From 9538992eafb868473f8d4b79ab54abe8df6d7430 Mon Sep 17 00:00:00 2001 From: Ubbe Date: Thu, 29 Jan 2026 18:13:28 +0700 Subject: [PATCH 01/31] hotfix(frontend): flags copilot redirects (#11878) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Changes šŸ—ļø - Refactor homepage redirect logic to always point to `/` - the `/` route handles whether to redirect to `/copilot` or `/library` based on flag - Simplify `useGetFlag` checks - Add `` and `` wrapper components - helpers to do 1 thing or the other, depending on chat enabled/disabled - avoids boilerplate code, checking flagss and redirects mistakes (especially around race conditions with LD init ) ## Checklist šŸ“‹ ### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Log in / out of AutoGPT with flag disabled/enabled - [x] Sign up to AutoGPT with flag disabled/enabled - [x] Redirects to homepage always work `/` - [x] Can't access Copilot with disabled flag --- .gitignore | 1 + .../src/app/(no-navbar)/onboarding/page.tsx | 13 ++-- .../src/app/(platform)/auth/callback/route.ts | 14 ++--- .../SessionsList/useSessionsPagination.ts | 4 +- .../src/app/(platform)/copilot/layout.tsx | 11 +++- .../src/app/(platform)/copilot/page.tsx | 12 +--- .../app/(platform)/copilot/useCopilotPage.ts | 31 +--------- .../src/app/(platform)/error/page.tsx | 6 +- .../src/app/(platform)/login/actions.ts | 7 +-- .../src/app/(platform)/login/useLoginPage.ts | 10 +--- .../src/app/(platform)/signup/actions.ts | 7 +-- .../app/(platform)/signup/useSignupPage.ts | 10 +--- .../frontend/src/app/api/helpers.ts | 1 - autogpt_platform/frontend/src/app/page.tsx | 29 +++------ .../layout/Navbar/components/NavbarLink.tsx | 7 +-- .../frontend/src/hooks/useAgentGraph.tsx | 2 +- .../frontend/src/lib/constants.ts | 7 --- .../frontend/src/lib/supabase/helpers.ts | 3 +- .../frontend/src/lib/supabase/middleware.ts | 3 +- .../onboarding/onboarding-provider.tsx | 10 +--- .../src/services/environment/index.ts | 10 +++- .../feature-flags/FeatureFlagPage.tsx | 59 +++++++++++++++++++ .../feature-flags/FeatureFlagRedirect.tsx | 51 ++++++++++++++++ .../feature-flags/feature-flag-provider.tsx | 10 ++-- .../services/feature-flags/use-get-flag.ts | 33 +++-------- classic/frontend/.gitignore | 1 + 26 files changed, 188 insertions(+), 164 deletions(-) create mode 100644 autogpt_platform/frontend/src/services/feature-flags/FeatureFlagPage.tsx create mode 100644 autogpt_platform/frontend/src/services/feature-flags/FeatureFlagRedirect.tsx diff --git a/.gitignore b/.gitignore index dfce8ba810..8660637ae5 100644 --- a/.gitignore +++ b/.gitignore @@ -179,3 +179,4 @@ autogpt_platform/backend/settings.py .test-contents .claude/settings.local.json /autogpt_platform/backend/logs +.next \ No newline at end of file diff --git a/autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx b/autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx index 70d9783ccd..246fe52826 100644 --- a/autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx +++ b/autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx @@ -1,10 +1,9 @@ "use client"; +import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding"; +import { getOnboardingStatus, resolveResponse } from "@/app/api/helpers"; import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner"; import { useRouter } from "next/navigation"; import { useEffect } from "react"; -import { resolveResponse, getOnboardingStatus } from "@/app/api/helpers"; -import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding"; -import { getHomepageRoute } from "@/lib/constants"; export default function OnboardingPage() { const router = useRouter(); @@ -13,12 +12,10 @@ export default function OnboardingPage() { async function redirectToStep() { try { // Check if onboarding is enabled (also gets chat flag for redirect) - const { shouldShowOnboarding, isChatEnabled } = - await getOnboardingStatus(); - const homepageRoute = getHomepageRoute(isChatEnabled); + const { shouldShowOnboarding } = await getOnboardingStatus(); if (!shouldShowOnboarding) { - router.replace(homepageRoute); + router.replace("/"); return; } @@ -26,7 +23,7 @@ export default function OnboardingPage() { // Handle completed onboarding if (onboarding.completedSteps.includes("GET_RESULTS")) { - router.replace(homepageRoute); + router.replace("/"); return; } diff --git a/autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts b/autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts index 15be137f63..e7e2997d0d 100644 --- a/autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts +++ b/autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts @@ -1,9 +1,8 @@ -import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase"; -import { getHomepageRoute } from "@/lib/constants"; -import BackendAPI from "@/lib/autogpt-server-api"; -import { NextResponse } from "next/server"; -import { revalidatePath } from "next/cache"; import { getOnboardingStatus } from "@/app/api/helpers"; +import BackendAPI from "@/lib/autogpt-server-api"; +import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase"; +import { revalidatePath } from "next/cache"; +import { NextResponse } from "next/server"; // Handle the callback to complete the user session login export async function GET(request: Request) { @@ -27,13 +26,12 @@ export async function GET(request: Request) { await api.createUser(); // Get onboarding status from backend (includes chat flag evaluated for this user) - const { shouldShowOnboarding, isChatEnabled } = - await getOnboardingStatus(); + const { shouldShowOnboarding } = await getOnboardingStatus(); if (shouldShowOnboarding) { next = "/onboarding"; revalidatePath("/onboarding", "layout"); } else { - next = getHomepageRoute(isChatEnabled); + next = "/"; revalidatePath(next, "layout"); } } catch (createUserError) { diff --git a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts index 11ddd937af..61e3e6f37f 100644 --- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts +++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts @@ -73,9 +73,9 @@ export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) { }; const reset = () => { + // Only reset the offset - keep existing sessions visible during refetch + // The effect will replace sessions when new data arrives at offset 0 setOffset(0); - setAccumulatedSessions([]); - setTotalCount(null); }; return { diff --git a/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx b/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx index 89cf72e2ba..876e5accfb 100644 --- a/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx +++ b/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx @@ -1,6 +1,13 @@ -import type { ReactNode } from "react"; +"use client"; +import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage"; +import { Flag } from "@/services/feature-flags/use-get-flag"; +import { type ReactNode } from "react"; import { CopilotShell } from "./components/CopilotShell/CopilotShell"; export default function CopilotLayout({ children }: { children: ReactNode }) { - return {children}; + return ( + + {children} + + ); } diff --git a/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx b/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx index 104b238895..e9bc018c1b 100644 --- a/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx +++ b/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx @@ -14,14 +14,8 @@ export default function CopilotPage() { const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen); const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt); const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt); - const { - greetingName, - quickActions, - isLoading, - hasSession, - initialPrompt, - isReady, - } = state; + const { greetingName, quickActions, isLoading, hasSession, initialPrompt } = + state; const { handleQuickAction, startChatWithPrompt, @@ -29,8 +23,6 @@ export default function CopilotPage() { handleStreamingChange, } = handlers; - if (!isReady) return null; - if (hasSession) { return (
diff --git a/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts b/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts index e4713cd24a..9d99f8e7bd 100644 --- a/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts +++ b/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts @@ -3,18 +3,11 @@ import { postV2CreateSession, } from "@/app/api/__generated__/endpoints/chat/chat"; import { useToast } from "@/components/molecules/Toast/use-toast"; -import { getHomepageRoute } from "@/lib/constants"; import { useSupabase } from "@/lib/supabase/hooks/useSupabase"; import { useOnboarding } from "@/providers/onboarding/onboarding-provider"; -import { - Flag, - type FlagValues, - useGetFlag, -} from "@/services/feature-flags/use-get-flag"; import { SessionKey, sessionStorage } from "@/services/storage/session-storage"; import * as Sentry from "@sentry/nextjs"; import { useQueryClient } from "@tanstack/react-query"; -import { useFlags } from "launchdarkly-react-client-sdk"; import { useRouter } from "next/navigation"; import { useEffect } from "react"; import { useCopilotStore } from "./copilot-page-store"; @@ -33,22 +26,6 @@ export function useCopilotPage() { const isCreating = useCopilotStore((s) => s.isCreatingSession); const setIsCreating = useCopilotStore((s) => s.setIsCreatingSession); - // Complete VISIT_COPILOT onboarding step to grant $5 welcome bonus - useEffect(() => { - if (isLoggedIn) { - completeStep("VISIT_COPILOT"); - } - }, [completeStep, isLoggedIn]); - - const isChatEnabled = useGetFlag(Flag.CHAT); - const flags = useFlags(); - const homepageRoute = getHomepageRoute(isChatEnabled); - const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true"; - const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID; - const isLaunchDarklyConfigured = envEnabled && Boolean(clientId); - const isFlagReady = - !isLaunchDarklyConfigured || flags[Flag.CHAT] !== undefined; - const greetingName = getGreetingName(user); const quickActions = getQuickActions(); @@ -58,11 +35,8 @@ export function useCopilotPage() { : undefined; useEffect(() => { - if (!isFlagReady) return; - if (isChatEnabled === false) { - router.replace(homepageRoute); - } - }, [homepageRoute, isChatEnabled, isFlagReady, router]); + if (isLoggedIn) completeStep("VISIT_COPILOT"); + }, [completeStep, isLoggedIn]); async function startChatWithPrompt(prompt: string) { if (!prompt?.trim()) return; @@ -116,7 +90,6 @@ export function useCopilotPage() { isLoading: isUserLoading, hasSession, initialPrompt, - isReady: isFlagReady && isChatEnabled !== false && isLoggedIn, }, handlers: { handleQuickAction, diff --git a/autogpt_platform/frontend/src/app/(platform)/error/page.tsx b/autogpt_platform/frontend/src/app/(platform)/error/page.tsx index b26ca4559b..3cf68178ad 100644 --- a/autogpt_platform/frontend/src/app/(platform)/error/page.tsx +++ b/autogpt_platform/frontend/src/app/(platform)/error/page.tsx @@ -1,8 +1,6 @@ "use client"; import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard"; -import { getHomepageRoute } from "@/lib/constants"; -import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag"; import { useSearchParams } from "next/navigation"; import { Suspense } from "react"; import { getErrorDetails } from "./helpers"; @@ -11,8 +9,6 @@ function ErrorPageContent() { const searchParams = useSearchParams(); const errorMessage = searchParams.get("message"); const errorDetails = getErrorDetails(errorMessage); - const isChatEnabled = useGetFlag(Flag.CHAT); - const homepageRoute = getHomepageRoute(isChatEnabled); function handleRetry() { // Auth-related errors should redirect to login @@ -30,7 +26,7 @@ function ErrorPageContent() { }, 2000); } else { // For server/network errors, go to home - window.location.href = homepageRoute; + window.location.href = "/"; } } diff --git a/autogpt_platform/frontend/src/app/(platform)/login/actions.ts b/autogpt_platform/frontend/src/app/(platform)/login/actions.ts index 447a25a41d..c4867dd123 100644 --- a/autogpt_platform/frontend/src/app/(platform)/login/actions.ts +++ b/autogpt_platform/frontend/src/app/(platform)/login/actions.ts @@ -1,6 +1,5 @@ "use server"; -import { getHomepageRoute } from "@/lib/constants"; import BackendAPI from "@/lib/autogpt-server-api"; import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase"; import { loginFormSchema } from "@/types/auth"; @@ -38,10 +37,8 @@ export async function login(email: string, password: string) { await api.createUser(); // Get onboarding status from backend (includes chat flag evaluated for this user) - const { shouldShowOnboarding, isChatEnabled } = await getOnboardingStatus(); - const next = shouldShowOnboarding - ? "/onboarding" - : getHomepageRoute(isChatEnabled); + const { shouldShowOnboarding } = await getOnboardingStatus(); + const next = shouldShowOnboarding ? "/onboarding" : "/"; return { success: true, diff --git a/autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts b/autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts index e64cc1858d..9b81965c31 100644 --- a/autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts +++ b/autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts @@ -1,8 +1,6 @@ import { useToast } from "@/components/molecules/Toast/use-toast"; -import { getHomepageRoute } from "@/lib/constants"; import { useSupabase } from "@/lib/supabase/hooks/useSupabase"; import { environment } from "@/services/environment"; -import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag"; import { loginFormSchema, LoginProvider } from "@/types/auth"; import { zodResolver } from "@hookform/resolvers/zod"; import { useRouter, useSearchParams } from "next/navigation"; @@ -22,17 +20,15 @@ export function useLoginPage() { const [isGoogleLoading, setIsGoogleLoading] = useState(false); const [showNotAllowedModal, setShowNotAllowedModal] = useState(false); const isCloudEnv = environment.isCloud(); - const isChatEnabled = useGetFlag(Flag.CHAT); - const homepageRoute = getHomepageRoute(isChatEnabled); // Get redirect destination from 'next' query parameter const nextUrl = searchParams.get("next"); useEffect(() => { if (isLoggedIn && !isLoggingIn) { - router.push(nextUrl || homepageRoute); + router.push(nextUrl || "/"); } - }, [homepageRoute, isLoggedIn, isLoggingIn, nextUrl, router]); + }, [isLoggedIn, isLoggingIn, nextUrl, router]); const form = useForm>({ resolver: zodResolver(loginFormSchema), @@ -98,7 +94,7 @@ export function useLoginPage() { } // Prefer URL's next parameter, then use backend-determined route - router.replace(nextUrl || result.next || homepageRoute); + router.replace(nextUrl || result.next || "/"); } catch (error) { toast({ title: diff --git a/autogpt_platform/frontend/src/app/(platform)/signup/actions.ts b/autogpt_platform/frontend/src/app/(platform)/signup/actions.ts index 0fbba54b8e..204482dbe9 100644 --- a/autogpt_platform/frontend/src/app/(platform)/signup/actions.ts +++ b/autogpt_platform/frontend/src/app/(platform)/signup/actions.ts @@ -1,6 +1,5 @@ "use server"; -import { getHomepageRoute } from "@/lib/constants"; import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase"; import { signupFormSchema } from "@/types/auth"; import * as Sentry from "@sentry/nextjs"; @@ -59,10 +58,8 @@ export async function signup( } // Get onboarding status from backend (includes chat flag evaluated for this user) - const { shouldShowOnboarding, isChatEnabled } = await getOnboardingStatus(); - const next = shouldShowOnboarding - ? "/onboarding" - : getHomepageRoute(isChatEnabled); + const { shouldShowOnboarding } = await getOnboardingStatus(); + const next = shouldShowOnboarding ? "/onboarding" : "/"; return { success: true, next }; } catch (err) { diff --git a/autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts b/autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts index 5fa8c2c159..fd78b48735 100644 --- a/autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts +++ b/autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts @@ -1,8 +1,6 @@ import { useToast } from "@/components/molecules/Toast/use-toast"; -import { getHomepageRoute } from "@/lib/constants"; import { useSupabase } from "@/lib/supabase/hooks/useSupabase"; import { environment } from "@/services/environment"; -import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag"; import { LoginProvider, signupFormSchema } from "@/types/auth"; import { zodResolver } from "@hookform/resolvers/zod"; import { useRouter, useSearchParams } from "next/navigation"; @@ -22,17 +20,15 @@ export function useSignupPage() { const [isGoogleLoading, setIsGoogleLoading] = useState(false); const [showNotAllowedModal, setShowNotAllowedModal] = useState(false); const isCloudEnv = environment.isCloud(); - const isChatEnabled = useGetFlag(Flag.CHAT); - const homepageRoute = getHomepageRoute(isChatEnabled); // Get redirect destination from 'next' query parameter const nextUrl = searchParams.get("next"); useEffect(() => { if (isLoggedIn && !isSigningUp) { - router.push(nextUrl || homepageRoute); + router.push(nextUrl || "/"); } - }, [homepageRoute, isLoggedIn, isSigningUp, nextUrl, router]); + }, [isLoggedIn, isSigningUp, nextUrl, router]); const form = useForm>({ resolver: zodResolver(signupFormSchema), @@ -133,7 +129,7 @@ export function useSignupPage() { } // Prefer the URL's next parameter, then result.next (for onboarding), then default - const redirectTo = nextUrl || result.next || homepageRoute; + const redirectTo = nextUrl || result.next || "/"; router.replace(redirectTo); } catch (error) { setIsLoading(false); diff --git a/autogpt_platform/frontend/src/app/api/helpers.ts b/autogpt_platform/frontend/src/app/api/helpers.ts index c2104d231a..226f5fa786 100644 --- a/autogpt_platform/frontend/src/app/api/helpers.ts +++ b/autogpt_platform/frontend/src/app/api/helpers.ts @@ -181,6 +181,5 @@ export async function getOnboardingStatus() { const isCompleted = onboarding.completedSteps.includes("CONGRATS"); return { shouldShowOnboarding: status.is_onboarding_enabled && !isCompleted, - isChatEnabled: status.is_chat_enabled, }; } diff --git a/autogpt_platform/frontend/src/app/page.tsx b/autogpt_platform/frontend/src/app/page.tsx index dbfab49469..31d1e96e48 100644 --- a/autogpt_platform/frontend/src/app/page.tsx +++ b/autogpt_platform/frontend/src/app/page.tsx @@ -1,27 +1,14 @@ "use client"; -import { getHomepageRoute } from "@/lib/constants"; -import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag"; -import { useRouter } from "next/navigation"; -import { useEffect } from "react"; +import { FeatureFlagRedirect } from "@/services/feature-flags/FeatureFlagRedirect"; +import { Flag } from "@/services/feature-flags/use-get-flag"; export default function Page() { - const isChatEnabled = useGetFlag(Flag.CHAT); - const router = useRouter(); - const homepageRoute = getHomepageRoute(isChatEnabled); - const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true"; - const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID; - const isLaunchDarklyConfigured = envEnabled && Boolean(clientId); - const isFlagReady = - !isLaunchDarklyConfigured || typeof isChatEnabled === "boolean"; - - useEffect( - function redirectToHomepage() { - if (!isFlagReady) return; - router.replace(homepageRoute); - }, - [homepageRoute, isFlagReady, router], + return ( + ); - - return null; } diff --git a/autogpt_platform/frontend/src/components/layout/Navbar/components/NavbarLink.tsx b/autogpt_platform/frontend/src/components/layout/Navbar/components/NavbarLink.tsx index eab5a7352f..dff1277384 100644 --- a/autogpt_platform/frontend/src/components/layout/Navbar/components/NavbarLink.tsx +++ b/autogpt_platform/frontend/src/components/layout/Navbar/components/NavbarLink.tsx @@ -1,7 +1,6 @@ "use client"; import { IconLaptop } from "@/components/__legacy__/ui/icons"; -import { getHomepageRoute } from "@/lib/constants"; import { cn } from "@/lib/utils"; import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag"; import { ListChecksIcon } from "@phosphor-icons/react/dist/ssr"; @@ -24,11 +23,11 @@ interface Props { export function NavbarLink({ name, href }: Props) { const pathname = usePathname(); const isChatEnabled = useGetFlag(Flag.CHAT); - const homepageRoute = getHomepageRoute(isChatEnabled); + const expectedHomeRoute = isChatEnabled ? "/copilot" : "/library"; const isActive = - href === homepageRoute - ? pathname === "/" || pathname.startsWith(homepageRoute) + href === expectedHomeRoute + ? pathname === "/" || pathname.startsWith(expectedHomeRoute) : pathname.includes(href); return ( diff --git a/autogpt_platform/frontend/src/hooks/useAgentGraph.tsx b/autogpt_platform/frontend/src/hooks/useAgentGraph.tsx index 6c097c395e..d422e389dd 100644 --- a/autogpt_platform/frontend/src/hooks/useAgentGraph.tsx +++ b/autogpt_platform/frontend/src/hooks/useAgentGraph.tsx @@ -66,7 +66,7 @@ export default function useAgentGraph( >(null); const [xyNodes, setXYNodes] = useState([]); const [xyEdges, setXYEdges] = useState([]); - const betaBlocks = useGetFlag(Flag.BETA_BLOCKS); + const betaBlocks = useGetFlag(Flag.BETA_BLOCKS) as string[]; // Filter blocks based on beta flags const availableBlocks = useMemo(() => { diff --git a/autogpt_platform/frontend/src/lib/constants.ts b/autogpt_platform/frontend/src/lib/constants.ts index de5aac1670..19365a56ac 100644 --- a/autogpt_platform/frontend/src/lib/constants.ts +++ b/autogpt_platform/frontend/src/lib/constants.ts @@ -11,10 +11,3 @@ export const API_KEY_HEADER_NAME = "X-API-Key"; // Layout export const NAVBAR_HEIGHT_PX = 60; - -// Routes -export function getHomepageRoute(isChatEnabled?: boolean | null): string { - if (isChatEnabled === true) return "/copilot"; - if (isChatEnabled === false) return "/library"; - return "/"; -} diff --git a/autogpt_platform/frontend/src/lib/supabase/helpers.ts b/autogpt_platform/frontend/src/lib/supabase/helpers.ts index 3fd0eacb5f..26f7711bde 100644 --- a/autogpt_platform/frontend/src/lib/supabase/helpers.ts +++ b/autogpt_platform/frontend/src/lib/supabase/helpers.ts @@ -1,4 +1,3 @@ -import { getHomepageRoute } from "@/lib/constants"; import { environment } from "@/services/environment"; import { Key, storage } from "@/services/storage/local-storage"; import { type CookieOptions } from "@supabase/ssr"; @@ -71,7 +70,7 @@ export function getRedirectPath( } if (isAdminPage(path) && userRole !== "admin") { - return getHomepageRoute(); + return "/"; } return null; diff --git a/autogpt_platform/frontend/src/lib/supabase/middleware.ts b/autogpt_platform/frontend/src/lib/supabase/middleware.ts index de8b867ef0..cd1f4a240e 100644 --- a/autogpt_platform/frontend/src/lib/supabase/middleware.ts +++ b/autogpt_platform/frontend/src/lib/supabase/middleware.ts @@ -1,4 +1,3 @@ -import { getHomepageRoute } from "@/lib/constants"; import { environment } from "@/services/environment"; import { createServerClient } from "@supabase/ssr"; import { NextResponse, type NextRequest } from "next/server"; @@ -67,7 +66,7 @@ export async function updateSession(request: NextRequest) { // 2. Check if user is authenticated but lacks admin role when accessing admin pages if (user && userRole !== "admin" && isAdminPage(pathname)) { - url.pathname = getHomepageRoute(); + url.pathname = "/"; return NextResponse.redirect(url); } diff --git a/autogpt_platform/frontend/src/providers/onboarding/onboarding-provider.tsx b/autogpt_platform/frontend/src/providers/onboarding/onboarding-provider.tsx index 1ee4b2b6db..42cb99f187 100644 --- a/autogpt_platform/frontend/src/providers/onboarding/onboarding-provider.tsx +++ b/autogpt_platform/frontend/src/providers/onboarding/onboarding-provider.tsx @@ -23,9 +23,7 @@ import { WebSocketNotification, } from "@/lib/autogpt-server-api"; import { useBackendAPI } from "@/lib/autogpt-server-api/context"; -import { getHomepageRoute } from "@/lib/constants"; import { useSupabase } from "@/lib/supabase/hooks/useSupabase"; -import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag"; import Link from "next/link"; import { usePathname, useRouter } from "next/navigation"; import { @@ -104,8 +102,6 @@ export default function OnboardingProvider({ const pathname = usePathname(); const router = useRouter(); const { isLoggedIn } = useSupabase(); - const isChatEnabled = useGetFlag(Flag.CHAT); - const homepageRoute = getHomepageRoute(isChatEnabled); useOnboardingTimezoneDetection(); @@ -150,7 +146,7 @@ export default function OnboardingProvider({ if (isOnOnboardingRoute) { const enabled = await resolveResponse(getV1IsOnboardingEnabled()); if (!enabled) { - router.push(homepageRoute); + router.push("/"); return; } } @@ -162,7 +158,7 @@ export default function OnboardingProvider({ isOnOnboardingRoute && shouldRedirectFromOnboarding(onboarding.completedSteps, pathname) ) { - router.push(homepageRoute); + router.push("/"); } } catch (error) { console.error("Failed to initialize onboarding:", error); @@ -177,7 +173,7 @@ export default function OnboardingProvider({ } initializeOnboarding(); - }, [api, homepageRoute, isOnOnboardingRoute, router, isLoggedIn, pathname]); + }, [api, isOnOnboardingRoute, router, isLoggedIn, pathname]); const handleOnboardingNotification = useCallback( (notification: WebSocketNotification) => { diff --git a/autogpt_platform/frontend/src/services/environment/index.ts b/autogpt_platform/frontend/src/services/environment/index.ts index f19bc417e3..0214dcb3c8 100644 --- a/autogpt_platform/frontend/src/services/environment/index.ts +++ b/autogpt_platform/frontend/src/services/environment/index.ts @@ -83,6 +83,10 @@ function getPostHogCredentials() { }; } +function getLaunchDarklyClientId() { + return process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID; +} + function isProductionBuild() { return process.env.NODE_ENV === "production"; } @@ -120,7 +124,10 @@ function isVercelPreview() { } function areFeatureFlagsEnabled() { - return process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "enabled"; + return ( + process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true" && + Boolean(process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID) + ); } function isPostHogEnabled() { @@ -143,6 +150,7 @@ export const environment = { getSupabaseAnonKey, getPreviewStealingDev, getPostHogCredentials, + getLaunchDarklyClientId, // Assertions isServerSide, isClientSide, diff --git a/autogpt_platform/frontend/src/services/feature-flags/FeatureFlagPage.tsx b/autogpt_platform/frontend/src/services/feature-flags/FeatureFlagPage.tsx new file mode 100644 index 0000000000..eef0691de2 --- /dev/null +++ b/autogpt_platform/frontend/src/services/feature-flags/FeatureFlagPage.tsx @@ -0,0 +1,59 @@ +"use client"; + +import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner"; +import { useLDClient } from "launchdarkly-react-client-sdk"; +import { useRouter } from "next/navigation"; +import { ReactNode, useEffect, useState } from "react"; +import { environment } from "../environment"; +import { Flag, useGetFlag } from "./use-get-flag"; + +interface FeatureFlagRedirectProps { + flag: Flag; + whenDisabled: string; + children: ReactNode; +} + +export function FeatureFlagPage({ + flag, + whenDisabled, + children, +}: FeatureFlagRedirectProps) { + const [isLoading, setIsLoading] = useState(true); + const router = useRouter(); + const flagValue = useGetFlag(flag); + const ldClient = useLDClient(); + const ldEnabled = environment.areFeatureFlagsEnabled(); + const ldReady = Boolean(ldClient); + const flagEnabled = Boolean(flagValue); + + useEffect(() => { + const initialize = async () => { + if (!ldEnabled) { + router.replace(whenDisabled); + setIsLoading(false); + return; + } + + // Wait for LaunchDarkly to initialize when enabled to prevent race conditions + if (ldEnabled && !ldReady) return; + + try { + await ldClient?.waitForInitialization(); + if (!flagEnabled) router.replace(whenDisabled); + } catch (error) { + console.error(error); + router.replace(whenDisabled); + } finally { + setIsLoading(false); + } + }; + + initialize(); + }, [ldReady, flagEnabled]); + + return isLoading || !flagEnabled ? ( + + ) : ( + <>{children} + ); +} diff --git a/autogpt_platform/frontend/src/services/feature-flags/FeatureFlagRedirect.tsx b/autogpt_platform/frontend/src/services/feature-flags/FeatureFlagRedirect.tsx new file mode 100644 index 0000000000..b843b5567c --- /dev/null +++ b/autogpt_platform/frontend/src/services/feature-flags/FeatureFlagRedirect.tsx @@ -0,0 +1,51 @@ +"use client"; + +import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner"; +import { useLDClient } from "launchdarkly-react-client-sdk"; +import { useRouter } from "next/navigation"; +import { useEffect } from "react"; +import { environment } from "../environment"; +import { Flag, useGetFlag } from "./use-get-flag"; + +interface FeatureFlagRedirectProps { + flag: Flag; + whenEnabled: string; + whenDisabled: string; +} + +export function FeatureFlagRedirect({ + flag, + whenEnabled, + whenDisabled, +}: FeatureFlagRedirectProps) { + const router = useRouter(); + const flagValue = useGetFlag(flag); + const ldEnabled = environment.areFeatureFlagsEnabled(); + const ldClient = useLDClient(); + const ldReady = Boolean(ldClient); + const flagEnabled = Boolean(flagValue); + + useEffect(() => { + const initialize = async () => { + if (!ldEnabled) { + router.replace(whenDisabled); + return; + } + + // Wait for LaunchDarkly to initialize when enabled to prevent race conditions + if (ldEnabled && !ldReady) return; + + try { + await ldClient?.waitForInitialization(); + router.replace(flagEnabled ? whenEnabled : whenDisabled); + } catch (error) { + console.error(error); + router.replace(whenDisabled); + } + }; + + initialize(); + }, [ldReady, flagEnabled]); + + return ; +} diff --git a/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx b/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx index 47e4bd738a..8b78f4c589 100644 --- a/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx +++ b/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx @@ -7,14 +7,12 @@ import type { ReactNode } from "react"; import { useMemo } from "react"; import { environment } from "../environment"; -const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID; -const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true"; const LAUNCHDARKLY_INIT_TIMEOUT_MS = 5000; export function LaunchDarklyProvider({ children }: { children: ReactNode }) { const { user, isUserLoading } = useSupabase(); - const isCloud = environment.isCloud(); - const isLaunchDarklyConfigured = isCloud && envEnabled && clientId; + const envEnabled = environment.areFeatureFlagsEnabled(); + const clientId = environment.getLaunchDarklyClientId(); const context = useMemo(() => { if (isUserLoading || !user) { @@ -36,7 +34,7 @@ export function LaunchDarklyProvider({ children }: { children: ReactNode }) { }; }, [user, isUserLoading]); - if (!isLaunchDarklyConfigured) { + if (!envEnabled) { return <>{children}; } @@ -44,7 +42,7 @@ export function LaunchDarklyProvider({ children }: { children: ReactNode }) { (flag: T): FlagValues[T] | null { +type FlagValues = typeof defaultFlags; + +export function useGetFlag(flag: T): FlagValues[T] { const currentFlags = useFlags(); const flagValue = currentFlags[flag]; + const areFlagsEnabled = environment.areFeatureFlagsEnabled(); - const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true"; - const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID; - const isLaunchDarklyConfigured = envEnabled && Boolean(clientId); - - if (!isLaunchDarklyConfigured || isPwMockEnabled) { - return mockFlags[flag]; + if (!areFlagsEnabled || isPwMockEnabled) { + return defaultFlags[flag]; } - return flagValue ?? mockFlags[flag]; + return flagValue ?? defaultFlags[flag]; } diff --git a/classic/frontend/.gitignore b/classic/frontend/.gitignore index 036283f834..eb060615c5 100644 --- a/classic/frontend/.gitignore +++ b/classic/frontend/.gitignore @@ -8,6 +8,7 @@ .buildlog/ .history .svn/ +.next/ migrate_working_dir/ # IntelliJ related From 582c6cad36b2ba0e675d22c617538e037f495d0b Mon Sep 17 00:00:00 2001 From: Otto Date: Fri, 30 Jan 2026 05:12:35 +0000 Subject: [PATCH 02/31] fix(e2e): Make E2E test data deterministic and fix flaky tests (#11890) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary Fixes flaky E2E marketplace and library tests that were causing PRs to be removed from the merge queue. ## Root Cause 1. **Test data was probabilistic** - `e2e_test_data.py` used random chances (40% approve, then 20-50% feature), which could result in 0 featured agents 2. **Library pagination threshold wrong** - Checked `>= 10`, but page size is 20 3. **Fixed timeouts** - Used `waitForTimeout(2000)` / `waitForTimeout(10000)` instead of proper waits ## Changes ### Backend (`e2e_test_data.py`) - Add guaranteed minimums: 8 featured agents, 5 featured creators, 10 top agents - First N submissions are deterministically approved and featured - Increase agents per user from 15 ā†’ 25 (for pagination with page_size=20) - Fix library agent creation to use constants instead of hardcoded `10` ### Frontend Tests - `library.spec.ts`: Fix pagination threshold to `PAGE_SIZE` (20) - `library.page.ts`: Replace 2s timeout with `networkidle` + `waitForFunction` - `marketplace.page.ts`: Add `networkidle` wait, 30s waits in `getFirst*` methods - `marketplace.spec.ts`: Replace 10s timeout with `waitForFunction` - `marketplace-creator.spec.ts`: Add `networkidle` + element waits ## Related - Closes SECRT-1848, SECRT-1849 - Should unblock #11841 and other PRs in merge queue --------- Co-authored-by: Ubbe --- .../backend/test/e2e_test_data.py | 162 ++++++++++-------- .../frontend/src/tests/library.spec.ts | 13 +- .../src/tests/marketplace-creator.spec.ts | 3 + .../frontend/src/tests/marketplace.spec.ts | 11 +- .../frontend/src/tests/pages/library.page.ts | 26 +-- .../src/tests/pages/marketplace.page.ts | 15 +- 6 files changed, 136 insertions(+), 94 deletions(-) diff --git a/autogpt_platform/backend/test/e2e_test_data.py b/autogpt_platform/backend/test/e2e_test_data.py index d7576cdad3..7288197a90 100644 --- a/autogpt_platform/backend/test/e2e_test_data.py +++ b/autogpt_platform/backend/test/e2e_test_data.py @@ -43,19 +43,24 @@ faker = Faker() # Constants for data generation limits (reduced for E2E tests) NUM_USERS = 15 NUM_AGENT_BLOCKS = 30 -MIN_GRAPHS_PER_USER = 15 -MAX_GRAPHS_PER_USER = 15 +MIN_GRAPHS_PER_USER = 25 +MAX_GRAPHS_PER_USER = 25 MIN_NODES_PER_GRAPH = 3 MAX_NODES_PER_GRAPH = 6 MIN_PRESETS_PER_USER = 2 MAX_PRESETS_PER_USER = 3 -MIN_AGENTS_PER_USER = 15 -MAX_AGENTS_PER_USER = 15 +MIN_AGENTS_PER_USER = 25 +MAX_AGENTS_PER_USER = 25 MIN_EXECUTIONS_PER_GRAPH = 2 MAX_EXECUTIONS_PER_GRAPH = 8 MIN_REVIEWS_PER_VERSION = 2 MAX_REVIEWS_PER_VERSION = 5 +# Guaranteed minimums for marketplace tests (deterministic) +GUARANTEED_FEATURED_AGENTS = 8 +GUARANTEED_FEATURED_CREATORS = 5 +GUARANTEED_TOP_AGENTS = 10 + def get_image(): """Generate a consistent image URL using picsum.photos service.""" @@ -385,7 +390,7 @@ class TestDataCreator: library_agents = [] for user in self.users: - num_agents = 10 # Create exactly 10 agents per user + num_agents = random.randint(MIN_AGENTS_PER_USER, MAX_AGENTS_PER_USER) # Get available graphs for this user user_graphs = [ @@ -507,14 +512,17 @@ class TestDataCreator: existing_profiles, min(num_creators, len(existing_profiles)) ) - # Mark about 50% of creators as featured (more for testing) - num_featured = max(2, int(num_creators * 0.5)) + # Guarantee at least GUARANTEED_FEATURED_CREATORS featured creators + num_featured = max(GUARANTEED_FEATURED_CREATORS, int(num_creators * 0.5)) num_featured = min( num_featured, len(selected_profiles) ) # Don't exceed available profiles featured_profile_ids = set( random.sample([p.id for p in selected_profiles], num_featured) ) + print( + f"šŸŽÆ Creating {num_featured} featured creators (min: {GUARANTEED_FEATURED_CREATORS})" + ) for profile in selected_profiles: try: @@ -545,21 +553,25 @@ class TestDataCreator: return profiles async def create_test_store_submissions(self) -> List[Dict[str, Any]]: - """Create test store submissions using the API function.""" + """Create test store submissions using the API function. + + DETERMINISTIC: Guarantees minimum featured agents for E2E tests. + """ print("Creating test store submissions...") submissions = [] approved_submissions = [] + featured_count = 0 + submission_counter = 0 - # Create a special test submission for test123@gmail.com + # Create a special test submission for test123@gmail.com (ALWAYS approved + featured) test_user = next( (user for user in self.users if user["email"] == "test123@gmail.com"), None ) - if test_user: - # Special test data for consistent testing + if test_user and self.agent_graphs: test_submission_data = { "user_id": test_user["id"], - "agent_id": self.agent_graphs[0]["id"], # Use first available graph + "agent_id": self.agent_graphs[0]["id"], "agent_version": 1, "slug": "test-agent-submission", "name": "Test Agent Submission", @@ -580,37 +592,24 @@ class TestDataCreator: submissions.append(test_submission.model_dump()) print("āœ… Created special test store submission for test123@gmail.com") - # Randomly approve, reject, or leave pending the test submission + # ALWAYS approve and feature the test submission if test_submission.store_listing_version_id: - random_value = random.random() - if random_value < 0.4: # 40% chance to approve - approved_submission = await review_store_submission( - store_listing_version_id=test_submission.store_listing_version_id, - is_approved=True, - external_comments="Test submission approved", - internal_comments="Auto-approved test submission", - reviewer_id=test_user["id"], - ) - approved_submissions.append(approved_submission.model_dump()) - print("āœ… Approved test store submission") + approved_submission = await review_store_submission( + store_listing_version_id=test_submission.store_listing_version_id, + is_approved=True, + external_comments="Test submission approved", + internal_comments="Auto-approved test submission", + reviewer_id=test_user["id"], + ) + approved_submissions.append(approved_submission.model_dump()) + print("āœ… Approved test store submission") - # Mark approved submission as featured - await prisma.storelistingversion.update( - where={"id": test_submission.store_listing_version_id}, - data={"isFeatured": True}, - ) - print("šŸŒŸ Marked test agent as FEATURED") - elif random_value < 0.7: # 30% chance to reject (40% to 70%) - await review_store_submission( - store_listing_version_id=test_submission.store_listing_version_id, - is_approved=False, - external_comments="Test submission rejected - needs improvements", - internal_comments="Auto-rejected test submission for E2E testing", - reviewer_id=test_user["id"], - ) - print("āŒ Rejected test store submission") - else: # 30% chance to leave pending (70% to 100%) - print("ā³ Left test submission pending for review") + await prisma.storelistingversion.update( + where={"id": test_submission.store_listing_version_id}, + data={"isFeatured": True}, + ) + featured_count += 1 + print("šŸŒŸ Marked test agent as FEATURED") except Exception as e: print(f"Error creating test store submission: {e}") @@ -620,7 +619,6 @@ class TestDataCreator: # Create regular submissions for all users for user in self.users: - # Get available graphs for this specific user user_graphs = [ g for g in self.agent_graphs if g.get("userId") == user["id"] ] @@ -631,18 +629,17 @@ class TestDataCreator: ) continue - # Create exactly 4 store submissions per user for submission_index in range(4): graph = random.choice(user_graphs) + submission_counter += 1 try: print( - f"Creating store submission for user {user['id']} with graph {graph['id']} (owner: {graph.get('userId')})" + f"Creating store submission for user {user['id']} with graph {graph['id']}" ) - # Use the API function to create store submission with correct parameters submission = await create_store_submission( - user_id=user["id"], # Must match graph's userId + user_id=user["id"], agent_id=graph["id"], agent_version=graph.get("version", 1), slug=faker.slug(), @@ -651,22 +648,24 @@ class TestDataCreator: video_url=get_video_url() if random.random() < 0.3 else None, image_urls=[get_image() for _ in range(3)], description=faker.text(), - categories=[ - get_category() - ], # Single category from predefined list + categories=[get_category()], changes_summary="Initial E2E test submission", ) submissions.append(submission.model_dump()) print(f"āœ… Created store submission: {submission.name}") - # Randomly approve, reject, or leave pending the submission if submission.store_listing_version_id: - random_value = random.random() - if random_value < 0.4: # 40% chance to approve - try: - # Pick a random user as the reviewer (admin) - reviewer_id = random.choice(self.users)["id"] + # DETERMINISTIC: First N submissions are always approved + # First GUARANTEED_FEATURED_AGENTS of those are always featured + should_approve = ( + submission_counter <= GUARANTEED_TOP_AGENTS + or random.random() < 0.4 + ) + should_feature = featured_count < GUARANTEED_FEATURED_AGENTS + if should_approve: + try: + reviewer_id = random.choice(self.users)["id"] approved_submission = await review_store_submission( store_listing_version_id=submission.store_listing_version_id, is_approved=True, @@ -681,16 +680,7 @@ class TestDataCreator: f"āœ… Approved store submission: {submission.name}" ) - # Mark some agents as featured during creation (30% chance) - # More likely for creators and first submissions - is_creator = user["id"] in [ - p.get("userId") for p in self.profiles - ] - feature_chance = ( - 0.5 if is_creator else 0.2 - ) # 50% for creators, 20% for others - - if random.random() < feature_chance: + if should_feature: try: await prisma.storelistingversion.update( where={ @@ -698,8 +688,25 @@ class TestDataCreator: }, data={"isFeatured": True}, ) + featured_count += 1 print( - f"šŸŒŸ Marked agent as FEATURED: {submission.name}" + f"šŸŒŸ Marked agent as FEATURED ({featured_count}/{GUARANTEED_FEATURED_AGENTS}): {submission.name}" + ) + except Exception as e: + print( + f"Warning: Could not mark submission as featured: {e}" + ) + elif random.random() < 0.2: + try: + await prisma.storelistingversion.update( + where={ + "id": submission.store_listing_version_id + }, + data={"isFeatured": True}, + ) + featured_count += 1 + print( + f"šŸŒŸ Marked agent as FEATURED (bonus): {submission.name}" ) except Exception as e: print( @@ -710,11 +717,9 @@ class TestDataCreator: print( f"Warning: Could not approve submission {submission.name}: {e}" ) - elif random_value < 0.7: # 30% chance to reject (40% to 70%) + elif random.random() < 0.5: try: - # Pick a random user as the reviewer (admin) reviewer_id = random.choice(self.users)["id"] - await review_store_submission( store_listing_version_id=submission.store_listing_version_id, is_approved=False, @@ -729,7 +734,7 @@ class TestDataCreator: print( f"Warning: Could not reject submission {submission.name}: {e}" ) - else: # 30% chance to leave pending (70% to 100%) + else: print( f"ā³ Left submission pending for review: {submission.name}" ) @@ -743,9 +748,13 @@ class TestDataCreator: traceback.print_exc() continue + print("\nšŸ“Š Store Submissions Summary:") + print(f" Created: {len(submissions)}") + print(f" Approved: {len(approved_submissions)}") print( - f"Created {len(submissions)} store submissions, approved {len(approved_submissions)}" + f" Featured: {featured_count} (guaranteed min: {GUARANTEED_FEATURED_AGENTS})" ) + self.store_submissions = submissions return submissions @@ -825,12 +834,15 @@ class TestDataCreator: print(f"āœ… Agent blocks available: {len(self.agent_blocks)}") print(f"āœ… Agent graphs created: {len(self.agent_graphs)}") print(f"āœ… Library agents created: {len(self.library_agents)}") - print(f"āœ… Creator profiles updated: {len(self.profiles)} (some featured)") - print( - f"āœ… Store submissions created: {len(self.store_submissions)} (some marked as featured during creation)" - ) + print(f"āœ… Creator profiles updated: {len(self.profiles)}") + print(f"āœ… Store submissions created: {len(self.store_submissions)}") print(f"āœ… API keys created: {len(self.api_keys)}") print(f"āœ… Presets created: {len(self.presets)}") + print("\nšŸŽÆ Deterministic Guarantees:") + print(f" ā€¢ Featured agents: >= {GUARANTEED_FEATURED_AGENTS}") + print(f" ā€¢ Featured creators: >= {GUARANTEED_FEATURED_CREATORS}") + print(f" ā€¢ Top agents (approved): >= {GUARANTEED_TOP_AGENTS}") + print(f" ā€¢ Library agents per user: >= {MIN_AGENTS_PER_USER}") print("\nšŸš€ Your E2E test database is ready to use!") diff --git a/autogpt_platform/frontend/src/tests/library.spec.ts b/autogpt_platform/frontend/src/tests/library.spec.ts index 1972e94522..52941785e3 100644 --- a/autogpt_platform/frontend/src/tests/library.spec.ts +++ b/autogpt_platform/frontend/src/tests/library.spec.ts @@ -59,12 +59,13 @@ test.describe("Library", () => { }); test("pagination works correctly", async ({ page }, testInfo) => { - test.setTimeout(testInfo.timeout * 3); // Increase timeout for pagination operations + test.setTimeout(testInfo.timeout * 3); await page.goto("/library"); + const PAGE_SIZE = 20; const paginationResult = await libraryPage.testPagination(); - if (paginationResult.initialCount >= 10) { + if (paginationResult.initialCount >= PAGE_SIZE) { expect(paginationResult.finalCount).toBeGreaterThanOrEqual( paginationResult.initialCount, ); @@ -133,7 +134,10 @@ test.describe("Library", () => { test.expect(clearedSearchValue).toBe(""); }); - test("pagination while searching works correctly", async ({ page }) => { + test("pagination while searching works correctly", async ({ + page, + }, testInfo) => { + test.setTimeout(testInfo.timeout * 3); await page.goto("/library"); const allAgents = await libraryPage.getAgents(); @@ -152,9 +156,10 @@ test.describe("Library", () => { ); expect(matchingResults.length).toEqual(initialSearchResults.length); + const PAGE_SIZE = 20; const searchPaginationResult = await libraryPage.testPagination(); - if (searchPaginationResult.initialCount >= 10) { + if (searchPaginationResult.initialCount >= PAGE_SIZE) { expect(searchPaginationResult.finalCount).toBeGreaterThanOrEqual( searchPaginationResult.initialCount, ); diff --git a/autogpt_platform/frontend/src/tests/marketplace-creator.spec.ts b/autogpt_platform/frontend/src/tests/marketplace-creator.spec.ts index 3558f0672c..a41b652afb 100644 --- a/autogpt_platform/frontend/src/tests/marketplace-creator.spec.ts +++ b/autogpt_platform/frontend/src/tests/marketplace-creator.spec.ts @@ -69,9 +69,12 @@ test.describe("Marketplace Creator Page ā€“ Basic Functionality", () => { await marketplacePage.getFirstCreatorProfile(page); await firstCreatorProfile.click(); await page.waitForURL("**/marketplace/creator/**"); + await page.waitForLoadState("networkidle").catch(() => {}); + const firstAgent = page .locator('[data-testid="store-card"]:visible') .first(); + await firstAgent.waitFor({ state: "visible", timeout: 30000 }); await firstAgent.click(); await page.waitForURL("**/marketplace/agent/**"); diff --git a/autogpt_platform/frontend/src/tests/marketplace.spec.ts b/autogpt_platform/frontend/src/tests/marketplace.spec.ts index 774713dc82..44d89bf351 100644 --- a/autogpt_platform/frontend/src/tests/marketplace.spec.ts +++ b/autogpt_platform/frontend/src/tests/marketplace.spec.ts @@ -77,7 +77,6 @@ test.describe("Marketplace ā€“ Basic Functionality", () => { const firstFeaturedAgent = await marketplacePage.getFirstFeaturedAgent(page); - await firstFeaturedAgent.waitFor({ state: "visible" }); await firstFeaturedAgent.click(); await page.waitForURL("**/marketplace/agent/**"); await matchesUrl(page, /\/marketplace\/agent\/.+/); @@ -116,7 +115,15 @@ test.describe("Marketplace ā€“ Basic Functionality", () => { const searchTerm = page.getByText("DummyInput").first(); await isVisible(searchTerm); - await page.waitForTimeout(10000); + await page.waitForLoadState("networkidle").catch(() => {}); + + await page + .waitForFunction( + () => + document.querySelectorAll('[data-testid="store-card"]').length > 0, + { timeout: 15000 }, + ) + .catch(() => console.log("No search results appeared within timeout")); const results = await marketplacePage.getSearchResultsCount(page); expect(results).toBeGreaterThan(0); diff --git a/autogpt_platform/frontend/src/tests/pages/library.page.ts b/autogpt_platform/frontend/src/tests/pages/library.page.ts index 3a7695ec3a..03e98598b4 100644 --- a/autogpt_platform/frontend/src/tests/pages/library.page.ts +++ b/autogpt_platform/frontend/src/tests/pages/library.page.ts @@ -300,21 +300,27 @@ export class LibraryPage extends BasePage { async scrollToLoadMore(): Promise { console.log(`scrolling to load more agents`); - // Get initial agent count - const initialCount = await this.getAgentCount(); - console.log(`Initial agent count: ${initialCount}`); + const initialCount = await this.getAgentCountByListLength(); + console.log(`Initial agent count (DOM cards): ${initialCount}`); - // Scroll down to trigger pagination await this.scrollToBottom(); - // Wait for potential new agents to load - await this.page.waitForTimeout(2000); + await this.page + .waitForLoadState("networkidle", { timeout: 10000 }) + .catch(() => console.log("Network idle timeout, continuing...")); - // Check if more agents loaded - const newCount = await this.getAgentCount(); - console.log(`New agent count after scroll: ${newCount}`); + await this.page + .waitForFunction( + (prevCount) => + document.querySelectorAll('[data-testid="library-agent-card"]') + .length > prevCount, + initialCount, + { timeout: 5000 }, + ) + .catch(() => {}); - return; + const newCount = await this.getAgentCountByListLength(); + console.log(`New agent count after scroll (DOM cards): ${newCount}`); } async testPagination(): Promise<{ diff --git a/autogpt_platform/frontend/src/tests/pages/marketplace.page.ts b/autogpt_platform/frontend/src/tests/pages/marketplace.page.ts index 20f60c371a..115a7b2f12 100644 --- a/autogpt_platform/frontend/src/tests/pages/marketplace.page.ts +++ b/autogpt_platform/frontend/src/tests/pages/marketplace.page.ts @@ -9,6 +9,7 @@ export class MarketplacePage extends BasePage { async goto(page: Page) { await page.goto("/marketplace"); + await page.waitForLoadState("networkidle").catch(() => {}); } async getMarketplaceTitle(page: Page) { @@ -109,16 +110,24 @@ export class MarketplacePage extends BasePage { async getFirstFeaturedAgent(page: Page) { const { getId } = getSelectors(page); - return getId("featured-store-card").first(); + const card = getId("featured-store-card").first(); + await card.waitFor({ state: "visible", timeout: 30000 }); + return card; } async getFirstTopAgent() { - return this.page.locator('[data-testid="store-card"]:visible').first(); + const card = this.page + .locator('[data-testid="store-card"]:visible') + .first(); + await card.waitFor({ state: "visible", timeout: 30000 }); + return card; } async getFirstCreatorProfile(page: Page) { const { getId } = getSelectors(page); - return getId("creator-card").first(); + const card = getId("creator-card").first(); + await card.waitFor({ state: "visible", timeout: 30000 }); + return card; } async getSearchResultsCount(page: Page) { From 7cb1e588b0d51eeb7b9991138b71a7f0ac96a5b5 Mon Sep 17 00:00:00 2001 From: Otto Date: Fri, 30 Jan 2026 07:49:05 +0000 Subject: [PATCH 03/31] fix(frontend): Refocus ChatInput after voice transcription completes (#11893) ## Summary Refocuses the chat input textarea after voice transcription finishes, allowing users to immediately use `spacebar+enter` to record and send their prompt. ## Changes - Added `inputId` parameter to `useVoiceRecording` hook - After transcription completes, the input is automatically focused - This improves the voice input UX flow ## Testing 1. Click mic button or press spacebar to record voice 2. Record a message and stop 3. After transcription completes, the input should be focused 4. User can now press Enter to send or spacebar to record again --------- Co-authored-by: Lluis Agusti --- .../Chat/components/ChatInput/ChatInput.tsx | 1 + .../Chat/components/ChatInput/useVoiceRecording.ts | 13 ++++++++++++- 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx index 521f6f6320..beb4678e73 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx @@ -57,6 +57,7 @@ export function ChatInput({ isStreaming, value, baseHandleKeyDown, + inputId, }); return ( diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts index 13b625e69c..4de74ef2e9 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts @@ -15,6 +15,7 @@ interface Args { isStreaming?: boolean; value: string; baseHandleKeyDown: (event: KeyboardEvent) => void; + inputId?: string; } export function useVoiceRecording({ @@ -23,6 +24,7 @@ export function useVoiceRecording({ isStreaming = false, value, baseHandleKeyDown, + inputId, }: Args) { const [isRecording, setIsRecording] = useState(false); const [isTranscribing, setIsTranscribing] = useState(false); @@ -103,7 +105,7 @@ export function useVoiceRecording({ setIsTranscribing(false); } }, - [handleTranscription], + [handleTranscription, inputId], ); const stopRecording = useCallback(() => { @@ -201,6 +203,15 @@ export function useVoiceRecording({ } }, [error, toast]); + useEffect(() => { + if (!isTranscribing && inputId) { + const inputElement = document.getElementById(inputId); + if (inputElement) { + inputElement.focus(); + } + } + }, [isTranscribing, inputId]); + const handleKeyDown = useCallback( (event: KeyboardEvent) => { if (event.key === " " && !value.trim() && !isTranscribing) { From e10ff8d37fb5b94a1dd814d36b8789b574d12be9 Mon Sep 17 00:00:00 2001 From: Otto Date: Fri, 30 Jan 2026 08:32:50 +0000 Subject: [PATCH 04/31] fix(frontend): remove double flag check on homepage redirect (#11894) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Changes šŸ—ļø Fixes the hard refresh redirect bug (SECRT-1845) by removing the double feature flag check. ### Before (buggy) ``` / ā†’ checks flag ā†’ /copilot or /library /copilot (layout) ā†’ checks flag ā†’ /library if OFF ``` On hard refresh, two sequential LD checks created a race condition window. ### After (fixed) ``` / ā†’ always redirects to /copilot /copilot (layout) ā†’ single flag check via FeatureFlagPage ``` Single check point = no double-check race condition. ## Root Cause As identified by @0ubbe: the root page and copilot layout were both checking the feature flag. On hard refresh with network latency, the second check could fire before LaunchDarkly fully initialized, causing users to be bounced to `/library`. ## Test Plan - [ ] Hard refresh on `/` ā†’ should go to `/copilot` (flag ON) - [ ] Hard refresh on `/copilot` ā†’ should stay on `/copilot` (flag ON) - [ ] With flag OFF ā†’ should redirect to `/library` - [ ] Normal navigation still works Fixes: SECRT-1845 cc @0ubbe --- autogpt_platform/frontend/src/app/page.tsx | 24 ++++++++++++++-------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/autogpt_platform/frontend/src/app/page.tsx b/autogpt_platform/frontend/src/app/page.tsx index 31d1e96e48..9a55e986bc 100644 --- a/autogpt_platform/frontend/src/app/page.tsx +++ b/autogpt_platform/frontend/src/app/page.tsx @@ -1,14 +1,20 @@ "use client"; -import { FeatureFlagRedirect } from "@/services/feature-flags/FeatureFlagRedirect"; -import { Flag } from "@/services/feature-flags/use-get-flag"; +import { useRouter } from "next/navigation"; +import { useEffect } from "react"; +/** + * Root page always redirects to /copilot. + * The /copilot page handles the feature flag check and redirects to /library if needed. + * This single-check approach avoids race conditions with LaunchDarkly initialization. + * See: SECRT-1845 + */ export default function Page() { - return ( - - ); + const router = useRouter(); + + useEffect(() => { + router.replace("/copilot"); + }, [router]); + + return null; } From de0ec3d388592c8e4e85832380a7105f63b7ef36 Mon Sep 17 00:00:00 2001 From: Bently Date: Fri, 30 Jan 2026 08:40:55 +0000 Subject: [PATCH 05/31] chore(llm): remove deprecated Claude 3.7 Sonnet model with migration and defensive handling (#11841) ## Summary Remove `claude-3-7-sonnet-20250219` from LLM model definitions ahead of Anthropic's API retirement, with comprehensive migration and defensive error handling. ## Background Anthropic is retiring Claude 3.7 Sonnet (`claude-3-7-sonnet-20250219`) on **February 19, 2026 at 9:00 AM PT**. This PR removes the model from the platform and migrates existing users to prevent service interruptions. ## Changes ### Code Changes - Remove `CLAUDE_3_7_SONNET` enum member from `LlmModel` in `llm.py` - Remove corresponding `ModelMetadata` entry - Remove `CLAUDE_3_7_SONNET` from `StagehandRecommendedLlmModel` enum - Remove `CLAUDE_3_7_SONNET` from block cost config - Add `CLAUDE_4_5_SONNET` to `StagehandRecommendedLlmModel` enum - Update Stagehand block defaults from `CLAUDE_3_7_SONNET` to `CLAUDE_4_5_SONNET` (staying in Claude family) - Add defensive error handling in `CredentialsFieldInfo.discriminate()` for deprecated model values ### Database Migration - Adds migration `20260126120000_migrate_claude_3_7_to_4_5_sonnet` - Migrates `AgentNode.constantInput` model references - Migrates `AgentNodeExecutionInputOutput.data` preset overrides ### Documentation - Updated `docs/integrations/block-integrations/llm.md` to remove deprecated model - Updated `docs/integrations/block-integrations/stagehand/blocks.md` to remove deprecated model and add Claude 4.5 Sonnet ## Notes - Agent JSON files in `autogpt_platform/backend/agents/` still reference this model in their provider mappings. These are auto-generated and should be regenerated separately. ## Testing - [ ] Verify LLM block still functions with remaining models - [ ] Confirm no import errors in affected files - [ ] Verify migration runs successfully - [ ] Verify deprecated model gives helpful error message instead of KeyError --- .../backend/backend/blocks/llm.py | 4 ---- .../backend/blocks/stagehand/blocks.py | 8 +++---- .../backend/backend/data/block_cost_config.py | 1 - .../backend/backend/data/model.py | 12 +++++++--- .../migration.sql | 22 +++++++++++++++++++ docs/integrations/block-integrations/llm.md | 14 ++++++------ .../block-integrations/stagehand/blocks.md | 6 ++--- 7 files changed, 45 insertions(+), 22 deletions(-) create mode 100644 autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql diff --git a/autogpt_platform/backend/backend/blocks/llm.py b/autogpt_platform/backend/backend/blocks/llm.py index fdcd7f3568..732fb1354c 100644 --- a/autogpt_platform/backend/backend/blocks/llm.py +++ b/autogpt_platform/backend/backend/blocks/llm.py @@ -115,7 +115,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta): CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101" CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929" CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001" - CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219" CLAUDE_3_HAIKU = "claude-3-haiku-20240307" # AI/ML API models AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo" @@ -280,9 +279,6 @@ MODEL_METADATA = { LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata( "anthropic", 200000, 64000, "Claude Haiku 4.5", "Anthropic", "Anthropic", 2 ), # claude-haiku-4-5-20251001 - LlmModel.CLAUDE_3_7_SONNET: ModelMetadata( - "anthropic", 200000, 64000, "Claude 3.7 Sonnet", "Anthropic", "Anthropic", 2 - ), # claude-3-7-sonnet-20250219 LlmModel.CLAUDE_3_HAIKU: ModelMetadata( "anthropic", 200000, 4096, "Claude 3 Haiku", "Anthropic", "Anthropic", 1 ), # claude-3-haiku-20240307 diff --git a/autogpt_platform/backend/backend/blocks/stagehand/blocks.py b/autogpt_platform/backend/backend/blocks/stagehand/blocks.py index be1d736962..4d5d6bf4f3 100644 --- a/autogpt_platform/backend/backend/blocks/stagehand/blocks.py +++ b/autogpt_platform/backend/backend/blocks/stagehand/blocks.py @@ -83,7 +83,7 @@ class StagehandRecommendedLlmModel(str, Enum): GPT41_MINI = "gpt-4.1-mini-2025-04-14" # Anthropic - CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219" + CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929" @property def provider_name(self) -> str: @@ -137,7 +137,7 @@ class StagehandObserveBlock(Block): model: StagehandRecommendedLlmModel = SchemaField( title="LLM Model", description="LLM to use for Stagehand (provider is inferred)", - default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET, + default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET, advanced=False, ) model_credentials: AICredentials = AICredentialsField() @@ -230,7 +230,7 @@ class StagehandActBlock(Block): model: StagehandRecommendedLlmModel = SchemaField( title="LLM Model", description="LLM to use for Stagehand (provider is inferred)", - default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET, + default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET, advanced=False, ) model_credentials: AICredentials = AICredentialsField() @@ -330,7 +330,7 @@ class StagehandExtractBlock(Block): model: StagehandRecommendedLlmModel = SchemaField( title="LLM Model", description="LLM to use for Stagehand (provider is inferred)", - default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET, + default=StagehandRecommendedLlmModel.CLAUDE_4_5_SONNET, advanced=False, ) model_credentials: AICredentials = AICredentialsField() diff --git a/autogpt_platform/backend/backend/data/block_cost_config.py b/autogpt_platform/backend/backend/data/block_cost_config.py index 1b54ae0942..f46cc726f0 100644 --- a/autogpt_platform/backend/backend/data/block_cost_config.py +++ b/autogpt_platform/backend/backend/data/block_cost_config.py @@ -81,7 +81,6 @@ MODEL_COST: dict[LlmModel, int] = { LlmModel.CLAUDE_4_5_HAIKU: 4, LlmModel.CLAUDE_4_5_OPUS: 14, LlmModel.CLAUDE_4_5_SONNET: 9, - LlmModel.CLAUDE_3_7_SONNET: 5, LlmModel.CLAUDE_3_HAIKU: 1, LlmModel.AIML_API_QWEN2_5_72B: 1, LlmModel.AIML_API_LLAMA3_1_70B: 1, diff --git a/autogpt_platform/backend/backend/data/model.py b/autogpt_platform/backend/backend/data/model.py index 2cc73f6b7b..331126fbd6 100644 --- a/autogpt_platform/backend/backend/data/model.py +++ b/autogpt_platform/backend/backend/data/model.py @@ -666,10 +666,16 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]): if not (self.discriminator and self.discriminator_mapping): return self + try: + provider = self.discriminator_mapping[discriminator_value] + except KeyError: + raise ValueError( + f"Model '{discriminator_value}' is not supported. " + "It may have been deprecated. Please update your agent configuration." + ) + return CredentialsFieldInfo( - credentials_provider=frozenset( - [self.discriminator_mapping[discriminator_value]] - ), + credentials_provider=frozenset([provider]), credentials_types=self.supported_types, credentials_scopes=self.required_scopes, discriminator=self.discriminator, diff --git a/autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql b/autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql new file mode 100644 index 0000000000..5746c80820 --- /dev/null +++ b/autogpt_platform/backend/migrations/20260126120000_migrate_claude_3_7_to_4_5_sonnet/migration.sql @@ -0,0 +1,22 @@ +-- Migrate Claude 3.7 Sonnet to Claude 4.5 Sonnet +-- This updates all AgentNode blocks that use the deprecated Claude 3.7 Sonnet model +-- Anthropic is retiring claude-3-7-sonnet-20250219 on February 19, 2026 + +-- Update AgentNode constant inputs +UPDATE "AgentNode" +SET "constantInput" = JSONB_SET( + "constantInput"::jsonb, + '{model}', + '"claude-sonnet-4-5-20250929"'::jsonb + ) +WHERE "constantInput"::jsonb->>'model' = 'claude-3-7-sonnet-20250219'; + +-- Update AgentPreset input overrides (stored in AgentNodeExecutionInputOutput) +UPDATE "AgentNodeExecutionInputOutput" +SET "data" = JSONB_SET( + "data"::jsonb, + '{model}', + '"claude-sonnet-4-5-20250929"'::jsonb + ) +WHERE "agentPresetId" IS NOT NULL + AND "data"::jsonb->>'model' = 'claude-3-7-sonnet-20250219'; diff --git a/docs/integrations/block-integrations/llm.md b/docs/integrations/block-integrations/llm.md index f4d69b912b..6a0a9e0987 100644 --- a/docs/integrations/block-integrations/llm.md +++ b/docs/integrations/block-integrations/llm.md @@ -65,7 +65,7 @@ The result routes data to yes_output or no_output, enabling intelligent branchin | condition | A plaintext English description of the condition to evaluate | str | Yes | | yes_value | (Optional) Value to output if the condition is true. If not provided, input_value will be used. | Yes Value | No | | no_value | (Optional) Value to output if the condition is false. If not provided, input_value will be used. | No Value | No | -| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | +| model | The language model to use for evaluating the condition. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | ### Outputs @@ -103,7 +103,7 @@ The block sends the entire conversation history to the chosen LLM, including sys |-------|-------------|------|----------| | prompt | The prompt to send to the language model. | str | No | | messages | List of messages in the conversation. | List[Any] | Yes | -| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | +| model | The language model to use for the conversation. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No | | ollama_host | Ollama host for local models | str | No | @@ -257,7 +257,7 @@ The block formulates a prompt based on the given focus or source data, sends it |-------|-------------|------|----------| | focus | The focus of the list to generate. | str | No | | source_data | The data to generate the list from. | str | No | -| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | +| model | The language model to use for generating the list. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | | max_retries | Maximum number of retries for generating a valid list. | int | No | | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No | | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No | @@ -424,7 +424,7 @@ The block sends the input prompt to a chosen LLM, along with any system prompts | prompt | The prompt to send to the language model. | str | Yes | | expected_format | Expected format of the response. If provided, the response will be validated against this format. The keys should be the expected fields in the response, and the values should be the description of the field. | Dict[str, str] | Yes | | list_result | Whether the response should be a list of objects in the expected format. | bool | No | -| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | +| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | | force_json_output | Whether to force the LLM to produce a JSON-only response. This can increase the block's reliability, but may also reduce the quality of the response because it prohibits the LLM from reasoning before providing its JSON response. | bool | No | | sys_prompt | The system prompt to provide additional context to the model. | str | No | | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No | @@ -464,7 +464,7 @@ The block sends the input prompt to a chosen LLM, processes the response, and re | Input | Description | Type | Required | |-------|-------------|------|----------| | prompt | The prompt to send to the language model. You can use any of the {keys} from Prompt Values to fill in the prompt with values from the prompt values dictionary by putting them in curly braces. | str | Yes | -| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | +| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | | sys_prompt | The system prompt to provide additional context to the model. | str | No | | retry | Number of times to retry the LLM call if the response does not match the expected format. | int | No | | prompt_values | Values used to fill in the prompt. The values can be used in the prompt by putting them in a double curly braces, e.g. {{variable_name}}. | Dict[str, str] | No | @@ -501,7 +501,7 @@ The block splits the input text into smaller chunks, sends each chunk to an LLM | Input | Description | Type | Required | |-------|-------------|------|----------| | text | The text to summarize. | str | Yes | -| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | +| model | The language model to use for summarizing the text. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | | focus | The topic to focus on in the summary | str | No | | style | The style of the summary to generate. | "concise" \| "detailed" \| "bullet points" \| "numbered list" | No | | max_tokens | The maximum number of tokens to generate in the chat completion. | int | No | @@ -763,7 +763,7 @@ Configure agent_mode_max_iterations to control loop behavior: 0 for single decis | Input | Description | Type | Required | |-------|-------------|------|----------| | prompt | The prompt to send to the language model. | str | Yes | -| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | +| model | The language model to use for answering the prompt. | "o3-mini" \| "o3-2025-04-16" \| "o1" \| "o1-mini" \| "gpt-5.2-2025-12-11" \| "gpt-5.1-2025-11-13" \| "gpt-5-2025-08-07" \| "gpt-5-mini-2025-08-07" \| "gpt-5-nano-2025-08-07" \| "gpt-5-chat-latest" \| "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "gpt-4o-mini" \| "gpt-4o" \| "gpt-4-turbo" \| "gpt-3.5-turbo" \| "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-haiku-20240307" \| "Qwen/Qwen2.5-72B-Instruct-Turbo" \| "nvidia/llama-3.1-nemotron-70b-instruct" \| "meta-llama/Llama-3.3-70B-Instruct-Turbo" \| "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" \| "meta-llama/Llama-3.2-3B-Instruct-Turbo" \| "llama-3.3-70b-versatile" \| "llama-3.1-8b-instant" \| "llama3.3" \| "llama3.2" \| "llama3" \| "llama3.1:405b" \| "dolphin-mistral:latest" \| "openai/gpt-oss-120b" \| "openai/gpt-oss-20b" \| "google/gemini-2.5-pro-preview-03-25" \| "google/gemini-3-pro-preview" \| "google/gemini-2.5-flash" \| "google/gemini-2.0-flash-001" \| "google/gemini-2.5-flash-lite-preview-06-17" \| "google/gemini-2.0-flash-lite-001" \| "mistralai/mistral-nemo" \| "cohere/command-r-08-2024" \| "cohere/command-r-plus-08-2024" \| "deepseek/deepseek-chat" \| "deepseek/deepseek-r1-0528" \| "perplexity/sonar" \| "perplexity/sonar-pro" \| "perplexity/sonar-deep-research" \| "nousresearch/hermes-3-llama-3.1-405b" \| "nousresearch/hermes-3-llama-3.1-70b" \| "amazon/nova-lite-v1" \| "amazon/nova-micro-v1" \| "amazon/nova-pro-v1" \| "microsoft/wizardlm-2-8x22b" \| "gryphe/mythomax-l2-13b" \| "meta-llama/llama-4-scout" \| "meta-llama/llama-4-maverick" \| "x-ai/grok-4" \| "x-ai/grok-4-fast" \| "x-ai/grok-4.1-fast" \| "x-ai/grok-code-fast-1" \| "moonshotai/kimi-k2" \| "qwen/qwen3-235b-a22b-thinking-2507" \| "qwen/qwen3-coder" \| "Llama-4-Scout-17B-16E-Instruct-FP8" \| "Llama-4-Maverick-17B-128E-Instruct-FP8" \| "Llama-3.3-8B-Instruct" \| "Llama-3.3-70B-Instruct" \| "v0-1.5-md" \| "v0-1.5-lg" \| "v0-1.0-md" | No | | multiple_tool_calls | Whether to allow multiple tool calls in a single response. | bool | No | | sys_prompt | The system prompt to provide additional context to the model. | str | No | | conversation_history | The conversation history to provide context for the prompt. | List[Dict[str, Any]] | No | diff --git a/docs/integrations/block-integrations/stagehand/blocks.md b/docs/integrations/block-integrations/stagehand/blocks.md index dac0586fa2..cc201d092b 100644 --- a/docs/integrations/block-integrations/stagehand/blocks.md +++ b/docs/integrations/block-integrations/stagehand/blocks.md @@ -20,7 +20,7 @@ Configure timeouts for DOM settlement and page loading. Variables can be passed | Input | Description | Type | Required | |-------|-------------|------|----------| | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes | -| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No | +| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No | | url | URL to navigate to. | str | Yes | | action | Action to perform. Suggested actions are: click, fill, type, press, scroll, select from dropdown. For multi-step actions, add an entry for each step. | List[str] | Yes | | variables | Variables to use in the action. Variables contains data you want the action to use. | Dict[str, str] | No | @@ -65,7 +65,7 @@ Supports searching within iframes and configurable timeouts for dynamic content | Input | Description | Type | Required | |-------|-------------|------|----------| | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes | -| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No | +| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No | | url | URL to navigate to. | str | Yes | | instruction | Natural language description of elements or actions to discover. | str | Yes | | iframes | Whether to search within iframes. If True, Stagehand will search for actions within iframes. | bool | No | @@ -106,7 +106,7 @@ Use this to explore a page's interactive elements before building automated work | Input | Description | Type | Required | |-------|-------------|------|----------| | browserbase_project_id | Browserbase project ID (required if using Browserbase) | str | Yes | -| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-3-7-sonnet-20250219" | No | +| model | LLM to use for Stagehand (provider is inferred) | "gpt-4.1-2025-04-14" \| "gpt-4.1-mini-2025-04-14" \| "claude-sonnet-4-5-20250929" | No | | url | URL to navigate to. | str | Yes | | instruction | Natural language description of elements or actions to discover. | str | Yes | | iframes | Whether to search within iframes. If True, Stagehand will search for actions within iframes. | bool | No | From e6438b9a764d26c08f368173fd1a1150608b7065 Mon Sep 17 00:00:00 2001 From: Ubbe Date: Fri, 30 Jan 2026 17:20:03 +0700 Subject: [PATCH 06/31] hotfix(frontend): use server redirect (#11900) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ### Changes šŸ—ļø The page used a client-side redirect (`useEffect` + `router.replace`) which only works after JavaScript loads and hydrates. On deployed sites, if there's any delay or failure in JS execution, users see an empty/black page because the component returns null. **Fix:** Converted to a server-side redirect using redirect() from next/navigation. This is a server component now, so: ### Checklist šŸ“‹ #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Tested locally but will see it fully working once deployed --- autogpt_platform/frontend/src/app/page.tsx | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/autogpt_platform/frontend/src/app/page.tsx b/autogpt_platform/frontend/src/app/page.tsx index 9a55e986bc..88e4e21644 100644 --- a/autogpt_platform/frontend/src/app/page.tsx +++ b/autogpt_platform/frontend/src/app/page.tsx @@ -1,7 +1,4 @@ -"use client"; - -import { useRouter } from "next/navigation"; -import { useEffect } from "react"; +import { redirect } from "next/navigation"; /** * Root page always redirects to /copilot. @@ -10,11 +7,5 @@ import { useEffect } from "react"; * See: SECRT-1845 */ export default function Page() { - const router = useRouter(); - - useEffect(() => { - router.replace("/copilot"); - }, [router]); - - return null; + redirect("/copilot"); } From 350ad3591b1e7a9f5ebf5a2d3053339c0f8b57e2 Mon Sep 17 00:00:00 2001 From: Reinier van der Leer Date: Fri, 30 Jan 2026 12:01:51 +0100 Subject: [PATCH 07/31] fix(backend/chat): Filter credentials for graph execution by scopes (#11881) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit [SECRT-1842: run_agent tool does not correctly use credentials - agents fail with insufficient auth scopes](https://linear.app/autogpt/issue/SECRT-1842) ### Changes šŸ—ļø - Include scopes in credentials filter in `backend.api.features.chat.tools.utils.match_user_credentials_to_graph` ### Checklist šŸ“‹ #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - CI must pass - It's broken now and a simple change so we'll test in the dev deployment --- .../backend/api/features/chat/tools/utils.py | 40 ++++++++++++++++--- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/utils.py b/autogpt_platform/backend/backend/api/features/chat/tools/utils.py index a2ac91dc65..0046d0b249 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/utils.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/utils.py @@ -8,7 +8,7 @@ from backend.api.features.library import model as library_model from backend.api.features.store import db as store_db from backend.data import graph as graph_db from backend.data.graph import GraphModel -from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput +from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput from backend.integrations.creds_manager import IntegrationCredentialsManager from backend.util.exceptions import NotFoundError @@ -266,13 +266,14 @@ async def match_user_credentials_to_graph( credential_requirements, _node_fields, ) in aggregated_creds.items(): - # Find first matching credential by provider and type + # Find first matching credential by provider, type, and scopes matching_cred = next( ( cred for cred in available_creds if cred.provider in credential_requirements.provider and cred.type in credential_requirements.supported_types + and _credential_has_required_scopes(cred, credential_requirements) ), None, ) @@ -296,10 +297,17 @@ async def match_user_credentials_to_graph( f"{credential_field_name} (validation failed: {e})" ) else: + # Build a helpful error message including scope requirements + error_parts = [ + f"provider in {list(credential_requirements.provider)}", + f"type in {list(credential_requirements.supported_types)}", + ] + if credential_requirements.required_scopes: + error_parts.append( + f"scopes including {list(credential_requirements.required_scopes)}" + ) missing_creds.append( - f"{credential_field_name} " - f"(requires provider in {list(credential_requirements.provider)}, " - f"type in {list(credential_requirements.supported_types)})" + f"{credential_field_name} (requires {', '.join(error_parts)})" ) logger.info( @@ -309,6 +317,28 @@ async def match_user_credentials_to_graph( return graph_credentials_inputs, missing_creds +def _credential_has_required_scopes( + credential: Credentials, + requirements: CredentialsFieldInfo, +) -> bool: + """ + Check if a credential has all the scopes required by the block. + + For OAuth2 credentials, verifies that the credential's scopes are a superset + of the required scopes. For other credential types, returns True (no scope check). + """ + # Only OAuth2 credentials have scopes to check + if credential.type != "oauth2": + return True + + # If no scopes are required, any credential matches + if not requirements.required_scopes: + return True + + # Check that credential scopes are a superset of required scopes + return set(credential.scopes).issuperset(requirements.required_scopes) + + async def check_user_has_required_credentials( user_id: str, required_credentials: list[CredentialsMetaInput], From dbbff046163020dc1d035926f037871befb713c7 Mon Sep 17 00:00:00 2001 From: Otto Date: Fri, 30 Jan 2026 12:08:26 +0000 Subject: [PATCH 08/31] hotfix(frontend): LD remount (#11903) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Changes šŸ—ļø Removes the `key` prop from `LDProvider` that was causing full remounts when user context changed. ### The Problem The `key={context.key}` prop was forcing React to unmount and remount the entire LDProvider when switching from anonymous ā†’ logged in user: ``` 1. Page loads, user loading ā†’ key="anonymous" ā†’ LD mounts ā†’ flags available āœ… 2. User finishes loading ā†’ key="user-123" ā†’ React sees key changed 3. LDProvider UNMOUNTS ā†’ flags become undefined āŒ 4. New LDProvider MOUNTS ā†’ initializes again ā†’ flags available āœ… ``` This caused the flag values to cycle: `undefined ā†’ value ā†’ undefined ā†’ value` ### The Fix Remove the `key` prop. The LDProvider handles context changes internally via the `context` prop, which triggers `identify()` without remounting the provider. ## Checklist šŸ“‹ - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [ ] I have tested my changes according to the test plan: - [ ] Flag values don't flicker on page load - [ ] Flag values update correctly when logging in/out - [ ] No redirect race conditions Related: SECRT-1845 --- .../src/services/feature-flags/feature-flag-provider.tsx | 2 -- 1 file changed, 2 deletions(-) diff --git a/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx b/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx index 8b78f4c589..9e1c812e85 100644 --- a/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx +++ b/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx @@ -40,8 +40,6 @@ export function LaunchDarklyProvider({ children }: { children: ReactNode }) { return ( Date: Fri, 30 Jan 2026 20:40:46 +0700 Subject: [PATCH 09/31] hotfix(frontend): fix home redirect (3) (#11904) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ### Changes šŸ—ļø Further improvements to LaunchDarkly initialisation and homepage redirect... ### Checklist šŸ“‹ #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Run the app locally with the flag disabled/enabled, and the redirects work --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Ubbe <0ubbe@users.noreply.github.com> --- autogpt_platform/frontend/src/app/page.tsx | 20 +++++++++++-------- .../feature-flags/feature-flag-provider.tsx | 9 ++++++++- 2 files changed, 20 insertions(+), 9 deletions(-) diff --git a/autogpt_platform/frontend/src/app/page.tsx b/autogpt_platform/frontend/src/app/page.tsx index 88e4e21644..ce67760eda 100644 --- a/autogpt_platform/frontend/src/app/page.tsx +++ b/autogpt_platform/frontend/src/app/page.tsx @@ -1,11 +1,15 @@ -import { redirect } from "next/navigation"; +"use client"; + +import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner"; +import { useRouter } from "next/navigation"; +import { useEffect } from "react"; -/** - * Root page always redirects to /copilot. - * The /copilot page handles the feature flag check and redirects to /library if needed. - * This single-check approach avoids race conditions with LaunchDarkly initialization. - * See: SECRT-1845 - */ export default function Page() { - redirect("/copilot"); + const router = useRouter(); + + useEffect(() => { + router.replace("/copilot"); + }, [router]); + + return ; } diff --git a/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx b/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx index 9e1c812e85..da073816ac 100644 --- a/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx +++ b/autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx @@ -1,5 +1,6 @@ "use client"; +import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner"; import { useSupabase } from "@/lib/supabase/hooks/useSupabase"; import * as Sentry from "@sentry/nextjs"; import { LDProvider } from "launchdarkly-react-client-sdk"; @@ -15,7 +16,9 @@ export function LaunchDarklyProvider({ children }: { children: ReactNode }) { const clientId = environment.getLaunchDarklyClientId(); const context = useMemo(() => { - if (isUserLoading || !user) { + if (isUserLoading) return; + + if (!user) { return { kind: "user" as const, key: "anonymous", @@ -38,6 +41,10 @@ export function LaunchDarklyProvider({ children }: { children: ReactNode }) { return <>{children}; } + if (isUserLoading) { + return ; + } + return ( Date: Fri, 30 Jan 2026 22:59:45 +0000 Subject: [PATCH 10/31] fix(readme): update broken self-hosting docs link (#11911) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary The self-hosting guide link in README.md was broken. **Old link:** `https://docs.agpt.co/platform/getting-started/` - Redirects to `https://agpt.co/docs/platform/getting-started` - Returns HTTP 400 āŒ **New link:** `https://agpt.co/docs/platform/getting-started/getting-started` - Works correctly āœ… ## Changes - Updated the self-hosting guide URL in README.md Fixes #OPEN-2973 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3572fe318b..349d8818ef 100644 --- a/README.md +++ b/README.md @@ -54,7 +54,7 @@ Before proceeding with the installation, ensure your system meets the following ### Updated Setup Instructions: We've moved to a fully maintained and regularly updated documentation site. -šŸ‘‰ [Follow the official self-hosting guide here](https://docs.agpt.co/platform/getting-started/) +šŸ‘‰ [Follow the official self-hosting guide here](https://agpt.co/docs/platform/getting-started/getting-started) This tutorial assumes you have Docker, VSCode, git and npm installed. From 18a1661fa3ae88ee84465d30d30b76d26c93f08d Mon Sep 17 00:00:00 2001 From: Zamil Majdy Date: Fri, 30 Jan 2026 18:18:21 -0600 Subject: [PATCH 11/31] feat: add library agent fetching with two-phase search for sub-agent support (#11889) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Context When users ask the chat to create agents, they may want to compose workflows that reuse their existing agents as sub-agents. For this to work, the Agent Generator service needs to know what agents the user has available. **Challenge:** Users can have large libraries with many agents. Fetching all of them would be slow and provide too much context to the LLM. ## Solution This PR implements **search-based library agent fetching** with a **two-phase search** strategy: 1. **Phase 1 (Initial Search):** When the user describes their goal, we search for relevant library agents using the goal as the search query 2. **Phase 2 (Step-Based Enrichment):** After the goal is decomposed into steps, we extract keywords from those steps and search for additional relevant agents This ensures we find agents that are relevant to both the high-level goal AND the specific steps identified. ### Example Flow ``` User goal: "Create an agent that fetches weather and sends a summary email" Phase 1: Search for "weather email summary" ā†’ finds "Weather Fetcher" agent Phase 2: After decomposition identifies steps like "send email notification" ā†’ searches "send email notification" ā†’ finds "Gmail Sender" agent ``` ### Changes **Library Agent Fetching:** - `get_library_agents_for_generation()` - Search-based fetching from user's library - `search_marketplace_agents_for_generation()` - Search public marketplace - `get_all_relevant_agents_for_generation()` - Combines both with deduplication **Two-Phase Search:** - `extract_search_terms_from_steps()` - Extracts keywords from decomposed steps - `enrich_library_agents_from_steps()` - Searches for additional agents based on steps - Integrated into `create_agent.py` as "Step 1.5" after goal decomposition **Type Safety:** - Added `TypedDict` definitions: `LibraryAgentSummary`, `MarketplaceAgentSummary`, `DecompositionStep`, `DecompositionResult` ### Design Decisions - **Search-based, not fetch-all:** Scalable for large libraries - **Library agents prioritized:** They have full schemas; marketplace agents have basic info only - **Deduplication by name and graph_id:** Prevents duplicates across searches - **Graceful degradation:** Failures don't block agent generation - **Limited to 3 search terms:** Avoids excessive API calls during enrichment ## Related PR - Agent Generator: https://github.com/Significant-Gravitas/AutoGPT-Agent-Generator/pull/103 ## Test plan - [x] `test_library_agents.py` - 19 tests covering all new functions - [x] `test_service.py` - 4 tests for library_agents passthrough - [ ] Integration test: Create agent with library sub-agent composition --- .../backend/api/features/chat/service.py | 5 + .../chat/tools/agent_generator/__init__.py | 42 +- .../chat/tools/agent_generator/core.py | 615 ++++++++++++- .../chat/tools/agent_generator/errors.py | 66 +- .../chat/tools/agent_generator/service.py | 40 +- .../api/features/chat/tools/agent_search.py | 132 ++- .../api/features/chat/tools/create_agent.py | 53 +- .../api/features/chat/tools/edit_agent.py | 32 +- .../backend/api/features/library/db.py | 8 +- .../backend/api/features/library/model.py | 118 ++- .../backend/snapshots/lib_agts_search | 8 + .../agent_generator/test_core_integration.py | 12 +- .../agent_generator/test_library_agents.py | 841 ++++++++++++++++++ .../test/agent_generator/test_service.py | 134 +++ .../legacy-builder/CustomNode/CustomNode.tsx | 2 +- .../frontend/src/app/api/openapi.json | 36 + .../components/ChatMessage/ChatMessage.tsx | 8 + .../ClarificationQuestionsWidget.tsx | 65 +- .../components/ToolResponseMessage/helpers.ts | 6 +- 19 files changed, 2069 insertions(+), 154 deletions(-) create mode 100644 autogpt_platform/backend/test/agent_generator/test_library_agents.py diff --git a/autogpt_platform/backend/backend/api/features/chat/service.py b/autogpt_platform/backend/backend/api/features/chat/service.py index 20216162b5..bcd6856503 100644 --- a/autogpt_platform/backend/backend/api/features/chat/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/service.py @@ -1834,6 +1834,11 @@ async def _execute_long_running_tool( tool_call_id=tool_call_id, result=error_response.model_dump_json(), ) + # Generate LLM continuation so user sees explanation even for errors + try: + await _generate_llm_continuation(session_id=session_id, user_id=user_id) + except Exception as llm_err: + logger.warning(f"Failed to generate LLM continuation for error: {llm_err}") finally: await _mark_operation_completed(tool_call_id) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py index 499025b7dc..b7650b3cbd 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py @@ -2,30 +2,54 @@ from .core import ( AgentGeneratorNotConfiguredError, + AgentJsonValidationError, + AgentSummary, + DecompositionResult, + DecompositionStep, + LibraryAgentSummary, + MarketplaceAgentSummary, decompose_goal, + enrich_library_agents_from_steps, + extract_search_terms_from_steps, + extract_uuids_from_text, generate_agent, generate_agent_patch, get_agent_as_json, + get_all_relevant_agents_for_generation, + get_library_agent_by_graph_id, + get_library_agent_by_id, + get_library_agents_for_generation, json_to_graph, save_agent_to_library, + search_marketplace_agents_for_generation, ) from .errors import get_user_message_for_error from .service import health_check as check_external_service_health from .service import is_external_service_configured __all__ = [ - # Core functions + "AgentGeneratorNotConfiguredError", + "AgentJsonValidationError", + "AgentSummary", + "DecompositionResult", + "DecompositionStep", + "LibraryAgentSummary", + "MarketplaceAgentSummary", + "check_external_service_health", "decompose_goal", + "enrich_library_agents_from_steps", + "extract_search_terms_from_steps", + "extract_uuids_from_text", "generate_agent", "generate_agent_patch", - "save_agent_to_library", "get_agent_as_json", - "json_to_graph", - # Exceptions - "AgentGeneratorNotConfiguredError", - # Service - "is_external_service_configured", - "check_external_service_health", - # Error handling + "get_all_relevant_agents_for_generation", + "get_library_agent_by_graph_id", + "get_library_agent_by_id", + "get_library_agents_for_generation", "get_user_message_for_error", + "is_external_service_configured", + "json_to_graph", + "save_agent_to_library", + "search_marketplace_agents_for_generation", ] diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py index d56e33cbb0..466f6438a3 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py @@ -1,11 +1,21 @@ """Core agent generation functions.""" import logging +import re import uuid -from typing import Any +from typing import Any, NotRequired, TypedDict from backend.api.features.library import db as library_db -from backend.data.graph import Graph, Link, Node, create_graph +from backend.api.features.store import db as store_db +from backend.data.graph import ( + Graph, + Link, + Node, + create_graph, + get_graph, + get_graph_all_versions, +) +from backend.util.exceptions import DatabaseError, NotFoundError from .service import ( decompose_goal_external, @@ -16,6 +26,74 @@ from .service import ( logger = logging.getLogger(__name__) +AGENT_EXECUTOR_BLOCK_ID = "e189baac-8c20-45a1-94a7-55177ea42565" + + +class ExecutionSummary(TypedDict): + """Summary of a single execution for quality assessment.""" + + status: str + correctness_score: NotRequired[float] + activity_summary: NotRequired[str] + + +class LibraryAgentSummary(TypedDict): + """Summary of a library agent for sub-agent composition. + + Includes recent executions to help the LLM decide whether to use this agent. + Each execution shows status, correctness_score (0-1), and activity_summary. + """ + + graph_id: str + graph_version: int + name: str + description: str + input_schema: dict[str, Any] + output_schema: dict[str, Any] + recent_executions: NotRequired[list[ExecutionSummary]] + + +class MarketplaceAgentSummary(TypedDict): + """Summary of a marketplace agent for sub-agent composition.""" + + name: str + description: str + sub_heading: str + creator: str + is_marketplace_agent: bool + + +class DecompositionStep(TypedDict, total=False): + """A single step in decomposed instructions.""" + + description: str + action: str + block_name: str + tool: str + name: str + + +class DecompositionResult(TypedDict, total=False): + """Result from decompose_goal - can be instructions, questions, or error.""" + + type: str + steps: list[DecompositionStep] + questions: list[dict[str, Any]] + error: str + error_type: str + + +AgentSummary = LibraryAgentSummary | MarketplaceAgentSummary | dict[str, Any] + + +def _to_dict_list( + agents: list[AgentSummary] | list[dict[str, Any]] | None, +) -> list[dict[str, Any]] | None: + """Convert typed agent summaries to plain dicts for external service calls.""" + if agents is None: + return None + return [dict(a) for a in agents] + class AgentGeneratorNotConfiguredError(Exception): """Raised when the external Agent Generator service is not configured.""" @@ -36,15 +114,414 @@ def _check_service_configured() -> None: ) -async def decompose_goal(description: str, context: str = "") -> dict[str, Any] | None: +_UUID_PATTERN = re.compile( + r"[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}", + re.IGNORECASE, +) + + +def extract_uuids_from_text(text: str) -> list[str]: + """Extract all UUID v4 strings from text. + + Args: + text: Text that may contain UUIDs (e.g., user's goal description) + + Returns: + List of unique UUIDs found in the text (lowercase) + """ + matches = _UUID_PATTERN.findall(text) + return list({m.lower() for m in matches}) + + +async def get_library_agent_by_id( + user_id: str, agent_id: str +) -> LibraryAgentSummary | None: + """Fetch a specific library agent by its ID (library agent ID or graph_id). + + This function tries multiple lookup strategies: + 1. First tries to find by graph_id (AgentGraph primary key) + 2. If not found, tries to find by library agent ID (LibraryAgent primary key) + + This handles both cases: + - User provides graph_id (e.g., from AgentExecutorBlock) + - User provides library agent ID (e.g., from library URL) + + Args: + user_id: The user ID + agent_id: The ID to look up (can be graph_id or library agent ID) + + Returns: + LibraryAgentSummary if found, None otherwise + """ + try: + agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id) + if agent: + logger.debug(f"Found library agent by graph_id: {agent.name}") + return LibraryAgentSummary( + graph_id=agent.graph_id, + graph_version=agent.graph_version, + name=agent.name, + description=agent.description, + input_schema=agent.input_schema, + output_schema=agent.output_schema, + ) + except DatabaseError: + raise + except Exception as e: + logger.debug(f"Could not fetch library agent by graph_id {agent_id}: {e}") + + try: + agent = await library_db.get_library_agent(agent_id, user_id) + if agent: + logger.debug(f"Found library agent by library_id: {agent.name}") + return LibraryAgentSummary( + graph_id=agent.graph_id, + graph_version=agent.graph_version, + name=agent.name, + description=agent.description, + input_schema=agent.input_schema, + output_schema=agent.output_schema, + ) + except NotFoundError: + logger.debug(f"Library agent not found by library_id: {agent_id}") + except DatabaseError: + raise + except Exception as e: + logger.warning( + f"Could not fetch library agent by library_id {agent_id}: {e}", + exc_info=True, + ) + + return None + + +get_library_agent_by_graph_id = get_library_agent_by_id + + +async def get_library_agents_for_generation( + user_id: str, + search_query: str | None = None, + exclude_graph_id: str | None = None, + max_results: int = 15, +) -> list[LibraryAgentSummary]: + """Fetch user's library agents formatted for Agent Generator. + + Uses search-based fetching to return relevant agents instead of all agents. + This is more scalable for users with large libraries. + + Includes recent_executions list to help the LLM assess agent quality: + - Each execution has status, correctness_score (0-1), and activity_summary + - This gives the LLM concrete examples of recent performance + + Args: + user_id: The user ID + search_query: Optional search term to find relevant agents (user's goal/description) + exclude_graph_id: Optional graph ID to exclude (prevents circular references) + max_results: Maximum number of agents to return (default 15) + + Returns: + List of LibraryAgentSummary with schemas and recent executions for sub-agent composition + """ + try: + response = await library_db.list_library_agents( + user_id=user_id, + search_term=search_query, + page=1, + page_size=max_results, + include_executions=True, + ) + + results: list[LibraryAgentSummary] = [] + for agent in response.agents: + if exclude_graph_id is not None and agent.graph_id == exclude_graph_id: + continue + + summary = LibraryAgentSummary( + graph_id=agent.graph_id, + graph_version=agent.graph_version, + name=agent.name, + description=agent.description, + input_schema=agent.input_schema, + output_schema=agent.output_schema, + ) + if agent.recent_executions: + exec_summaries: list[ExecutionSummary] = [] + for ex in agent.recent_executions: + exec_sum = ExecutionSummary(status=ex.status) + if ex.correctness_score is not None: + exec_sum["correctness_score"] = ex.correctness_score + if ex.activity_summary: + exec_sum["activity_summary"] = ex.activity_summary + exec_summaries.append(exec_sum) + summary["recent_executions"] = exec_summaries + results.append(summary) + return results + except DatabaseError: + raise + except Exception as e: + logger.warning(f"Failed to fetch library agents: {e}") + return [] + + +async def search_marketplace_agents_for_generation( + search_query: str, + max_results: int = 10, +) -> list[MarketplaceAgentSummary]: + """Search marketplace agents formatted for Agent Generator. + + Note: This returns basic agent info. Full input/output schemas would require + additional graph fetches and is a potential future enhancement. + + Args: + search_query: Search term to find relevant public agents + max_results: Maximum number of agents to return (default 10) + + Returns: + List of MarketplaceAgentSummary (without detailed schemas for now) + """ + try: + response = await store_db.get_store_agents( + search_query=search_query, + page=1, + page_size=max_results, + ) + + results: list[MarketplaceAgentSummary] = [] + for agent in response.agents: + results.append( + MarketplaceAgentSummary( + name=agent.agent_name, + description=agent.description, + sub_heading=agent.sub_heading, + creator=agent.creator, + is_marketplace_agent=True, + ) + ) + return results + except Exception as e: + logger.warning(f"Failed to search marketplace agents: {e}") + return [] + + +async def get_all_relevant_agents_for_generation( + user_id: str, + search_query: str | None = None, + exclude_graph_id: str | None = None, + include_library: bool = True, + include_marketplace: bool = True, + max_library_results: int = 15, + max_marketplace_results: int = 10, +) -> list[AgentSummary]: + """Fetch relevant agents from library and/or marketplace. + + Searches both user's library and marketplace by default. + Explicitly mentioned UUIDs in the search query are always looked up. + + Args: + user_id: The user ID + search_query: Search term to find relevant agents (user's goal/description) + exclude_graph_id: Optional graph ID to exclude (prevents circular references) + include_library: Whether to search user's library (default True) + include_marketplace: Whether to also search marketplace (default True) + max_library_results: Max library agents to return (default 15) + max_marketplace_results: Max marketplace agents to return (default 10) + + Returns: + List of AgentSummary, library agents first (with full schemas), + then marketplace agents (basic info only) + """ + agents: list[AgentSummary] = [] + seen_graph_ids: set[str] = set() + + if search_query: + mentioned_uuids = extract_uuids_from_text(search_query) + for graph_id in mentioned_uuids: + if graph_id == exclude_graph_id: + continue + agent = await get_library_agent_by_graph_id(user_id, graph_id) + agent_graph_id = agent.get("graph_id") if agent else None + if agent and agent_graph_id and agent_graph_id not in seen_graph_ids: + agents.append(agent) + seen_graph_ids.add(agent_graph_id) + logger.debug( + f"Found explicitly mentioned agent: {agent.get('name') or 'Unknown'}" + ) + + if include_library: + library_agents = await get_library_agents_for_generation( + user_id=user_id, + search_query=search_query, + exclude_graph_id=exclude_graph_id, + max_results=max_library_results, + ) + for agent in library_agents: + graph_id = agent.get("graph_id") + if graph_id and graph_id not in seen_graph_ids: + agents.append(agent) + seen_graph_ids.add(graph_id) + + if include_marketplace and search_query: + marketplace_agents = await search_marketplace_agents_for_generation( + search_query=search_query, + max_results=max_marketplace_results, + ) + library_names: set[str] = set() + for a in agents: + name = a.get("name") + if name and isinstance(name, str): + library_names.add(name.lower()) + for agent in marketplace_agents: + agent_name = agent.get("name") + if agent_name and isinstance(agent_name, str): + if agent_name.lower() not in library_names: + agents.append(agent) + + return agents + + +def extract_search_terms_from_steps( + decomposition_result: DecompositionResult | dict[str, Any], +) -> list[str]: + """Extract search terms from decomposed instruction steps. + + Analyzes the decomposition result to extract relevant keywords + for additional library agent searches. + + Args: + decomposition_result: Result from decompose_goal containing steps + + Returns: + List of unique search terms extracted from steps + """ + search_terms: list[str] = [] + + if decomposition_result.get("type") != "instructions": + return search_terms + + steps = decomposition_result.get("steps", []) + if not steps: + return search_terms + + step_keys: list[str] = ["description", "action", "block_name", "tool", "name"] + + for step in steps: + for key in step_keys: + value = step.get(key) # type: ignore[union-attr] + if isinstance(value, str) and len(value) > 3: + search_terms.append(value) + + seen: set[str] = set() + unique_terms: list[str] = [] + for term in search_terms: + term_lower = term.lower() + if term_lower not in seen: + seen.add(term_lower) + unique_terms.append(term) + + return unique_terms + + +async def enrich_library_agents_from_steps( + user_id: str, + decomposition_result: DecompositionResult | dict[str, Any], + existing_agents: list[AgentSummary] | list[dict[str, Any]], + exclude_graph_id: str | None = None, + include_marketplace: bool = True, + max_additional_results: int = 10, +) -> list[AgentSummary] | list[dict[str, Any]]: + """Enrich library agents list with additional searches based on decomposed steps. + + This implements two-phase search: after decomposition, we search for additional + relevant agents based on the specific steps identified. + + Args: + user_id: The user ID + decomposition_result: Result from decompose_goal containing steps + existing_agents: Already fetched library agents from initial search + exclude_graph_id: Optional graph ID to exclude + include_marketplace: Whether to also search marketplace + max_additional_results: Max additional agents per search term (default 10) + + Returns: + Combined list of library agents (existing + newly discovered) + """ + search_terms = extract_search_terms_from_steps(decomposition_result) + + if not search_terms: + return existing_agents + + existing_ids: set[str] = set() + existing_names: set[str] = set() + + for agent in existing_agents: + agent_name = agent.get("name") + if agent_name and isinstance(agent_name, str): + existing_names.add(agent_name.lower()) + graph_id = agent.get("graph_id") # type: ignore[call-overload] + if graph_id and isinstance(graph_id, str): + existing_ids.add(graph_id) + + all_agents: list[AgentSummary] | list[dict[str, Any]] = list(existing_agents) + + for term in search_terms[:3]: + try: + additional_agents = await get_all_relevant_agents_for_generation( + user_id=user_id, + search_query=term, + exclude_graph_id=exclude_graph_id, + include_marketplace=include_marketplace, + max_library_results=max_additional_results, + max_marketplace_results=5, + ) + + for agent in additional_agents: + agent_name = agent.get("name") + if not agent_name or not isinstance(agent_name, str): + continue + agent_name_lower = agent_name.lower() + + if agent_name_lower in existing_names: + continue + + graph_id = agent.get("graph_id") # type: ignore[call-overload] + if graph_id and graph_id in existing_ids: + continue + + all_agents.append(agent) + existing_names.add(agent_name_lower) + if graph_id and isinstance(graph_id, str): + existing_ids.add(graph_id) + + except DatabaseError: + logger.error(f"Database error searching for agents with term '{term}'") + raise + except Exception as e: + logger.warning( + f"Failed to search for additional agents with term '{term}': {e}" + ) + + logger.debug( + f"Enriched library agents: {len(existing_agents)} initial + " + f"{len(all_agents) - len(existing_agents)} additional = {len(all_agents)} total" + ) + + return all_agents + + +async def decompose_goal( + description: str, + context: str = "", + library_agents: list[AgentSummary] | None = None, +) -> DecompositionResult | None: """Break down a goal into steps or return clarifying questions. Args: description: Natural language goal description context: Additional context (e.g., answers to previous questions) + library_agents: User's library agents available for sub-agent composition Returns: - Dict with either: + DecompositionResult with either: - {"type": "clarifying_questions", "questions": [...]} - {"type": "instructions", "steps": [...]} Or None on error @@ -54,14 +531,21 @@ async def decompose_goal(description: str, context: str = "") -> dict[str, Any] """ _check_service_configured() logger.info("Calling external Agent Generator service for decompose_goal") - return await decompose_goal_external(description, context) + result = await decompose_goal_external( + description, context, _to_dict_list(library_agents) + ) + return result # type: ignore[return-value] -async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None: +async def generate_agent( + instructions: DecompositionResult | dict[str, Any], + library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None, +) -> dict[str, Any] | None: """Generate agent JSON from instructions. Args: instructions: Structured instructions from decompose_goal + library_agents: User's library agents available for sub-agent composition Returns: Agent JSON dict, error dict {"type": "error", ...}, or None on error @@ -71,12 +555,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None: """ _check_service_configured() logger.info("Calling external Agent Generator service for generate_agent") - result = await generate_agent_external(instructions) + result = await generate_agent_external( + dict(instructions), _to_dict_list(library_agents) + ) if result: - # Check if it's an error response - pass through as-is if isinstance(result, dict) and result.get("type") == "error": return result - # Ensure required fields for successful agent generation if "id" not in result: result["id"] = str(uuid.uuid4()) if "version" not in result: @@ -86,6 +570,12 @@ async def generate_agent(instructions: dict[str, Any]) -> dict[str, Any] | None: return result +class AgentJsonValidationError(Exception): + """Raised when agent JSON is invalid or missing required fields.""" + + pass + + def json_to_graph(agent_json: dict[str, Any]) -> Graph: """Convert agent JSON dict to Graph model. @@ -94,25 +584,55 @@ def json_to_graph(agent_json: dict[str, Any]) -> Graph: Returns: Graph ready for saving + + Raises: + AgentJsonValidationError: If required fields are missing from nodes or links """ nodes = [] - for n in agent_json.get("nodes", []): + for idx, n in enumerate(agent_json.get("nodes", [])): + block_id = n.get("block_id") + if not block_id: + node_id = n.get("id", f"index_{idx}") + raise AgentJsonValidationError( + f"Node '{node_id}' is missing required field 'block_id'" + ) node = Node( id=n.get("id", str(uuid.uuid4())), - block_id=n["block_id"], + block_id=block_id, input_default=n.get("input_default", {}), metadata=n.get("metadata", {}), ) nodes.append(node) links = [] - for link_data in agent_json.get("links", []): + for idx, link_data in enumerate(agent_json.get("links", [])): + source_id = link_data.get("source_id") + sink_id = link_data.get("sink_id") + source_name = link_data.get("source_name") + sink_name = link_data.get("sink_name") + + missing_fields = [] + if not source_id: + missing_fields.append("source_id") + if not sink_id: + missing_fields.append("sink_id") + if not source_name: + missing_fields.append("source_name") + if not sink_name: + missing_fields.append("sink_name") + + if missing_fields: + link_id = link_data.get("id", f"index_{idx}") + raise AgentJsonValidationError( + f"Link '{link_id}' is missing required fields: {', '.join(missing_fields)}" + ) + link = Link( id=link_data.get("id", str(uuid.uuid4())), - source_id=link_data["source_id"], - sink_id=link_data["sink_id"], - source_name=link_data["source_name"], - sink_name=link_data["sink_name"], + source_id=source_id, + sink_id=sink_id, + source_name=source_name, + sink_name=sink_name, is_static=link_data.get("is_static", False), ) links.append(link) @@ -133,22 +653,40 @@ def _reassign_node_ids(graph: Graph) -> None: This is needed when creating a new version to avoid unique constraint violations. """ - # Create mapping from old node IDs to new UUIDs id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes} - # Reassign node IDs for node in graph.nodes: node.id = id_map[node.id] - # Update link references to use new node IDs for link in graph.links: - link.id = str(uuid.uuid4()) # Also give links new IDs + link.id = str(uuid.uuid4()) if link.source_id in id_map: link.source_id = id_map[link.source_id] if link.sink_id in id_map: link.sink_id = id_map[link.sink_id] +def _populate_agent_executor_user_ids(agent_json: dict[str, Any], user_id: str) -> None: + """Populate user_id in AgentExecutorBlock nodes. + + The external agent generator creates AgentExecutorBlock nodes with empty user_id. + This function fills in the actual user_id so sub-agents run with correct permissions. + + Args: + agent_json: Agent JSON dict (modified in place) + user_id: User ID to set + """ + for node in agent_json.get("nodes", []): + if node.get("block_id") == AGENT_EXECUTOR_BLOCK_ID: + input_default = node.get("input_default") or {} + if not input_default.get("user_id"): + input_default["user_id"] = user_id + node["input_default"] = input_default + logger.debug( + f"Set user_id for AgentExecutorBlock node {node.get('id')}" + ) + + async def save_agent_to_library( agent_json: dict[str, Any], user_id: str, is_update: bool = False ) -> tuple[Graph, Any]: @@ -162,33 +700,27 @@ async def save_agent_to_library( Returns: Tuple of (created Graph, LibraryAgent) """ - from backend.data.graph import get_graph_all_versions + # Populate user_id in AgentExecutorBlock nodes before conversion + _populate_agent_executor_user_ids(agent_json, user_id) graph = json_to_graph(agent_json) if is_update: - # For updates, keep the same graph ID but increment version - # and reassign node/link IDs to avoid conflicts if graph.id: existing_versions = await get_graph_all_versions(graph.id, user_id) if existing_versions: latest_version = max(v.version for v in existing_versions) graph.version = latest_version + 1 - # Reassign node IDs (but keep graph ID the same) _reassign_node_ids(graph) logger.info(f"Updating agent {graph.id} to version {graph.version}") else: - # For new agents, always generate a fresh UUID to avoid collisions graph.id = str(uuid.uuid4()) graph.version = 1 - # Reassign all node IDs as well _reassign_node_ids(graph) logger.info(f"Creating new agent with ID {graph.id}") - # Save to database created_graph = await create_graph(graph, user_id) - # Add to user's library (or update existing library agent) library_agents = await library_db.create_library_agent( graph=created_graph, user_id=user_id, @@ -200,25 +732,31 @@ async def save_agent_to_library( async def get_agent_as_json( - graph_id: str, user_id: str | None + agent_id: str, user_id: str | None ) -> dict[str, Any] | None: """Fetch an agent and convert to JSON format for editing. Args: - graph_id: Graph ID or library agent ID + agent_id: Graph ID or library agent ID user_id: User ID Returns: Agent as JSON dict or None if not found """ - from backend.data.graph import get_graph + graph = await get_graph(agent_id, version=None, user_id=user_id) + + if not graph and user_id: + try: + library_agent = await library_db.get_library_agent(agent_id, user_id) + graph = await get_graph( + library_agent.graph_id, version=None, user_id=user_id + ) + except NotFoundError: + pass - # Try to get the graph (version=None gets the active version) - graph = await get_graph(graph_id, version=None, user_id=user_id) if not graph: return None - # Convert to JSON format nodes = [] for node in graph.nodes: nodes.append( @@ -256,7 +794,9 @@ async def get_agent_as_json( async def generate_agent_patch( - update_request: str, current_agent: dict[str, Any] + update_request: str, + current_agent: dict[str, Any], + library_agents: list[AgentSummary] | None = None, ) -> dict[str, Any] | None: """Update an existing agent using natural language. @@ -268,6 +808,7 @@ async def generate_agent_patch( Args: update_request: Natural language description of changes current_agent: Current agent JSON + library_agents: User's library agents available for sub-agent composition Returns: Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...}, @@ -278,4 +819,6 @@ async def generate_agent_patch( """ _check_service_configured() logger.info("Calling external Agent Generator service for generate_agent_patch") - return await generate_agent_patch_external(update_request, current_agent) + return await generate_agent_patch_external( + update_request, current_agent, _to_dict_list(library_agents) + ) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/errors.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/errors.py index bf71a95df9..282d8cf9aa 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/errors.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/errors.py @@ -1,11 +1,43 @@ """Error handling utilities for agent generator.""" +import re + + +def _sanitize_error_details(details: str) -> str: + """Sanitize error details to remove sensitive information. + + Strips common patterns that could expose internal system info: + - File paths (Unix and Windows) + - Database connection strings + - URLs with credentials + - Stack trace internals + + Args: + details: Raw error details string + + Returns: + Sanitized error details safe for user display + """ + sanitized = re.sub( + r"/[a-zA-Z0-9_./\-]+\.(py|js|ts|json|yaml|yml)", "[path]", details + ) + sanitized = re.sub(r"[A-Z]:\\[a-zA-Z0-9_\\.\\-]+", "[path]", sanitized) + sanitized = re.sub( + r"(postgres|mysql|mongodb|redis)://[^\s]+", "[database_url]", sanitized + ) + sanitized = re.sub(r"https?://[^:]+:[^@]+@[^\s]+", "[url]", sanitized) + sanitized = re.sub(r", line \d+", "", sanitized) + sanitized = re.sub(r'File "[^"]+",?', "", sanitized) + + return sanitized.strip() + def get_user_message_for_error( error_type: str, operation: str = "process the request", llm_parse_message: str | None = None, validation_message: str | None = None, + error_details: str | None = None, ) -> str: """Get a user-friendly error message based on error type. @@ -19,25 +51,45 @@ def get_user_message_for_error( message (e.g., "analyze the goal", "generate the agent") llm_parse_message: Custom message for llm_parse_error type validation_message: Custom message for validation_error type + error_details: Optional additional details about the error Returns: User-friendly error message suitable for display to the user """ + base_message = "" + if error_type == "llm_parse_error": - return ( + base_message = ( llm_parse_message or "The AI had trouble processing this request. Please try again." ) elif error_type == "validation_error": - return ( + base_message = ( validation_message - or "The request failed validation. Please try rephrasing." + or "The generated agent failed validation. " + "This usually happens when the agent structure doesn't match " + "what the platform expects. Please try simplifying your goal " + "or breaking it into smaller parts." ) elif error_type == "patch_error": - return "Failed to apply the changes. Please try a different approach." + base_message = ( + "Failed to apply the changes. The modification couldn't be " + "validated. Please try a different approach or simplify the change." + ) elif error_type in ("timeout", "llm_timeout"): - return "The request took too long. Please try again." + base_message = ( + "The request took too long to process. This can happen with " + "complex agents. Please try again or simplify your goal." + ) elif error_type in ("rate_limit", "llm_rate_limit"): - return "The service is currently busy. Please try again in a moment." + base_message = "The service is currently busy. Please try again in a moment." else: - return f"Failed to {operation}. Please try again." + base_message = f"Failed to {operation}. Please try again." + + if error_details: + details = _sanitize_error_details(error_details) + if len(details) > 200: + details = details[:200] + "..." + base_message += f"\n\nTechnical details: {details}" + + return base_message diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py index 1df1faaaef..c6242b0ba9 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py @@ -117,13 +117,16 @@ def _get_client() -> httpx.AsyncClient: async def decompose_goal_external( - description: str, context: str = "" + description: str, + context: str = "", + library_agents: list[dict[str, Any]] | None = None, ) -> dict[str, Any] | None: """Call the external service to decompose a goal. Args: description: Natural language goal description context: Additional context (e.g., answers to previous questions) + library_agents: User's library agents available for sub-agent composition Returns: Dict with either: @@ -141,6 +144,8 @@ async def decompose_goal_external( if context: # The external service uses user_instruction for additional context payload["user_instruction"] = context + if library_agents: + payload["library_agents"] = library_agents try: response = await client.post("/api/decompose-description", json=payload) @@ -207,21 +212,25 @@ async def decompose_goal_external( async def generate_agent_external( instructions: dict[str, Any], + library_agents: list[dict[str, Any]] | None = None, ) -> dict[str, Any] | None: """Call the external service to generate an agent from instructions. Args: instructions: Structured instructions from decompose_goal + library_agents: User's library agents available for sub-agent composition Returns: Agent JSON dict on success, or error dict {"type": "error", ...} on error """ client = _get_client() + payload: dict[str, Any] = {"instructions": instructions} + if library_agents: + payload["library_agents"] = library_agents + try: - response = await client.post( - "/api/generate-agent", json={"instructions": instructions} - ) + response = await client.post("/api/generate-agent", json=payload) response.raise_for_status() data = response.json() @@ -229,8 +238,7 @@ async def generate_agent_external( error_msg = data.get("error", "Unknown error from Agent Generator") error_type = data.get("error_type", "unknown") logger.error( - f"Agent Generator generation failed: {error_msg} " - f"(type: {error_type})" + f"Agent Generator generation failed: {error_msg} (type: {error_type})" ) return _create_error_response(error_msg, error_type) @@ -251,27 +259,31 @@ async def generate_agent_external( async def generate_agent_patch_external( - update_request: str, current_agent: dict[str, Any] + update_request: str, + current_agent: dict[str, Any], + library_agents: list[dict[str, Any]] | None = None, ) -> dict[str, Any] | None: """Call the external service to generate a patch for an existing agent. Args: update_request: Natural language description of changes current_agent: Current agent JSON + library_agents: User's library agents available for sub-agent composition Returns: Updated agent JSON, clarifying questions dict, or error dict on error """ client = _get_client() + payload: dict[str, Any] = { + "update_request": update_request, + "current_agent_json": current_agent, + } + if library_agents: + payload["library_agents"] = library_agents + try: - response = await client.post( - "/api/update-agent", - json={ - "update_request": update_request, - "current_agent_json": current_agent, - }, - ) + response = await client.post("/api/update-agent", json=payload) response.raise_for_status() data = response.json() diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py index 5fa74ba04e..62d59c470e 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py @@ -1,6 +1,7 @@ """Shared agent search functionality for find_agent and find_library_agent tools.""" import logging +import re from typing import Literal from backend.api.features.library import db as library_db @@ -19,6 +20,85 @@ logger = logging.getLogger(__name__) SearchSource = Literal["marketplace", "library"] +_UUID_PATTERN = re.compile( + r"^[a-f0-9]{8}-[a-f0-9]{4}-4[a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$", + re.IGNORECASE, +) + + +def _is_uuid(text: str) -> bool: + """Check if text is a valid UUID v4.""" + return bool(_UUID_PATTERN.match(text.strip())) + + +async def _get_library_agent_by_id(user_id: str, agent_id: str) -> AgentInfo | None: + """Fetch a library agent by ID (library agent ID or graph_id). + + Tries multiple lookup strategies: + 1. First by graph_id (AgentGraph primary key) + 2. Then by library agent ID (LibraryAgent primary key) + + Args: + user_id: The user ID + agent_id: The ID to look up (can be graph_id or library agent ID) + + Returns: + AgentInfo if found, None otherwise + """ + try: + agent = await library_db.get_library_agent_by_graph_id(user_id, agent_id) + if agent: + logger.debug(f"Found library agent by graph_id: {agent.name}") + return AgentInfo( + id=agent.id, + name=agent.name, + description=agent.description or "", + source="library", + in_library=True, + creator=agent.creator_name, + status=agent.status.value, + can_access_graph=agent.can_access_graph, + has_external_trigger=agent.has_external_trigger, + new_output=agent.new_output, + graph_id=agent.graph_id, + ) + except DatabaseError: + raise + except Exception as e: + logger.warning( + f"Could not fetch library agent by graph_id {agent_id}: {e}", + exc_info=True, + ) + + try: + agent = await library_db.get_library_agent(agent_id, user_id) + if agent: + logger.debug(f"Found library agent by library_id: {agent.name}") + return AgentInfo( + id=agent.id, + name=agent.name, + description=agent.description or "", + source="library", + in_library=True, + creator=agent.creator_name, + status=agent.status.value, + can_access_graph=agent.can_access_graph, + has_external_trigger=agent.has_external_trigger, + new_output=agent.new_output, + graph_id=agent.graph_id, + ) + except NotFoundError: + logger.debug(f"Library agent not found by library_id: {agent_id}") + except DatabaseError: + raise + except Exception as e: + logger.warning( + f"Could not fetch library agent by library_id {agent_id}: {e}", + exc_info=True, + ) + + return None + async def search_agents( query: str, @@ -69,29 +149,37 @@ async def search_agents( is_featured=False, ) ) - else: # library - logger.info(f"Searching user library for: {query}") - results = await library_db.list_library_agents( - user_id=user_id, # type: ignore[arg-type] - search_term=query, - page_size=10, - ) - for agent in results.agents: - agents.append( - AgentInfo( - id=agent.id, - name=agent.name, - description=agent.description or "", - source="library", - in_library=True, - creator=agent.creator_name, - status=agent.status.value, - can_access_graph=agent.can_access_graph, - has_external_trigger=agent.has_external_trigger, - new_output=agent.new_output, - graph_id=agent.graph_id, - ) + else: + if _is_uuid(query): + logger.info(f"Query looks like UUID, trying direct lookup: {query}") + agent = await _get_library_agent_by_id(user_id, query) # type: ignore[arg-type] + if agent: + agents.append(agent) + logger.info(f"Found agent by direct ID lookup: {agent.name}") + + if not agents: + logger.info(f"Searching user library for: {query}") + results = await library_db.list_library_agents( + user_id=user_id, # type: ignore[arg-type] + search_term=query, + page_size=10, ) + for agent in results.agents: + agents.append( + AgentInfo( + id=agent.id, + name=agent.name, + description=agent.description or "", + source="library", + in_library=True, + creator=agent.creator_name, + status=agent.status.value, + can_access_graph=agent.can_access_graph, + has_external_trigger=agent.has_external_trigger, + new_output=agent.new_output, + graph_id=agent.graph_id, + ) + ) logger.info(f"Found {len(agents)} agents in {source}") except NotFoundError: pass diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py b/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py index 74011c7e95..adb2c78fce 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py @@ -8,7 +8,9 @@ from backend.api.features.chat.model import ChatSession from .agent_generator import ( AgentGeneratorNotConfiguredError, decompose_goal, + enrich_library_agents_from_steps, generate_agent, + get_all_relevant_agents_for_generation, get_user_message_for_error, save_agent_to_library, ) @@ -103,9 +105,24 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) - # Step 1: Decompose goal into steps + library_agents = None + if user_id: + try: + library_agents = await get_all_relevant_agents_for_generation( + user_id=user_id, + search_query=description, + include_marketplace=True, + ) + logger.debug( + f"Found {len(library_agents)} relevant agents for sub-agent composition" + ) + except Exception as e: + logger.warning(f"Failed to fetch library agents: {e}") + try: - decomposition_result = await decompose_goal(description, context) + decomposition_result = await decompose_goal( + description, context, library_agents + ) except AgentGeneratorNotConfiguredError: return ErrorResponse( message=( @@ -124,7 +141,6 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) - # Check if the result is an error from the external service if decomposition_result.get("type") == "error": error_msg = decomposition_result.get("error", "Unknown error") error_type = decomposition_result.get("error_type", "unknown") @@ -144,7 +160,6 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) - # Check if LLM returned clarifying questions if decomposition_result.get("type") == "clarifying_questions": questions = decomposition_result.get("questions", []) return ClarificationNeededResponse( @@ -163,7 +178,6 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) - # Check for unachievable/vague goals if decomposition_result.get("type") == "unachievable_goal": suggested = decomposition_result.get("suggested_goal", "") reason = decomposition_result.get("reason", "") @@ -190,9 +204,22 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) - # Step 2: Generate agent JSON (external service handles fixing and validation) + if user_id and library_agents is not None: + try: + library_agents = await enrich_library_agents_from_steps( + user_id=user_id, + decomposition_result=decomposition_result, + existing_agents=library_agents, + include_marketplace=True, + ) + logger.debug( + f"After enrichment: {len(library_agents)} total agents for sub-agent composition" + ) + except Exception as e: + logger.warning(f"Failed to enrich library agents from steps: {e}") + try: - agent_json = await generate_agent(decomposition_result) + agent_json = await generate_agent(decomposition_result, library_agents) except AgentGeneratorNotConfiguredError: return ErrorResponse( message=( @@ -211,7 +238,6 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) - # Check if the result is an error from the external service if isinstance(agent_json, dict) and agent_json.get("type") == "error": error_msg = agent_json.get("error", "Unknown error") error_type = agent_json.get("error_type", "unknown") @@ -219,7 +245,12 @@ class CreateAgentTool(BaseTool): error_type, operation="generate the agent", llm_parse_message="The AI had trouble generating the agent. Please try again or simplify your goal.", - validation_message="The generated agent failed validation. Please try rephrasing your goal.", + validation_message=( + "I wasn't able to create a valid agent for this request. " + "The generated workflow had some structural issues. " + "Please try simplifying your goal or breaking it into smaller steps." + ), + error_details=error_msg, ) return ErrorResponse( message=user_message, @@ -237,7 +268,6 @@ class CreateAgentTool(BaseTool): node_count = len(agent_json.get("nodes", [])) link_count = len(agent_json.get("links", [])) - # Step 3: Preview or save if not save: return AgentPreviewResponse( message=( @@ -252,7 +282,6 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) - # Save to library if not user_id: return ErrorResponse( message="You must be logged in to save agents.", @@ -270,7 +299,7 @@ class CreateAgentTool(BaseTool): agent_id=created_graph.id, agent_name=created_graph.name, library_agent_id=library_agent.id, - library_agent_link=f"/library/{library_agent.id}", + library_agent_link=f"/library/agents/{library_agent.id}", agent_page_link=f"/build?flowID={created_graph.id}", session_id=session_id, ) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py b/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py index ee8eee53ce..2c2c48226b 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py @@ -9,6 +9,7 @@ from .agent_generator import ( AgentGeneratorNotConfiguredError, generate_agent_patch, get_agent_as_json, + get_all_relevant_agents_for_generation, get_user_message_for_error, save_agent_to_library, ) @@ -117,7 +118,6 @@ class EditAgentTool(BaseTool): session_id=session_id, ) - # Step 1: Fetch current agent current_agent = await get_agent_as_json(agent_id, user_id) if current_agent is None: @@ -127,14 +127,30 @@ class EditAgentTool(BaseTool): session_id=session_id, ) - # Build the update request with context + library_agents = None + if user_id: + try: + graph_id = current_agent.get("id") + library_agents = await get_all_relevant_agents_for_generation( + user_id=user_id, + search_query=changes, + exclude_graph_id=graph_id, + include_marketplace=True, + ) + logger.debug( + f"Found {len(library_agents)} relevant agents for sub-agent composition" + ) + except Exception as e: + logger.warning(f"Failed to fetch library agents: {e}") + update_request = changes if context: update_request = f"{changes}\n\nAdditional context:\n{context}" - # Step 2: Generate updated agent (external service handles fixing and validation) try: - result = await generate_agent_patch(update_request, current_agent) + result = await generate_agent_patch( + update_request, current_agent, library_agents + ) except AgentGeneratorNotConfiguredError: return ErrorResponse( message=( @@ -153,7 +169,6 @@ class EditAgentTool(BaseTool): session_id=session_id, ) - # Check if the result is an error from the external service if isinstance(result, dict) and result.get("type") == "error": error_msg = result.get("error", "Unknown error") error_type = result.get("error_type", "unknown") @@ -162,6 +177,7 @@ class EditAgentTool(BaseTool): operation="generate the changes", llm_parse_message="The AI had trouble generating the changes. Please try again or simplify your request.", validation_message="The generated changes failed validation. Please try rephrasing your request.", + error_details=error_msg, ) return ErrorResponse( message=user_message, @@ -175,7 +191,6 @@ class EditAgentTool(BaseTool): session_id=session_id, ) - # Check if LLM returned clarifying questions if result.get("type") == "clarifying_questions": questions = result.get("questions", []) return ClarificationNeededResponse( @@ -194,7 +209,6 @@ class EditAgentTool(BaseTool): session_id=session_id, ) - # Result is the updated agent JSON updated_agent = result agent_name = updated_agent.get("name", "Updated Agent") @@ -202,7 +216,6 @@ class EditAgentTool(BaseTool): node_count = len(updated_agent.get("nodes", [])) link_count = len(updated_agent.get("links", [])) - # Step 3: Preview or save if not save: return AgentPreviewResponse( message=( @@ -218,7 +231,6 @@ class EditAgentTool(BaseTool): session_id=session_id, ) - # Save to library (creates a new version) if not user_id: return ErrorResponse( message="You must be logged in to save agents.", @@ -236,7 +248,7 @@ class EditAgentTool(BaseTool): agent_id=created_graph.id, agent_name=created_graph.name, library_agent_id=library_agent.id, - library_agent_link=f"/library/{library_agent.id}", + library_agent_link=f"/library/agents/{library_agent.id}", agent_page_link=f"/build?flowID={created_graph.id}", session_id=session_id, ) diff --git a/autogpt_platform/backend/backend/api/features/library/db.py b/autogpt_platform/backend/backend/api/features/library/db.py index 872fe66b28..394f959953 100644 --- a/autogpt_platform/backend/backend/api/features/library/db.py +++ b/autogpt_platform/backend/backend/api/features/library/db.py @@ -39,6 +39,7 @@ async def list_library_agents( sort_by: library_model.LibraryAgentSort = library_model.LibraryAgentSort.UPDATED_AT, page: int = 1, page_size: int = 50, + include_executions: bool = False, ) -> library_model.LibraryAgentResponse: """ Retrieves a paginated list of LibraryAgent records for a given user. @@ -49,6 +50,9 @@ async def list_library_agents( sort_by: Sorting field (createdAt, updatedAt, isFavorite, isCreatedByUser). page: Current page (1-indexed). page_size: Number of items per page. + include_executions: Whether to include execution data for status calculation. + Defaults to False for performance (UI fetches status separately). + Set to True when accurate status/metrics are needed (e.g., agent generator). Returns: A LibraryAgentResponse containing the list of agents and pagination details. @@ -76,7 +80,6 @@ async def list_library_agents( "isArchived": False, } - # Build search filter if applicable if search_term: where_clause["OR"] = [ { @@ -93,7 +96,6 @@ async def list_library_agents( }, ] - # Determine sorting order_by: prisma.types.LibraryAgentOrderByInput | None = None if sort_by == library_model.LibraryAgentSort.CREATED_AT: @@ -105,7 +107,7 @@ async def list_library_agents( library_agents = await prisma.models.LibraryAgent.prisma().find_many( where=where_clause, include=library_agent_include( - user_id, include_nodes=False, include_executions=False + user_id, include_nodes=False, include_executions=include_executions ), order=order_by, skip=(page - 1) * page_size, diff --git a/autogpt_platform/backend/backend/api/features/library/model.py b/autogpt_platform/backend/backend/api/features/library/model.py index 14d7c7be81..c6bc0e0427 100644 --- a/autogpt_platform/backend/backend/api/features/library/model.py +++ b/autogpt_platform/backend/backend/api/features/library/model.py @@ -9,6 +9,7 @@ import pydantic from backend.data.block import BlockInput from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo from backend.data.model import CredentialsMetaInput, is_credentials_field_name +from backend.util.json import loads as json_loads from backend.util.models import Pagination if TYPE_CHECKING: @@ -16,10 +17,10 @@ if TYPE_CHECKING: class LibraryAgentStatus(str, Enum): - COMPLETED = "COMPLETED" # All runs completed - HEALTHY = "HEALTHY" # Agent is running (not all runs have completed) - WAITING = "WAITING" # Agent is queued or waiting to start - ERROR = "ERROR" # Agent is in an error state + COMPLETED = "COMPLETED" + HEALTHY = "HEALTHY" + WAITING = "WAITING" + ERROR = "ERROR" class MarketplaceListingCreator(pydantic.BaseModel): @@ -39,6 +40,30 @@ class MarketplaceListing(pydantic.BaseModel): creator: MarketplaceListingCreator +class RecentExecution(pydantic.BaseModel): + """Summary of a recent execution for quality assessment. + + Used by the LLM to understand the agent's recent performance with specific examples + rather than just aggregate statistics. + """ + + status: str + correctness_score: float | None = None + activity_summary: str | None = None + + +def _parse_settings(settings: dict | str | None) -> GraphSettings: + """Parse settings from database, handling both dict and string formats.""" + if settings is None: + return GraphSettings() + try: + if isinstance(settings, str): + settings = json_loads(settings) + return GraphSettings.model_validate(settings) + except Exception: + return GraphSettings() + + class LibraryAgent(pydantic.BaseModel): """ Represents an agent in the library, including metadata for display and @@ -48,7 +73,7 @@ class LibraryAgent(pydantic.BaseModel): id: str graph_id: str graph_version: int - owner_user_id: str # ID of user who owns/created this agent graph + owner_user_id: str image_url: str | None @@ -64,7 +89,7 @@ class LibraryAgent(pydantic.BaseModel): description: str instructions: str | None = None - input_schema: dict[str, Any] # Should be BlockIOObjectSubSchema in frontend + input_schema: dict[str, Any] output_schema: dict[str, Any] credentials_input_schema: dict[str, Any] | None = pydantic.Field( description="Input schema for credentials required by the agent", @@ -81,25 +106,19 @@ class LibraryAgent(pydantic.BaseModel): ) trigger_setup_info: Optional[GraphTriggerInfo] = None - # Indicates whether there's a new output (based on recent runs) new_output: bool - - # Whether the user can access the underlying graph + execution_count: int = 0 + success_rate: float | None = None + avg_correctness_score: float | None = None + recent_executions: list[RecentExecution] = pydantic.Field( + default_factory=list, + description="List of recent executions with status, score, and summary", + ) can_access_graph: bool - - # Indicates if this agent is the latest version is_latest_version: bool - - # Whether the agent is marked as favorite by the user is_favorite: bool - - # Recommended schedule cron (from marketplace agents) recommended_schedule_cron: str | None = None - - # User-specific settings for this library agent settings: GraphSettings = pydantic.Field(default_factory=GraphSettings) - - # Marketplace listing information if the agent has been published marketplace_listing: Optional["MarketplaceListing"] = None @staticmethod @@ -123,7 +142,6 @@ class LibraryAgent(pydantic.BaseModel): agent_updated_at = agent.AgentGraph.updatedAt lib_agent_updated_at = agent.updatedAt - # Compute updated_at as the latest between library agent and graph updated_at = ( max(agent_updated_at, lib_agent_updated_at) if agent_updated_at @@ -136,7 +154,6 @@ class LibraryAgent(pydantic.BaseModel): creator_name = agent.Creator.name or "Unknown" creator_image_url = agent.Creator.avatarUrl or "" - # Logic to calculate status and new_output week_ago = datetime.datetime.now(datetime.timezone.utc) - datetime.timedelta( days=7 ) @@ -145,13 +162,55 @@ class LibraryAgent(pydantic.BaseModel): status = status_result.status new_output = status_result.new_output - # Check if user can access the graph - can_access_graph = agent.AgentGraph.userId == agent.userId + execution_count = len(executions) + success_rate: float | None = None + avg_correctness_score: float | None = None + if execution_count > 0: + success_count = sum( + 1 + for e in executions + if e.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED + ) + success_rate = (success_count / execution_count) * 100 - # Hard-coded to True until a method to check is implemented + correctness_scores = [] + for e in executions: + if e.stats and isinstance(e.stats, dict): + score = e.stats.get("correctness_score") + if score is not None and isinstance(score, (int, float)): + correctness_scores.append(float(score)) + if correctness_scores: + avg_correctness_score = sum(correctness_scores) / len( + correctness_scores + ) + + recent_executions: list[RecentExecution] = [] + for e in executions: + exec_score: float | None = None + exec_summary: str | None = None + if e.stats and isinstance(e.stats, dict): + score = e.stats.get("correctness_score") + if score is not None and isinstance(score, (int, float)): + exec_score = float(score) + summary = e.stats.get("activity_status") + if summary is not None and isinstance(summary, str): + exec_summary = summary + exec_status = ( + e.executionStatus.value + if hasattr(e.executionStatus, "value") + else str(e.executionStatus) + ) + recent_executions.append( + RecentExecution( + status=exec_status, + correctness_score=exec_score, + activity_summary=exec_summary, + ) + ) + + can_access_graph = agent.AgentGraph.userId == agent.userId is_latest_version = True - # Build marketplace_listing if available marketplace_listing_data = None if store_listing and store_listing.ActiveVersion and profile: creator_data = MarketplaceListingCreator( @@ -190,11 +249,15 @@ class LibraryAgent(pydantic.BaseModel): has_sensitive_action=graph.has_sensitive_action, trigger_setup_info=graph.trigger_setup_info, new_output=new_output, + execution_count=execution_count, + success_rate=success_rate, + avg_correctness_score=avg_correctness_score, + recent_executions=recent_executions, can_access_graph=can_access_graph, is_latest_version=is_latest_version, is_favorite=agent.isFavorite, recommended_schedule_cron=agent.AgentGraph.recommendedScheduleCron, - settings=GraphSettings.model_validate(agent.settings), + settings=_parse_settings(agent.settings), marketplace_listing=marketplace_listing_data, ) @@ -220,18 +283,15 @@ def _calculate_agent_status( if not executions: return AgentStatusResult(status=LibraryAgentStatus.COMPLETED, new_output=False) - # Track how many times each execution status appears status_counts = {status: 0 for status in prisma.enums.AgentExecutionStatus} new_output = False for execution in executions: - # Check if there's a completed run more recent than `recent_threshold` if execution.createdAt >= recent_threshold: if execution.executionStatus == prisma.enums.AgentExecutionStatus.COMPLETED: new_output = True status_counts[execution.executionStatus] += 1 - # Determine the final status based on counts if status_counts[prisma.enums.AgentExecutionStatus.FAILED] > 0: return AgentStatusResult(status=LibraryAgentStatus.ERROR, new_output=new_output) elif status_counts[prisma.enums.AgentExecutionStatus.QUEUED] > 0: diff --git a/autogpt_platform/backend/snapshots/lib_agts_search b/autogpt_platform/backend/snapshots/lib_agts_search index 67c307b09e..3ce8402b63 100644 --- a/autogpt_platform/backend/snapshots/lib_agts_search +++ b/autogpt_platform/backend/snapshots/lib_agts_search @@ -31,6 +31,10 @@ "has_sensitive_action": false, "trigger_setup_info": null, "new_output": false, + "execution_count": 0, + "success_rate": null, + "avg_correctness_score": null, + "recent_executions": [], "can_access_graph": true, "is_latest_version": true, "is_favorite": false, @@ -72,6 +76,10 @@ "has_sensitive_action": false, "trigger_setup_info": null, "new_output": false, + "execution_count": 0, + "success_rate": null, + "avg_correctness_score": null, + "recent_executions": [], "can_access_graph": false, "is_latest_version": true, "is_favorite": false, diff --git a/autogpt_platform/backend/test/agent_generator/test_core_integration.py b/autogpt_platform/backend/test/agent_generator/test_core_integration.py index bdcc24ba79..05ce4a3aff 100644 --- a/autogpt_platform/backend/test/agent_generator/test_core_integration.py +++ b/autogpt_platform/backend/test/agent_generator/test_core_integration.py @@ -57,7 +57,8 @@ class TestDecomposeGoal: result = await core.decompose_goal("Build a chatbot") - mock_external.assert_called_once_with("Build a chatbot", "") + # library_agents defaults to None + mock_external.assert_called_once_with("Build a chatbot", "", None) assert result == expected_result @pytest.mark.asyncio @@ -74,7 +75,8 @@ class TestDecomposeGoal: await core.decompose_goal("Build a chatbot", "Use Python") - mock_external.assert_called_once_with("Build a chatbot", "Use Python") + # library_agents defaults to None + mock_external.assert_called_once_with("Build a chatbot", "Use Python", None) @pytest.mark.asyncio async def test_returns_none_on_service_failure(self): @@ -109,7 +111,8 @@ class TestGenerateAgent: instructions = {"type": "instructions", "steps": ["Step 1"]} result = await core.generate_agent(instructions) - mock_external.assert_called_once_with(instructions) + # library_agents defaults to None + mock_external.assert_called_once_with(instructions, None) # Result should have id, version, is_active added if not present assert result is not None assert result["name"] == "Test Agent" @@ -174,7 +177,8 @@ class TestGenerateAgentPatch: current_agent = {"nodes": [], "links": []} result = await core.generate_agent_patch("Add a node", current_agent) - mock_external.assert_called_once_with("Add a node", current_agent) + # library_agents defaults to None + mock_external.assert_called_once_with("Add a node", current_agent, None) assert result == expected_result @pytest.mark.asyncio diff --git a/autogpt_platform/backend/test/agent_generator/test_library_agents.py b/autogpt_platform/backend/test/agent_generator/test_library_agents.py new file mode 100644 index 0000000000..e62b0746e7 --- /dev/null +++ b/autogpt_platform/backend/test/agent_generator/test_library_agents.py @@ -0,0 +1,841 @@ +""" +Tests for library agent fetching functionality in agent generator. + +This test suite verifies the search-based library agent fetching, +including the combination of library and marketplace agents. +""" + +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from backend.api.features.chat.tools.agent_generator import core + + +class TestGetLibraryAgentsForGeneration: + """Test get_library_agents_for_generation function.""" + + @pytest.mark.asyncio + async def test_fetches_agents_with_search_term(self): + """Test that search_term is passed to the library db.""" + # Create a mock agent with proper attribute values + mock_agent = MagicMock() + mock_agent.graph_id = "agent-123" + mock_agent.graph_version = 1 + mock_agent.name = "Email Agent" + mock_agent.description = "Sends emails" + mock_agent.input_schema = {"properties": {}} + mock_agent.output_schema = {"properties": {}} + mock_agent.recent_executions = [] + + mock_response = MagicMock() + mock_response.agents = [mock_agent] + + with patch.object( + core.library_db, + "list_library_agents", + new_callable=AsyncMock, + return_value=mock_response, + ) as mock_list: + result = await core.get_library_agents_for_generation( + user_id="user-123", + search_query="send email", + ) + + mock_list.assert_called_once_with( + user_id="user-123", + search_term="send email", + page=1, + page_size=15, + include_executions=True, + ) + + # Verify result format + assert len(result) == 1 + assert result[0]["graph_id"] == "agent-123" + assert result[0]["name"] == "Email Agent" + + @pytest.mark.asyncio + async def test_excludes_specified_graph_id(self): + """Test that agents with excluded graph_id are filtered out.""" + mock_response = MagicMock() + mock_response.agents = [ + MagicMock( + graph_id="agent-123", + graph_version=1, + name="Agent 1", + description="First agent", + input_schema={}, + output_schema={}, + recent_executions=[], + ), + MagicMock( + graph_id="agent-456", + graph_version=1, + name="Agent 2", + description="Second agent", + input_schema={}, + output_schema={}, + recent_executions=[], + ), + ] + + with patch.object( + core.library_db, + "list_library_agents", + new_callable=AsyncMock, + return_value=mock_response, + ): + result = await core.get_library_agents_for_generation( + user_id="user-123", + exclude_graph_id="agent-123", + ) + + # Verify the excluded agent is not in results + assert len(result) == 1 + assert result[0]["graph_id"] == "agent-456" + + @pytest.mark.asyncio + async def test_respects_max_results(self): + """Test that max_results parameter limits the page_size.""" + mock_response = MagicMock() + mock_response.agents = [] + + with patch.object( + core.library_db, + "list_library_agents", + new_callable=AsyncMock, + return_value=mock_response, + ) as mock_list: + await core.get_library_agents_for_generation( + user_id="user-123", + max_results=5, + ) + + mock_list.assert_called_once_with( + user_id="user-123", + search_term=None, + page=1, + page_size=5, + include_executions=True, + ) + + +class TestSearchMarketplaceAgentsForGeneration: + """Test search_marketplace_agents_for_generation function.""" + + @pytest.mark.asyncio + async def test_searches_marketplace_with_query(self): + """Test that marketplace is searched with the query.""" + mock_response = MagicMock() + mock_response.agents = [ + MagicMock( + agent_name="Public Agent", + description="A public agent", + sub_heading="Does something useful", + creator="creator-1", + ) + ] + + # The store_db is dynamically imported, so patch the import path + with patch( + "backend.api.features.store.db.get_store_agents", + new_callable=AsyncMock, + return_value=mock_response, + ) as mock_search: + result = await core.search_marketplace_agents_for_generation( + search_query="automation", + max_results=10, + ) + + mock_search.assert_called_once_with( + search_query="automation", + page=1, + page_size=10, + ) + + assert len(result) == 1 + assert result[0]["name"] == "Public Agent" + assert result[0]["is_marketplace_agent"] is True + + @pytest.mark.asyncio + async def test_handles_marketplace_error_gracefully(self): + """Test that marketplace errors don't crash the function.""" + with patch( + "backend.api.features.store.db.get_store_agents", + new_callable=AsyncMock, + side_effect=Exception("Marketplace unavailable"), + ): + result = await core.search_marketplace_agents_for_generation( + search_query="test" + ) + + # Should return empty list, not raise exception + assert result == [] + + +class TestGetAllRelevantAgentsForGeneration: + """Test get_all_relevant_agents_for_generation function.""" + + @pytest.mark.asyncio + async def test_combines_library_and_marketplace_agents(self): + """Test that agents from both sources are combined.""" + library_agents = [ + { + "graph_id": "lib-123", + "graph_version": 1, + "name": "Library Agent", + "description": "From library", + "input_schema": {}, + "output_schema": {}, + } + ] + + marketplace_agents = [ + { + "name": "Market Agent", + "description": "From marketplace", + "sub_heading": "Sub heading", + "creator": "creator-1", + "is_marketplace_agent": True, + } + ] + + with patch.object( + core, + "get_library_agents_for_generation", + new_callable=AsyncMock, + return_value=library_agents, + ): + with patch.object( + core, + "search_marketplace_agents_for_generation", + new_callable=AsyncMock, + return_value=marketplace_agents, + ): + result = await core.get_all_relevant_agents_for_generation( + user_id="user-123", + search_query="test query", + include_marketplace=True, + ) + + # Library agents should come first + assert len(result) == 2 + assert result[0]["name"] == "Library Agent" + assert result[1]["name"] == "Market Agent" + + @pytest.mark.asyncio + async def test_deduplicates_by_name(self): + """Test that marketplace agents with same name as library are excluded.""" + library_agents = [ + { + "graph_id": "lib-123", + "graph_version": 1, + "name": "Shared Agent", + "description": "From library", + "input_schema": {}, + "output_schema": {}, + } + ] + + marketplace_agents = [ + { + "name": "Shared Agent", # Same name, should be deduplicated + "description": "From marketplace", + "sub_heading": "Sub heading", + "creator": "creator-1", + "is_marketplace_agent": True, + }, + { + "name": "Unique Agent", + "description": "Only in marketplace", + "sub_heading": "Sub heading", + "creator": "creator-2", + "is_marketplace_agent": True, + }, + ] + + with patch.object( + core, + "get_library_agents_for_generation", + new_callable=AsyncMock, + return_value=library_agents, + ): + with patch.object( + core, + "search_marketplace_agents_for_generation", + new_callable=AsyncMock, + return_value=marketplace_agents, + ): + result = await core.get_all_relevant_agents_for_generation( + user_id="user-123", + search_query="test", + include_marketplace=True, + ) + + # Shared Agent from marketplace should be excluded + assert len(result) == 2 + names = [a["name"] for a in result] + assert "Shared Agent" in names + assert "Unique Agent" in names + + @pytest.mark.asyncio + async def test_skips_marketplace_when_disabled(self): + """Test that marketplace is not searched when include_marketplace=False.""" + library_agents = [ + { + "graph_id": "lib-123", + "graph_version": 1, + "name": "Library Agent", + "description": "From library", + "input_schema": {}, + "output_schema": {}, + } + ] + + with patch.object( + core, + "get_library_agents_for_generation", + new_callable=AsyncMock, + return_value=library_agents, + ): + with patch.object( + core, + "search_marketplace_agents_for_generation", + new_callable=AsyncMock, + ) as mock_marketplace: + result = await core.get_all_relevant_agents_for_generation( + user_id="user-123", + search_query="test", + include_marketplace=False, + ) + + # Marketplace should not be called + mock_marketplace.assert_not_called() + assert len(result) == 1 + + @pytest.mark.asyncio + async def test_skips_marketplace_when_no_search_query(self): + """Test that marketplace is not searched without a search query.""" + library_agents = [ + { + "graph_id": "lib-123", + "graph_version": 1, + "name": "Library Agent", + "description": "From library", + "input_schema": {}, + "output_schema": {}, + } + ] + + with patch.object( + core, + "get_library_agents_for_generation", + new_callable=AsyncMock, + return_value=library_agents, + ): + with patch.object( + core, + "search_marketplace_agents_for_generation", + new_callable=AsyncMock, + ) as mock_marketplace: + result = await core.get_all_relevant_agents_for_generation( + user_id="user-123", + search_query=None, # No search query + include_marketplace=True, + ) + + # Marketplace should not be called without search query + mock_marketplace.assert_not_called() + assert len(result) == 1 + + +class TestExtractSearchTermsFromSteps: + """Test extract_search_terms_from_steps function.""" + + def test_extracts_terms_from_instructions_type(self): + """Test extraction from valid instructions decomposition result.""" + decomposition_result = { + "type": "instructions", + "steps": [ + { + "description": "Send an email notification", + "block_name": "GmailSendBlock", + }, + {"description": "Fetch weather data", "action": "Get weather API"}, + ], + } + + result = core.extract_search_terms_from_steps(decomposition_result) + + assert "Send an email notification" in result + assert "GmailSendBlock" in result + assert "Fetch weather data" in result + assert "Get weather API" in result + + def test_returns_empty_for_non_instructions_type(self): + """Test that non-instructions types return empty list.""" + decomposition_result = { + "type": "clarifying_questions", + "questions": [{"question": "What email?"}], + } + + result = core.extract_search_terms_from_steps(decomposition_result) + + assert result == [] + + def test_deduplicates_terms_case_insensitively(self): + """Test that duplicate terms are removed (case-insensitive).""" + decomposition_result = { + "type": "instructions", + "steps": [ + {"description": "Send Email", "name": "send email"}, + {"description": "Other task"}, + ], + } + + result = core.extract_search_terms_from_steps(decomposition_result) + + # Should only have one "send email" variant + email_terms = [t for t in result if "email" in t.lower()] + assert len(email_terms) == 1 + + def test_filters_short_terms(self): + """Test that terms with 3 or fewer characters are filtered out.""" + decomposition_result = { + "type": "instructions", + "steps": [ + {"description": "ab", "action": "xyz"}, # Both too short + {"description": "Valid term here"}, + ], + } + + result = core.extract_search_terms_from_steps(decomposition_result) + + assert "ab" not in result + assert "xyz" not in result + assert "Valid term here" in result + + def test_handles_empty_steps(self): + """Test handling of empty steps list.""" + decomposition_result = { + "type": "instructions", + "steps": [], + } + + result = core.extract_search_terms_from_steps(decomposition_result) + + assert result == [] + + +class TestEnrichLibraryAgentsFromSteps: + """Test enrich_library_agents_from_steps function.""" + + @pytest.mark.asyncio + async def test_enriches_with_additional_agents(self): + """Test that additional agents are found based on steps.""" + existing_agents = [ + { + "graph_id": "existing-123", + "graph_version": 1, + "name": "Existing Agent", + "description": "Already fetched", + "input_schema": {}, + "output_schema": {}, + } + ] + + additional_agents = [ + { + "graph_id": "new-456", + "graph_version": 1, + "name": "Email Agent", + "description": "For sending emails", + "input_schema": {}, + "output_schema": {}, + } + ] + + decomposition_result = { + "type": "instructions", + "steps": [ + {"description": "Send email notification"}, + ], + } + + with patch.object( + core, + "get_all_relevant_agents_for_generation", + new_callable=AsyncMock, + return_value=additional_agents, + ): + result = await core.enrich_library_agents_from_steps( + user_id="user-123", + decomposition_result=decomposition_result, + existing_agents=existing_agents, + ) + + # Should have both existing and new agents + assert len(result) == 2 + names = [a["name"] for a in result] + assert "Existing Agent" in names + assert "Email Agent" in names + + @pytest.mark.asyncio + async def test_deduplicates_by_graph_id(self): + """Test that agents with same graph_id are not duplicated.""" + existing_agents = [ + { + "graph_id": "agent-123", + "graph_version": 1, + "name": "Existing Agent", + "description": "Already fetched", + "input_schema": {}, + "output_schema": {}, + } + ] + + # Additional search returns same agent + additional_agents = [ + { + "graph_id": "agent-123", # Same ID + "graph_version": 1, + "name": "Existing Agent Copy", + "description": "Same agent different name", + "input_schema": {}, + "output_schema": {}, + } + ] + + decomposition_result = { + "type": "instructions", + "steps": [{"description": "Some action"}], + } + + with patch.object( + core, + "get_all_relevant_agents_for_generation", + new_callable=AsyncMock, + return_value=additional_agents, + ): + result = await core.enrich_library_agents_from_steps( + user_id="user-123", + decomposition_result=decomposition_result, + existing_agents=existing_agents, + ) + + # Should not duplicate + assert len(result) == 1 + + @pytest.mark.asyncio + async def test_deduplicates_by_name(self): + """Test that agents with same name are not duplicated.""" + existing_agents = [ + { + "graph_id": "agent-123", + "graph_version": 1, + "name": "Email Agent", + "description": "Already fetched", + "input_schema": {}, + "output_schema": {}, + } + ] + + # Additional search returns agent with same name but different ID + additional_agents = [ + { + "graph_id": "agent-456", # Different ID + "graph_version": 1, + "name": "Email Agent", # Same name + "description": "Different agent same name", + "input_schema": {}, + "output_schema": {}, + } + ] + + decomposition_result = { + "type": "instructions", + "steps": [{"description": "Send email"}], + } + + with patch.object( + core, + "get_all_relevant_agents_for_generation", + new_callable=AsyncMock, + return_value=additional_agents, + ): + result = await core.enrich_library_agents_from_steps( + user_id="user-123", + decomposition_result=decomposition_result, + existing_agents=existing_agents, + ) + + # Should not duplicate by name + assert len(result) == 1 + assert result[0].get("graph_id") == "agent-123" # Original kept + + @pytest.mark.asyncio + async def test_returns_existing_when_no_steps(self): + """Test that existing agents are returned when no search terms extracted.""" + existing_agents = [ + { + "graph_id": "existing-123", + "graph_version": 1, + "name": "Existing Agent", + "description": "Already fetched", + "input_schema": {}, + "output_schema": {}, + } + ] + + decomposition_result = { + "type": "clarifying_questions", # Not instructions type + "questions": [], + } + + result = await core.enrich_library_agents_from_steps( + user_id="user-123", + decomposition_result=decomposition_result, + existing_agents=existing_agents, + ) + + # Should return existing unchanged + assert result == existing_agents + + @pytest.mark.asyncio + async def test_limits_search_terms_to_three(self): + """Test that only first 3 search terms are used.""" + existing_agents = [] + + decomposition_result = { + "type": "instructions", + "steps": [ + {"description": "First action"}, + {"description": "Second action"}, + {"description": "Third action"}, + {"description": "Fourth action"}, + {"description": "Fifth action"}, + ], + } + + call_count = 0 + + async def mock_get_agents(*args, **kwargs): + nonlocal call_count + call_count += 1 + return [] + + with patch.object( + core, + "get_all_relevant_agents_for_generation", + side_effect=mock_get_agents, + ): + await core.enrich_library_agents_from_steps( + user_id="user-123", + decomposition_result=decomposition_result, + existing_agents=existing_agents, + ) + + # Should only make 3 calls (limited to first 3 terms) + assert call_count == 3 + + +class TestExtractUuidsFromText: + """Test extract_uuids_from_text function.""" + + def test_extracts_single_uuid(self): + """Test extraction of a single UUID from text.""" + text = "Use my agent 46631191-e8a8-486f-ad90-84f89738321d for this task" + result = core.extract_uuids_from_text(text) + assert len(result) == 1 + assert "46631191-e8a8-486f-ad90-84f89738321d" in result + + def test_extracts_multiple_uuids(self): + """Test extraction of multiple UUIDs from text.""" + text = ( + "Combine agents 11111111-1111-4111-8111-111111111111 " + "and 22222222-2222-4222-9222-222222222222" + ) + result = core.extract_uuids_from_text(text) + assert len(result) == 2 + assert "11111111-1111-4111-8111-111111111111" in result + assert "22222222-2222-4222-9222-222222222222" in result + + def test_deduplicates_uuids(self): + """Test that duplicate UUIDs are deduplicated.""" + text = ( + "Use 46631191-e8a8-486f-ad90-84f89738321d twice: " + "46631191-e8a8-486f-ad90-84f89738321d" + ) + result = core.extract_uuids_from_text(text) + assert len(result) == 1 + + def test_normalizes_to_lowercase(self): + """Test that UUIDs are normalized to lowercase.""" + text = "Use 46631191-E8A8-486F-AD90-84F89738321D" + result = core.extract_uuids_from_text(text) + assert result[0] == "46631191-e8a8-486f-ad90-84f89738321d" + + def test_returns_empty_for_no_uuids(self): + """Test that empty list is returned when no UUIDs found.""" + text = "Create an email agent that sends notifications" + result = core.extract_uuids_from_text(text) + assert result == [] + + def test_ignores_invalid_uuids(self): + """Test that invalid UUID-like strings are ignored.""" + text = "Not a valid UUID: 12345678-1234-1234-1234-123456789abc" + result = core.extract_uuids_from_text(text) + # UUID v4 requires specific patterns (4 in third group, 8/9/a/b in fourth) + assert len(result) == 0 + + +class TestGetLibraryAgentById: + """Test get_library_agent_by_id function (and its alias get_library_agent_by_graph_id).""" + + @pytest.mark.asyncio + async def test_returns_agent_when_found_by_graph_id(self): + """Test that agent is returned when found by graph_id.""" + mock_agent = MagicMock() + mock_agent.graph_id = "agent-123" + mock_agent.graph_version = 1 + mock_agent.name = "Test Agent" + mock_agent.description = "Test description" + mock_agent.input_schema = {"properties": {}} + mock_agent.output_schema = {"properties": {}} + + with patch.object( + core.library_db, + "get_library_agent_by_graph_id", + new_callable=AsyncMock, + return_value=mock_agent, + ): + result = await core.get_library_agent_by_id("user-123", "agent-123") + + assert result is not None + assert result["graph_id"] == "agent-123" + assert result["name"] == "Test Agent" + + @pytest.mark.asyncio + async def test_falls_back_to_library_agent_id(self): + """Test that lookup falls back to library agent ID when graph_id not found.""" + mock_agent = MagicMock() + mock_agent.graph_id = "graph-456" # Different from the lookup ID + mock_agent.graph_version = 1 + mock_agent.name = "Library Agent" + mock_agent.description = "Found by library ID" + mock_agent.input_schema = {"properties": {}} + mock_agent.output_schema = {"properties": {}} + + with ( + patch.object( + core.library_db, + "get_library_agent_by_graph_id", + new_callable=AsyncMock, + return_value=None, # Not found by graph_id + ), + patch.object( + core.library_db, + "get_library_agent", + new_callable=AsyncMock, + return_value=mock_agent, # Found by library ID + ), + ): + result = await core.get_library_agent_by_id("user-123", "library-id-123") + + assert result is not None + assert result["graph_id"] == "graph-456" + assert result["name"] == "Library Agent" + + @pytest.mark.asyncio + async def test_returns_none_when_not_found_by_either_method(self): + """Test that None is returned when agent not found by either method.""" + with ( + patch.object( + core.library_db, + "get_library_agent_by_graph_id", + new_callable=AsyncMock, + return_value=None, + ), + patch.object( + core.library_db, + "get_library_agent", + new_callable=AsyncMock, + side_effect=core.NotFoundError("Not found"), + ), + ): + result = await core.get_library_agent_by_id("user-123", "nonexistent") + + assert result is None + + @pytest.mark.asyncio + async def test_returns_none_on_exception(self): + """Test that None is returned when exception occurs in both lookups.""" + with ( + patch.object( + core.library_db, + "get_library_agent_by_graph_id", + new_callable=AsyncMock, + side_effect=Exception("Database error"), + ), + patch.object( + core.library_db, + "get_library_agent", + new_callable=AsyncMock, + side_effect=Exception("Database error"), + ), + ): + result = await core.get_library_agent_by_id("user-123", "agent-123") + + assert result is None + + @pytest.mark.asyncio + async def test_alias_works(self): + """Test that get_library_agent_by_graph_id is an alias for get_library_agent_by_id.""" + assert core.get_library_agent_by_graph_id is core.get_library_agent_by_id + + +class TestGetAllRelevantAgentsWithUuids: + """Test UUID extraction in get_all_relevant_agents_for_generation.""" + + @pytest.mark.asyncio + async def test_fetches_explicitly_mentioned_agents(self): + """Test that agents mentioned by UUID are fetched directly.""" + mock_agent = MagicMock() + mock_agent.graph_id = "46631191-e8a8-486f-ad90-84f89738321d" + mock_agent.graph_version = 1 + mock_agent.name = "Mentioned Agent" + mock_agent.description = "Explicitly mentioned" + mock_agent.input_schema = {} + mock_agent.output_schema = {} + + mock_response = MagicMock() + mock_response.agents = [] + + with ( + patch.object( + core.library_db, + "get_library_agent_by_graph_id", + new_callable=AsyncMock, + return_value=mock_agent, + ), + patch.object( + core.library_db, + "list_library_agents", + new_callable=AsyncMock, + return_value=mock_response, + ), + ): + result = await core.get_all_relevant_agents_for_generation( + user_id="user-123", + search_query="Use agent 46631191-e8a8-486f-ad90-84f89738321d", + include_marketplace=False, + ) + + assert len(result) == 1 + assert result[0].get("graph_id") == "46631191-e8a8-486f-ad90-84f89738321d" + assert result[0].get("name") == "Mentioned Agent" + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/autogpt_platform/backend/test/agent_generator/test_service.py b/autogpt_platform/backend/test/agent_generator/test_service.py index fe7a1a7fdd..d62dca1729 100644 --- a/autogpt_platform/backend/test/agent_generator/test_service.py +++ b/autogpt_platform/backend/test/agent_generator/test_service.py @@ -433,5 +433,139 @@ class TestGetBlocksExternal: assert result is None +class TestLibraryAgentsPassthrough: + """Test that library_agents are passed correctly in all requests.""" + + def setup_method(self): + """Reset client singleton before each test.""" + service._settings = None + service._client = None + + @pytest.mark.asyncio + async def test_decompose_goal_passes_library_agents(self): + """Test that library_agents are included in decompose goal payload.""" + library_agents = [ + { + "graph_id": "agent-123", + "graph_version": 1, + "name": "Email Sender", + "description": "Sends emails", + "input_schema": {"properties": {"to": {"type": "string"}}}, + "output_schema": {"properties": {"sent": {"type": "boolean"}}}, + }, + ] + + mock_response = MagicMock() + mock_response.json.return_value = { + "success": True, + "type": "instructions", + "steps": ["Step 1"], + } + mock_response.raise_for_status = MagicMock() + + mock_client = AsyncMock() + mock_client.post.return_value = mock_response + + with patch.object(service, "_get_client", return_value=mock_client): + await service.decompose_goal_external( + "Send an email", + library_agents=library_agents, + ) + + # Verify library_agents was passed in the payload + call_args = mock_client.post.call_args + assert call_args[1]["json"]["library_agents"] == library_agents + + @pytest.mark.asyncio + async def test_generate_agent_passes_library_agents(self): + """Test that library_agents are included in generate agent payload.""" + library_agents = [ + { + "graph_id": "agent-456", + "graph_version": 2, + "name": "Data Fetcher", + "description": "Fetches data from API", + "input_schema": {"properties": {"url": {"type": "string"}}}, + "output_schema": {"properties": {"data": {"type": "object"}}}, + }, + ] + + mock_response = MagicMock() + mock_response.json.return_value = { + "success": True, + "agent_json": {"name": "Test Agent", "nodes": []}, + } + mock_response.raise_for_status = MagicMock() + + mock_client = AsyncMock() + mock_client.post.return_value = mock_response + + with patch.object(service, "_get_client", return_value=mock_client): + await service.generate_agent_external( + {"steps": ["Step 1"]}, + library_agents=library_agents, + ) + + # Verify library_agents was passed in the payload + call_args = mock_client.post.call_args + assert call_args[1]["json"]["library_agents"] == library_agents + + @pytest.mark.asyncio + async def test_generate_agent_patch_passes_library_agents(self): + """Test that library_agents are included in patch generation payload.""" + library_agents = [ + { + "graph_id": "agent-789", + "graph_version": 1, + "name": "Slack Notifier", + "description": "Sends Slack messages", + "input_schema": {"properties": {"message": {"type": "string"}}}, + "output_schema": {"properties": {"success": {"type": "boolean"}}}, + }, + ] + + mock_response = MagicMock() + mock_response.json.return_value = { + "success": True, + "agent_json": {"name": "Updated Agent", "nodes": []}, + } + mock_response.raise_for_status = MagicMock() + + mock_client = AsyncMock() + mock_client.post.return_value = mock_response + + with patch.object(service, "_get_client", return_value=mock_client): + await service.generate_agent_patch_external( + "Add error handling", + {"name": "Original Agent", "nodes": []}, + library_agents=library_agents, + ) + + # Verify library_agents was passed in the payload + call_args = mock_client.post.call_args + assert call_args[1]["json"]["library_agents"] == library_agents + + @pytest.mark.asyncio + async def test_decompose_goal_without_library_agents(self): + """Test that decompose goal works without library_agents.""" + mock_response = MagicMock() + mock_response.json.return_value = { + "success": True, + "type": "instructions", + "steps": ["Step 1"], + } + mock_response.raise_for_status = MagicMock() + + mock_client = AsyncMock() + mock_client.post.return_value = mock_response + + with patch.object(service, "_get_client", return_value=mock_client): + await service.decompose_goal_external("Build a workflow") + + # Verify library_agents was NOT passed when not provided + call_args = mock_client.post.call_args + assert "library_agents" not in call_args[1]["json"] + + if __name__ == "__main__": pytest.main([__file__, "-v"]) diff --git a/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode.tsx b/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode.tsx index 94e917a4ac..834603cc4a 100644 --- a/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode.tsx +++ b/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode.tsx @@ -857,7 +857,7 @@ export const CustomNode = React.memo( })(); const hasAdvancedFields = - data.inputSchema && + data.inputSchema?.properties && Object.entries(data.inputSchema.properties).some(([key, value]) => { return ( value.advanced === true && !data.inputSchema.required?.includes(key) diff --git a/autogpt_platform/frontend/src/app/api/openapi.json b/autogpt_platform/frontend/src/app/api/openapi.json index 6692c30e72..a44ceb8388 100644 --- a/autogpt_platform/frontend/src/app/api/openapi.json +++ b/autogpt_platform/frontend/src/app/api/openapi.json @@ -7981,6 +7981,25 @@ ] }, "new_output": { "type": "boolean", "title": "New Output" }, + "execution_count": { + "type": "integer", + "title": "Execution Count", + "default": 0 + }, + "success_rate": { + "anyOf": [{ "type": "number" }, { "type": "null" }], + "title": "Success Rate" + }, + "avg_correctness_score": { + "anyOf": [{ "type": "number" }, { "type": "null" }], + "title": "Avg Correctness Score" + }, + "recent_executions": { + "items": { "$ref": "#/components/schemas/RecentExecution" }, + "type": "array", + "title": "Recent Executions", + "description": "List of recent executions with status, score, and summary" + }, "can_access_graph": { "type": "boolean", "title": "Can Access Graph" @@ -9374,6 +9393,23 @@ "required": ["providers", "pagination"], "title": "ProviderResponse" }, + "RecentExecution": { + "properties": { + "status": { "type": "string", "title": "Status" }, + "correctness_score": { + "anyOf": [{ "type": "number" }, { "type": "null" }], + "title": "Correctness Score" + }, + "activity_summary": { + "anyOf": [{ "type": "string" }, { "type": "null" }], + "title": "Activity Summary" + } + }, + "type": "object", + "required": ["status"], + "title": "RecentExecution", + "description": "Summary of a recent execution for quality assessment.\n\nUsed by the LLM to understand the agent's recent performance with specific examples\nrather than just aggregate statistics." + }, "RefundRequest": { "properties": { "id": { "type": "string", "title": "Id" }, diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx index c922d0da76..2ac433a272 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx @@ -156,11 +156,19 @@ export function ChatMessage({ } if (isClarificationNeeded && message.type === "clarification_needed") { + const hasUserReplyAfter = + index >= 0 && + messages + .slice(index + 1) + .some((m) => m.type === "message" && m.role === "user"); + return ( ); diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx index a3bd17dd3f..3b225d1ef1 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx @@ -6,7 +6,7 @@ import { Input } from "@/components/atoms/Input/Input"; import { Text } from "@/components/atoms/Text/Text"; import { cn } from "@/lib/utils"; import { CheckCircleIcon, QuestionIcon } from "@phosphor-icons/react"; -import { useState } from "react"; +import { useState, useEffect, useRef } from "react"; export interface ClarifyingQuestion { question: string; @@ -17,39 +17,96 @@ export interface ClarifyingQuestion { interface Props { questions: ClarifyingQuestion[]; message: string; + sessionId?: string; onSubmitAnswers: (answers: Record) => void; onCancel?: () => void; + isAnswered?: boolean; className?: string; } +function getStorageKey(sessionId?: string): string | null { + if (!sessionId) return null; + return `clarification_answers_${sessionId}`; +} + export function ClarificationQuestionsWidget({ questions, message, + sessionId, onSubmitAnswers, onCancel, + isAnswered = false, className, }: Props) { const [answers, setAnswers] = useState>({}); const [isSubmitted, setIsSubmitted] = useState(false); + const lastSessionIdRef = useRef(undefined); + + useEffect(() => { + const storageKey = getStorageKey(sessionId); + if (!storageKey) { + setAnswers({}); + setIsSubmitted(false); + lastSessionIdRef.current = sessionId; + return; + } + + try { + const saved = localStorage.getItem(storageKey); + if (saved) { + const parsed = JSON.parse(saved) as Record; + setAnswers(parsed); + } else { + setAnswers({}); + } + setIsSubmitted(false); + } catch { + setAnswers({}); + setIsSubmitted(false); + } + lastSessionIdRef.current = sessionId; + }, [sessionId]); + + useEffect(() => { + if (lastSessionIdRef.current !== sessionId) { + return; + } + const storageKey = getStorageKey(sessionId); + if (!storageKey) return; + + const hasAnswers = Object.values(answers).some((v) => v.trim()); + try { + if (hasAnswers) { + localStorage.setItem(storageKey, JSON.stringify(answers)); + } else { + localStorage.removeItem(storageKey); + } + } catch {} + }, [answers, sessionId]); function handleAnswerChange(keyword: string, value: string) { setAnswers((prev) => ({ ...prev, [keyword]: value })); } function handleSubmit() { - // Check if all questions are answered const allAnswered = questions.every((q) => answers[q.keyword]?.trim()); if (!allAnswered) { return; } setIsSubmitted(true); onSubmitAnswers(answers); + + const storageKey = getStorageKey(sessionId); + try { + if (storageKey) { + localStorage.removeItem(storageKey); + } + } catch {} } const allAnswered = questions.every((q) => answers[q.keyword]?.trim()); - // Show submitted state after answers are submitted - if (isSubmitted) { + if (isAnswered || isSubmitted) { return (
; - if (response.error) return stripInternalReasoning(String(response.error)); if (response.message) return stripInternalReasoning(String(response.message)); + if (response.error) return stripInternalReasoning(String(response.error)); } return "An error occurred"; } @@ -363,8 +363,8 @@ export function formatToolResponse(result: unknown, toolName: string): string { case "error": const errorMsg = - (response.error as string) || response.message || "An error occurred"; - return `Error: ${errorMsg}`; + (response.message as string) || response.error || "An error occurred"; + return stripInternalReasoning(String(errorMsg)); case "no_results": const suggestions = (response.suggestions as string[]) || []; From 05b60db554db82562b38c9c35c11b027e7ec8e02 Mon Sep 17 00:00:00 2001 From: Nicholas Tindle Date: Fri, 30 Jan 2026 21:00:43 -0600 Subject: [PATCH 12/31] fix(backend/chat): Include input schema in discovery and validate unknown fields (#11916) Co-authored-by: Claude Opus 4.5 --- .../api/features/chat/tools/agent_search.py | 56 ++++++++++++++++++- .../backend/api/features/chat/tools/models.py | 20 +++++++ .../api/features/chat/tools/run_agent.py | 17 ++++++ .../api/features/chat/tools/run_agent_test.py | 39 +++++++++++++ 4 files changed, 130 insertions(+), 2 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py index 5fa74ba04e..3613bb568d 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py @@ -1,10 +1,13 @@ """Shared agent search functionality for find_agent and find_library_agent tools.""" +import asyncio import logging from typing import Literal from backend.api.features.library import db as library_db from backend.api.features.store import db as store_db +from backend.data import graph as graph_db +from backend.data.graph import GraphModel from backend.util.exceptions import DatabaseError, NotFoundError from .models import ( @@ -14,6 +17,7 @@ from .models import ( NoResultsResponse, ToolResponseBase, ) +from .utils import fetch_graph_from_store_slug logger = logging.getLogger(__name__) @@ -54,7 +58,28 @@ async def search_agents( if source == "marketplace": logger.info(f"Searching marketplace for: {query}") results = await store_db.get_store_agents(search_query=query, page_size=5) - for agent in results.agents: + + # Fetch all graphs in parallel for better performance + async def fetch_marketplace_graph( + creator: str, slug: str + ) -> GraphModel | None: + try: + graph, _ = await fetch_graph_from_store_slug(creator, slug) + return graph + except Exception as e: + logger.warning( + f"Failed to fetch input schema for {creator}/{slug}: {e}" + ) + return None + + graphs = await asyncio.gather( + *( + fetch_marketplace_graph(agent.creator, agent.slug) + for agent in results.agents + ) + ) + + for agent, graph in zip(results.agents, graphs): agents.append( AgentInfo( id=f"{agent.creator}/{agent.slug}", @@ -67,6 +92,7 @@ async def search_agents( rating=agent.rating, runs=agent.runs, is_featured=False, + inputs=graph.input_schema if graph else None, ) ) else: # library @@ -76,7 +102,32 @@ async def search_agents( search_term=query, page_size=10, ) - for agent in results.agents: + + # Fetch all graphs in parallel for better performance + # (list_library_agents doesn't include nodes for performance) + async def fetch_library_graph( + graph_id: str, graph_version: int + ) -> GraphModel | None: + try: + return await graph_db.get_graph( + graph_id=graph_id, + version=graph_version, + user_id=user_id, + ) + except Exception as e: + logger.warning( + f"Failed to fetch input schema for graph {graph_id}: {e}" + ) + return None + + graphs = await asyncio.gather( + *( + fetch_library_graph(agent.graph_id, agent.graph_version) + for agent in results.agents + ) + ) + + for agent, graph in zip(results.agents, graphs): agents.append( AgentInfo( id=agent.id, @@ -90,6 +141,7 @@ async def search_agents( has_external_trigger=agent.has_external_trigger, new_output=agent.new_output, graph_id=agent.graph_id, + inputs=graph.input_schema if graph else None, ) ) logger.info(f"Found {len(agents)} agents in {source}") diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/models.py b/autogpt_platform/backend/backend/api/features/chat/tools/models.py index 8552681d03..e635578149 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/models.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/models.py @@ -32,6 +32,8 @@ class ResponseType(str, Enum): OPERATION_STARTED = "operation_started" OPERATION_PENDING = "operation_pending" OPERATION_IN_PROGRESS = "operation_in_progress" + # Input validation + INPUT_VALIDATION_ERROR = "input_validation_error" # Base response model @@ -62,6 +64,10 @@ class AgentInfo(BaseModel): has_external_trigger: bool | None = None new_output: bool | None = None graph_id: str | None = None + inputs: dict[str, Any] | None = Field( + default=None, + description="Input schema for the agent, including field names, types, and defaults", + ) class AgentsFoundResponse(ToolResponseBase): @@ -188,6 +194,20 @@ class ErrorResponse(ToolResponseBase): details: dict[str, Any] | None = None +class InputValidationErrorResponse(ToolResponseBase): + """Response when run_agent receives unknown input fields.""" + + type: ResponseType = ResponseType.INPUT_VALIDATION_ERROR + unrecognized_fields: list[str] = Field( + description="List of input field names that were not recognized" + ) + inputs: dict[str, Any] = Field( + description="The agent's valid input schema for reference" + ) + graph_id: str | None = None + graph_version: int | None = None + + # Agent output models class ExecutionOutputInfo(BaseModel): """Summary of a single execution's outputs.""" diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py index a7fa65348a..73d4cf81f2 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py @@ -30,6 +30,7 @@ from .models import ( ErrorResponse, ExecutionOptions, ExecutionStartedResponse, + InputValidationErrorResponse, SetupInfo, SetupRequirementsResponse, ToolResponseBase, @@ -273,6 +274,22 @@ class RunAgentTool(BaseTool): input_properties = graph.input_schema.get("properties", {}) required_fields = set(graph.input_schema.get("required", [])) provided_inputs = set(params.inputs.keys()) + valid_fields = set(input_properties.keys()) + + # Check for unknown input fields + unrecognized_fields = provided_inputs - valid_fields + if unrecognized_fields: + return InputValidationErrorResponse( + message=( + f"Unknown input field(s) provided: {', '.join(sorted(unrecognized_fields))}. " + f"Agent was not executed. Please use the correct field names from the schema." + ), + session_id=session_id, + unrecognized_fields=sorted(unrecognized_fields), + inputs=graph.input_schema, + graph_id=graph.id, + graph_version=graph.version, + ) # If agent has inputs but none were provided AND use_defaults is not set, # always show what's available first so user can decide diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py index 404df2adb6..d5da394fa6 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py @@ -402,3 +402,42 @@ async def test_run_agent_schedule_without_name(setup_test_data): # Should return error about missing schedule_name assert result_data.get("type") == "error" assert "schedule_name" in result_data["message"].lower() + + +@pytest.mark.asyncio(loop_scope="session") +async def test_run_agent_rejects_unknown_input_fields(setup_test_data): + """Test that run_agent returns input_validation_error for unknown input fields.""" + user = setup_test_data["user"] + store_submission = setup_test_data["store_submission"] + + tool = RunAgentTool() + agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}" + session = make_session(user_id=user.id) + + # Execute with unknown input field names + response = await tool.execute( + user_id=user.id, + session_id=str(uuid.uuid4()), + tool_call_id=str(uuid.uuid4()), + username_agent_slug=agent_marketplace_id, + inputs={ + "unknown_field": "some value", + "another_unknown": "another value", + }, + session=session, + ) + + assert response is not None + assert hasattr(response, "output") + assert isinstance(response.output, str) + result_data = orjson.loads(response.output) + + # Should return input_validation_error type with unrecognized fields + assert result_data.get("type") == "input_validation_error" + assert "unrecognized_fields" in result_data + assert set(result_data["unrecognized_fields"]) == { + "another_unknown", + "unknown_field", + } + assert "inputs" in result_data # Contains the valid schema + assert "Agent was not executed" in result_data["message"] From 7ee94d986cd39ba1c39ab0cd4577039cdeb83b92 Mon Sep 17 00:00:00 2001 From: Otto Date: Sat, 31 Jan 2026 03:05:31 +0000 Subject: [PATCH 13/31] docs: add credentials prerequisites to create-basic-agent guide (#11913) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary Addresses #11785 - users were encountering `openai_api_key_credentials` errors when following the create-basic-agent guide because it didn't mention the need to configure API credentials before using AI blocks. ## Changes Added a **Prerequisites** section to `docs/platform/create-basic-agent.md` explaining: - **Cloud users:** Go to Profile ā†’ Integrations to add API keys - **Self-hosted (Docker):** Add keys to `autogpt_platform/backend/.env` and restart services Also added a note that the Calculator example doesn't need credentials, making it a good first test. ## Related - Issue: #11785 --- docs/platform/create-basic-agent.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/docs/platform/create-basic-agent.md b/docs/platform/create-basic-agent.md index 7721fb9b9c..ffe654ba99 100644 --- a/docs/platform/create-basic-agent.md +++ b/docs/platform/create-basic-agent.md @@ -4,6 +4,28 @@ This guide walks through creating a simple question-answer AI agent using AutoGPT's visual builder. This is a basic example that can be expanded into more complex agents. +## **Prerequisites** + +### **Cloud-Hosted AutoGPT** +If you're using the cloud-hosted version at [agpt.co](https://agpt.co), you're ready to go! AI blocks come with **built-in credits** ā€” no API keys required to get started. If you'd prefer to use your own API keys, you can add them via **Profile ā†’ Integrations**. + +### **Self-Hosted (Docker)** +If you're running AutoGPT locally with Docker, you'll need to add your own API keys to `autogpt_platform/backend/.env`: + +```bash +# Create or edit backend/.env +OPENAI_API_KEY=sk-your-key-here +ANTHROPIC_API_KEY=sk-ant-your-key-here +# Add other provider keys as needed +``` + +After adding keys, restart the services: +```bash +docker compose down && docker compose up -d +``` + +**Note:** The Calculator example below doesn't require any API credentials ā€” it's a good way to test your setup before adding AI blocks. + ## **Example Agent: Q&A (with AI)** A step-by-step guide to creating a simple Q&A agent using input and output blocks. From 2abbb7fbc8abb5853993e4a27161b8b5d96fb9f4 Mon Sep 17 00:00:00 2001 From: Otto Date: Sat, 31 Jan 2026 03:50:21 +0000 Subject: [PATCH 14/31] hotfix(backend): use discriminator for credential matching in run_block (#11908) Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Nicholas Tindle Co-authored-by: Nicholas Tindle Co-authored-by: Claude Opus 4.5 --- .../api/features/chat/tools/run_block.py | 43 +++++++++++++++---- 1 file changed, 35 insertions(+), 8 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py b/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py index 3f57236564..3a7118eb87 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py @@ -4,6 +4,8 @@ import logging from collections import defaultdict from typing import Any +from pydantic_core import PydanticUndefined + from backend.api.features.chat.model import ChatSession from backend.data.block import get_block from backend.data.execution import ExecutionContext @@ -73,15 +75,22 @@ class RunBlockTool(BaseTool): self, user_id: str, block: Any, + input_data: dict[str, Any] | None = None, ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]: """ Check if user has required credentials for a block. + Args: + user_id: User ID + block: Block to check credentials for + input_data: Input data for the block (used to determine provider via discriminator) + Returns: tuple[matched_credentials, missing_credentials] """ matched_credentials: dict[str, CredentialsMetaInput] = {} missing_credentials: list[CredentialsMetaInput] = [] + input_data = input_data or {} # Get credential field info from block's input schema credentials_fields_info = block.input_schema.get_credentials_fields_info() @@ -94,14 +103,33 @@ class RunBlockTool(BaseTool): available_creds = await creds_manager.store.get_all_creds(user_id) for field_name, field_info in credentials_fields_info.items(): - # field_info.provider is a frozenset of acceptable providers - # field_info.supported_types is a frozenset of acceptable types + effective_field_info = field_info + if field_info.discriminator and field_info.discriminator_mapping: + # Get discriminator from input, falling back to schema default + discriminator_value = input_data.get(field_info.discriminator) + if discriminator_value is None: + field = block.input_schema.model_fields.get( + field_info.discriminator + ) + if field and field.default is not PydanticUndefined: + discriminator_value = field.default + + if ( + discriminator_value + and discriminator_value in field_info.discriminator_mapping + ): + effective_field_info = field_info.discriminate(discriminator_value) + logger.debug( + f"Discriminated provider for {field_name}: " + f"{discriminator_value} -> {effective_field_info.provider}" + ) + matching_cred = next( ( cred for cred in available_creds - if cred.provider in field_info.provider - and cred.type in field_info.supported_types + if cred.provider in effective_field_info.provider + and cred.type in effective_field_info.supported_types ), None, ) @@ -115,8 +143,8 @@ class RunBlockTool(BaseTool): ) else: # Create a placeholder for the missing credential - provider = next(iter(field_info.provider), "unknown") - cred_type = next(iter(field_info.supported_types), "api_key") + provider = next(iter(effective_field_info.provider), "unknown") + cred_type = next(iter(effective_field_info.supported_types), "api_key") missing_credentials.append( CredentialsMetaInput( id=field_name, @@ -184,10 +212,9 @@ class RunBlockTool(BaseTool): logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}") - # Check credentials creds_manager = IntegrationCredentialsManager() matched_credentials, missing_credentials = await self._check_block_credentials( - user_id, block + user_id, block, input_data ) if missing_credentials: From 7e37de8e30529bd9b8c2ee3e3e74bf689ff13783 Mon Sep 17 00:00:00 2001 From: Otto Date: Sat, 31 Jan 2026 19:17:36 +0000 Subject: [PATCH 15/31] fix: Include graph schemas for marketplace agents in Agent Generator (#11920) ## Problem When marketplace agents are included in the `library_agents` payload sent to the Agent Generator service, they were missing required fields (`graph_id`, `graph_version`, `input_schema`, `output_schema`). This caused Pydantic validation to fail with HTTP 422 Unprocessable Entity. **Root cause:** The `MarketplaceAgentSummary` TypedDict had a different shape than `LibraryAgentInfo` expected by the Agent Generator: - Agent Generator expects: `graph_id`, `graph_version`, `name`, `description`, `input_schema`, `output_schema` - MarketplaceAgentSummary had: `name`, `description`, `sub_heading`, `creator`, `is_marketplace_agent` ## Solution 1. **Add `agent_graph_id` to `StoreAgent` model** - The field was already in the database view but not exposed 2. **Include `agentGraphId` in hybrid search SQL query** - Carry the field through the search CTEs 3. **Update `search_marketplace_agents_for_generation()`** - Now fetches full graph schemas using `get_graph()` and returns `LibraryAgentSummary` (same type as library agents) 4. **Update deduplication logic** - Use `graph_id` instead of name for more accurate deduplication ## Changes - `backend/api/features/store/model.py`: Add optional `agent_graph_id` field to `StoreAgent` - `backend/api/features/store/hybrid_search.py`: Include `agentGraphId` in SQL query columns - `backend/api/features/store/db.py`: Map `agentGraphId` when creating `StoreAgent` objects - `backend/api/features/chat/tools/agent_generator/core.py`: Update `search_marketplace_agents_for_generation()` to fetch and include full graph schemas ## Testing - [ ] Agent creation on dev with marketplace agents in context - [ ] Verify no 422 errors from Agent Generator - [ ] Verify marketplace agents can be used as sub-agents Fixes: SECRT-1817 --------- Co-authored-by: majdyz Co-authored-by: Zamil Majdy --- .../chat/tools/agent_generator/core.py | 59 +++++++++++-------- .../backend/backend/api/features/store/db.py | 2 + .../api/features/store/hybrid_search.py | 2 + .../backend/api/features/store/model.py | 1 + .../backend/api/features/store/model_test.py | 3 + .../backend/api/features/store/routes_test.py | 6 ++ .../api/features/store/test_cache_delete.py | 1 + .../backend/backend/data/graph.py | 33 +++++++++++ .../backend/snapshots/agts_by_creator | 3 +- .../backend/snapshots/agts_category | 3 +- .../backend/snapshots/agts_pagination | 15 +++-- .../backend/snapshots/agts_search | 3 +- .../backend/snapshots/agts_sorted | 3 +- autogpt_platform/backend/snapshots/feat_agts | 3 +- .../agent_generator/test_library_agents.py | 58 +++++++++++------- .../frontend/src/app/api/openapi.json | 6 +- 16 files changed, 143 insertions(+), 58 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py index 466f6438a3..0ddd2aa86b 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py @@ -14,6 +14,7 @@ from backend.data.graph import ( create_graph, get_graph, get_graph_all_versions, + get_store_listed_graphs, ) from backend.util.exceptions import DatabaseError, NotFoundError @@ -266,18 +267,18 @@ async def get_library_agents_for_generation( async def search_marketplace_agents_for_generation( search_query: str, max_results: int = 10, -) -> list[MarketplaceAgentSummary]: +) -> list[LibraryAgentSummary]: """Search marketplace agents formatted for Agent Generator. - Note: This returns basic agent info. Full input/output schemas would require - additional graph fetches and is a potential future enhancement. + Fetches marketplace agents and their full schemas so they can be used + as sub-agents in generated workflows. Args: search_query: Search term to find relevant public agents max_results: Maximum number of agents to return (default 10) Returns: - List of MarketplaceAgentSummary (without detailed schemas for now) + List of LibraryAgentSummary with full input/output schemas """ try: response = await store_db.get_store_agents( @@ -286,17 +287,31 @@ async def search_marketplace_agents_for_generation( page_size=max_results, ) - results: list[MarketplaceAgentSummary] = [] - for agent in response.agents: - results.append( - MarketplaceAgentSummary( - name=agent.agent_name, - description=agent.description, - sub_heading=agent.sub_heading, - creator=agent.creator, - is_marketplace_agent=True, + agents_with_graphs = [ + agent for agent in response.agents if agent.agent_graph_id + ] + + if not agents_with_graphs: + return [] + + graph_ids = [agent.agent_graph_id for agent in agents_with_graphs] + graphs = await get_store_listed_graphs(*graph_ids) + + results: list[LibraryAgentSummary] = [] + for agent in agents_with_graphs: + graph_id = agent.agent_graph_id + if graph_id and graph_id in graphs: + graph = graphs[graph_id] + results.append( + LibraryAgentSummary( + graph_id=graph.id, + graph_version=graph.version, + name=agent.agent_name, + description=agent.description, + input_schema=graph.input_schema, + output_schema=graph.output_schema, + ) ) - ) return results except Exception as e: logger.warning(f"Failed to search marketplace agents: {e}") @@ -327,8 +342,7 @@ async def get_all_relevant_agents_for_generation( max_marketplace_results: Max marketplace agents to return (default 10) Returns: - List of AgentSummary, library agents first (with full schemas), - then marketplace agents (basic info only) + List of AgentSummary with full schemas (both library and marketplace agents) """ agents: list[AgentSummary] = [] seen_graph_ids: set[str] = set() @@ -365,16 +379,11 @@ async def get_all_relevant_agents_for_generation( search_query=search_query, max_results=max_marketplace_results, ) - library_names: set[str] = set() - for a in agents: - name = a.get("name") - if name and isinstance(name, str): - library_names.add(name.lower()) for agent in marketplace_agents: - agent_name = agent.get("name") - if agent_name and isinstance(agent_name, str): - if agent_name.lower() not in library_names: - agents.append(agent) + graph_id = agent.get("graph_id") + if graph_id and graph_id not in seen_graph_ids: + agents.append(agent) + seen_graph_ids.add(graph_id) return agents diff --git a/autogpt_platform/backend/backend/api/features/store/db.py b/autogpt_platform/backend/backend/api/features/store/db.py index 956fdfa7da..850a2bc3e9 100644 --- a/autogpt_platform/backend/backend/api/features/store/db.py +++ b/autogpt_platform/backend/backend/api/features/store/db.py @@ -112,6 +112,7 @@ async def get_store_agents( description=agent["description"], runs=agent["runs"], rating=agent["rating"], + agent_graph_id=agent.get("agentGraphId", ""), ) store_agents.append(store_agent) except Exception as e: @@ -170,6 +171,7 @@ async def get_store_agents( description=agent.description, runs=agent.runs, rating=agent.rating, + agent_graph_id=agent.agentGraphId, ) # Add to the list only if creation was successful store_agents.append(store_agent) diff --git a/autogpt_platform/backend/backend/api/features/store/hybrid_search.py b/autogpt_platform/backend/backend/api/features/store/hybrid_search.py index 8b0884bb24..e1b8f402c8 100644 --- a/autogpt_platform/backend/backend/api/features/store/hybrid_search.py +++ b/autogpt_platform/backend/backend/api/features/store/hybrid_search.py @@ -600,6 +600,7 @@ async def hybrid_search( sa.featured, sa.is_available, sa.updated_at, + sa."agentGraphId", -- Searchable text for BM25 reranking COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text, -- Semantic score @@ -659,6 +660,7 @@ async def hybrid_search( featured, is_available, updated_at, + "agentGraphId", searchable_text, semantic_score, lexical_score, diff --git a/autogpt_platform/backend/backend/api/features/store/model.py b/autogpt_platform/backend/backend/api/features/store/model.py index a3310b96fc..d66b91807d 100644 --- a/autogpt_platform/backend/backend/api/features/store/model.py +++ b/autogpt_platform/backend/backend/api/features/store/model.py @@ -38,6 +38,7 @@ class StoreAgent(pydantic.BaseModel): description: str runs: int rating: float + agent_graph_id: str class StoreAgentsResponse(pydantic.BaseModel): diff --git a/autogpt_platform/backend/backend/api/features/store/model_test.py b/autogpt_platform/backend/backend/api/features/store/model_test.py index fd09a0cf77..c4109f4603 100644 --- a/autogpt_platform/backend/backend/api/features/store/model_test.py +++ b/autogpt_platform/backend/backend/api/features/store/model_test.py @@ -26,11 +26,13 @@ def test_store_agent(): description="Test description", runs=50, rating=4.5, + agent_graph_id="test-graph-id", ) assert agent.slug == "test-agent" assert agent.agent_name == "Test Agent" assert agent.runs == 50 assert agent.rating == 4.5 + assert agent.agent_graph_id == "test-graph-id" def test_store_agents_response(): @@ -46,6 +48,7 @@ def test_store_agents_response(): description="Test description", runs=50, rating=4.5, + agent_graph_id="test-graph-id", ) ], pagination=store_model.Pagination( diff --git a/autogpt_platform/backend/backend/api/features/store/routes_test.py b/autogpt_platform/backend/backend/api/features/store/routes_test.py index 36431c20ec..fcef3f845a 100644 --- a/autogpt_platform/backend/backend/api/features/store/routes_test.py +++ b/autogpt_platform/backend/backend/api/features/store/routes_test.py @@ -82,6 +82,7 @@ def test_get_agents_featured( description="Featured agent description", runs=100, rating=4.5, + agent_graph_id="test-graph-1", ) ], pagination=store_model.Pagination( @@ -127,6 +128,7 @@ def test_get_agents_by_creator( description="Creator agent description", runs=50, rating=4.0, + agent_graph_id="test-graph-2", ) ], pagination=store_model.Pagination( @@ -172,6 +174,7 @@ def test_get_agents_sorted( description="Top agent description", runs=1000, rating=5.0, + agent_graph_id="test-graph-3", ) ], pagination=store_model.Pagination( @@ -217,6 +220,7 @@ def test_get_agents_search( description="Specific search term description", runs=75, rating=4.2, + agent_graph_id="test-graph-search", ) ], pagination=store_model.Pagination( @@ -262,6 +266,7 @@ def test_get_agents_category( description="Category agent description", runs=60, rating=4.1, + agent_graph_id="test-graph-category", ) ], pagination=store_model.Pagination( @@ -306,6 +311,7 @@ def test_get_agents_pagination( description=f"Agent {i} description", runs=i * 10, rating=4.0, + agent_graph_id="test-graph-2", ) for i in range(5) ], diff --git a/autogpt_platform/backend/backend/api/features/store/test_cache_delete.py b/autogpt_platform/backend/backend/api/features/store/test_cache_delete.py index dd9be1f4ab..298c51d47c 100644 --- a/autogpt_platform/backend/backend/api/features/store/test_cache_delete.py +++ b/autogpt_platform/backend/backend/api/features/store/test_cache_delete.py @@ -33,6 +33,7 @@ class TestCacheDeletion: description="Test description", runs=100, rating=4.5, + agent_graph_id="test-graph-id", ) ], pagination=Pagination( diff --git a/autogpt_platform/backend/backend/data/graph.py b/autogpt_platform/backend/backend/data/graph.py index c1f38f81d5..ee6cd2e4b0 100644 --- a/autogpt_platform/backend/backend/data/graph.py +++ b/autogpt_platform/backend/backend/data/graph.py @@ -1028,6 +1028,39 @@ async def get_graph( return GraphModel.from_db(graph, for_export) +async def get_store_listed_graphs(*graph_ids: str) -> dict[str, GraphModel]: + """Batch-fetch multiple store-listed graphs by their IDs. + + Only returns graphs that have approved store listings (publicly available). + Does not require permission checks since store-listed graphs are public. + + Args: + *graph_ids: Variable number of graph IDs to fetch + + Returns: + Dict mapping graph_id to GraphModel for graphs with approved store listings + """ + if not graph_ids: + return {} + + store_listings = await StoreListingVersion.prisma().find_many( + where={ + "agentGraphId": {"in": list(graph_ids)}, + "submissionStatus": SubmissionStatus.APPROVED, + "isDeleted": False, + }, + include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}}, + distinct=["agentGraphId"], + order={"agentGraphVersion": "desc"}, + ) + + return { + listing.agentGraphId: GraphModel.from_db(listing.AgentGraph) + for listing in store_listings + if listing.AgentGraph + } + + async def get_graph_as_admin( graph_id: str, version: int | None = None, diff --git a/autogpt_platform/backend/snapshots/agts_by_creator b/autogpt_platform/backend/snapshots/agts_by_creator index 4d6dd12920..3f2e128a0d 100644 --- a/autogpt_platform/backend/snapshots/agts_by_creator +++ b/autogpt_platform/backend/snapshots/agts_by_creator @@ -9,7 +9,8 @@ "sub_heading": "Creator agent subheading", "description": "Creator agent description", "runs": 50, - "rating": 4.0 + "rating": 4.0, + "agent_graph_id": "test-graph-2" } ], "pagination": { diff --git a/autogpt_platform/backend/snapshots/agts_category b/autogpt_platform/backend/snapshots/agts_category index f65925ead3..4d0531763c 100644 --- a/autogpt_platform/backend/snapshots/agts_category +++ b/autogpt_platform/backend/snapshots/agts_category @@ -9,7 +9,8 @@ "sub_heading": "Category agent subheading", "description": "Category agent description", "runs": 60, - "rating": 4.1 + "rating": 4.1, + "agent_graph_id": "test-graph-category" } ], "pagination": { diff --git a/autogpt_platform/backend/snapshots/agts_pagination b/autogpt_platform/backend/snapshots/agts_pagination index 82e7f5f9bf..7b946157fb 100644 --- a/autogpt_platform/backend/snapshots/agts_pagination +++ b/autogpt_platform/backend/snapshots/agts_pagination @@ -9,7 +9,8 @@ "sub_heading": "Agent 0 subheading", "description": "Agent 0 description", "runs": 0, - "rating": 4.0 + "rating": 4.0, + "agent_graph_id": "test-graph-2" }, { "slug": "agent-1", @@ -20,7 +21,8 @@ "sub_heading": "Agent 1 subheading", "description": "Agent 1 description", "runs": 10, - "rating": 4.0 + "rating": 4.0, + "agent_graph_id": "test-graph-2" }, { "slug": "agent-2", @@ -31,7 +33,8 @@ "sub_heading": "Agent 2 subheading", "description": "Agent 2 description", "runs": 20, - "rating": 4.0 + "rating": 4.0, + "agent_graph_id": "test-graph-2" }, { "slug": "agent-3", @@ -42,7 +45,8 @@ "sub_heading": "Agent 3 subheading", "description": "Agent 3 description", "runs": 30, - "rating": 4.0 + "rating": 4.0, + "agent_graph_id": "test-graph-2" }, { "slug": "agent-4", @@ -53,7 +57,8 @@ "sub_heading": "Agent 4 subheading", "description": "Agent 4 description", "runs": 40, - "rating": 4.0 + "rating": 4.0, + "agent_graph_id": "test-graph-2" } ], "pagination": { diff --git a/autogpt_platform/backend/snapshots/agts_search b/autogpt_platform/backend/snapshots/agts_search index ca3f504584..ae9cc116bc 100644 --- a/autogpt_platform/backend/snapshots/agts_search +++ b/autogpt_platform/backend/snapshots/agts_search @@ -9,7 +9,8 @@ "sub_heading": "Search agent subheading", "description": "Specific search term description", "runs": 75, - "rating": 4.2 + "rating": 4.2, + "agent_graph_id": "test-graph-search" } ], "pagination": { diff --git a/autogpt_platform/backend/snapshots/agts_sorted b/autogpt_platform/backend/snapshots/agts_sorted index cddead76a5..b182256b2c 100644 --- a/autogpt_platform/backend/snapshots/agts_sorted +++ b/autogpt_platform/backend/snapshots/agts_sorted @@ -9,7 +9,8 @@ "sub_heading": "Top agent subheading", "description": "Top agent description", "runs": 1000, - "rating": 5.0 + "rating": 5.0, + "agent_graph_id": "test-graph-3" } ], "pagination": { diff --git a/autogpt_platform/backend/snapshots/feat_agts b/autogpt_platform/backend/snapshots/feat_agts index d57996a768..4f85786434 100644 --- a/autogpt_platform/backend/snapshots/feat_agts +++ b/autogpt_platform/backend/snapshots/feat_agts @@ -9,7 +9,8 @@ "sub_heading": "Featured agent subheading", "description": "Featured agent description", "runs": 100, - "rating": 4.5 + "rating": 4.5, + "agent_graph_id": "test-graph-1" } ], "pagination": { diff --git a/autogpt_platform/backend/test/agent_generator/test_library_agents.py b/autogpt_platform/backend/test/agent_generator/test_library_agents.py index e62b0746e7..8387339582 100644 --- a/autogpt_platform/backend/test/agent_generator/test_library_agents.py +++ b/autogpt_platform/backend/test/agent_generator/test_library_agents.py @@ -134,15 +134,28 @@ class TestSearchMarketplaceAgentsForGeneration: description="A public agent", sub_heading="Does something useful", creator="creator-1", + agent_graph_id="graph-123", ) ] - # The store_db is dynamically imported, so patch the import path - with patch( - "backend.api.features.store.db.get_store_agents", - new_callable=AsyncMock, - return_value=mock_response, - ) as mock_search: + mock_graph = MagicMock() + mock_graph.id = "graph-123" + mock_graph.version = 1 + mock_graph.input_schema = {"type": "object"} + mock_graph.output_schema = {"type": "object"} + + with ( + patch( + "backend.api.features.store.db.get_store_agents", + new_callable=AsyncMock, + return_value=mock_response, + ) as mock_search, + patch( + "backend.api.features.chat.tools.agent_generator.core.get_store_listed_graphs", + new_callable=AsyncMock, + return_value={"graph-123": mock_graph}, + ), + ): result = await core.search_marketplace_agents_for_generation( search_query="automation", max_results=10, @@ -156,7 +169,7 @@ class TestSearchMarketplaceAgentsForGeneration: assert len(result) == 1 assert result[0]["name"] == "Public Agent" - assert result[0]["is_marketplace_agent"] is True + assert result[0]["graph_id"] == "graph-123" @pytest.mark.asyncio async def test_handles_marketplace_error_gracefully(self): @@ -193,11 +206,12 @@ class TestGetAllRelevantAgentsForGeneration: marketplace_agents = [ { + "graph_id": "market-456", + "graph_version": 1, "name": "Market Agent", "description": "From marketplace", - "sub_heading": "Sub heading", - "creator": "creator-1", - "is_marketplace_agent": True, + "input_schema": {}, + "output_schema": {}, } ] @@ -225,11 +239,11 @@ class TestGetAllRelevantAgentsForGeneration: assert result[1]["name"] == "Market Agent" @pytest.mark.asyncio - async def test_deduplicates_by_name(self): - """Test that marketplace agents with same name as library are excluded.""" + async def test_deduplicates_by_graph_id(self): + """Test that marketplace agents with same graph_id as library are excluded.""" library_agents = [ { - "graph_id": "lib-123", + "graph_id": "shared-123", "graph_version": 1, "name": "Shared Agent", "description": "From library", @@ -240,18 +254,20 @@ class TestGetAllRelevantAgentsForGeneration: marketplace_agents = [ { - "name": "Shared Agent", # Same name, should be deduplicated + "graph_id": "shared-123", # Same graph_id, should be deduplicated + "graph_version": 1, + "name": "Shared Agent", "description": "From marketplace", - "sub_heading": "Sub heading", - "creator": "creator-1", - "is_marketplace_agent": True, + "input_schema": {}, + "output_schema": {}, }, { + "graph_id": "unique-456", + "graph_version": 1, "name": "Unique Agent", "description": "Only in marketplace", - "sub_heading": "Sub heading", - "creator": "creator-2", - "is_marketplace_agent": True, + "input_schema": {}, + "output_schema": {}, }, ] @@ -273,7 +289,7 @@ class TestGetAllRelevantAgentsForGeneration: include_marketplace=True, ) - # Shared Agent from marketplace should be excluded + # Shared Agent from marketplace should be excluded by graph_id assert len(result) == 2 names = [a["name"] for a in result] assert "Shared Agent" in names diff --git a/autogpt_platform/frontend/src/app/api/openapi.json b/autogpt_platform/frontend/src/app/api/openapi.json index a44ceb8388..aa4c49b1a2 100644 --- a/autogpt_platform/frontend/src/app/api/openapi.json +++ b/autogpt_platform/frontend/src/app/api/openapi.json @@ -9833,7 +9833,8 @@ "sub_heading": { "type": "string", "title": "Sub Heading" }, "description": { "type": "string", "title": "Description" }, "runs": { "type": "integer", "title": "Runs" }, - "rating": { "type": "number", "title": "Rating" } + "rating": { "type": "number", "title": "Rating" }, + "agent_graph_id": { "type": "string", "title": "Agent Graph Id" } }, "type": "object", "required": [ @@ -9845,7 +9846,8 @@ "sub_heading", "description", "runs", - "rating" + "rating", + "agent_graph_id" ], "title": "StoreAgent" }, From 1081590384c5355cc7af95f7f3936b25f16aa4aa Mon Sep 17 00:00:00 2001 From: "Guofang.Tang" Date: Mon, 2 Feb 2026 04:29:15 +0800 Subject: [PATCH 16/31] feat(backend): cover webhook ingress URL route (#11747) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ### Changes šŸ—ļø - Add a unit test to verify webhook ingress URL generation matches the FastAPI route. ### Checklist šŸ“‹ #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] poetry run pytest backend/integrations/webhooks/utils_test.py --confcutdir=backend/integrations/webhooks #### For configuration changes: - [x] .env.default is updated or already compatible with my changes - [x] docker-compose.yml is updated or already compatible with my changes - [x] I have included a list of my configuration changes in the PR description (under Changes) ## Summary by CodeRabbit * **Tests** * Added a unit test that validates webhook ingress URL generation matches the application's resolved route (scheme, host, and path) for provider-specific webhook endpoints, improving confidence in routing behavior and helping prevent regressions. āœļø Tip: You can customize this high-level summary in your review settings. --------- Co-authored-by: Reinier van der Leer --- .../integrations/webhooks/utils_test.py | 39 +++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 autogpt_platform/backend/backend/integrations/webhooks/utils_test.py diff --git a/autogpt_platform/backend/backend/integrations/webhooks/utils_test.py b/autogpt_platform/backend/backend/integrations/webhooks/utils_test.py new file mode 100644 index 0000000000..bc502a8e44 --- /dev/null +++ b/autogpt_platform/backend/backend/integrations/webhooks/utils_test.py @@ -0,0 +1,39 @@ +from urllib.parse import urlparse + +import fastapi +from fastapi.routing import APIRoute + +from backend.api.features.integrations.router import router as integrations_router +from backend.integrations.providers import ProviderName +from backend.integrations.webhooks import utils as webhooks_utils + + +def test_webhook_ingress_url_matches_route(monkeypatch) -> None: + app = fastapi.FastAPI() + app.include_router(integrations_router, prefix="/api/integrations") + + provider = ProviderName.GITHUB + webhook_id = "webhook_123" + base_url = "https://example.com" + + monkeypatch.setattr(webhooks_utils.app_config, "platform_base_url", base_url) + + route = next( + route + for route in integrations_router.routes + if isinstance(route, APIRoute) + and route.path == "/{provider}/webhooks/{webhook_id}/ingress" + and "POST" in route.methods + ) + expected_path = f"/api/integrations{route.path}".format( + provider=provider.value, + webhook_id=webhook_id, + ) + actual_url = urlparse(webhooks_utils.webhook_ingress_url(provider, webhook_id)) + expected_base = urlparse(base_url) + + assert (actual_url.scheme, actual_url.netloc) == ( + expected_base.scheme, + expected_base.netloc, + ) + assert actual_url.path == expected_path From f7350c797a537703eaa8303770296b1a9c7f54b0 Mon Sep 17 00:00:00 2001 From: Otto Date: Mon, 2 Feb 2026 13:01:05 +0000 Subject: [PATCH 17/31] fix(copilot): use messages_dict in fallback context compaction (#11922) ## Summary Fixes a bug where the fallback path in context compaction passes `recent_messages` (already sliced) instead of `messages_dict` (full conversation) to `_ensure_tool_pairs_intact`. This caused the function to fail to find assistant messages that exist in the original conversation but were outside the sliced window, resulting in orphan tool_results being sent to Anthropic and rejected with: ``` messages.66.content.0: unexpected tool_use_id found in tool_result blocks: toolu_vrtx_019bi1PDvEn7o5ByAxcS3VdA ``` ## Changes - Pass `messages_dict` and `slice_start` (relative to full conversation) instead of `recent_messages` and `reduced_slice_start` (relative to already-sliced list) ## Testing This is a targeted fix for the fallback path. The bug only manifests when: 1. Token count > 120k (triggers compaction) 2. Initial compaction + summary still exceeds limit (triggers fallback) 3. A tool_result's corresponding assistant is in `messages_dict` but not in `recent_messages` ## Related - Fixes SECRT-1861 - Related: SECRT-1839 (original fix that missed this code path) --- .../backend/backend/api/features/chat/service.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/service.py b/autogpt_platform/backend/backend/api/features/chat/service.py index 20216162b5..ddc64d64b2 100644 --- a/autogpt_platform/backend/backend/api/features/chat/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/service.py @@ -1184,11 +1184,14 @@ async def _stream_chat_chunks( else recent_messages ) # Ensure tool pairs stay intact in the reduced slice - reduced_slice_start = max( + # Note: Search in messages_dict (full conversation) not recent_messages + # (already sliced), so we can find assistants outside the current slice. + # Calculate where reduced_recent starts in messages_dict + reduced_start_in_dict = slice_start + max( 0, len(recent_messages) - keep_count ) reduced_recent = _ensure_tool_pairs_intact( - reduced_recent, recent_messages, reduced_slice_start + reduced_recent, messages_dict, reduced_start_in_dict ) if has_system_prompt: messages = [ From 2dd341c3696f2dca9d8b2d4725b75bd5ebe3f954 Mon Sep 17 00:00:00 2001 From: Zamil Majdy Date: Mon, 2 Feb 2026 20:31:07 -0600 Subject: [PATCH 18/31] refactor: enrich description with context before calling Agent Generator (#11932) ## Summary Updates the Agent Generator client to enrich the description with context before calling, instead of sending `user_instruction` as a separate parameter. ## Context Companion PR to Significant-Gravitas/AutoGPT-Agent-Generator#105 which removes unused parameters from the decompose API. ## Changes - Enrich `description` with `context` (e.g., clarifying question answers) before sending - Remove `user_instruction` from request payload ## How it works Both input boxes and chat box work the same way - the frontend constructs a formatted message with answers and sends it as a user message. The backend then enriches the description with this context before calling the external Agent Generator service. --- .../api/features/chat/tools/agent_generator/service.py | 7 +++---- .../backend/test/agent_generator/test_service.py | 7 +++++-- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py index c6242b0ba9..c9c960d1ae 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py @@ -139,11 +139,10 @@ async def decompose_goal_external( """ client = _get_client() - # Build the request payload - payload: dict[str, Any] = {"description": description} if context: - # The external service uses user_instruction for additional context - payload["user_instruction"] = context + description = f"{description}\n\nAdditional context from user:\n{context}" + + payload: dict[str, Any] = {"description": description} if library_agents: payload["library_agents"] = library_agents diff --git a/autogpt_platform/backend/test/agent_generator/test_service.py b/autogpt_platform/backend/test/agent_generator/test_service.py index d62dca1729..cc37c428c0 100644 --- a/autogpt_platform/backend/test/agent_generator/test_service.py +++ b/autogpt_platform/backend/test/agent_generator/test_service.py @@ -102,7 +102,7 @@ class TestDecomposeGoalExternal: @pytest.mark.asyncio async def test_decompose_goal_with_context(self): - """Test decomposition with additional context.""" + """Test decomposition with additional context enriched into description.""" mock_response = MagicMock() mock_response.json.return_value = { "success": True, @@ -119,9 +119,12 @@ class TestDecomposeGoalExternal: "Build a chatbot", context="Use Python" ) + expected_description = ( + "Build a chatbot\n\nAdditional context from user:\nUse Python" + ) mock_client.post.assert_called_once_with( "/api/decompose-description", - json={"description": "Build a chatbot", "user_instruction": "Use Python"}, + json={"description": expected_description}, ) @pytest.mark.asyncio From d81d1ce02406c98bdfdcb7c96f7a2f8c4783770c Mon Sep 17 00:00:00 2001 From: Zamil Majdy Date: Tue, 3 Feb 2026 11:41:43 +0700 Subject: [PATCH 19/31] refactor(backend): extract context window management and fix LLM continuation (#11936) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary Fixes CoPilot becoming unresponsive after long-running tools complete, and refactors context window management into a reusable function. ## Problem After `create_agent` completes, `_generate_llm_continuation()` was sending ALL messages to OpenRouter without any context compaction. When conversations exceeded ~50 messages, OpenRouter rejected requests with `provider_name: 'unknown'` (no provider would accept). **Evidence:** Langfuse session [44fbb803-092e-4ebd-b288-852959f4faf5](https://cloud.langfuse.com/project/cmk5qhf210003ad079sd8utjt/sessions/44fbb803-092e-4ebd-b288-852959f4faf5) showed: - Successful calls: 32-50 messages, known providers - Failed calls: 52+ messages, `provider: unknown`, `completion: null` ## Changes ### Refactor: Extract reusable `_manage_context_window()` - Counts tokens and checks against 120k threshold - Summarizes old messages while keeping recent 15 - Ensures tool_call/tool_response pairs stay intact - Progressive truncation if still over limit - Returns `ContextWindowResult` dataclass with messages, token count, compaction status, and errors - Helper `_messages_to_dicts()` reduces code duplication ### Fix: Update `_generate_llm_continuation()` - Now calls `_manage_context_window()` before making LLM calls - Adds retry logic with exponential backoff (matching `_stream_chat_chunks` behavior) ### Cleanup: Update `_stream_chat_chunks()` - Replaced inline context management with call to `_manage_context_window()` - Eliminates code duplication between the two functions ## Testing - Syntax check: āœ… - Ruff lint: āœ… - Import verification: āœ… ## Checklist - [x] My code follows the style guidelines of this project - [x] I have performed a self-review of my own code - [x] My changes generate no new warnings - [x] I have checked that my changes do not break existing functionality --------- Co-authored-by: Otto --- .../backend/api/features/chat/service.py | 625 +++++++++--------- 1 file changed, 298 insertions(+), 327 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/service.py b/autogpt_platform/backend/backend/api/features/chat/service.py index bcd6856503..f1f3156713 100644 --- a/autogpt_platform/backend/backend/api/features/chat/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/service.py @@ -3,7 +3,8 @@ import logging import time from asyncio import CancelledError from collections.abc import AsyncGenerator -from typing import Any +from dataclasses import dataclass +from typing import Any, cast import openai import orjson @@ -15,7 +16,14 @@ from openai import ( PermissionDeniedError, RateLimitError, ) -from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam +from openai.types.chat import ( + ChatCompletionAssistantMessageParam, + ChatCompletionChunk, + ChatCompletionMessageParam, + ChatCompletionStreamOptionsParam, + ChatCompletionSystemMessageParam, + ChatCompletionToolParam, +) from backend.data.redis_client import get_redis_async from backend.data.understanding import ( @@ -23,6 +31,7 @@ from backend.data.understanding import ( get_business_understanding, ) from backend.util.exceptions import NotFoundError +from backend.util.prompt import estimate_token_count from backend.util.settings import Settings from . import db as chat_db @@ -794,6 +803,201 @@ def _is_region_blocked_error(error: Exception) -> bool: return "not available in your region" in str(error).lower() +# Context window management constants +TOKEN_THRESHOLD = 120_000 +KEEP_RECENT_MESSAGES = 15 + + +@dataclass +class ContextWindowResult: + """Result of context window management.""" + + messages: list[dict[str, Any]] + token_count: int + was_compacted: bool + error: str | None = None + + +def _messages_to_dicts(messages: list) -> list[dict[str, Any]]: + """Convert message objects to dicts, filtering None values. + + Handles both TypedDict (dict-like) and other message formats. + """ + result = [] + for msg in messages: + if msg is None: + continue + if isinstance(msg, dict): + msg_dict = {k: v for k, v in msg.items() if v is not None} + else: + msg_dict = dict(msg) + result.append(msg_dict) + return result + + +async def _manage_context_window( + messages: list, + model: str, + api_key: str | None = None, + base_url: str | None = None, +) -> ContextWindowResult: + """ + Manage context window by summarizing old messages if token count exceeds threshold. + + This function handles context compaction for LLM calls by: + 1. Counting tokens in the message list + 2. If over threshold, summarizing old messages while keeping recent ones + 3. Ensuring tool_call/tool_response pairs stay intact + 4. Progressively reducing message count if still over limit + + Args: + messages: List of messages in OpenAI format (with system prompt if present) + model: Model name for token counting + api_key: API key for summarization calls + base_url: Base URL for summarization calls + + Returns: + ContextWindowResult with compacted messages and metadata + """ + if not messages: + return ContextWindowResult([], 0, False, "No messages to compact") + + messages_dict = _messages_to_dicts(messages) + + # Normalize model name for token counting (tiktoken only supports OpenAI models) + token_count_model = model.split("/")[-1] if "/" in model else model + if "claude" in token_count_model.lower() or not any( + known in token_count_model.lower() + for known in ["gpt", "o1", "chatgpt", "text-"] + ): + token_count_model = "gpt-4o" + + try: + token_count = estimate_token_count(messages_dict, model=token_count_model) + except Exception as e: + logger.warning(f"Token counting failed: {e}. Using gpt-4o approximation.") + token_count_model = "gpt-4o" + token_count = estimate_token_count(messages_dict, model=token_count_model) + + if token_count <= TOKEN_THRESHOLD: + return ContextWindowResult(messages, token_count, False) + + has_system_prompt = messages[0].get("role") == "system" + slice_start = max(0, len(messages_dict) - KEEP_RECENT_MESSAGES) + recent_messages = _ensure_tool_pairs_intact( + messages_dict[-KEEP_RECENT_MESSAGES:], messages_dict, slice_start + ) + + # Determine old messages to summarize (explicit bounds to avoid slice edge cases) + system_msg = messages[0] if has_system_prompt else None + if has_system_prompt: + old_messages_dict = ( + messages_dict[1:-KEEP_RECENT_MESSAGES] + if len(messages_dict) > KEEP_RECENT_MESSAGES + 1 + else [] + ) + else: + old_messages_dict = ( + messages_dict[:-KEEP_RECENT_MESSAGES] + if len(messages_dict) > KEEP_RECENT_MESSAGES + else [] + ) + + # Try to summarize old messages, fall back to truncation on failure + summary_msg = None + if old_messages_dict: + try: + summary_text = await _summarize_messages( + old_messages_dict, model=model, api_key=api_key, base_url=base_url + ) + summary_msg = ChatCompletionAssistantMessageParam( + role="assistant", + content=f"[Previous conversation summary ā€” for context only]: {summary_text}", + ) + base = [system_msg, summary_msg] if has_system_prompt else [summary_msg] + messages = base + recent_messages + logger.info( + f"Context summarized: {token_count} tokens, " + f"summarized {len(old_messages_dict)} msgs, kept {KEEP_RECENT_MESSAGES}" + ) + except Exception as e: + logger.warning(f"Summarization failed, falling back to truncation: {e}") + messages = ( + [system_msg] + recent_messages if has_system_prompt else recent_messages + ) + else: + logger.warning( + f"Token count {token_count} exceeds threshold but no old messages to summarize" + ) + + new_token_count = estimate_token_count( + _messages_to_dicts(messages), model=token_count_model + ) + + # Progressive truncation if still over limit + if new_token_count > TOKEN_THRESHOLD: + logger.warning( + f"Still over limit: {new_token_count} tokens. Reducing messages." + ) + base_msgs = ( + recent_messages + if old_messages_dict + else (messages_dict[1:] if has_system_prompt else messages_dict) + ) + + def build_messages(recent: list) -> list: + """Build message list with optional system prompt and summary.""" + prefix = [] + if has_system_prompt and system_msg: + prefix.append(system_msg) + if summary_msg: + prefix.append(summary_msg) + return prefix + recent + + for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]: + if keep_count == 0: + messages = build_messages([]) + if not messages: + continue + elif len(base_msgs) < keep_count: + continue + else: + reduced = _ensure_tool_pairs_intact( + base_msgs[-keep_count:], + base_msgs, + max(0, len(base_msgs) - keep_count), + ) + messages = build_messages(reduced) + + new_token_count = estimate_token_count( + _messages_to_dicts(messages), model=token_count_model + ) + if new_token_count <= TOKEN_THRESHOLD: + logger.info( + f"Reduced to {keep_count} messages, {new_token_count} tokens" + ) + break + else: + logger.error( + f"Cannot reduce below threshold. Final: {new_token_count} tokens" + ) + if has_system_prompt and len(messages) > 1: + messages = messages[1:] + logger.critical("Dropped system prompt as last resort") + return ContextWindowResult( + messages, new_token_count, True, "System prompt dropped" + ) + # No system prompt to drop - return error so callers don't proceed with oversized context + return ContextWindowResult( + messages, + new_token_count, + True, + "Unable to reduce context below token limit", + ) + + return ContextWindowResult(messages, new_token_count, True) + + async def _summarize_messages( messages: list, model: str, @@ -1022,11 +1226,8 @@ async def _stream_chat_chunks( logger.info("Starting pure chat stream") - # Build messages with system prompt prepended messages = session.to_openai_messages() if system_prompt: - from openai.types.chat import ChatCompletionSystemMessageParam - system_message = ChatCompletionSystemMessageParam( role="system", content=system_prompt, @@ -1034,314 +1235,38 @@ async def _stream_chat_chunks( messages = [system_message] + messages # Apply context window management - token_count = 0 # Initialize for exception handler - try: - from backend.util.prompt import estimate_token_count + context_result = await _manage_context_window( + messages=messages, + model=model, + api_key=config.api_key, + base_url=config.base_url, + ) - # Convert to dict for token counting - # OpenAI message types are TypedDicts, so they're already dict-like - messages_dict = [] - for msg in messages: - # TypedDict objects are already dicts, just filter None values - if isinstance(msg, dict): - msg_dict = {k: v for k, v in msg.items() if v is not None} - else: - # Fallback for unexpected types - msg_dict = dict(msg) - messages_dict.append(msg_dict) - - # Estimate tokens using appropriate tokenizer - # Normalize model name for token counting (tiktoken only supports OpenAI models) - token_count_model = model - if "/" in model: - # Strip provider prefix (e.g., "anthropic/claude-opus-4.5" -> "claude-opus-4.5") - token_count_model = model.split("/")[-1] - - # For Claude and other non-OpenAI models, approximate with gpt-4o tokenizer - # Most modern LLMs have similar tokenization (~1 token per 4 chars) - if "claude" in token_count_model.lower() or not any( - known in token_count_model.lower() - for known in ["gpt", "o1", "chatgpt", "text-"] - ): - token_count_model = "gpt-4o" - - # Attempt token counting with error handling - try: - token_count = estimate_token_count(messages_dict, model=token_count_model) - except Exception as token_error: - # If token counting fails, use gpt-4o as fallback approximation - logger.warning( - f"Token counting failed for model {token_count_model}: {token_error}. " - "Using gpt-4o approximation." - ) - token_count = estimate_token_count(messages_dict, model="gpt-4o") - - # If over threshold, summarize old messages - if token_count > 120_000: - KEEP_RECENT = 15 - - # Check if we have a system prompt at the start - has_system_prompt = ( - len(messages) > 0 and messages[0].get("role") == "system" - ) - - # Always attempt mitigation when over limit, even with few messages - if messages: - # Split messages based on whether system prompt exists - # Calculate start index for the slice - slice_start = max(0, len(messages_dict) - KEEP_RECENT) - recent_messages = messages_dict[-KEEP_RECENT:] - - # Ensure tool_call/tool_response pairs stay together - # This prevents API errors from orphan tool responses - recent_messages = _ensure_tool_pairs_intact( - recent_messages, messages_dict, slice_start - ) - - if has_system_prompt: - # Keep system prompt separate, summarize everything between system and recent - system_msg = messages[0] - old_messages_dict = messages_dict[1:-KEEP_RECENT] - else: - # No system prompt, summarize everything except recent - system_msg = None - old_messages_dict = messages_dict[:-KEEP_RECENT] - - # Summarize any non-empty old messages (no minimum threshold) - # If we're over the token limit, we need to compress whatever we can - if old_messages_dict: - # Summarize old messages using the same model as chat - summary_text = await _summarize_messages( - old_messages_dict, - model=model, - api_key=config.api_key, - base_url=config.base_url, - ) - - # Build new message list - # Use assistant role (not system) to prevent privilege escalation - # of user-influenced content to instruction-level authority - from openai.types.chat import ChatCompletionAssistantMessageParam - - summary_msg = ChatCompletionAssistantMessageParam( - role="assistant", - content=( - "[Previous conversation summary ā€” for context only]: " - f"{summary_text}" - ), - ) - - # Rebuild messages based on whether we have a system prompt - if has_system_prompt: - # system_prompt + summary + recent_messages - messages = [system_msg, summary_msg] + recent_messages - else: - # summary + recent_messages (no original system prompt) - messages = [summary_msg] + recent_messages - - logger.info( - f"Context summarized: {token_count} tokens, " - f"summarized {len(old_messages_dict)} old messages, " - f"kept last {KEEP_RECENT} messages" - ) - - # Fallback: If still over limit after summarization, progressively drop recent messages - # This handles edge cases where recent messages are extremely large - new_messages_dict = [] - for msg in messages: - if isinstance(msg, dict): - msg_dict = {k: v for k, v in msg.items() if v is not None} - else: - msg_dict = dict(msg) - new_messages_dict.append(msg_dict) - - new_token_count = estimate_token_count( - new_messages_dict, model=token_count_model - ) - - if new_token_count > 120_000: - # Still over limit - progressively reduce KEEP_RECENT - logger.warning( - f"Still over limit after summarization: {new_token_count} tokens. " - "Reducing number of recent messages kept." - ) - - for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]: - if keep_count == 0: - # Try with just system prompt + summary (no recent messages) - if has_system_prompt: - messages = [system_msg, summary_msg] - else: - messages = [summary_msg] - logger.info( - "Trying with 0 recent messages (system + summary only)" - ) - else: - # Slice from ORIGINAL recent_messages to avoid duplicating summary - reduced_recent = ( - recent_messages[-keep_count:] - if len(recent_messages) >= keep_count - else recent_messages - ) - # Ensure tool pairs stay intact in the reduced slice - reduced_slice_start = max( - 0, len(recent_messages) - keep_count - ) - reduced_recent = _ensure_tool_pairs_intact( - reduced_recent, recent_messages, reduced_slice_start - ) - if has_system_prompt: - messages = [ - system_msg, - summary_msg, - ] + reduced_recent - else: - messages = [summary_msg] + reduced_recent - - new_messages_dict = [] - for msg in messages: - if isinstance(msg, dict): - msg_dict = { - k: v for k, v in msg.items() if v is not None - } - else: - msg_dict = dict(msg) - new_messages_dict.append(msg_dict) - - new_token_count = estimate_token_count( - new_messages_dict, model=token_count_model - ) - - if new_token_count <= 120_000: - logger.info( - f"Reduced to {keep_count} recent messages, " - f"now {new_token_count} tokens" - ) - break - else: - logger.error( - f"Unable to reduce token count below threshold even with 0 messages. " - f"Final count: {new_token_count} tokens" - ) - # ABSOLUTE LAST RESORT: Drop system prompt - # This should only happen if summary itself is massive - if has_system_prompt and len(messages) > 1: - messages = messages[1:] # Drop system prompt - logger.critical( - "CRITICAL: Dropped system prompt as absolute last resort. " - "Behavioral consistency may be affected." - ) - # Yield error to user - yield StreamError( - errorText=( - "Warning: System prompt dropped due to size constraints. " - "Assistant behavior may be affected." - ) - ) - else: - # No old messages to summarize - all messages are "recent" - # Apply progressive truncation to reduce token count - logger.warning( - f"Token count {token_count} exceeds threshold but no old messages to summarize. " - f"Applying progressive truncation to recent messages." - ) - - # Create a base list excluding system prompt to avoid duplication - # This is the pool of messages we'll slice from in the loop - # Use messages_dict for type consistency with _ensure_tool_pairs_intact - base_msgs = ( - messages_dict[1:] if has_system_prompt else messages_dict - ) - - # Try progressively smaller keep counts - new_token_count = token_count # Initialize with current count - for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]: - if keep_count == 0: - # Try with just system prompt (no recent messages) - if has_system_prompt: - messages = [system_msg] - logger.info( - "Trying with 0 recent messages (system prompt only)" - ) - else: - # No system prompt and no recent messages = empty messages list - # This is invalid, skip this iteration - continue - else: - if len(base_msgs) < keep_count: - continue # Skip if we don't have enough messages - - # Slice from base_msgs to get recent messages (without system prompt) - recent_messages = base_msgs[-keep_count:] - - # Ensure tool pairs stay intact in the reduced slice - reduced_slice_start = max(0, len(base_msgs) - keep_count) - recent_messages = _ensure_tool_pairs_intact( - recent_messages, base_msgs, reduced_slice_start - ) - - if has_system_prompt: - messages = [system_msg] + recent_messages - else: - messages = recent_messages - - new_messages_dict = [] - for msg in messages: - if msg is None: - continue # Skip None messages (type safety) - if isinstance(msg, dict): - msg_dict = { - k: v for k, v in msg.items() if v is not None - } - else: - msg_dict = dict(msg) - new_messages_dict.append(msg_dict) - - new_token_count = estimate_token_count( - new_messages_dict, model=token_count_model - ) - - if new_token_count <= 120_000: - logger.info( - f"Reduced to {keep_count} recent messages, " - f"now {new_token_count} tokens" - ) - break - else: - # Even with 0 messages still over limit - logger.error( - f"Unable to reduce token count below threshold even with 0 messages. " - f"Final count: {new_token_count} tokens. Messages may be extremely large." - ) - # ABSOLUTE LAST RESORT: Drop system prompt - if has_system_prompt and len(messages) > 1: - messages = messages[1:] # Drop system prompt - logger.critical( - "CRITICAL: Dropped system prompt as absolute last resort. " - "Behavioral consistency may be affected." - ) - # Yield error to user - yield StreamError( - errorText=( - "Warning: System prompt dropped due to size constraints. " - "Assistant behavior may be affected." - ) - ) - - except Exception as e: - logger.error(f"Context summarization failed: {e}", exc_info=True) - # If we were over the token limit, yield error to user - # Don't silently continue with oversized messages that will fail - if token_count > 120_000: + if context_result.error: + if "System prompt dropped" in context_result.error: + # Warning only - continue with reduced context yield StreamError( errorText=( - f"Unable to manage context window (token limit exceeded: {token_count} tokens). " - "Context summarization failed. Please start a new conversation." + "Warning: System prompt dropped due to size constraints. " + "Assistant behavior may be affected." + ) + ) + else: + # Any other error - abort to prevent failed LLM calls + yield StreamError( + errorText=( + f"Context window management failed: {context_result.error}. " + "Please start a new conversation." ) ) yield StreamFinish() return - # Otherwise, continue with original messages (under limit) + + messages = context_result.messages + if context_result.was_compacted: + logger.info( + f"Context compacted for streaming: {context_result.token_count} tokens" + ) # Loop to handle tool calls and continue conversation while True: @@ -1369,14 +1294,6 @@ async def _stream_chat_chunks( :128 ] # OpenRouter limit - # Create the stream with proper types - from typing import cast - - from openai.types.chat import ( - ChatCompletionMessageParam, - ChatCompletionStreamOptionsParam, - ) - stream = await client.chat.completions.create( model=model, messages=cast(list[ChatCompletionMessageParam], messages), @@ -1900,17 +1817,36 @@ async def _generate_llm_continuation( # Build system prompt system_prompt, _ = await _build_system_prompt(user_id) - # Build messages in OpenAI format messages = session.to_openai_messages() if system_prompt: - from openai.types.chat import ChatCompletionSystemMessageParam - system_message = ChatCompletionSystemMessageParam( role="system", content=system_prompt, ) messages = [system_message] + messages + # Apply context window management to prevent oversized requests + context_result = await _manage_context_window( + messages=messages, + model=config.model, + api_key=config.api_key, + base_url=config.base_url, + ) + + if context_result.error and "System prompt dropped" not in context_result.error: + logger.error( + f"Context window management failed for session {session_id}: " + f"{context_result.error} (tokens={context_result.token_count})" + ) + return + + messages = context_result.messages + if context_result.was_compacted: + logger.info( + f"Context compacted for LLM continuation: " + f"{context_result.token_count} tokens" + ) + # Build extra_body for tracing extra_body: dict[str, Any] = { "posthogProperties": { @@ -1923,19 +1859,54 @@ async def _generate_llm_continuation( if session_id: extra_body["session_id"] = session_id[:128] - # Make non-streaming LLM call (no tools - just text response) - from typing import cast + retry_count = 0 + last_error: Exception | None = None + response = None - from openai.types.chat import ChatCompletionMessageParam + while retry_count <= MAX_RETRIES: + try: + logger.info( + f"Generating LLM continuation for session {session_id}" + f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}" + ) - # No tools parameter = text-only response (no tool calls) - response = await client.chat.completions.create( - model=config.model, - messages=cast(list[ChatCompletionMessageParam], messages), - extra_body=extra_body, - ) + response = await client.chat.completions.create( + model=config.model, + messages=cast(list[ChatCompletionMessageParam], messages), + extra_body=extra_body, + ) + last_error = None # Clear any previous error on success + break # Success, exit retry loop + except Exception as e: + last_error = e + if _is_retryable_error(e) and retry_count < MAX_RETRIES: + retry_count += 1 + delay = min( + BASE_DELAY_SECONDS * (2 ** (retry_count - 1)), + MAX_DELAY_SECONDS, + ) + logger.warning( + f"Retryable error in LLM continuation: {e!s}. " + f"Retrying in {delay:.1f}s (attempt {retry_count}/{MAX_RETRIES})" + ) + await asyncio.sleep(delay) + continue + else: + # Non-retryable error - log and exit gracefully + logger.error( + f"Non-retryable error in LLM continuation: {e!s}", + exc_info=True, + ) + return - if response.choices and response.choices[0].message.content: + if last_error: + logger.error( + f"Max retries ({MAX_RETRIES}) exceeded for LLM continuation. " + f"Last error: {last_error!s}" + ) + return + + if response and response.choices and response.choices[0].message.content: assistant_content = response.choices[0].message.content # Reload session from DB to avoid race condition with user messages From 14cee1670ace3497f62db406770afa663c210056 Mon Sep 17 00:00:00 2001 From: Krzysztof Czerwinski <34861343+kcze@users.noreply.github.com> Date: Tue, 3 Feb 2026 17:07:48 +0900 Subject: [PATCH 20/31] fix(backend): Prevent leaking Redis connections in `ws_api` (#11869) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fixing https://github.com/Significant-Gravitas/AutoGPT/pull/11297#discussion_r2496833421 ### Changes šŸ—ļø 1. event_bus.py - Added close method to AsyncRedisEventBus - Added __init__ method to track the _pubsub instance attribute - Added async def close() method that closes the PubSub connection safely - Modified listen_events() to store the pubsub reference in self._pubsub 2. ws_api.py - Added cleanup in event_broadcaster - Wrapped the worker coroutines in try/finally block - The finally block calls close() on both event buses to ensure cleanup happens on any exit (including exceptions before retry) --- .../backend/backend/api/ws_api.py | 26 ++++++++++++------- .../backend/backend/data/event_bus.py | 14 ++++++++++ 2 files changed, 30 insertions(+), 10 deletions(-) diff --git a/autogpt_platform/backend/backend/api/ws_api.py b/autogpt_platform/backend/backend/api/ws_api.py index b71fdb3526..e254d4b4db 100644 --- a/autogpt_platform/backend/backend/api/ws_api.py +++ b/autogpt_platform/backend/backend/api/ws_api.py @@ -66,18 +66,24 @@ async def event_broadcaster(manager: ConnectionManager): execution_bus = AsyncRedisExecutionEventBus() notification_bus = AsyncRedisNotificationEventBus() - async def execution_worker(): - async for event in execution_bus.listen("*"): - await manager.send_execution_update(event) + try: - async def notification_worker(): - async for notification in notification_bus.listen("*"): - await manager.send_notification( - user_id=notification.user_id, - payload=notification.payload, - ) + async def execution_worker(): + async for event in execution_bus.listen("*"): + await manager.send_execution_update(event) - await asyncio.gather(execution_worker(), notification_worker()) + async def notification_worker(): + async for notification in notification_bus.listen("*"): + await manager.send_notification( + user_id=notification.user_id, + payload=notification.payload, + ) + + await asyncio.gather(execution_worker(), notification_worker()) + finally: + # Ensure PubSub connections are closed on any exit to prevent leaks + await execution_bus.close() + await notification_bus.close() async def authenticate_websocket(websocket: WebSocket) -> str: diff --git a/autogpt_platform/backend/backend/data/event_bus.py b/autogpt_platform/backend/backend/data/event_bus.py index d8a1c5b729..614fb158b2 100644 --- a/autogpt_platform/backend/backend/data/event_bus.py +++ b/autogpt_platform/backend/backend/data/event_bus.py @@ -133,10 +133,23 @@ class RedisEventBus(BaseRedisEventBus[M], ABC): class AsyncRedisEventBus(BaseRedisEventBus[M], ABC): + def __init__(self): + self._pubsub: AsyncPubSub | None = None + @property async def connection(self) -> redis.AsyncRedis: return await redis.get_redis_async() + async def close(self) -> None: + """Close the PubSub connection if it exists.""" + if self._pubsub is not None: + try: + await self._pubsub.close() + except Exception: + logger.warning("Failed to close PubSub connection", exc_info=True) + finally: + self._pubsub = None + async def publish_event(self, event: M, channel_key: str): """ Publish an event to Redis. Gracefully handles connection failures @@ -157,6 +170,7 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC): await self.connection, channel_key ) assert isinstance(pubsub, AsyncPubSub) + self._pubsub = pubsub if "*" in channel_key: await pubsub.psubscribe(full_channel_name) From aef6f57cfd5feec005c51710161d0ee04f8721f2 Mon Sep 17 00:00:00 2001 From: Otto Date: Tue, 3 Feb 2026 09:54:49 +0000 Subject: [PATCH 21/31] fix(scheduler): route db calls through DatabaseManager (#11941) ## Summary Routes `increment_onboarding_runs` and `cleanup_expired_oauth_tokens` through the DatabaseManager RPC client instead of calling Prisma directly. ## Problem The Scheduler service never connects its Prisma client. While `add_graph_execution()` in `utils.py` has a fallback that routes through DatabaseManager when Prisma isn't connected, subsequent calls in the scheduler were hitting Prisma directly: - `increment_onboarding_runs()` after successful graph execution - `cleanup_expired_oauth_tokens()` in the scheduled job These threw `ClientNotConnectedError`, caught by generic exception handlers but spamming Sentry (~696K events since December per the original analysis in #11926). ## Solution Follow the same pattern as `utils.py`: 1. Add `cleanup_expired_oauth_tokens` to `DatabaseManager` and `DatabaseManagerAsyncClient` 2. Update scheduler to use `get_database_manager_async_client()` for both calls ## Changes - **database.py**: Import and expose `cleanup_expired_oauth_tokens` in both manager classes - **scheduler.py**: Use `db.increment_onboarding_runs()` and `db.cleanup_expired_oauth_tokens()` via the async client ## Impact - Eliminates Sentry error spam from scheduler - Onboarding run counters now actually increment for scheduled executions - OAuth token cleanup now actually runs ## Testing Deploy to staging with scheduled graphs and verify: 1. No more `ClientNotConnectedError` in scheduler logs 2. `UserOnboarding.agentRuns` increments on scheduled runs 3. Expired OAuth tokens get cleaned up Refs: #11926 (original fix that was closed) --- .../backend/backend/executor/database.py | 7 +++++++ .../backend/backend/executor/scheduler.py | 18 +++++++++++++----- 2 files changed, 20 insertions(+), 5 deletions(-) diff --git a/autogpt_platform/backend/backend/executor/database.py b/autogpt_platform/backend/backend/executor/database.py index ae7474fc1d..d44439d51c 100644 --- a/autogpt_platform/backend/backend/executor/database.py +++ b/autogpt_platform/backend/backend/executor/database.py @@ -17,6 +17,7 @@ from backend.data.analytics import ( get_accuracy_trends_and_alerts, get_marketplace_graphs_for_monitoring, ) +from backend.data.auth.oauth import cleanup_expired_oauth_tokens from backend.data.credit import UsageTransactionMetadata, get_user_credit_model from backend.data.execution import ( create_graph_execution, @@ -219,6 +220,9 @@ class DatabaseManager(AppService): # Onboarding increment_onboarding_runs = _(increment_onboarding_runs) + # OAuth + cleanup_expired_oauth_tokens = _(cleanup_expired_oauth_tokens) + # Store get_store_agents = _(get_store_agents) get_store_agent_details = _(get_store_agent_details) @@ -349,6 +353,9 @@ class DatabaseManagerAsyncClient(AppServiceClient): # Onboarding increment_onboarding_runs = d.increment_onboarding_runs + # OAuth + cleanup_expired_oauth_tokens = d.cleanup_expired_oauth_tokens + # Store get_store_agents = d.get_store_agents get_store_agent_details = d.get_store_agent_details diff --git a/autogpt_platform/backend/backend/executor/scheduler.py b/autogpt_platform/backend/backend/executor/scheduler.py index 44b77fc018..cbdc441718 100644 --- a/autogpt_platform/backend/backend/executor/scheduler.py +++ b/autogpt_platform/backend/backend/executor/scheduler.py @@ -24,11 +24,9 @@ from dotenv import load_dotenv from pydantic import BaseModel, Field, ValidationError from sqlalchemy import MetaData, create_engine -from backend.data.auth.oauth import cleanup_expired_oauth_tokens from backend.data.block import BlockInput from backend.data.execution import GraphExecutionWithNodes from backend.data.model import CredentialsMetaInput -from backend.data.onboarding import increment_onboarding_runs from backend.executor import utils as execution_utils from backend.monitoring import ( NotificationJobArgs, @@ -38,7 +36,11 @@ from backend.monitoring import ( report_execution_accuracy_alerts, report_late_executions, ) -from backend.util.clients import get_database_manager_client, get_scheduler_client +from backend.util.clients import ( + get_database_manager_async_client, + get_database_manager_client, + get_scheduler_client, +) from backend.util.cloud_storage import cleanup_expired_files_async from backend.util.exceptions import ( GraphNotFoundError, @@ -148,6 +150,7 @@ def execute_graph(**kwargs): async def _execute_graph(**kwargs): args = GraphExecutionJobArgs(**kwargs) start_time = asyncio.get_event_loop().time() + db = get_database_manager_async_client() try: logger.info(f"Executing recurring job for graph #{args.graph_id}") graph_exec: GraphExecutionWithNodes = await execution_utils.add_graph_execution( @@ -157,7 +160,7 @@ async def _execute_graph(**kwargs): inputs=args.input_data, graph_credentials_inputs=args.input_credentials, ) - await increment_onboarding_runs(args.user_id) + await db.increment_onboarding_runs(args.user_id) elapsed = asyncio.get_event_loop().time() - start_time logger.info( f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} " @@ -246,8 +249,13 @@ def cleanup_expired_files(): def cleanup_oauth_tokens(): """Clean up expired OAuth tokens from the database.""" + # Wait for completion - run_async(cleanup_expired_oauth_tokens()) + async def _cleanup(): + db = get_database_manager_async_client() + return await db.cleanup_expired_oauth_tokens() + + run_async(_cleanup()) def execution_accuracy_alerts(): From 678ddde7511c23d273c9fe82e9485c3854b8b011 Mon Sep 17 00:00:00 2001 From: Zamil Majdy Date: Tue, 3 Feb 2026 17:36:10 +0700 Subject: [PATCH 22/31] refactor(backend): unify context compression into compress_context() (#11937) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Background This PR consolidates and unifies context window management for the CoPilot backend. ### Problem The CoPilot backend had **two separate implementations** of context window management: 1. **`service.py` ā†’ `_manage_context_window()`** - Chat service streaming/continuation 2. **`prompt.py` ā†’ `compress_prompt()`** - Sync LLM blocks This duplication led to inconsistent behavior, maintenance burden, and duplicate code. --- ## Solution: Unified `compress_context()` A single async function that handles both use cases: | Caller | Usage | Behavior | |--------|-------|----------| | **Chat service** | `compress_context(msgs, client=openai_client)` | Summarization ā†’ Truncation | | **LLM blocks** | `compress_context(msgs, client=None)` | Truncation only (no API call) | --- ## Strategy Order | Step | Description | Runs When | |------|-------------|-----------| | **1. LLM Summarization** | Summarize old messages into single context message, keep recent 15 | Only if `client` provided | | **2. Content Truncation** | Progressively truncate message content (8192ā†’4096ā†’...ā†’128 tokens) | If still over limit | | **3. Middle-out Deletion** | Delete messages one at a time from center outward | If still over limit | | **4. First/Last Trim** | Truncate system prompt and last message content | Last resort | ### Why This Order? 1. **Summarization first** (if available) - Preserves semantic meaning of old messages 2. **Content truncation before deletion** - Keeps all conversation turns, just shorter 3. **Middle-out deletion** - More granular than dropping all old messages at once 4. **First/last trim** - Only touch system prompt as last resort --- ## Key Fixes | Issue | Before | After | |-------|--------|-------| | **Socket leak** | `AsyncOpenAI` client never closed | `async with` context manager | | **Timeout ignored** | `timeout=30` passed to `create()` (invalid) | `client.with_options(timeout=30)` | | **OpenAI tool messages** | Not truncated | Properly truncated | | **Tool pair integrity** | OpenAI format only | Both OpenAI + Anthropic formats | --- ## Tool Format Support `_ensure_tool_pairs_intact()` now supports both formats: ### OpenAI Format ```python # Assistant with tool_calls {"role": "assistant", "tool_calls": [{"id": "call_1", ...}]} # Tool response {"role": "tool", "tool_call_id": "call_1", "content": "result"} ``` ### Anthropic Format ```python # Assistant with tool_use {"role": "assistant", "content": [{"type": "tool_use", "id": "toolu_1", ...}]} # Tool result {"role": "user", "content": [{"type": "tool_result", "tool_use_id": "toolu_1", ...}]} ``` --- ## Files Changed | File | Change | |------|--------| | `backend/util/prompt.py` | +450 lines: Add `CompressResult`, `compress_context()`, helpers | | `backend/api/features/chat/service.py` | -380 lines: Remove duplicate, use thin wrapper | | `backend/blocks/llm.py` | Migrate `llm_call()` to use `compress_context(client=None)` | | `backend/util/prompt_test.py` | +400 lines: Comprehensive tests (OpenAI + Anthropic) | ### Removed - `compress_prompt()` - Replaced by `compress_context(client=None)` - `_manage_context_window()` - Replaced by `compress_context(client=openai_client)` --- ## API ```python async def compress_context( messages: list[dict], target_tokens: int = 120_000, *, model: str = "gpt-4o", client: AsyncOpenAI | None = None, # None = truncation only keep_recent: int = 15, reserve: int = 2_048, start_cap: int = 8_192, floor_cap: int = 128, ) -> CompressResult: ... @dataclass class CompressResult: messages: list[dict] token_count: int was_compacted: bool error: str | None = None original_token_count: int = 0 messages_summarized: int = 0 messages_dropped: int = 0 ``` --- ## Tests Added | Test Class | Coverage | |------------|----------| | `TestMsgTokens` | Token counting for regular messages, OpenAI tool calls, Anthropic tool_use | | `TestTruncateToolMessageContent` | OpenAI + Anthropic tool message truncation | | `TestEnsureToolPairsIntact` | OpenAI format (3 tests), Anthropic format (3 tests), edge cases (3 tests) | | `TestCompressContext` | No compression, truncation-only, tool pair preservation, error handling | --- ## Checklist - [x] Code follows project conventions - [x] Linting passes (`poetry run format`) - [x] Type checking passes (`pyright`) - [x] Tests added for all new functions - [x] Both OpenAI and Anthropic tool formats supported - [x] Backward compatible behavior preserved - [x] All review comments addressed --- .../backend/api/features/chat/service.py | 417 +---------- .../backend/backend/blocks/llm.py | 13 +- .../backend/backend/util/prompt.py | 698 +++++++++++++---- .../backend/backend/util/prompt_test.py | 700 +++++++++++++++++- 4 files changed, 1305 insertions(+), 523 deletions(-) diff --git a/autogpt_platform/backend/backend/api/features/chat/service.py b/autogpt_platform/backend/backend/api/features/chat/service.py index f1f3156713..6336d1c5af 100644 --- a/autogpt_platform/backend/backend/api/features/chat/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/service.py @@ -3,10 +3,13 @@ import logging import time from asyncio import CancelledError from collections.abc import AsyncGenerator -from dataclasses import dataclass -from typing import Any, cast +from typing import TYPE_CHECKING, Any, cast import openai + +if TYPE_CHECKING: + from backend.util.prompt import CompressResult + import orjson from langfuse import get_client from openai import ( @@ -17,7 +20,6 @@ from openai import ( RateLimitError, ) from openai.types.chat import ( - ChatCompletionAssistantMessageParam, ChatCompletionChunk, ChatCompletionMessageParam, ChatCompletionStreamOptionsParam, @@ -31,7 +33,6 @@ from backend.data.understanding import ( get_business_understanding, ) from backend.util.exceptions import NotFoundError -from backend.util.prompt import estimate_token_count from backend.util.settings import Settings from . import db as chat_db @@ -803,402 +804,58 @@ def _is_region_blocked_error(error: Exception) -> bool: return "not available in your region" in str(error).lower() -# Context window management constants -TOKEN_THRESHOLD = 120_000 -KEEP_RECENT_MESSAGES = 15 - - -@dataclass -class ContextWindowResult: - """Result of context window management.""" - - messages: list[dict[str, Any]] - token_count: int - was_compacted: bool - error: str | None = None - - -def _messages_to_dicts(messages: list) -> list[dict[str, Any]]: - """Convert message objects to dicts, filtering None values. - - Handles both TypedDict (dict-like) and other message formats. - """ - result = [] - for msg in messages: - if msg is None: - continue - if isinstance(msg, dict): - msg_dict = {k: v for k, v in msg.items() if v is not None} - else: - msg_dict = dict(msg) - result.append(msg_dict) - return result - - async def _manage_context_window( messages: list, model: str, api_key: str | None = None, base_url: str | None = None, -) -> ContextWindowResult: +) -> "CompressResult": """ - Manage context window by summarizing old messages if token count exceeds threshold. + Manage context window using the unified compress_context function. - This function handles context compaction for LLM calls by: - 1. Counting tokens in the message list - 2. If over threshold, summarizing old messages while keeping recent ones - 3. Ensuring tool_call/tool_response pairs stay intact - 4. Progressively reducing message count if still over limit + This is a thin wrapper that creates an OpenAI client for summarization + and delegates to the shared compression logic in prompt.py. Args: - messages: List of messages in OpenAI format (with system prompt if present) - model: Model name for token counting + messages: List of messages in OpenAI format + model: Model name for token counting and summarization api_key: API key for summarization calls base_url: Base URL for summarization calls Returns: - ContextWindowResult with compacted messages and metadata + CompressResult with compacted messages and metadata """ - if not messages: - return ContextWindowResult([], 0, False, "No messages to compact") - - messages_dict = _messages_to_dicts(messages) - - # Normalize model name for token counting (tiktoken only supports OpenAI models) - token_count_model = model.split("/")[-1] if "/" in model else model - if "claude" in token_count_model.lower() or not any( - known in token_count_model.lower() - for known in ["gpt", "o1", "chatgpt", "text-"] - ): - token_count_model = "gpt-4o" - - try: - token_count = estimate_token_count(messages_dict, model=token_count_model) - except Exception as e: - logger.warning(f"Token counting failed: {e}. Using gpt-4o approximation.") - token_count_model = "gpt-4o" - token_count = estimate_token_count(messages_dict, model=token_count_model) - - if token_count <= TOKEN_THRESHOLD: - return ContextWindowResult(messages, token_count, False) - - has_system_prompt = messages[0].get("role") == "system" - slice_start = max(0, len(messages_dict) - KEEP_RECENT_MESSAGES) - recent_messages = _ensure_tool_pairs_intact( - messages_dict[-KEEP_RECENT_MESSAGES:], messages_dict, slice_start - ) - - # Determine old messages to summarize (explicit bounds to avoid slice edge cases) - system_msg = messages[0] if has_system_prompt else None - if has_system_prompt: - old_messages_dict = ( - messages_dict[1:-KEEP_RECENT_MESSAGES] - if len(messages_dict) > KEEP_RECENT_MESSAGES + 1 - else [] - ) - else: - old_messages_dict = ( - messages_dict[:-KEEP_RECENT_MESSAGES] - if len(messages_dict) > KEEP_RECENT_MESSAGES - else [] - ) - - # Try to summarize old messages, fall back to truncation on failure - summary_msg = None - if old_messages_dict: - try: - summary_text = await _summarize_messages( - old_messages_dict, model=model, api_key=api_key, base_url=base_url - ) - summary_msg = ChatCompletionAssistantMessageParam( - role="assistant", - content=f"[Previous conversation summary ā€” for context only]: {summary_text}", - ) - base = [system_msg, summary_msg] if has_system_prompt else [summary_msg] - messages = base + recent_messages - logger.info( - f"Context summarized: {token_count} tokens, " - f"summarized {len(old_messages_dict)} msgs, kept {KEEP_RECENT_MESSAGES}" - ) - except Exception as e: - logger.warning(f"Summarization failed, falling back to truncation: {e}") - messages = ( - [system_msg] + recent_messages if has_system_prompt else recent_messages - ) - else: - logger.warning( - f"Token count {token_count} exceeds threshold but no old messages to summarize" - ) - - new_token_count = estimate_token_count( - _messages_to_dicts(messages), model=token_count_model - ) - - # Progressive truncation if still over limit - if new_token_count > TOKEN_THRESHOLD: - logger.warning( - f"Still over limit: {new_token_count} tokens. Reducing messages." - ) - base_msgs = ( - recent_messages - if old_messages_dict - else (messages_dict[1:] if has_system_prompt else messages_dict) - ) - - def build_messages(recent: list) -> list: - """Build message list with optional system prompt and summary.""" - prefix = [] - if has_system_prompt and system_msg: - prefix.append(system_msg) - if summary_msg: - prefix.append(summary_msg) - return prefix + recent - - for keep_count in [12, 10, 8, 5, 3, 2, 1, 0]: - if keep_count == 0: - messages = build_messages([]) - if not messages: - continue - elif len(base_msgs) < keep_count: - continue - else: - reduced = _ensure_tool_pairs_intact( - base_msgs[-keep_count:], - base_msgs, - max(0, len(base_msgs) - keep_count), - ) - messages = build_messages(reduced) - - new_token_count = estimate_token_count( - _messages_to_dicts(messages), model=token_count_model - ) - if new_token_count <= TOKEN_THRESHOLD: - logger.info( - f"Reduced to {keep_count} messages, {new_token_count} tokens" - ) - break - else: - logger.error( - f"Cannot reduce below threshold. Final: {new_token_count} tokens" - ) - if has_system_prompt and len(messages) > 1: - messages = messages[1:] - logger.critical("Dropped system prompt as last resort") - return ContextWindowResult( - messages, new_token_count, True, "System prompt dropped" - ) - # No system prompt to drop - return error so callers don't proceed with oversized context - return ContextWindowResult( - messages, - new_token_count, - True, - "Unable to reduce context below token limit", - ) - - return ContextWindowResult(messages, new_token_count, True) - - -async def _summarize_messages( - messages: list, - model: str, - api_key: str | None = None, - base_url: str | None = None, - timeout: float = 30.0, -) -> str: - """Summarize a list of messages into concise context. - - Uses the same model as the chat for higher quality summaries. - - Args: - messages: List of message dicts to summarize - model: Model to use for summarization (same as chat model) - api_key: API key for OpenAI client - base_url: Base URL for OpenAI client - timeout: Request timeout in seconds (default: 30.0) - - Returns: - Summarized text - """ - # Format messages for summarization - conversation = [] - for msg in messages: - role = msg.get("role", "") - content = msg.get("content", "") - # Include user, assistant, and tool messages (tool outputs are important context) - if content and role in ("user", "assistant", "tool"): - conversation.append(f"{role.upper()}: {content}") - - conversation_text = "\n\n".join(conversation) - - # Handle empty conversation - if not conversation_text: - return "No conversation history available." - - # Truncate conversation to fit within summarization model's context - # gpt-4o-mini has 128k context, but we limit to ~25k tokens (~100k chars) for safety - MAX_CHARS = 100_000 - if len(conversation_text) > MAX_CHARS: - conversation_text = conversation_text[:MAX_CHARS] + "\n\n[truncated]" - - # Call LLM to summarize import openai - summarization_client = openai.AsyncOpenAI( - api_key=api_key, base_url=base_url, timeout=timeout - ) + from backend.util.prompt import compress_context - response = await summarization_client.chat.completions.create( - model=model, - messages=[ - { - "role": "system", - "content": ( - "Create a detailed summary of the conversation so far. " - "This summary will be used as context when continuing the conversation.\n\n" - "Before writing the summary, analyze each message chronologically to identify:\n" - "- User requests and their explicit goals\n" - "- Your approach and key decisions made\n" - "- Technical specifics (file names, tool outputs, function signatures)\n" - "- Errors encountered and resolutions applied\n\n" - "You MUST include ALL of the following sections:\n\n" - "## 1. Primary Request and Intent\n" - "The user's explicit goals and what they are trying to accomplish.\n\n" - "## 2. Key Technical Concepts\n" - "Technologies, frameworks, tools, and patterns being used or discussed.\n\n" - "## 3. Files and Resources Involved\n" - "Specific files examined or modified, with relevant snippets and identifiers.\n\n" - "## 4. Errors and Fixes\n" - "Problems encountered, error messages, and their resolutions. " - "Include any user feedback on fixes.\n\n" - "## 5. Problem Solving\n" - "Issues that have been resolved and how they were addressed.\n\n" - "## 6. All User Messages\n" - "A complete list of all user inputs (excluding tool outputs) to preserve their exact requests.\n\n" - "## 7. Pending Tasks\n" - "Work items the user explicitly requested that have not yet been completed.\n\n" - "## 8. Current Work\n" - "Precise description of what was being worked on most recently, including relevant context.\n\n" - "## 9. Next Steps\n" - "What should happen next, aligned with the user's most recent requests. " - "Include verbatim quotes of recent instructions if relevant." - ), - }, - {"role": "user", "content": f"Summarize:\n\n{conversation_text}"}, - ], - max_tokens=1500, - temperature=0.3, - ) + # Convert messages to dict format + messages_dict = [] + for msg in messages: + if isinstance(msg, dict): + msg_dict = {k: v for k, v in msg.items() if v is not None} + else: + msg_dict = dict(msg) + messages_dict.append(msg_dict) - summary = response.choices[0].message.content - return summary or "No summary available." - - -def _ensure_tool_pairs_intact( - recent_messages: list[dict], - all_messages: list[dict], - start_index: int, -) -> list[dict]: - """ - Ensure tool_call/tool_response pairs stay together after slicing. - - When slicing messages for context compaction, a naive slice can separate - an assistant message containing tool_calls from its corresponding tool - response messages. This causes API validation errors (e.g., Anthropic's - "unexpected tool_use_id found in tool_result blocks"). - - This function checks for orphan tool responses in the slice and extends - backwards to include their corresponding assistant messages. - - Args: - recent_messages: The sliced messages to validate - all_messages: The complete message list (for looking up missing assistants) - start_index: The index in all_messages where recent_messages begins - - Returns: - A potentially extended list of messages with tool pairs intact - """ - if not recent_messages: - return recent_messages - - # Collect all tool_call_ids from assistant messages in the slice - available_tool_call_ids: set[str] = set() - for msg in recent_messages: - if msg.get("role") == "assistant" and msg.get("tool_calls"): - for tc in msg["tool_calls"]: - tc_id = tc.get("id") - if tc_id: - available_tool_call_ids.add(tc_id) - - # Find orphan tool responses (tool messages whose tool_call_id is missing) - orphan_tool_call_ids: set[str] = set() - for msg in recent_messages: - if msg.get("role") == "tool": - tc_id = msg.get("tool_call_id") - if tc_id and tc_id not in available_tool_call_ids: - orphan_tool_call_ids.add(tc_id) - - if not orphan_tool_call_ids: - # No orphans, slice is valid - return recent_messages - - # Find the assistant messages that contain the orphan tool_call_ids - # Search backwards from start_index in all_messages - messages_to_prepend: list[dict] = [] - for i in range(start_index - 1, -1, -1): - msg = all_messages[i] - if msg.get("role") == "assistant" and msg.get("tool_calls"): - msg_tool_ids = {tc.get("id") for tc in msg["tool_calls"] if tc.get("id")} - if msg_tool_ids & orphan_tool_call_ids: - # This assistant message has tool_calls we need - # Also collect its contiguous tool responses that follow it - assistant_and_responses: list[dict] = [msg] - - # Scan forward from this assistant to collect tool responses - for j in range(i + 1, start_index): - following_msg = all_messages[j] - if following_msg.get("role") == "tool": - tool_id = following_msg.get("tool_call_id") - if tool_id and tool_id in msg_tool_ids: - assistant_and_responses.append(following_msg) - else: - # Stop at first non-tool message - break - - # Prepend the assistant and its tool responses (maintain order) - messages_to_prepend = assistant_and_responses + messages_to_prepend - # Mark these as found - orphan_tool_call_ids -= msg_tool_ids - # Also add this assistant's tool_call_ids to available set - available_tool_call_ids |= msg_tool_ids - - if not orphan_tool_call_ids: - # Found all missing assistants - break - - if orphan_tool_call_ids: - # Some tool_call_ids couldn't be resolved - remove those tool responses - # This shouldn't happen in normal operation but handles edge cases - logger.warning( - f"Could not find assistant messages for tool_call_ids: {orphan_tool_call_ids}. " - "Removing orphan tool responses." - ) - recent_messages = [ - msg - for msg in recent_messages - if not ( - msg.get("role") == "tool" - and msg.get("tool_call_id") in orphan_tool_call_ids + # Only create client if api_key is provided (enables summarization) + # Use context manager to avoid socket leaks + if api_key: + async with openai.AsyncOpenAI( + api_key=api_key, base_url=base_url, timeout=30.0 + ) as client: + return await compress_context( + messages=messages_dict, + model=model, + client=client, ) - ] - - if messages_to_prepend: - logger.info( - f"Extended recent messages by {len(messages_to_prepend)} to preserve " - f"tool_call/tool_response pairs" + else: + # No API key - use truncation-only mode + return await compress_context( + messages=messages_dict, + model=model, + client=None, ) - return messages_to_prepend + recent_messages - - return recent_messages async def _stream_chat_chunks( diff --git a/autogpt_platform/backend/backend/blocks/llm.py b/autogpt_platform/backend/backend/blocks/llm.py index 732fb1354c..54295da1f1 100644 --- a/autogpt_platform/backend/backend/blocks/llm.py +++ b/autogpt_platform/backend/backend/blocks/llm.py @@ -32,7 +32,7 @@ from backend.data.model import ( from backend.integrations.providers import ProviderName from backend.util import json from backend.util.logging import TruncatedLogger -from backend.util.prompt import compress_prompt, estimate_token_count +from backend.util.prompt import compress_context, estimate_token_count from backend.util.text import TextFormatter logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]") @@ -634,11 +634,18 @@ async def llm_call( context_window = llm_model.context_window if compress_prompt_to_fit: - prompt = compress_prompt( + result = await compress_context( messages=prompt, target_tokens=llm_model.context_window // 2, - lossy_ok=True, + client=None, # Truncation-only, no LLM summarization + reserve=0, # Caller handles response token budget separately ) + if result.error: + logger.warning( + f"Prompt compression did not meet target: {result.error}. " + f"Proceeding with {result.token_count} tokens." + ) + prompt = result.messages # Calculate available tokens based on context window and input length estimated_input_tokens = estimate_token_count(prompt) diff --git a/autogpt_platform/backend/backend/util/prompt.py b/autogpt_platform/backend/backend/util/prompt.py index 775d1c932b..5f904bbc8a 100644 --- a/autogpt_platform/backend/backend/util/prompt.py +++ b/autogpt_platform/backend/backend/util/prompt.py @@ -1,10 +1,19 @@ +from __future__ import annotations + +import logging from copy import deepcopy -from typing import Any +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any from tiktoken import encoding_for_model from backend.util import json +if TYPE_CHECKING: + from openai import AsyncOpenAI + +logger = logging.getLogger(__name__) + # ---------------------------------------------------------------------------# # CONSTANTS # # ---------------------------------------------------------------------------# @@ -100,9 +109,17 @@ def _is_objective_message(msg: dict) -> bool: def _truncate_tool_message_content(msg: dict, enc, max_tokens: int) -> None: """ Carefully truncate tool message content while preserving tool structure. - Only truncates tool_result content, leaves tool_use intact. + Handles both Anthropic-style (list content) and OpenAI-style (string content) tool messages. """ content = msg.get("content") + + # OpenAI-style tool message: role="tool" with string content + if msg.get("role") == "tool" and isinstance(content, str): + if _tok_len(content, enc) > max_tokens: + msg["content"] = _truncate_middle_tokens(content, enc, max_tokens) + return + + # Anthropic-style: list content with tool_result items if not isinstance(content, list): return @@ -140,141 +157,6 @@ def _truncate_middle_tokens(text: str, enc, max_tok: int) -> str: # ---------------------------------------------------------------------------# -def compress_prompt( - messages: list[dict], - target_tokens: int, - *, - model: str = "gpt-4o", - reserve: int = 2_048, - start_cap: int = 8_192, - floor_cap: int = 128, - lossy_ok: bool = True, -) -> list[dict]: - """ - Shrink *messages* so that:: - - token_count(prompt) + reserve ā‰¤ target_tokens - - Strategy - -------- - 1. **Token-aware truncation** ā€“ progressively halve a per-message cap - (`start_cap`, `start_cap/2`, ā€¦ `floor_cap`) and apply it to the - *content* of every message except the first and last. Tool shells - are included: we keep the envelope but shorten huge payloads. - 2. **Middle-out deletion** ā€“ if still over the limit, delete whole - messages working outward from the centre, **skipping** any message - that contains ``tool_calls`` or has ``role == "tool"``. - 3. **Last-chance trim** ā€“ if still too big, truncate the *first* and - *last* message bodies down to `floor_cap` tokens. - 4. If the prompt is *still* too large: - ā€¢ raise ``ValueError`` when ``lossy_ok == False`` (default) - ā€¢ return the partially-trimmed prompt when ``lossy_ok == True`` - - Parameters - ---------- - messages Complete chat history (will be deep-copied). - model Model name; passed to tiktoken to pick the right - tokenizer (gpt-4o ā†’ 'o200k_base', others fallback). - target_tokens Hard ceiling for prompt size **excluding** the model's - forthcoming answer. - reserve How many tokens you want to leave available for that - answer (`max_tokens` in your subsequent completion call). - start_cap Initial per-message truncation ceiling (tokens). - floor_cap Lowest cap we'll accept before moving to deletions. - lossy_ok If *True* return best-effort prompt instead of raising - after all trim passes have been exhausted. - - Returns - ------- - list[dict] ā€“ A *new* messages list that abides by the rules above. - """ - enc = encoding_for_model(model) # best-match tokenizer - msgs = deepcopy(messages) # never mutate caller - - def total_tokens() -> int: - """Current size of *msgs* in tokens.""" - return sum(_msg_tokens(m, enc) for m in msgs) - - original_token_count = total_tokens() - - if original_token_count + reserve <= target_tokens: - return msgs - - # ---- STEP 0 : normalise content -------------------------------------- - # Convert non-string payloads to strings so token counting is coherent. - for i, m in enumerate(msgs): - if not isinstance(m.get("content"), str) and m.get("content") is not None: - if _is_tool_message(m): - continue - - # Keep first and last messages intact (unless they're tool messages) - if i == 0 or i == len(msgs) - 1: - continue - - # Reasonable 20k-char ceiling prevents pathological blobs - content_str = json.dumps(m["content"], separators=(",", ":")) - if len(content_str) > 20_000: - content_str = _truncate_middle_tokens(content_str, enc, 20_000) - m["content"] = content_str - - # ---- STEP 1 : token-aware truncation --------------------------------- - cap = start_cap - while total_tokens() + reserve > target_tokens and cap >= floor_cap: - for m in msgs[1:-1]: # keep first & last intact - if _is_tool_message(m): - # For tool messages, only truncate tool result content, preserve structure - _truncate_tool_message_content(m, enc, cap) - continue - - if _is_objective_message(m): - # Never truncate objective messages - they contain the core task - continue - - content = m.get("content") or "" - if _tok_len(content, enc) > cap: - m["content"] = _truncate_middle_tokens(content, enc, cap) - cap //= 2 # tighten the screw - - # ---- STEP 2 : middle-out deletion ----------------------------------- - while total_tokens() + reserve > target_tokens and len(msgs) > 2: - # Identify all deletable messages (not first/last, not tool messages, not objective messages) - deletable_indices = [] - for i in range(1, len(msgs) - 1): # Skip first and last - if not _is_tool_message(msgs[i]) and not _is_objective_message(msgs[i]): - deletable_indices.append(i) - - if not deletable_indices: - break # nothing more we can drop - - # Delete from center outward - find the index closest to center - centre = len(msgs) // 2 - to_delete = min(deletable_indices, key=lambda i: abs(i - centre)) - del msgs[to_delete] - - # ---- STEP 3 : final safety-net trim on first & last ------------------ - cap = start_cap - while total_tokens() + reserve > target_tokens and cap >= floor_cap: - for idx in (0, -1): # first and last - if _is_tool_message(msgs[idx]): - # For tool messages at first/last position, truncate tool result content only - _truncate_tool_message_content(msgs[idx], enc, cap) - continue - - text = msgs[idx].get("content") or "" - if _tok_len(text, enc) > cap: - msgs[idx]["content"] = _truncate_middle_tokens(text, enc, cap) - cap //= 2 # tighten the screw - - # ---- STEP 4 : success or fail-gracefully ----------------------------- - if total_tokens() + reserve > target_tokens and not lossy_ok: - raise ValueError( - "compress_prompt: prompt still exceeds budget " - f"({total_tokens() + reserve} > {target_tokens})." - ) - - return msgs - - def estimate_token_count( messages: list[dict], *, @@ -293,7 +175,8 @@ def estimate_token_count( ------- int ā€“ Token count. """ - enc = encoding_for_model(model) # best-match tokenizer + token_model = _normalize_model_for_tokenizer(model) + enc = encoding_for_model(token_model) return sum(_msg_tokens(m, enc) for m in messages) @@ -315,6 +198,543 @@ def estimate_token_count_str( ------- int ā€“ Token count. """ - enc = encoding_for_model(model) # best-match tokenizer + token_model = _normalize_model_for_tokenizer(model) + enc = encoding_for_model(token_model) text = json.dumps(text) if not isinstance(text, str) else text return _tok_len(text, enc) + + +# ---------------------------------------------------------------------------# +# UNIFIED CONTEXT COMPRESSION # +# ---------------------------------------------------------------------------# + +# Default thresholds +DEFAULT_TOKEN_THRESHOLD = 120_000 +DEFAULT_KEEP_RECENT = 15 + + +@dataclass +class CompressResult: + """Result of context compression.""" + + messages: list[dict] + token_count: int + was_compacted: bool + error: str | None = None + original_token_count: int = 0 + messages_summarized: int = 0 + messages_dropped: int = 0 + + +def _normalize_model_for_tokenizer(model: str) -> str: + """Normalize model name for tiktoken tokenizer selection.""" + if "/" in model: + model = model.split("/")[-1] + if "claude" in model.lower() or not any( + known in model.lower() for known in ["gpt", "o1", "chatgpt", "text-"] + ): + return "gpt-4o" + return model + + +def _extract_tool_call_ids_from_message(msg: dict) -> set[str]: + """ + Extract tool_call IDs from an assistant message. + + Supports both formats: + - OpenAI: {"role": "assistant", "tool_calls": [{"id": "..."}]} + - Anthropic: {"role": "assistant", "content": [{"type": "tool_use", "id": "..."}]} + + Returns: + Set of tool_call IDs found in the message. + """ + ids: set[str] = set() + if msg.get("role") != "assistant": + return ids + + # OpenAI format: tool_calls array + if msg.get("tool_calls"): + for tc in msg["tool_calls"]: + tc_id = tc.get("id") + if tc_id: + ids.add(tc_id) + + # Anthropic format: content list with tool_use blocks + content = msg.get("content") + if isinstance(content, list): + for block in content: + if isinstance(block, dict) and block.get("type") == "tool_use": + tc_id = block.get("id") + if tc_id: + ids.add(tc_id) + + return ids + + +def _extract_tool_response_ids_from_message(msg: dict) -> set[str]: + """ + Extract tool_call IDs that this message is responding to. + + Supports both formats: + - OpenAI: {"role": "tool", "tool_call_id": "..."} + - Anthropic: {"role": "user", "content": [{"type": "tool_result", "tool_use_id": "..."}]} + + Returns: + Set of tool_call IDs this message responds to. + """ + ids: set[str] = set() + + # OpenAI format: role=tool with tool_call_id + if msg.get("role") == "tool": + tc_id = msg.get("tool_call_id") + if tc_id: + ids.add(tc_id) + + # Anthropic format: content list with tool_result blocks + content = msg.get("content") + if isinstance(content, list): + for block in content: + if isinstance(block, dict) and block.get("type") == "tool_result": + tc_id = block.get("tool_use_id") + if tc_id: + ids.add(tc_id) + + return ids + + +def _is_tool_response_message(msg: dict) -> bool: + """Check if message is a tool response (OpenAI or Anthropic format).""" + # OpenAI format + if msg.get("role") == "tool": + return True + # Anthropic format + content = msg.get("content") + if isinstance(content, list): + for block in content: + if isinstance(block, dict) and block.get("type") == "tool_result": + return True + return False + + +def _remove_orphan_tool_responses( + messages: list[dict], orphan_ids: set[str] +) -> list[dict]: + """ + Remove tool response messages/blocks that reference orphan tool_call IDs. + + Supports both OpenAI and Anthropic formats. + For Anthropic messages with mixed valid/orphan tool_result blocks, + filters out only the orphan blocks instead of dropping the entire message. + """ + result = [] + for msg in messages: + # OpenAI format: role=tool - drop entire message if orphan + if msg.get("role") == "tool": + tc_id = msg.get("tool_call_id") + if tc_id and tc_id in orphan_ids: + continue + result.append(msg) + continue + + # Anthropic format: content list may have mixed tool_result blocks + content = msg.get("content") + if isinstance(content, list): + has_tool_results = any( + isinstance(b, dict) and b.get("type") == "tool_result" for b in content + ) + if has_tool_results: + # Filter out orphan tool_result blocks, keep valid ones + filtered_content = [ + block + for block in content + if not ( + isinstance(block, dict) + and block.get("type") == "tool_result" + and block.get("tool_use_id") in orphan_ids + ) + ] + # Only keep message if it has remaining content + if filtered_content: + msg = msg.copy() + msg["content"] = filtered_content + result.append(msg) + continue + + result.append(msg) + return result + + +def _ensure_tool_pairs_intact( + recent_messages: list[dict], + all_messages: list[dict], + start_index: int, +) -> list[dict]: + """ + Ensure tool_call/tool_response pairs stay together after slicing. + + When slicing messages for context compaction, a naive slice can separate + an assistant message containing tool_calls from its corresponding tool + response messages. This causes API validation errors (e.g., Anthropic's + "unexpected tool_use_id found in tool_result blocks"). + + This function checks for orphan tool responses in the slice and extends + backwards to include their corresponding assistant messages. + + Supports both formats: + - OpenAI: tool_calls array + role="tool" responses + - Anthropic: tool_use blocks + tool_result blocks + + Args: + recent_messages: The sliced messages to validate + all_messages: The complete message list (for looking up missing assistants) + start_index: The index in all_messages where recent_messages begins + + Returns: + A potentially extended list of messages with tool pairs intact + """ + if not recent_messages: + return recent_messages + + # Collect all tool_call_ids from assistant messages in the slice + available_tool_call_ids: set[str] = set() + for msg in recent_messages: + available_tool_call_ids |= _extract_tool_call_ids_from_message(msg) + + # Find orphan tool responses (responses whose tool_call_id is missing) + orphan_tool_call_ids: set[str] = set() + for msg in recent_messages: + response_ids = _extract_tool_response_ids_from_message(msg) + for tc_id in response_ids: + if tc_id not in available_tool_call_ids: + orphan_tool_call_ids.add(tc_id) + + if not orphan_tool_call_ids: + # No orphans, slice is valid + return recent_messages + + # Find the assistant messages that contain the orphan tool_call_ids + # Search backwards from start_index in all_messages + messages_to_prepend: list[dict] = [] + for i in range(start_index - 1, -1, -1): + msg = all_messages[i] + msg_tool_ids = _extract_tool_call_ids_from_message(msg) + if msg_tool_ids & orphan_tool_call_ids: + # This assistant message has tool_calls we need + # Also collect its contiguous tool responses that follow it + assistant_and_responses: list[dict] = [msg] + + # Scan forward from this assistant to collect tool responses + for j in range(i + 1, start_index): + following_msg = all_messages[j] + following_response_ids = _extract_tool_response_ids_from_message( + following_msg + ) + if following_response_ids and following_response_ids & msg_tool_ids: + assistant_and_responses.append(following_msg) + elif not _is_tool_response_message(following_msg): + # Stop at first non-tool-response message + break + + # Prepend the assistant and its tool responses (maintain order) + messages_to_prepend = assistant_and_responses + messages_to_prepend + # Mark these as found + orphan_tool_call_ids -= msg_tool_ids + # Also add this assistant's tool_call_ids to available set + available_tool_call_ids |= msg_tool_ids + + if not orphan_tool_call_ids: + # Found all missing assistants + break + + if orphan_tool_call_ids: + # Some tool_call_ids couldn't be resolved - remove those tool responses + # This shouldn't happen in normal operation but handles edge cases + logger.warning( + f"Could not find assistant messages for tool_call_ids: {orphan_tool_call_ids}. " + "Removing orphan tool responses." + ) + recent_messages = _remove_orphan_tool_responses( + recent_messages, orphan_tool_call_ids + ) + + if messages_to_prepend: + logger.info( + f"Extended recent messages by {len(messages_to_prepend)} to preserve " + f"tool_call/tool_response pairs" + ) + return messages_to_prepend + recent_messages + + return recent_messages + + +async def _summarize_messages_llm( + messages: list[dict], + client: AsyncOpenAI, + model: str, + timeout: float = 30.0, +) -> str: + """Summarize messages using an LLM.""" + conversation = [] + for msg in messages: + role = msg.get("role", "") + content = msg.get("content", "") + if content and role in ("user", "assistant", "tool"): + conversation.append(f"{role.upper()}: {content}") + + conversation_text = "\n\n".join(conversation) + + if not conversation_text: + return "No conversation history available." + + # Limit to ~100k chars for safety + MAX_CHARS = 100_000 + if len(conversation_text) > MAX_CHARS: + conversation_text = conversation_text[:MAX_CHARS] + "\n\n[truncated]" + + response = await client.with_options(timeout=timeout).chat.completions.create( + model=model, + messages=[ + { + "role": "system", + "content": ( + "Create a detailed summary of the conversation so far. " + "This summary will be used as context when continuing the conversation.\n\n" + "Before writing the summary, analyze each message chronologically to identify:\n" + "- User requests and their explicit goals\n" + "- Your approach and key decisions made\n" + "- Technical specifics (file names, tool outputs, function signatures)\n" + "- Errors encountered and resolutions applied\n\n" + "You MUST include ALL of the following sections:\n\n" + "## 1. Primary Request and Intent\n" + "The user's explicit goals and what they are trying to accomplish.\n\n" + "## 2. Key Technical Concepts\n" + "Technologies, frameworks, tools, and patterns being used or discussed.\n\n" + "## 3. Files and Resources Involved\n" + "Specific files examined or modified, with relevant snippets and identifiers.\n\n" + "## 4. Errors and Fixes\n" + "Problems encountered, error messages, and their resolutions. " + "Include any user feedback on fixes.\n\n" + "## 5. Problem Solving\n" + "Issues that have been resolved and how they were addressed.\n\n" + "## 6. All User Messages\n" + "A complete list of all user inputs (excluding tool outputs) to preserve their exact requests.\n\n" + "## 7. Pending Tasks\n" + "Work items the user explicitly requested that have not yet been completed.\n\n" + "## 8. Current Work\n" + "Precise description of what was being worked on most recently, including relevant context.\n\n" + "## 9. Next Steps\n" + "What should happen next, aligned with the user's most recent requests. " + "Include verbatim quotes of recent instructions if relevant." + ), + }, + {"role": "user", "content": f"Summarize:\n\n{conversation_text}"}, + ], + max_tokens=1500, + temperature=0.3, + ) + + return response.choices[0].message.content or "No summary available." + + +async def compress_context( + messages: list[dict], + target_tokens: int = DEFAULT_TOKEN_THRESHOLD, + *, + model: str = "gpt-4o", + client: AsyncOpenAI | None = None, + keep_recent: int = DEFAULT_KEEP_RECENT, + reserve: int = 2_048, + start_cap: int = 8_192, + floor_cap: int = 128, +) -> CompressResult: + """ + Unified context compression that combines summarization and truncation strategies. + + Strategy (in order): + 1. **LLM summarization** ā€“ If client provided, summarize old messages into a + single context message while keeping recent messages intact. This is the + primary strategy for chat service. + 2. **Content truncation** ā€“ Progressively halve a per-message cap and truncate + bloated message content (tool outputs, large pastes). Preserves all messages + but shortens their content. Primary strategy when client=None (LLM blocks). + 3. **Middle-out deletion** ā€“ Delete whole messages one at a time from the center + outward, skipping tool messages and objective messages. + 4. **First/last trim** ā€“ Truncate first and last message content as last resort. + + Parameters + ---------- + messages Complete chat history (will be deep-copied). + target_tokens Hard ceiling for prompt size. + model Model name for tokenization and summarization. + client AsyncOpenAI client. If provided, enables LLM summarization + as the first strategy. If None, skips to truncation strategies. + keep_recent Number of recent messages to preserve during summarization. + reserve Tokens to reserve for model response. + start_cap Initial per-message truncation ceiling (tokens). + floor_cap Lowest cap before moving to deletions. + + Returns + ------- + CompressResult with compressed messages and metadata. + """ + # Guard clause for empty messages + if not messages: + return CompressResult( + messages=[], + token_count=0, + was_compacted=False, + original_token_count=0, + ) + + token_model = _normalize_model_for_tokenizer(model) + enc = encoding_for_model(token_model) + msgs = deepcopy(messages) + + def total_tokens() -> int: + return sum(_msg_tokens(m, enc) for m in msgs) + + original_count = total_tokens() + + # Already under limit + if original_count + reserve <= target_tokens: + return CompressResult( + messages=msgs, + token_count=original_count, + was_compacted=False, + original_token_count=original_count, + ) + + messages_summarized = 0 + messages_dropped = 0 + + # ---- STEP 1: LLM summarization (if client provided) ------------------- + # This is the primary compression strategy for chat service. + # Summarize old messages while keeping recent ones intact. + if client is not None: + has_system = len(msgs) > 0 and msgs[0].get("role") == "system" + system_msg = msgs[0] if has_system else None + + # Calculate old vs recent messages + if has_system: + if len(msgs) > keep_recent + 1: + old_msgs = msgs[1:-keep_recent] + recent_msgs = msgs[-keep_recent:] + else: + old_msgs = [] + recent_msgs = msgs[1:] if len(msgs) > 1 else [] + else: + if len(msgs) > keep_recent: + old_msgs = msgs[:-keep_recent] + recent_msgs = msgs[-keep_recent:] + else: + old_msgs = [] + recent_msgs = msgs + + # Ensure tool pairs stay intact + slice_start = max(0, len(msgs) - keep_recent) + recent_msgs = _ensure_tool_pairs_intact(recent_msgs, msgs, slice_start) + + if old_msgs: + try: + summary_text = await _summarize_messages_llm(old_msgs, client, model) + summary_msg = { + "role": "assistant", + "content": f"[Previous conversation summary ā€” for context only]: {summary_text}", + } + messages_summarized = len(old_msgs) + + if has_system: + msgs = [system_msg, summary_msg] + recent_msgs + else: + msgs = [summary_msg] + recent_msgs + + logger.info( + f"Context summarized: {original_count} -> {total_tokens()} tokens, " + f"summarized {messages_summarized} messages" + ) + except Exception as e: + logger.warning(f"Summarization failed, continuing with truncation: {e}") + # Fall through to content truncation + + # ---- STEP 2: Normalize content ---------------------------------------- + # Convert non-string payloads to strings so token counting is coherent. + # Always run this before truncation to ensure consistent token counting. + for i, m in enumerate(msgs): + if not isinstance(m.get("content"), str) and m.get("content") is not None: + if _is_tool_message(m): + continue + if i == 0 or i == len(msgs) - 1: + continue + content_str = json.dumps(m["content"], separators=(",", ":")) + if len(content_str) > 20_000: + content_str = _truncate_middle_tokens(content_str, enc, 20_000) + m["content"] = content_str + + # ---- STEP 3: Token-aware content truncation --------------------------- + # Progressively halve per-message cap and truncate bloated content. + # This preserves all messages but shortens their content. + cap = start_cap + while total_tokens() + reserve > target_tokens and cap >= floor_cap: + for m in msgs[1:-1]: + if _is_tool_message(m): + _truncate_tool_message_content(m, enc, cap) + continue + if _is_objective_message(m): + continue + content = m.get("content") or "" + if _tok_len(content, enc) > cap: + m["content"] = _truncate_middle_tokens(content, enc, cap) + cap //= 2 + + # ---- STEP 4: Middle-out deletion -------------------------------------- + # Delete messages one at a time from the center outward. + # This is more granular than dropping all old messages at once. + while total_tokens() + reserve > target_tokens and len(msgs) > 2: + deletable: list[int] = [] + for i in range(1, len(msgs) - 1): + msg = msgs[i] + if ( + msg is not None + and not _is_tool_message(msg) + and not _is_objective_message(msg) + ): + deletable.append(i) + if not deletable: + break + centre = len(msgs) // 2 + to_delete = min(deletable, key=lambda i: abs(i - centre)) + del msgs[to_delete] + messages_dropped += 1 + + # ---- STEP 5: Final trim on first/last --------------------------------- + cap = start_cap + while total_tokens() + reserve > target_tokens and cap >= floor_cap: + for idx in (0, -1): + msg = msgs[idx] + if msg is None: + continue + if _is_tool_message(msg): + _truncate_tool_message_content(msg, enc, cap) + continue + text = msg.get("content") or "" + if _tok_len(text, enc) > cap: + msg["content"] = _truncate_middle_tokens(text, enc, cap) + cap //= 2 + + # Filter out any None values that may have been introduced + final_msgs: list[dict] = [m for m in msgs if m is not None] + final_count = sum(_msg_tokens(m, enc) for m in final_msgs) + error = None + if final_count + reserve > target_tokens: + error = f"Could not compress below target ({final_count + reserve} > {target_tokens})" + logger.warning(error) + + return CompressResult( + messages=final_msgs, + token_count=final_count, + was_compacted=True, + error=error, + original_token_count=original_count, + messages_summarized=messages_summarized, + messages_dropped=messages_dropped, + ) diff --git a/autogpt_platform/backend/backend/util/prompt_test.py b/autogpt_platform/backend/backend/util/prompt_test.py index af6b230f8f..2d4bf090b3 100644 --- a/autogpt_platform/backend/backend/util/prompt_test.py +++ b/autogpt_platform/backend/backend/util/prompt_test.py @@ -1,10 +1,21 @@ """Tests for prompt utility functions, especially tool call token counting.""" +from unittest.mock import AsyncMock, MagicMock + import pytest from tiktoken import encoding_for_model from backend.util import json -from backend.util.prompt import _msg_tokens, estimate_token_count +from backend.util.prompt import ( + CompressResult, + _ensure_tool_pairs_intact, + _msg_tokens, + _normalize_model_for_tokenizer, + _truncate_middle_tokens, + _truncate_tool_message_content, + compress_context, + estimate_token_count, +) class TestMsgTokens: @@ -276,3 +287,690 @@ class TestEstimateTokenCount: assert total_tokens == expected_total assert total_tokens > 20 # Should be substantial + + +class TestNormalizeModelForTokenizer: + """Test model name normalization for tiktoken.""" + + def test_openai_models_unchanged(self): + """Test that OpenAI models are returned as-is.""" + assert _normalize_model_for_tokenizer("gpt-4o") == "gpt-4o" + assert _normalize_model_for_tokenizer("gpt-4") == "gpt-4" + assert _normalize_model_for_tokenizer("gpt-3.5-turbo") == "gpt-3.5-turbo" + + def test_claude_models_normalized(self): + """Test that Claude models are normalized to gpt-4o.""" + assert _normalize_model_for_tokenizer("claude-3-opus") == "gpt-4o" + assert _normalize_model_for_tokenizer("claude-3-sonnet") == "gpt-4o" + assert _normalize_model_for_tokenizer("anthropic/claude-3-haiku") == "gpt-4o" + + def test_openrouter_paths_extracted(self): + """Test that OpenRouter model paths are handled.""" + assert _normalize_model_for_tokenizer("openai/gpt-4o") == "gpt-4o" + assert _normalize_model_for_tokenizer("anthropic/claude-3-opus") == "gpt-4o" + + def test_unknown_models_default_to_gpt4o(self): + """Test that unknown models default to gpt-4o.""" + assert _normalize_model_for_tokenizer("some-random-model") == "gpt-4o" + assert _normalize_model_for_tokenizer("llama-3-70b") == "gpt-4o" + + +class TestTruncateToolMessageContent: + """Test tool message content truncation.""" + + @pytest.fixture + def enc(self): + return encoding_for_model("gpt-4o") + + def test_truncate_openai_tool_message(self, enc): + """Test truncation of OpenAI-style tool message with string content.""" + long_content = "x" * 10000 + msg = {"role": "tool", "tool_call_id": "call_123", "content": long_content} + + _truncate_tool_message_content(msg, enc, max_tokens=100) + + # Content should be truncated + assert len(msg["content"]) < len(long_content) + assert "ā€¦" in msg["content"] # Has ellipsis marker + + def test_truncate_anthropic_tool_result(self, enc): + """Test truncation of Anthropic-style tool_result.""" + long_content = "y" * 10000 + msg = { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_123", + "content": long_content, + } + ], + } + + _truncate_tool_message_content(msg, enc, max_tokens=100) + + # Content should be truncated + result_content = msg["content"][0]["content"] + assert len(result_content) < len(long_content) + assert "ā€¦" in result_content + + def test_preserve_tool_use_blocks(self, enc): + """Test that tool_use blocks are not truncated.""" + msg = { + "role": "assistant", + "content": [ + { + "type": "tool_use", + "id": "toolu_123", + "name": "some_function", + "input": {"key": "value" * 1000}, # Large input + } + ], + } + + original = json.dumps(msg["content"][0]["input"]) + _truncate_tool_message_content(msg, enc, max_tokens=10) + + # tool_use should be unchanged + assert json.dumps(msg["content"][0]["input"]) == original + + def test_no_truncation_when_under_limit(self, enc): + """Test that short content is not modified.""" + msg = {"role": "tool", "tool_call_id": "call_123", "content": "Short content"} + + original = msg["content"] + _truncate_tool_message_content(msg, enc, max_tokens=1000) + + assert msg["content"] == original + + +class TestTruncateMiddleTokens: + """Test middle truncation of text.""" + + @pytest.fixture + def enc(self): + return encoding_for_model("gpt-4o") + + def test_truncates_long_text(self, enc): + """Test that long text is truncated with ellipsis in middle.""" + long_text = "word " * 1000 + result = _truncate_middle_tokens(long_text, enc, max_tok=50) + + assert len(enc.encode(result)) <= 52 # Allow some slack for ellipsis + assert "ā€¦" in result + assert result.startswith("word") # Head preserved + assert result.endswith("word ") # Tail preserved + + def test_preserves_short_text(self, enc): + """Test that short text is not modified.""" + short_text = "Hello world" + result = _truncate_middle_tokens(short_text, enc, max_tok=100) + + assert result == short_text + + +class TestEnsureToolPairsIntact: + """Test tool call/response pair preservation for both OpenAI and Anthropic formats.""" + + # ---- OpenAI Format Tests ---- + + def test_openai_adds_missing_tool_call(self): + """Test that orphaned OpenAI tool_response gets its tool_call prepended.""" + all_msgs = [ + {"role": "system", "content": "You are helpful."}, + { + "role": "assistant", + "tool_calls": [ + {"id": "call_1", "type": "function", "function": {"name": "f1"}} + ], + }, + {"role": "tool", "tool_call_id": "call_1", "content": "result"}, + {"role": "user", "content": "Thanks!"}, + ] + # Recent messages start at index 2 (the tool response) + recent = [all_msgs[2], all_msgs[3]] + start_index = 2 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + # Should prepend the tool_call message + assert len(result) == 3 + assert result[0]["role"] == "assistant" + assert "tool_calls" in result[0] + + def test_openai_keeps_complete_pairs(self): + """Test that complete OpenAI pairs are unchanged.""" + all_msgs = [ + {"role": "system", "content": "System"}, + { + "role": "assistant", + "tool_calls": [ + {"id": "call_1", "type": "function", "function": {"name": "f1"}} + ], + }, + {"role": "tool", "tool_call_id": "call_1", "content": "result"}, + ] + recent = all_msgs[1:] # Include both tool_call and response + start_index = 1 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + assert len(result) == 2 # No messages added + + def test_openai_multiple_tool_calls(self): + """Test multiple OpenAI tool calls in one assistant message.""" + all_msgs = [ + {"role": "system", "content": "System"}, + { + "role": "assistant", + "tool_calls": [ + {"id": "call_1", "type": "function", "function": {"name": "f1"}}, + {"id": "call_2", "type": "function", "function": {"name": "f2"}}, + ], + }, + {"role": "tool", "tool_call_id": "call_1", "content": "result1"}, + {"role": "tool", "tool_call_id": "call_2", "content": "result2"}, + {"role": "user", "content": "Thanks!"}, + ] + # Recent messages start at index 2 (first tool response) + recent = [all_msgs[2], all_msgs[3], all_msgs[4]] + start_index = 2 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + # Should prepend the assistant message with both tool_calls + assert len(result) == 4 + assert result[0]["role"] == "assistant" + assert len(result[0]["tool_calls"]) == 2 + + # ---- Anthropic Format Tests ---- + + def test_anthropic_adds_missing_tool_use(self): + """Test that orphaned Anthropic tool_result gets its tool_use prepended.""" + all_msgs = [ + {"role": "system", "content": "You are helpful."}, + { + "role": "assistant", + "content": [ + { + "type": "tool_use", + "id": "toolu_123", + "name": "get_weather", + "input": {"location": "SF"}, + } + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_123", + "content": "22Ā°C and sunny", + } + ], + }, + {"role": "user", "content": "Thanks!"}, + ] + # Recent messages start at index 2 (the tool_result) + recent = [all_msgs[2], all_msgs[3]] + start_index = 2 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + # Should prepend the tool_use message + assert len(result) == 3 + assert result[0]["role"] == "assistant" + assert result[0]["content"][0]["type"] == "tool_use" + + def test_anthropic_keeps_complete_pairs(self): + """Test that complete Anthropic pairs are unchanged.""" + all_msgs = [ + {"role": "system", "content": "System"}, + { + "role": "assistant", + "content": [ + { + "type": "tool_use", + "id": "toolu_456", + "name": "calculator", + "input": {"expr": "2+2"}, + } + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_456", + "content": "4", + } + ], + }, + ] + recent = all_msgs[1:] # Include both tool_use and result + start_index = 1 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + assert len(result) == 2 # No messages added + + def test_anthropic_multiple_tool_uses(self): + """Test multiple Anthropic tool_use blocks in one message.""" + all_msgs = [ + {"role": "system", "content": "System"}, + { + "role": "assistant", + "content": [ + {"type": "text", "text": "Let me check both..."}, + { + "type": "tool_use", + "id": "toolu_1", + "name": "get_weather", + "input": {"city": "NYC"}, + }, + { + "type": "tool_use", + "id": "toolu_2", + "name": "get_weather", + "input": {"city": "LA"}, + }, + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_1", + "content": "Cold", + }, + { + "type": "tool_result", + "tool_use_id": "toolu_2", + "content": "Warm", + }, + ], + }, + {"role": "user", "content": "Thanks!"}, + ] + # Recent messages start at index 2 (tool_result) + recent = [all_msgs[2], all_msgs[3]] + start_index = 2 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + # Should prepend the assistant message with both tool_uses + assert len(result) == 3 + assert result[0]["role"] == "assistant" + tool_use_count = sum( + 1 for b in result[0]["content"] if b.get("type") == "tool_use" + ) + assert tool_use_count == 2 + + # ---- Mixed/Edge Case Tests ---- + + def test_anthropic_with_type_message_field(self): + """Test Anthropic format with 'type': 'message' field (smart_decision_maker style).""" + all_msgs = [ + {"role": "system", "content": "You are helpful."}, + { + "role": "assistant", + "content": [ + { + "type": "tool_use", + "id": "toolu_abc", + "name": "search", + "input": {"q": "test"}, + } + ], + }, + { + "role": "user", + "type": "message", # Extra field from smart_decision_maker + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_abc", + "content": "Found results", + } + ], + }, + {"role": "user", "content": "Thanks!"}, + ] + # Recent messages start at index 2 (the tool_result with 'type': 'message') + recent = [all_msgs[2], all_msgs[3]] + start_index = 2 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + # Should prepend the tool_use message + assert len(result) == 3 + assert result[0]["role"] == "assistant" + assert result[0]["content"][0]["type"] == "tool_use" + + def test_handles_no_tool_messages(self): + """Test messages without tool calls.""" + all_msgs = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + ] + recent = all_msgs + start_index = 0 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + assert result == all_msgs + + def test_handles_empty_messages(self): + """Test empty message list.""" + result = _ensure_tool_pairs_intact([], [], 0) + assert result == [] + + def test_mixed_text_and_tool_content(self): + """Test Anthropic message with mixed text and tool_use content.""" + all_msgs = [ + { + "role": "assistant", + "content": [ + {"type": "text", "text": "I'll help you with that."}, + { + "type": "tool_use", + "id": "toolu_mixed", + "name": "search", + "input": {"q": "test"}, + }, + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_mixed", + "content": "Found results", + } + ], + }, + {"role": "assistant", "content": "Here are the results..."}, + ] + # Start from tool_result + recent = [all_msgs[1], all_msgs[2]] + start_index = 1 + + result = _ensure_tool_pairs_intact(recent, all_msgs, start_index) + + # Should prepend the assistant message with tool_use + assert len(result) == 3 + assert result[0]["content"][0]["type"] == "text" + assert result[0]["content"][1]["type"] == "tool_use" + + +class TestCompressContext: + """Test the async compress_context function.""" + + @pytest.mark.asyncio + async def test_no_compression_needed(self): + """Test messages under limit return without compression.""" + messages = [ + {"role": "system", "content": "You are helpful."}, + {"role": "user", "content": "Hello!"}, + ] + + result = await compress_context(messages, target_tokens=100000) + + assert isinstance(result, CompressResult) + assert result.was_compacted is False + assert len(result.messages) == 2 + assert result.error is None + + @pytest.mark.asyncio + async def test_truncation_without_client(self): + """Test that truncation works without LLM client.""" + long_content = "x" * 50000 + messages = [ + {"role": "system", "content": "System"}, + {"role": "user", "content": long_content}, + {"role": "assistant", "content": "Response"}, + ] + + result = await compress_context( + messages, target_tokens=1000, client=None, reserve=100 + ) + + assert result.was_compacted is True + # Should have truncated without summarization + assert result.messages_summarized == 0 + + @pytest.mark.asyncio + async def test_with_mocked_llm_client(self): + """Test summarization with mocked LLM client.""" + # Create many messages to trigger summarization + messages = [{"role": "system", "content": "System prompt"}] + for i in range(30): + messages.append({"role": "user", "content": f"User message {i} " * 100}) + messages.append( + {"role": "assistant", "content": f"Assistant response {i} " * 100} + ) + + # Mock the AsyncOpenAI client + mock_client = AsyncMock() + mock_response = MagicMock() + mock_response.choices = [MagicMock()] + mock_response.choices[0].message.content = "Summary of conversation" + mock_client.with_options.return_value.chat.completions.create = AsyncMock( + return_value=mock_response + ) + + result = await compress_context( + messages, + target_tokens=5000, + client=mock_client, + keep_recent=5, + reserve=500, + ) + + assert result.was_compacted is True + # Should have attempted summarization + assert mock_client.with_options.called or result.messages_summarized > 0 + + @pytest.mark.asyncio + async def test_preserves_tool_pairs(self): + """Test that tool call/response pairs stay together.""" + messages = [ + {"role": "system", "content": "System"}, + {"role": "user", "content": "Do something"}, + { + "role": "assistant", + "tool_calls": [ + {"id": "call_1", "type": "function", "function": {"name": "func"}} + ], + }, + {"role": "tool", "tool_call_id": "call_1", "content": "Result " * 1000}, + {"role": "assistant", "content": "Done!"}, + ] + + result = await compress_context( + messages, target_tokens=500, client=None, reserve=50 + ) + + # Check that if tool response exists, its call exists too + tool_call_ids = set() + tool_response_ids = set() + for msg in result.messages: + if "tool_calls" in msg: + for tc in msg["tool_calls"]: + tool_call_ids.add(tc["id"]) + if msg.get("role") == "tool": + tool_response_ids.add(msg.get("tool_call_id")) + + # All tool responses should have their calls + assert tool_response_ids <= tool_call_ids + + @pytest.mark.asyncio + async def test_returns_error_when_cannot_compress(self): + """Test that error is returned when compression fails.""" + # Single huge message that can't be compressed enough + messages = [ + {"role": "user", "content": "x" * 100000}, + ] + + result = await compress_context( + messages, target_tokens=100, client=None, reserve=50 + ) + + # Should have an error since we can't get below 100 tokens + assert result.error is not None + assert result.was_compacted is True + + @pytest.mark.asyncio + async def test_empty_messages(self): + """Test that empty messages list returns early without error.""" + result = await compress_context([], target_tokens=1000) + + assert result.messages == [] + assert result.token_count == 0 + assert result.was_compacted is False + assert result.error is None + + +class TestRemoveOrphanToolResponses: + """Test _remove_orphan_tool_responses helper function.""" + + def test_removes_openai_orphan(self): + """Test removal of orphan OpenAI tool response.""" + from backend.util.prompt import _remove_orphan_tool_responses + + messages = [ + {"role": "tool", "tool_call_id": "call_orphan", "content": "result"}, + {"role": "user", "content": "Hello"}, + ] + orphan_ids = {"call_orphan"} + + result = _remove_orphan_tool_responses(messages, orphan_ids) + + assert len(result) == 1 + assert result[0]["role"] == "user" + + def test_keeps_valid_openai_tool(self): + """Test that valid OpenAI tool responses are kept.""" + from backend.util.prompt import _remove_orphan_tool_responses + + messages = [ + {"role": "tool", "tool_call_id": "call_valid", "content": "result"}, + ] + orphan_ids = {"call_other"} + + result = _remove_orphan_tool_responses(messages, orphan_ids) + + assert len(result) == 1 + assert result[0]["tool_call_id"] == "call_valid" + + def test_filters_anthropic_mixed_blocks(self): + """Test filtering individual orphan blocks from Anthropic message with mixed valid/orphan.""" + from backend.util.prompt import _remove_orphan_tool_responses + + messages = [ + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_valid", + "content": "valid result", + }, + { + "type": "tool_result", + "tool_use_id": "toolu_orphan", + "content": "orphan result", + }, + ], + }, + ] + orphan_ids = {"toolu_orphan"} + + result = _remove_orphan_tool_responses(messages, orphan_ids) + + assert len(result) == 1 + # Should only have the valid tool_result, orphan filtered out + assert len(result[0]["content"]) == 1 + assert result[0]["content"][0]["tool_use_id"] == "toolu_valid" + + def test_removes_anthropic_all_orphan(self): + """Test removal of Anthropic message when all tool_results are orphans.""" + from backend.util.prompt import _remove_orphan_tool_responses + + messages = [ + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_orphan1", + "content": "result1", + }, + { + "type": "tool_result", + "tool_use_id": "toolu_orphan2", + "content": "result2", + }, + ], + }, + ] + orphan_ids = {"toolu_orphan1", "toolu_orphan2"} + + result = _remove_orphan_tool_responses(messages, orphan_ids) + + # Message should be completely removed since no content left + assert len(result) == 0 + + def test_preserves_non_tool_messages(self): + """Test that non-tool messages are preserved.""" + from backend.util.prompt import _remove_orphan_tool_responses + + messages = [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there!"}, + ] + orphan_ids = {"some_id"} + + result = _remove_orphan_tool_responses(messages, orphan_ids) + + assert result == messages + + +class TestCompressResultDataclass: + """Test CompressResult dataclass.""" + + def test_default_values(self): + """Test default values are set correctly.""" + result = CompressResult( + messages=[{"role": "user", "content": "test"}], + token_count=10, + was_compacted=False, + ) + + assert result.error is None + assert result.original_token_count == 0 # Defaults to 0, not None + assert result.messages_summarized == 0 + assert result.messages_dropped == 0 + + def test_all_fields(self): + """Test all fields can be set.""" + result = CompressResult( + messages=[{"role": "user", "content": "test"}], + token_count=100, + was_compacted=True, + error="Some error", + original_token_count=500, + messages_summarized=10, + messages_dropped=5, + ) + + assert result.token_count == 100 + assert result.was_compacted is True + assert result.error == "Some error" + assert result.original_token_count == 500 + assert result.messages_summarized == 10 + assert result.messages_dropped == 5 From 7dc53071e8a9f293f729886e39524bae6358490c Mon Sep 17 00:00:00 2001 From: Otto Date: Tue, 3 Feb 2026 12:43:30 +0000 Subject: [PATCH 23/31] fix(backend): Add retry and error handling to block initialization (#11946) ## Summary Adds retry logic and graceful error handling to `initialize_blocks()` to prevent transient DB errors from crashing server startup. ## Problem When a transient database error occurs during block initialization (e.g., Prisma P1017 "Server has closed the connection"), the entire server fails to start. This is overly aggressive since: 1. Blocks are already registered in memory 2. The DB sync is primarily for tracking/schema storage 3. One flaky connection shouldn't prevent the server from starting **Triggered by:** [Sentry AUTOGPT-SERVER-7PW](https://significant-gravitas.sentry.io/issues/7238733543/) ## Solution - Add retry decorator (3 attempts with exponential backoff) for DB operations - On failure after retries, log a warning and continue to the next block - Blocks remain available in memory even if DB sync fails - Log summary of any failed blocks at the end ## Changes - `autogpt_platform/backend/backend/data/block.py`: Wrap block DB sync in retry logic with graceful fallback ## Testing - Existing block initialization behavior unchanged on success - On transient DB errors: retries up to 3 times, then continues with warning --- .../backend/backend/data/block.py | 28 +++++++++++++++---- 1 file changed, 23 insertions(+), 5 deletions(-) diff --git a/autogpt_platform/backend/backend/data/block.py b/autogpt_platform/backend/backend/data/block.py index 8d9ecfff4c..eb9360b037 100644 --- a/autogpt_platform/backend/backend/data/block.py +++ b/autogpt_platform/backend/backend/data/block.py @@ -873,14 +873,13 @@ def is_block_auth_configured( async def initialize_blocks() -> None: - # First, sync all provider costs to blocks - # Imported here to avoid circular import from backend.sdk.cost_integration import sync_all_provider_costs + from backend.util.retry import func_retry sync_all_provider_costs() - for cls in get_blocks().values(): - block = cls() + @func_retry + async def sync_block_to_db(block: Block) -> None: existing_block = await AgentBlock.prisma().find_first( where={"OR": [{"id": block.id}, {"name": block.name}]} ) @@ -893,7 +892,7 @@ async def initialize_blocks() -> None: outputSchema=json.dumps(block.output_schema.jsonschema()), ) ) - continue + return input_schema = json.dumps(block.input_schema.jsonschema()) output_schema = json.dumps(block.output_schema.jsonschema()) @@ -913,6 +912,25 @@ async def initialize_blocks() -> None: }, ) + failed_blocks: list[str] = [] + for cls in get_blocks().values(): + block = cls() + try: + await sync_block_to_db(block) + except Exception as e: + logger.warning( + f"Failed to sync block {block.name} to database: {e}. " + "Block is still available in memory.", + exc_info=True, + ) + failed_blocks.append(block.name) + + if failed_blocks: + logger.error( + f"Failed to sync {len(failed_blocks)} block(s) to database: " + f"{', '.join(failed_blocks)}. These blocks are still available in memory." + ) + # Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281 def get_block(block_id: str) -> AnyBlockSchema | None: From 81e48c00a430e2bd924c52b493c37eda1296e6c0 Mon Sep 17 00:00:00 2001 From: Zamil Majdy Date: Tue, 3 Feb 2026 21:59:25 +0700 Subject: [PATCH 24/31] feat(copilot): add customize_agent tool for marketplace templates (#11943) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Summary Adds a new copilot tool that allows users to customize marketplace/template agents using natural language before adding them to their library. This exposes the Agent Generator's `/api/template-modification` endpoint to the copilot, which was previously not available. ## Changes - **service.py**: Add `customize_template_external` to call Agent Generator's template modification endpoint - **core.py**: - Add `customize_template` wrapper function - Extract `graph_to_json` as a reusable function (was previously inline in `get_agent_as_json`) - **customize_agent.py**: New tool that: - Takes marketplace agent ID (format: `creator/slug`) - Fetches template from store via `store_db.get_agent()` - Calls Agent Generator for customization - Handles clarifying questions from the generator - Saves customized agent to user's library - **__init__.py**: Register the tool in `TOOL_REGISTRY` for auto-discovery ## Usage Flow 1. User searches marketplace: *"Find me a newsletter agent"* 2. Copilot calls `find_agent` ā†’ returns `autogpt/newsletter-writer` 3. User: *"Customize that agent to post to Discord instead of email"* 4. Copilot calls: ``` customize_agent( agent_id="autogpt/newsletter-writer", modifications="Post to Discord instead of sending email" ) ``` 5. Agent Generator may ask clarifying questions (e.g., "What Discord channel?") 6. Customized agent is saved to user's library ## Test plan - [x] Verified tool imports correctly - [x] Verified tool is registered in `TOOL_REGISTRY` - [x] Verified OpenAI function schema is valid - [x] Ran existing tests (`pytest backend/api/features/chat/tools/`) - all pass - [x] Type checker (`pyright`) passes with 0 errors - [ ] Manual testing with copilot (requires Agent Generator service) --- .../api/features/chat/tools/__init__.py | 2 + .../chat/tools/agent_generator/__init__.py | 4 + .../chat/tools/agent_generator/core.py | 89 +++-- .../chat/tools/agent_generator/service.py | 71 ++++ .../features/chat/tools/customize_agent.py | 337 ++++++++++++++++++ 5 files changed, 482 insertions(+), 21 deletions(-) create mode 100644 autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/__init__.py b/autogpt_platform/backend/backend/api/features/chat/tools/__init__.py index d078860c3a..dcbc35ef37 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/__init__.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/__init__.py @@ -10,6 +10,7 @@ from .add_understanding import AddUnderstandingTool from .agent_output import AgentOutputTool from .base import BaseTool from .create_agent import CreateAgentTool +from .customize_agent import CustomizeAgentTool from .edit_agent import EditAgentTool from .find_agent import FindAgentTool from .find_block import FindBlockTool @@ -34,6 +35,7 @@ logger = logging.getLogger(__name__) TOOL_REGISTRY: dict[str, BaseTool] = { "add_understanding": AddUnderstandingTool(), "create_agent": CreateAgentTool(), + "customize_agent": CustomizeAgentTool(), "edit_agent": EditAgentTool(), "find_agent": FindAgentTool(), "find_block": FindBlockTool(), diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py index b7650b3cbd..4266834220 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/__init__.py @@ -8,6 +8,7 @@ from .core import ( DecompositionStep, LibraryAgentSummary, MarketplaceAgentSummary, + customize_template, decompose_goal, enrich_library_agents_from_steps, extract_search_terms_from_steps, @@ -19,6 +20,7 @@ from .core import ( get_library_agent_by_graph_id, get_library_agent_by_id, get_library_agents_for_generation, + graph_to_json, json_to_graph, save_agent_to_library, search_marketplace_agents_for_generation, @@ -36,6 +38,7 @@ __all__ = [ "LibraryAgentSummary", "MarketplaceAgentSummary", "check_external_service_health", + "customize_template", "decompose_goal", "enrich_library_agents_from_steps", "extract_search_terms_from_steps", @@ -48,6 +51,7 @@ __all__ = [ "get_library_agent_by_id", "get_library_agents_for_generation", "get_user_message_for_error", + "graph_to_json", "is_external_service_configured", "json_to_graph", "save_agent_to_library", diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py index 0ddd2aa86b..5b40091bbb 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py @@ -19,6 +19,7 @@ from backend.data.graph import ( from backend.util.exceptions import DatabaseError, NotFoundError from .service import ( + customize_template_external, decompose_goal_external, generate_agent_external, generate_agent_patch_external, @@ -740,32 +741,15 @@ async def save_agent_to_library( return created_graph, library_agents[0] -async def get_agent_as_json( - agent_id: str, user_id: str | None -) -> dict[str, Any] | None: - """Fetch an agent and convert to JSON format for editing. +def graph_to_json(graph: Graph) -> dict[str, Any]: + """Convert a Graph object to JSON format for the agent generator. Args: - agent_id: Graph ID or library agent ID - user_id: User ID + graph: Graph object to convert Returns: - Agent as JSON dict or None if not found + Agent as JSON dict """ - graph = await get_graph(agent_id, version=None, user_id=user_id) - - if not graph and user_id: - try: - library_agent = await library_db.get_library_agent(agent_id, user_id) - graph = await get_graph( - library_agent.graph_id, version=None, user_id=user_id - ) - except NotFoundError: - pass - - if not graph: - return None - nodes = [] for node in graph.nodes: nodes.append( @@ -802,6 +786,35 @@ async def get_agent_as_json( } +async def get_agent_as_json( + agent_id: str, user_id: str | None +) -> dict[str, Any] | None: + """Fetch an agent and convert to JSON format for editing. + + Args: + agent_id: Graph ID or library agent ID + user_id: User ID + + Returns: + Agent as JSON dict or None if not found + """ + graph = await get_graph(agent_id, version=None, user_id=user_id) + + if not graph and user_id: + try: + library_agent = await library_db.get_library_agent(agent_id, user_id) + graph = await get_graph( + library_agent.graph_id, version=None, user_id=user_id + ) + except NotFoundError: + pass + + if not graph: + return None + + return graph_to_json(graph) + + async def generate_agent_patch( update_request: str, current_agent: dict[str, Any], @@ -831,3 +844,37 @@ async def generate_agent_patch( return await generate_agent_patch_external( update_request, current_agent, _to_dict_list(library_agents) ) + + +async def customize_template( + template_agent: dict[str, Any], + modification_request: str, + context: str = "", +) -> dict[str, Any] | None: + """Customize a template/marketplace agent using natural language. + + This is used when users want to modify a template or marketplace agent + to fit their specific needs before adding it to their library. + + The external Agent Generator service handles: + - Understanding the modification request + - Applying changes to the template + - Fixing and validating the result + + Args: + template_agent: The template agent JSON to customize + modification_request: Natural language description of customizations + context: Additional context (e.g., answers to previous questions) + + Returns: + Customized agent JSON, clarifying questions dict {"type": "clarifying_questions", ...}, + error dict {"type": "error", ...}, or None on unexpected error + + Raises: + AgentGeneratorNotConfiguredError: If the external service is not configured. + """ + _check_service_configured() + logger.info("Calling external Agent Generator service for customize_template") + return await customize_template_external( + template_agent, modification_request, context + ) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py index c9c960d1ae..780247a776 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py @@ -326,6 +326,77 @@ async def generate_agent_patch_external( return _create_error_response(error_msg, "unexpected_error") +async def customize_template_external( + template_agent: dict[str, Any], + modification_request: str, + context: str = "", +) -> dict[str, Any] | None: + """Call the external service to customize a template/marketplace agent. + + Args: + template_agent: The template agent JSON to customize + modification_request: Natural language description of customizations + context: Additional context (e.g., answers to previous questions) + + Returns: + Customized agent JSON, clarifying questions dict, or error dict on error + """ + client = _get_client() + + request = modification_request + if context: + request = f"{modification_request}\n\nAdditional context from user:\n{context}" + + payload: dict[str, Any] = { + "template_agent_json": template_agent, + "modification_request": request, + } + + try: + response = await client.post("/api/template-modification", json=payload) + response.raise_for_status() + data = response.json() + + if not data.get("success"): + error_msg = data.get("error", "Unknown error from Agent Generator") + error_type = data.get("error_type", "unknown") + logger.error( + f"Agent Generator template customization failed: {error_msg} " + f"(type: {error_type})" + ) + return _create_error_response(error_msg, error_type) + + # Check if it's clarifying questions + if data.get("type") == "clarifying_questions": + return { + "type": "clarifying_questions", + "questions": data.get("questions", []), + } + + # Check if it's an error passed through + if data.get("type") == "error": + return _create_error_response( + data.get("error", "Unknown error"), + data.get("error_type", "unknown"), + ) + + # Otherwise return the customized agent JSON + return data.get("agent_json") + + except httpx.HTTPStatusError as e: + error_type, error_msg = _classify_http_error(e) + logger.error(error_msg) + return _create_error_response(error_msg, error_type) + except httpx.RequestError as e: + error_type, error_msg = _classify_request_error(e) + logger.error(error_msg) + return _create_error_response(error_msg, error_type) + except Exception as e: + error_msg = f"Unexpected error calling Agent Generator: {e}" + logger.error(error_msg) + return _create_error_response(error_msg, "unexpected_error") + + async def get_blocks_external() -> list[dict[str, Any]] | None: """Get available blocks from the external service. diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py b/autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py new file mode 100644 index 0000000000..c0568bd936 --- /dev/null +++ b/autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py @@ -0,0 +1,337 @@ +"""CustomizeAgentTool - Customizes marketplace/template agents using natural language.""" + +import logging +from typing import Any + +from backend.api.features.chat.model import ChatSession +from backend.api.features.store import db as store_db +from backend.api.features.store.exceptions import AgentNotFoundError + +from .agent_generator import ( + AgentGeneratorNotConfiguredError, + customize_template, + get_user_message_for_error, + graph_to_json, + save_agent_to_library, +) +from .base import BaseTool +from .models import ( + AgentPreviewResponse, + AgentSavedResponse, + ClarificationNeededResponse, + ClarifyingQuestion, + ErrorResponse, + ToolResponseBase, +) + +logger = logging.getLogger(__name__) + + +class CustomizeAgentTool(BaseTool): + """Tool for customizing marketplace/template agents using natural language.""" + + @property + def name(self) -> str: + return "customize_agent" + + @property + def description(self) -> str: + return ( + "Customize a marketplace or template agent using natural language. " + "Takes an existing agent from the marketplace and modifies it based on " + "the user's requirements before adding to their library." + ) + + @property + def requires_auth(self) -> bool: + return True + + @property + def is_long_running(self) -> bool: + return True + + @property + def parameters(self) -> dict[str, Any]: + return { + "type": "object", + "properties": { + "agent_id": { + "type": "string", + "description": ( + "The marketplace agent ID in format 'creator/slug' " + "(e.g., 'autogpt/newsletter-writer'). " + "Get this from find_agent results." + ), + }, + "modifications": { + "type": "string", + "description": ( + "Natural language description of how to customize the agent. " + "Be specific about what changes you want to make." + ), + }, + "context": { + "type": "string", + "description": ( + "Additional context or answers to previous clarifying questions." + ), + }, + "save": { + "type": "boolean", + "description": ( + "Whether to save the customized agent to the user's library. " + "Default is true. Set to false for preview only." + ), + "default": True, + }, + }, + "required": ["agent_id", "modifications"], + } + + async def _execute( + self, + user_id: str | None, + session: ChatSession, + **kwargs, + ) -> ToolResponseBase: + """Execute the customize_agent tool. + + Flow: + 1. Parse the agent ID to get creator/slug + 2. Fetch the template agent from the marketplace + 3. Call customize_template with the modification request + 4. Preview or save based on the save parameter + """ + agent_id = kwargs.get("agent_id", "").strip() + modifications = kwargs.get("modifications", "").strip() + context = kwargs.get("context", "") + save = kwargs.get("save", True) + session_id = session.session_id if session else None + + if not agent_id: + return ErrorResponse( + message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').", + error="missing_agent_id", + session_id=session_id, + ) + + if not modifications: + return ErrorResponse( + message="Please describe how you want to customize this agent.", + error="missing_modifications", + session_id=session_id, + ) + + # Parse agent_id in format "creator/slug" + parts = [p.strip() for p in agent_id.split("/")] + if len(parts) != 2 or not parts[0] or not parts[1]: + return ErrorResponse( + message=( + f"Invalid agent ID format: '{agent_id}'. " + "Expected format is 'creator/agent-name' " + "(e.g., 'autogpt/newsletter-writer')." + ), + error="invalid_agent_id_format", + session_id=session_id, + ) + + creator_username, agent_slug = parts + + # Fetch the marketplace agent details + try: + agent_details = await store_db.get_store_agent_details( + username=creator_username, agent_name=agent_slug + ) + except AgentNotFoundError: + return ErrorResponse( + message=( + f"Could not find marketplace agent '{agent_id}'. " + "Please check the agent ID and try again." + ), + error="agent_not_found", + session_id=session_id, + ) + except Exception as e: + logger.error(f"Error fetching marketplace agent {agent_id}: {e}") + return ErrorResponse( + message="Failed to fetch the marketplace agent. Please try again.", + error="fetch_error", + session_id=session_id, + ) + + if not agent_details.store_listing_version_id: + return ErrorResponse( + message=( + f"The agent '{agent_id}' does not have an available version. " + "Please try a different agent." + ), + error="no_version_available", + session_id=session_id, + ) + + # Get the full agent graph + try: + graph = await store_db.get_agent(agent_details.store_listing_version_id) + template_agent = graph_to_json(graph) + except Exception as e: + logger.error(f"Error fetching agent graph for {agent_id}: {e}") + return ErrorResponse( + message="Failed to fetch the agent configuration. Please try again.", + error="graph_fetch_error", + session_id=session_id, + ) + + # Call customize_template + try: + result = await customize_template( + template_agent=template_agent, + modification_request=modifications, + context=context, + ) + except AgentGeneratorNotConfiguredError: + return ErrorResponse( + message=( + "Agent customization is not available. " + "The Agent Generator service is not configured." + ), + error="service_not_configured", + session_id=session_id, + ) + except Exception as e: + logger.error(f"Error calling customize_template for {agent_id}: {e}") + return ErrorResponse( + message=( + "Failed to customize the agent due to a service error. " + "Please try again." + ), + error="customization_service_error", + session_id=session_id, + ) + + if result is None: + return ErrorResponse( + message=( + "Failed to customize the agent. " + "The agent generation service may be unavailable or timed out. " + "Please try again." + ), + error="customization_failed", + session_id=session_id, + ) + + # Handle error response + if isinstance(result, dict) and result.get("type") == "error": + error_msg = result.get("error", "Unknown error") + error_type = result.get("error_type", "unknown") + user_message = get_user_message_for_error( + error_type, + operation="customize the agent", + llm_parse_message=( + "The AI had trouble customizing the agent. " + "Please try again or simplify your request." + ), + validation_message=( + "The customized agent failed validation. " + "Please try rephrasing your request." + ), + error_details=error_msg, + ) + return ErrorResponse( + message=user_message, + error=f"customization_failed:{error_type}", + session_id=session_id, + ) + + # Handle clarifying questions + if isinstance(result, dict) and result.get("type") == "clarifying_questions": + questions = result.get("questions") or [] + if not isinstance(questions, list): + logger.error( + f"Unexpected clarifying questions format: {type(questions)}" + ) + questions = [] + return ClarificationNeededResponse( + message=( + "I need some more information to customize this agent. " + "Please answer the following questions:" + ), + questions=[ + ClarifyingQuestion( + question=q.get("question", ""), + keyword=q.get("keyword", ""), + example=q.get("example"), + ) + for q in questions + if isinstance(q, dict) + ], + session_id=session_id, + ) + + # Result should be the customized agent JSON + if not isinstance(result, dict): + logger.error(f"Unexpected customize_template response type: {type(result)}") + return ErrorResponse( + message="Failed to customize the agent due to an unexpected response.", + error="unexpected_response_type", + session_id=session_id, + ) + + customized_agent = result + + agent_name = customized_agent.get( + "name", f"Customized {agent_details.agent_name}" + ) + agent_description = customized_agent.get("description", "") + nodes = customized_agent.get("nodes") + links = customized_agent.get("links") + node_count = len(nodes) if isinstance(nodes, list) else 0 + link_count = len(links) if isinstance(links, list) else 0 + + if not save: + return AgentPreviewResponse( + message=( + f"I've customized the agent '{agent_details.agent_name}'. " + f"The customized agent has {node_count} blocks. " + f"Review it and call customize_agent with save=true to save it." + ), + agent_json=customized_agent, + agent_name=agent_name, + description=agent_description, + node_count=node_count, + link_count=link_count, + session_id=session_id, + ) + + if not user_id: + return ErrorResponse( + message="You must be logged in to save agents.", + error="auth_required", + session_id=session_id, + ) + + # Save to user's library + try: + created_graph, library_agent = await save_agent_to_library( + customized_agent, user_id, is_update=False + ) + + return AgentSavedResponse( + message=( + f"Customized agent '{created_graph.name}' " + f"(based on '{agent_details.agent_name}') " + f"has been saved to your library!" + ), + agent_id=created_graph.id, + agent_name=created_graph.name, + library_agent_id=library_agent.id, + library_agent_link=f"/library/agents/{library_agent.id}", + agent_page_link=f"/build?flowID={created_graph.id}", + session_id=session_id, + ) + except Exception as e: + logger.error(f"Error saving customized agent: {e}") + return ErrorResponse( + message="Failed to save the customized agent. Please try again.", + error="save_failed", + session_id=session_id, + ) From f4bf492f24da4790dbf9f9df78061144d34034ac Mon Sep 17 00:00:00 2001 From: Swifty Date: Tue, 3 Feb 2026 16:52:06 +0100 Subject: [PATCH 25/31] feat(platform): Add Redis-based SSE reconnection for long-running CoPilot operations (#11877) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Changes šŸ—ļø Adds Redis-based SSE reconnection support for long-running CoPilot operations (like Agent Generator), enabling clients to reconnect and resume receiving updates after disconnection. ### What this does: - **Stream Registry** - Redis-backed task tracking with message persistence via Redis Streams - **SSE Reconnection** - Clients can reconnect to active tasks using `task_id` and `last_message_id` - **Duplicate Message Fix** - Filters out in-progress assistant messages from session response when active stream exists - **Completion Consumer** - Handles background task completion notifications via Redis Streams ### Architecture: ``` 1. User sends message ā†’ Backend creates task in Redis 2. SSE chunks written to Redis Stream for persistence 3. Client receives chunks via SSE subscription 4. If client disconnects ā†’ Task continues in background 5. Client reconnects ā†’ GET /sessions/{id} returns active_stream info 6. Client subscribes to /tasks/{task_id}/stream with last_message_id 7. Missed messages replayed from Redis Stream ``` ### Key endpoints: - `GET /sessions/{session_id}` - Returns `active_stream` info if task is running - `GET /tasks/{task_id}/stream?last_message_id=X` - SSE endpoint for reconnection - `GET /tasks/{task_id}` - Get task status - `POST /operations/{op_id}/complete` - Webhook for external service completion ### Duplicate message fix: When `GET /sessions/{id}` detects an active stream: 1. Filters out the in-progress assistant message from response 2. Returns `last_message_id="0-0"` so client replays stream from beginning 3. Client receives complete response only through SSE (single source of truth) ### Frontend changes: - Task persistence in localStorage for cross-tab reconnection - Stream event dispatcher handles reconnection flow - Deduplication logic prevents duplicate messages ### Testing: - Manual testing of reconnection scenarios - Verified duplicate message fix works correctly ## Related - Resolves SSE timeout issues for Agent Generator - Fixes duplicate message bug on reconnection --- .../api/features/chat/completion_consumer.py | 368 +++++++++ .../api/features/chat/completion_handler.py | 344 +++++++++ .../backend/api/features/chat/config.py | 50 ++ .../api/features/chat/response_model.py | 4 + .../backend/api/features/chat/routes.py | 427 ++++++++++- .../backend/api/features/chat/service.py | 291 +++++++- .../api/features/chat/stream_registry.py | 704 ++++++++++++++++++ .../chat/tools/agent_generator/core.py | 27 +- .../chat/tools/agent_generator/service.py | 46 +- .../api/features/chat/tools/create_agent.py | 25 +- .../api/features/chat/tools/edit_agent.py | 25 +- .../backend/api/features/chat/tools/models.py | 21 + .../api/features/store/embeddings_e2e_test.py | 9 +- .../backend/backend/api/rest_api.py | 16 + .../agent_generator/test_core_integration.py | 9 +- .../CopilotShell/useCopilotShell.ts | 73 +- .../api/chat/tasks/[taskId]/stream/route.ts | 81 ++ .../frontend/src/app/api/openapi.json | 210 +++++- .../src/components/contextual/Chat/Chat.tsx | 36 +- .../contextual/Chat/SSE_RECONNECTION.md | 159 ++++ .../contextual/Chat/chat-constants.ts | 16 + .../components/contextual/Chat/chat-store.ts | 348 +++++++-- .../components/contextual/Chat/chat-types.ts | 71 +- .../ChatContainer/ChatContainer.tsx | 9 + .../createStreamEventDispatcher.ts | 27 +- .../Chat/components/ChatContainer/handlers.ts | 154 +++- .../Chat/components/ChatContainer/helpers.ts | 1 + .../ChatContainer/useChatContainer.ts | 343 +++++++-- .../components/ChatMessage/useChatMessage.ts | 1 + .../components/MessageList/MessageList.tsx | 5 - .../contextual/Chat/stream-executor.ts | 189 ++++- .../contextual/Chat/stream-utils.ts | 5 + 32 files changed, 3747 insertions(+), 347 deletions(-) create mode 100644 autogpt_platform/backend/backend/api/features/chat/completion_consumer.py create mode 100644 autogpt_platform/backend/backend/api/features/chat/completion_handler.py create mode 100644 autogpt_platform/backend/backend/api/features/chat/stream_registry.py create mode 100644 autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts create mode 100644 autogpt_platform/frontend/src/components/contextual/Chat/SSE_RECONNECTION.md create mode 100644 autogpt_platform/frontend/src/components/contextual/Chat/chat-constants.ts diff --git a/autogpt_platform/backend/backend/api/features/chat/completion_consumer.py b/autogpt_platform/backend/backend/api/features/chat/completion_consumer.py new file mode 100644 index 0000000000..f447d46bd7 --- /dev/null +++ b/autogpt_platform/backend/backend/api/features/chat/completion_consumer.py @@ -0,0 +1,368 @@ +"""Redis Streams consumer for operation completion messages. + +This module provides a consumer (ChatCompletionConsumer) that listens for +completion notifications (OperationCompleteMessage) from external services +(like Agent Generator) and triggers the appropriate stream registry and +chat service updates via process_operation_success/process_operation_failure. + +Why Redis Streams instead of RabbitMQ? +-------------------------------------- +While the project typically uses RabbitMQ for async task queues (e.g., execution +queue), Redis Streams was chosen for chat completion notifications because: + +1. **Unified Infrastructure**: The SSE reconnection feature already uses Redis + Streams (via stream_registry) for message persistence and replay. Using Redis + Streams for completion notifications keeps all chat streaming infrastructure + in one system, simplifying operations and reducing cross-system coordination. + +2. **Message Replay**: Redis Streams support XREAD with arbitrary message IDs, + allowing consumers to replay missed messages after reconnection. This aligns + with the SSE reconnection pattern where clients can resume from last_message_id. + +3. **Consumer Groups with XAUTOCLAIM**: Redis consumer groups provide automatic + load balancing across pods with explicit message claiming (XAUTOCLAIM) for + recovering from dead consumers - ideal for the completion callback pattern. + +4. **Lower Latency**: For real-time SSE updates, Redis (already in-memory for + stream_registry) provides lower latency than an additional RabbitMQ hop. + +5. **Atomicity with Task State**: Completion processing often needs to update + task metadata stored in Redis. Keeping both in Redis enables simpler + transactional semantics without distributed coordination. + +The consumer uses Redis Streams with consumer groups for reliable message +processing across multiple platform pods, with XAUTOCLAIM for reclaiming +stale pending messages from dead consumers. +""" + +import asyncio +import logging +import os +import uuid +from typing import Any + +import orjson +from prisma import Prisma +from pydantic import BaseModel +from redis.exceptions import ResponseError + +from backend.data.redis_client import get_redis_async + +from . import stream_registry +from .completion_handler import process_operation_failure, process_operation_success +from .config import ChatConfig + +logger = logging.getLogger(__name__) +config = ChatConfig() + + +class OperationCompleteMessage(BaseModel): + """Message format for operation completion notifications.""" + + operation_id: str + task_id: str + success: bool + result: dict | str | None = None + error: str | None = None + + +class ChatCompletionConsumer: + """Consumer for chat operation completion messages from Redis Streams. + + This consumer initializes its own Prisma client in start() to ensure + database operations work correctly within this async context. + + Uses Redis consumer groups to allow multiple platform pods to consume + messages reliably with automatic redelivery on failure. + """ + + def __init__(self): + self._consumer_task: asyncio.Task | None = None + self._running = False + self._prisma: Prisma | None = None + self._consumer_name = f"consumer-{uuid.uuid4().hex[:8]}" + + async def start(self) -> None: + """Start the completion consumer.""" + if self._running: + logger.warning("Completion consumer already running") + return + + # Create consumer group if it doesn't exist + try: + redis = await get_redis_async() + await redis.xgroup_create( + config.stream_completion_name, + config.stream_consumer_group, + id="0", + mkstream=True, + ) + logger.info( + f"Created consumer group '{config.stream_consumer_group}' " + f"on stream '{config.stream_completion_name}'" + ) + except ResponseError as e: + if "BUSYGROUP" in str(e): + logger.debug( + f"Consumer group '{config.stream_consumer_group}' already exists" + ) + else: + raise + + self._running = True + self._consumer_task = asyncio.create_task(self._consume_messages()) + logger.info( + f"Chat completion consumer started (consumer: {self._consumer_name})" + ) + + async def _ensure_prisma(self) -> Prisma: + """Lazily initialize Prisma client on first use.""" + if self._prisma is None: + database_url = os.getenv("DATABASE_URL", "postgresql://localhost:5432") + self._prisma = Prisma(datasource={"url": database_url}) + await self._prisma.connect() + logger.info("[COMPLETION] Consumer Prisma client connected (lazy init)") + return self._prisma + + async def stop(self) -> None: + """Stop the completion consumer.""" + self._running = False + + if self._consumer_task: + self._consumer_task.cancel() + try: + await self._consumer_task + except asyncio.CancelledError: + pass + self._consumer_task = None + + if self._prisma: + await self._prisma.disconnect() + self._prisma = None + logger.info("[COMPLETION] Consumer Prisma client disconnected") + + logger.info("Chat completion consumer stopped") + + async def _consume_messages(self) -> None: + """Main message consumption loop with retry logic.""" + max_retries = 10 + retry_delay = 5 # seconds + retry_count = 0 + block_timeout = 5000 # milliseconds + + while self._running and retry_count < max_retries: + try: + redis = await get_redis_async() + + # Reset retry count on successful connection + retry_count = 0 + + while self._running: + # First, claim any stale pending messages from dead consumers + # Redis does NOT auto-redeliver pending messages; we must explicitly + # claim them using XAUTOCLAIM + try: + claimed_result = await redis.xautoclaim( + name=config.stream_completion_name, + groupname=config.stream_consumer_group, + consumername=self._consumer_name, + min_idle_time=config.stream_claim_min_idle_ms, + start_id="0-0", + count=10, + ) + # xautoclaim returns: (next_start_id, [(id, data), ...], [deleted_ids]) + if claimed_result and len(claimed_result) >= 2: + claimed_entries = claimed_result[1] + if claimed_entries: + logger.info( + f"Claimed {len(claimed_entries)} stale pending messages" + ) + for entry_id, data in claimed_entries: + if not self._running: + return + await self._process_entry(redis, entry_id, data) + except Exception as e: + logger.warning(f"XAUTOCLAIM failed (non-fatal): {e}") + + # Read new messages from the stream + messages = await redis.xreadgroup( + groupname=config.stream_consumer_group, + consumername=self._consumer_name, + streams={config.stream_completion_name: ">"}, + block=block_timeout, + count=10, + ) + + if not messages: + continue + + for stream_name, entries in messages: + for entry_id, data in entries: + if not self._running: + return + await self._process_entry(redis, entry_id, data) + + except asyncio.CancelledError: + logger.info("Consumer cancelled") + return + except Exception as e: + retry_count += 1 + logger.error( + f"Consumer error (retry {retry_count}/{max_retries}): {e}", + exc_info=True, + ) + if self._running and retry_count < max_retries: + await asyncio.sleep(retry_delay) + else: + logger.error("Max retries reached, stopping consumer") + return + + async def _process_entry( + self, redis: Any, entry_id: str, data: dict[str, Any] + ) -> None: + """Process a single stream entry and acknowledge it on success. + + Args: + redis: Redis client connection + entry_id: The stream entry ID + data: The entry data dict + """ + try: + # Handle the message + message_data = data.get("data") + if message_data: + await self._handle_message( + message_data.encode() + if isinstance(message_data, str) + else message_data + ) + + # Acknowledge the message after successful processing + await redis.xack( + config.stream_completion_name, + config.stream_consumer_group, + entry_id, + ) + except Exception as e: + logger.error( + f"Error processing completion message {entry_id}: {e}", + exc_info=True, + ) + # Message remains in pending state and will be claimed by + # XAUTOCLAIM after min_idle_time expires + + async def _handle_message(self, body: bytes) -> None: + """Handle a completion message using our own Prisma client.""" + try: + data = orjson.loads(body) + message = OperationCompleteMessage(**data) + except Exception as e: + logger.error(f"Failed to parse completion message: {e}") + return + + logger.info( + f"[COMPLETION] Received completion for operation {message.operation_id} " + f"(task_id={message.task_id}, success={message.success})" + ) + + # Find task in registry + task = await stream_registry.find_task_by_operation_id(message.operation_id) + if task is None: + task = await stream_registry.get_task(message.task_id) + + if task is None: + logger.warning( + f"[COMPLETION] Task not found for operation {message.operation_id} " + f"(task_id={message.task_id})" + ) + return + + logger.info( + f"[COMPLETION] Found task: task_id={task.task_id}, " + f"session_id={task.session_id}, tool_call_id={task.tool_call_id}" + ) + + # Guard against empty task fields + if not task.task_id or not task.session_id or not task.tool_call_id: + logger.error( + f"[COMPLETION] Task has empty critical fields! " + f"task_id={task.task_id!r}, session_id={task.session_id!r}, " + f"tool_call_id={task.tool_call_id!r}" + ) + return + + if message.success: + await self._handle_success(task, message) + else: + await self._handle_failure(task, message) + + async def _handle_success( + self, + task: stream_registry.ActiveTask, + message: OperationCompleteMessage, + ) -> None: + """Handle successful operation completion.""" + prisma = await self._ensure_prisma() + await process_operation_success(task, message.result, prisma) + + async def _handle_failure( + self, + task: stream_registry.ActiveTask, + message: OperationCompleteMessage, + ) -> None: + """Handle failed operation completion.""" + prisma = await self._ensure_prisma() + await process_operation_failure(task, message.error, prisma) + + +# Module-level consumer instance +_consumer: ChatCompletionConsumer | None = None + + +async def start_completion_consumer() -> None: + """Start the global completion consumer.""" + global _consumer + if _consumer is None: + _consumer = ChatCompletionConsumer() + await _consumer.start() + + +async def stop_completion_consumer() -> None: + """Stop the global completion consumer.""" + global _consumer + if _consumer: + await _consumer.stop() + _consumer = None + + +async def publish_operation_complete( + operation_id: str, + task_id: str, + success: bool, + result: dict | str | None = None, + error: str | None = None, +) -> None: + """Publish an operation completion message to Redis Streams. + + Args: + operation_id: The operation ID that completed. + task_id: The task ID associated with the operation. + success: Whether the operation succeeded. + result: The result data (for success). + error: The error message (for failure). + """ + message = OperationCompleteMessage( + operation_id=operation_id, + task_id=task_id, + success=success, + result=result, + error=error, + ) + + redis = await get_redis_async() + await redis.xadd( + config.stream_completion_name, + {"data": message.model_dump_json()}, + maxlen=config.stream_max_length, + ) + logger.info(f"Published completion for operation {operation_id}") diff --git a/autogpt_platform/backend/backend/api/features/chat/completion_handler.py b/autogpt_platform/backend/backend/api/features/chat/completion_handler.py new file mode 100644 index 0000000000..905fa2ddba --- /dev/null +++ b/autogpt_platform/backend/backend/api/features/chat/completion_handler.py @@ -0,0 +1,344 @@ +"""Shared completion handling for operation success and failure. + +This module provides common logic for handling operation completion from both: +- The Redis Streams consumer (completion_consumer.py) +- The HTTP webhook endpoint (routes.py) +""" + +import logging +from typing import Any + +import orjson +from prisma import Prisma + +from . import service as chat_service +from . import stream_registry +from .response_model import StreamError, StreamToolOutputAvailable +from .tools.models import ErrorResponse + +logger = logging.getLogger(__name__) + +# Tools that produce agent_json that needs to be saved to library +AGENT_GENERATION_TOOLS = {"create_agent", "edit_agent"} + +# Keys that should be stripped from agent_json when returning in error responses +SENSITIVE_KEYS = frozenset( + { + "api_key", + "apikey", + "api_secret", + "password", + "secret", + "credentials", + "credential", + "token", + "access_token", + "refresh_token", + "private_key", + "privatekey", + "auth", + "authorization", + } +) + + +def _sanitize_agent_json(obj: Any) -> Any: + """Recursively sanitize agent_json by removing sensitive keys. + + Args: + obj: The object to sanitize (dict, list, or primitive) + + Returns: + Sanitized copy with sensitive keys removed/redacted + """ + if isinstance(obj, dict): + return { + k: "[REDACTED]" if k.lower() in SENSITIVE_KEYS else _sanitize_agent_json(v) + for k, v in obj.items() + } + elif isinstance(obj, list): + return [_sanitize_agent_json(item) for item in obj] + else: + return obj + + +class ToolMessageUpdateError(Exception): + """Raised when updating a tool message in the database fails.""" + + pass + + +async def _update_tool_message( + session_id: str, + tool_call_id: str, + content: str, + prisma_client: Prisma | None, +) -> None: + """Update tool message in database. + + Args: + session_id: The session ID + tool_call_id: The tool call ID to update + content: The new content for the message + prisma_client: Optional Prisma client. If None, uses chat_service. + + Raises: + ToolMessageUpdateError: If the database update fails. The caller should + handle this to avoid marking the task as completed with inconsistent state. + """ + try: + if prisma_client: + # Use provided Prisma client (for consumer with its own connection) + updated_count = await prisma_client.chatmessage.update_many( + where={ + "sessionId": session_id, + "toolCallId": tool_call_id, + }, + data={"content": content}, + ) + # Check if any rows were updated - 0 means message not found + if updated_count == 0: + raise ToolMessageUpdateError( + f"No message found with tool_call_id={tool_call_id} in session {session_id}" + ) + else: + # Use service function (for webhook endpoint) + await chat_service._update_pending_operation( + session_id=session_id, + tool_call_id=tool_call_id, + result=content, + ) + except ToolMessageUpdateError: + raise + except Exception as e: + logger.error(f"[COMPLETION] Failed to update tool message: {e}", exc_info=True) + raise ToolMessageUpdateError( + f"Failed to update tool message for tool_call_id={tool_call_id}: {e}" + ) from e + + +def serialize_result(result: dict | list | str | int | float | bool | None) -> str: + """Serialize result to JSON string with sensible defaults. + + Args: + result: The result to serialize. Can be a dict, list, string, + number, boolean, or None. + + Returns: + JSON string representation of the result. Returns '{"status": "completed"}' + only when result is explicitly None. + """ + if isinstance(result, str): + return result + if result is None: + return '{"status": "completed"}' + return orjson.dumps(result).decode("utf-8") + + +async def _save_agent_from_result( + result: dict[str, Any], + user_id: str | None, + tool_name: str, +) -> dict[str, Any]: + """Save agent to library if result contains agent_json. + + Args: + result: The result dict that may contain agent_json + user_id: The user ID to save the agent for + tool_name: The tool name (create_agent or edit_agent) + + Returns: + Updated result dict with saved agent details, or original result if no agent_json + """ + if not user_id: + logger.warning("[COMPLETION] Cannot save agent: no user_id in task") + return result + + agent_json = result.get("agent_json") + if not agent_json: + logger.warning( + f"[COMPLETION] {tool_name} completed but no agent_json in result" + ) + return result + + try: + from .tools.agent_generator import save_agent_to_library + + is_update = tool_name == "edit_agent" + created_graph, library_agent = await save_agent_to_library( + agent_json, user_id, is_update=is_update + ) + + logger.info( + f"[COMPLETION] Saved agent '{created_graph.name}' to library " + f"(graph_id={created_graph.id}, library_agent_id={library_agent.id})" + ) + + # Return a response similar to AgentSavedResponse + return { + "type": "agent_saved", + "message": f"Agent '{created_graph.name}' has been saved to your library!", + "agent_id": created_graph.id, + "agent_name": created_graph.name, + "library_agent_id": library_agent.id, + "library_agent_link": f"/library/agents/{library_agent.id}", + "agent_page_link": f"/build?flowID={created_graph.id}", + } + except Exception as e: + logger.error( + f"[COMPLETION] Failed to save agent to library: {e}", + exc_info=True, + ) + # Return error but don't fail the whole operation + # Sanitize agent_json to remove sensitive keys before returning + return { + "type": "error", + "message": f"Agent was generated but failed to save: {str(e)}", + "error": str(e), + "agent_json": _sanitize_agent_json(agent_json), + } + + +async def process_operation_success( + task: stream_registry.ActiveTask, + result: dict | str | None, + prisma_client: Prisma | None = None, +) -> None: + """Handle successful operation completion. + + Publishes the result to the stream registry, updates the database, + generates LLM continuation, and marks the task as completed. + + Args: + task: The active task that completed + result: The result data from the operation + prisma_client: Optional Prisma client for database operations. + If None, uses chat_service._update_pending_operation instead. + + Raises: + ToolMessageUpdateError: If the database update fails. The task will be + marked as failed instead of completed to avoid inconsistent state. + """ + # For agent generation tools, save the agent to library + if task.tool_name in AGENT_GENERATION_TOOLS and isinstance(result, dict): + result = await _save_agent_from_result(result, task.user_id, task.tool_name) + + # Serialize result for output (only substitute default when result is exactly None) + result_output = result if result is not None else {"status": "completed"} + output_str = ( + result_output + if isinstance(result_output, str) + else orjson.dumps(result_output).decode("utf-8") + ) + + # Publish result to stream registry + await stream_registry.publish_chunk( + task.task_id, + StreamToolOutputAvailable( + toolCallId=task.tool_call_id, + toolName=task.tool_name, + output=output_str, + success=True, + ), + ) + + # Update pending operation in database + # If this fails, we must not continue to mark the task as completed + result_str = serialize_result(result) + try: + await _update_tool_message( + session_id=task.session_id, + tool_call_id=task.tool_call_id, + content=result_str, + prisma_client=prisma_client, + ) + except ToolMessageUpdateError: + # DB update failed - mark task as failed to avoid inconsistent state + logger.error( + f"[COMPLETION] DB update failed for task {task.task_id}, " + "marking as failed instead of completed" + ) + await stream_registry.publish_chunk( + task.task_id, + StreamError(errorText="Failed to save operation result to database"), + ) + await stream_registry.mark_task_completed(task.task_id, status="failed") + raise + + # Generate LLM continuation with streaming + try: + await chat_service._generate_llm_continuation_with_streaming( + session_id=task.session_id, + user_id=task.user_id, + task_id=task.task_id, + ) + except Exception as e: + logger.error( + f"[COMPLETION] Failed to generate LLM continuation: {e}", + exc_info=True, + ) + + # Mark task as completed and release Redis lock + await stream_registry.mark_task_completed(task.task_id, status="completed") + try: + await chat_service._mark_operation_completed(task.tool_call_id) + except Exception as e: + logger.error(f"[COMPLETION] Failed to mark operation completed: {e}") + + logger.info( + f"[COMPLETION] Successfully processed completion for task {task.task_id}" + ) + + +async def process_operation_failure( + task: stream_registry.ActiveTask, + error: str | None, + prisma_client: Prisma | None = None, +) -> None: + """Handle failed operation completion. + + Publishes the error to the stream registry, updates the database with + the error response, and marks the task as failed. + + Args: + task: The active task that failed + error: The error message from the operation + prisma_client: Optional Prisma client for database operations. + If None, uses chat_service._update_pending_operation instead. + """ + error_msg = error or "Operation failed" + + # Publish error to stream registry + await stream_registry.publish_chunk( + task.task_id, + StreamError(errorText=error_msg), + ) + + # Update pending operation with error + # If this fails, we still continue to mark the task as failed + error_response = ErrorResponse( + message=error_msg, + error=error, + ) + try: + await _update_tool_message( + session_id=task.session_id, + tool_call_id=task.tool_call_id, + content=error_response.model_dump_json(), + prisma_client=prisma_client, + ) + except ToolMessageUpdateError: + # DB update failed - log but continue with cleanup + logger.error( + f"[COMPLETION] DB update failed while processing failure for task {task.task_id}, " + "continuing with cleanup" + ) + + # Mark task as failed and release Redis lock + await stream_registry.mark_task_completed(task.task_id, status="failed") + try: + await chat_service._mark_operation_completed(task.tool_call_id) + except Exception as e: + logger.error(f"[COMPLETION] Failed to mark operation completed: {e}") + + logger.info(f"[COMPLETION] Processed failure for task {task.task_id}: {error_msg}") diff --git a/autogpt_platform/backend/backend/api/features/chat/config.py b/autogpt_platform/backend/backend/api/features/chat/config.py index dba7934877..2e8dbf5413 100644 --- a/autogpt_platform/backend/backend/api/features/chat/config.py +++ b/autogpt_platform/backend/backend/api/features/chat/config.py @@ -44,6 +44,48 @@ class ChatConfig(BaseSettings): description="TTL in seconds for long-running operation tracking in Redis (safety net if pod dies)", ) + # Stream registry configuration for SSE reconnection + stream_ttl: int = Field( + default=3600, + description="TTL in seconds for stream data in Redis (1 hour)", + ) + stream_max_length: int = Field( + default=10000, + description="Maximum number of messages to store per stream", + ) + + # Redis Streams configuration for completion consumer + stream_completion_name: str = Field( + default="chat:completions", + description="Redis Stream name for operation completions", + ) + stream_consumer_group: str = Field( + default="chat_consumers", + description="Consumer group name for completion stream", + ) + stream_claim_min_idle_ms: int = Field( + default=60000, + description="Minimum idle time in milliseconds before claiming pending messages from dead consumers", + ) + + # Redis key prefixes for stream registry + task_meta_prefix: str = Field( + default="chat:task:meta:", + description="Prefix for task metadata hash keys", + ) + task_stream_prefix: str = Field( + default="chat:stream:", + description="Prefix for task message stream keys", + ) + task_op_prefix: str = Field( + default="chat:task:op:", + description="Prefix for operation ID to task ID mapping keys", + ) + internal_api_key: str | None = Field( + default=None, + description="API key for internal webhook callbacks (env: CHAT_INTERNAL_API_KEY)", + ) + # Langfuse Prompt Management Configuration # Note: Langfuse credentials are in Settings().secrets (settings.py) langfuse_prompt_name: str = Field( @@ -82,6 +124,14 @@ class ChatConfig(BaseSettings): v = "https://openrouter.ai/api/v1" return v + @field_validator("internal_api_key", mode="before") + @classmethod + def get_internal_api_key(cls, v): + """Get internal API key from environment if not provided.""" + if v is None: + v = os.getenv("CHAT_INTERNAL_API_KEY") + return v + # Prompt paths for different contexts PROMPT_PATHS: dict[str, str] = { "default": "prompts/chat_system.md", diff --git a/autogpt_platform/backend/backend/api/features/chat/response_model.py b/autogpt_platform/backend/backend/api/features/chat/response_model.py index 53a8cf3a1f..f627a42fcc 100644 --- a/autogpt_platform/backend/backend/api/features/chat/response_model.py +++ b/autogpt_platform/backend/backend/api/features/chat/response_model.py @@ -52,6 +52,10 @@ class StreamStart(StreamBaseResponse): type: ResponseType = ResponseType.START messageId: str = Field(..., description="Unique message ID") + taskId: str | None = Field( + default=None, + description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream", + ) class StreamFinish(StreamBaseResponse): diff --git a/autogpt_platform/backend/backend/api/features/chat/routes.py b/autogpt_platform/backend/backend/api/features/chat/routes.py index cab51543b1..3e731d86ac 100644 --- a/autogpt_platform/backend/backend/api/features/chat/routes.py +++ b/autogpt_platform/backend/backend/api/features/chat/routes.py @@ -1,19 +1,23 @@ """Chat API routes for chat session management and streaming via SSE.""" import logging +import uuid as uuid_module from collections.abc import AsyncGenerator from typing import Annotated from autogpt_libs import auth -from fastapi import APIRouter, Depends, Query, Security +from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security from fastapi.responses import StreamingResponse from pydantic import BaseModel from backend.util.exceptions import NotFoundError from . import service as chat_service +from . import stream_registry +from .completion_handler import process_operation_failure, process_operation_success from .config import ChatConfig from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions +from .response_model import StreamFinish, StreamHeartbeat, StreamStart config = ChatConfig() @@ -55,6 +59,15 @@ class CreateSessionResponse(BaseModel): user_id: str | None +class ActiveStreamInfo(BaseModel): + """Information about an active stream for reconnection.""" + + task_id: str + last_message_id: str # Redis Stream message ID for resumption + operation_id: str # Operation ID for completion tracking + tool_name: str # Name of the tool being executed + + class SessionDetailResponse(BaseModel): """Response model providing complete details for a chat session, including messages.""" @@ -63,6 +76,7 @@ class SessionDetailResponse(BaseModel): updated_at: str user_id: str | None messages: list[dict] + active_stream: ActiveStreamInfo | None = None # Present if stream is still active class SessionSummaryResponse(BaseModel): @@ -81,6 +95,14 @@ class ListSessionsResponse(BaseModel): total: int +class OperationCompleteRequest(BaseModel): + """Request model for external completion webhook.""" + + success: bool + result: dict | str | None = None + error: str | None = None + + # ========== Routes ========== @@ -166,13 +188,14 @@ async def get_session( Retrieve the details of a specific chat session. Looks up a chat session by ID for the given user (if authenticated) and returns all session data including messages. + If there's an active stream for this session, returns the task_id for reconnection. Args: session_id: The unique identifier for the desired chat session. user_id: The optional authenticated user ID, or None for anonymous access. Returns: - SessionDetailResponse: Details for the requested session, or None if not found. + SessionDetailResponse: Details for the requested session, including active_stream info if applicable. """ session = await get_chat_session(session_id, user_id) @@ -180,11 +203,28 @@ async def get_session( raise NotFoundError(f"Session {session_id} not found.") messages = [message.model_dump() for message in session.messages] - logger.info( - f"Returning session {session_id}: " - f"message_count={len(messages)}, " - f"roles={[m.get('role') for m in messages]}" + + # Check if there's an active stream for this session + active_stream_info = None + active_task, last_message_id = await stream_registry.get_active_task_for_session( + session_id, user_id ) + if active_task: + # Filter out the in-progress assistant message from the session response. + # The client will receive the complete assistant response through the SSE + # stream replay instead, preventing duplicate content. + if messages and messages[-1].get("role") == "assistant": + messages = messages[:-1] + + # Use "0-0" as last_message_id to replay the stream from the beginning. + # Since we filtered out the cached assistant message, the client needs + # the full stream to reconstruct the response. + active_stream_info = ActiveStreamInfo( + task_id=active_task.task_id, + last_message_id="0-0", + operation_id=active_task.operation_id, + tool_name=active_task.tool_name, + ) return SessionDetailResponse( id=session.session_id, @@ -192,6 +232,7 @@ async def get_session( updated_at=session.updated_at.isoformat(), user_id=session.user_id or None, messages=messages, + active_stream=active_stream_info, ) @@ -211,49 +252,112 @@ async def stream_chat_post( - Tool call UI elements (if invoked) - Tool execution results + The AI generation runs in a background task that continues even if the client disconnects. + All chunks are written to Redis for reconnection support. If the client disconnects, + they can reconnect using GET /tasks/{task_id}/stream to resume from where they left off. + Args: session_id: The chat session identifier to associate with the streamed messages. request: Request body containing message, is_user_message, and optional context. user_id: Optional authenticated user ID. Returns: - StreamingResponse: SSE-formatted response chunks. + StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event + containing the task_id for reconnection. """ + import asyncio + session = await _validate_and_get_session(session_id, user_id) + # Create a task in the stream registry for reconnection support + task_id = str(uuid_module.uuid4()) + operation_id = str(uuid_module.uuid4()) + await stream_registry.create_task( + task_id=task_id, + session_id=session_id, + user_id=user_id, + tool_call_id="chat_stream", # Not a tool call, but needed for the model + tool_name="chat", + operation_id=operation_id, + ) + + # Background task that runs the AI generation independently of SSE connection + async def run_ai_generation(): + try: + # Emit a start event with task_id for reconnection + start_chunk = StreamStart(messageId=task_id, taskId=task_id) + await stream_registry.publish_chunk(task_id, start_chunk) + + async for chunk in chat_service.stream_chat_completion( + session_id, + request.message, + is_user_message=request.is_user_message, + user_id=user_id, + session=session, # Pass pre-fetched session to avoid double-fetch + context=request.context, + ): + # Write to Redis (subscribers will receive via XREAD) + await stream_registry.publish_chunk(task_id, chunk) + + # Mark task as completed + await stream_registry.mark_task_completed(task_id, "completed") + except Exception as e: + logger.error( + f"Error in background AI generation for session {session_id}: {e}" + ) + await stream_registry.mark_task_completed(task_id, "failed") + + # Start the AI generation in a background task + bg_task = asyncio.create_task(run_ai_generation()) + await stream_registry.set_task_asyncio_task(task_id, bg_task) + + # SSE endpoint that subscribes to the task's stream async def event_generator() -> AsyncGenerator[str, None]: - chunk_count = 0 - first_chunk_type: str | None = None - async for chunk in chat_service.stream_chat_completion( - session_id, - request.message, - is_user_message=request.is_user_message, - user_id=user_id, - session=session, # Pass pre-fetched session to avoid double-fetch - context=request.context, - ): - if chunk_count < 3: - logger.info( - "Chat stream chunk", - extra={ - "session_id": session_id, - "chunk_type": str(chunk.type), - }, - ) - if not first_chunk_type: - first_chunk_type = str(chunk.type) - chunk_count += 1 - yield chunk.to_sse() - logger.info( - "Chat stream completed", - extra={ - "session_id": session_id, - "chunk_count": chunk_count, - "first_chunk_type": first_chunk_type, - }, - ) - # AI SDK protocol termination - yield "data: [DONE]\n\n" + subscriber_queue = None + try: + # Subscribe to the task stream (this replays existing messages + live updates) + subscriber_queue = await stream_registry.subscribe_to_task( + task_id=task_id, + user_id=user_id, + last_message_id="0-0", # Get all messages from the beginning + ) + + if subscriber_queue is None: + yield StreamFinish().to_sse() + yield "data: [DONE]\n\n" + return + + # Read from the subscriber queue and yield to SSE + while True: + try: + chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0) + yield chunk.to_sse() + + # Check for finish signal + if isinstance(chunk, StreamFinish): + break + except asyncio.TimeoutError: + # Send heartbeat to keep connection alive + yield StreamHeartbeat().to_sse() + + except GeneratorExit: + pass # Client disconnected - background task continues + except Exception as e: + logger.error(f"Error in SSE stream for task {task_id}: {e}") + finally: + # Unsubscribe when client disconnects or stream ends to prevent resource leak + if subscriber_queue is not None: + try: + await stream_registry.unsubscribe_from_task( + task_id, subscriber_queue + ) + except Exception as unsub_err: + logger.error( + f"Error unsubscribing from task {task_id}: {unsub_err}", + exc_info=True, + ) + # AI SDK protocol termination - always yield even if unsubscribe fails + yield "data: [DONE]\n\n" return StreamingResponse( event_generator(), @@ -366,6 +470,251 @@ async def session_assign_user( return {"status": "ok"} +# ========== Task Streaming (SSE Reconnection) ========== + + +@router.get( + "/tasks/{task_id}/stream", +) +async def stream_task( + task_id: str, + user_id: str | None = Depends(auth.get_user_id), + last_message_id: str = Query( + default="0-0", + description="Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.", + ), +): + """ + Reconnect to a long-running task's SSE stream. + + When a long-running operation (like agent generation) starts, the client + receives a task_id. If the connection drops, the client can reconnect + using this endpoint to resume receiving updates. + + Args: + task_id: The task ID from the operation_started response. + user_id: Authenticated user ID for ownership validation. + last_message_id: Last Redis Stream message ID received ("0-0" for full replay). + + Returns: + StreamingResponse: SSE-formatted response chunks starting after last_message_id. + + Raises: + HTTPException: 404 if task not found, 410 if task expired, 403 if access denied. + """ + # Check task existence and expiry before subscribing + task, error_code = await stream_registry.get_task_with_expiry_info(task_id) + + if error_code == "TASK_EXPIRED": + raise HTTPException( + status_code=410, + detail={ + "code": "TASK_EXPIRED", + "message": "This operation has expired. Please try again.", + }, + ) + + if error_code == "TASK_NOT_FOUND": + raise HTTPException( + status_code=404, + detail={ + "code": "TASK_NOT_FOUND", + "message": f"Task {task_id} not found.", + }, + ) + + # Validate ownership if task has an owner + if task and task.user_id and user_id != task.user_id: + raise HTTPException( + status_code=403, + detail={ + "code": "ACCESS_DENIED", + "message": "You do not have access to this task.", + }, + ) + + # Get subscriber queue from stream registry + subscriber_queue = await stream_registry.subscribe_to_task( + task_id=task_id, + user_id=user_id, + last_message_id=last_message_id, + ) + + if subscriber_queue is None: + raise HTTPException( + status_code=404, + detail={ + "code": "TASK_NOT_FOUND", + "message": f"Task {task_id} not found or access denied.", + }, + ) + + async def event_generator() -> AsyncGenerator[str, None]: + import asyncio + + heartbeat_interval = 15.0 # Send heartbeat every 15 seconds + try: + while True: + try: + # Wait for next chunk with timeout for heartbeats + chunk = await asyncio.wait_for( + subscriber_queue.get(), timeout=heartbeat_interval + ) + yield chunk.to_sse() + + # Check for finish signal + if isinstance(chunk, StreamFinish): + break + except asyncio.TimeoutError: + # Send heartbeat to keep connection alive + yield StreamHeartbeat().to_sse() + except Exception as e: + logger.error(f"Error in task stream {task_id}: {e}", exc_info=True) + finally: + # Unsubscribe when client disconnects or stream ends + try: + await stream_registry.unsubscribe_from_task(task_id, subscriber_queue) + except Exception as unsub_err: + logger.error( + f"Error unsubscribing from task {task_id}: {unsub_err}", + exc_info=True, + ) + # AI SDK protocol termination - always yield even if unsubscribe fails + yield "data: [DONE]\n\n" + + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", + "x-vercel-ai-ui-message-stream": "v1", + }, + ) + + +@router.get( + "/tasks/{task_id}", +) +async def get_task_status( + task_id: str, + user_id: str | None = Depends(auth.get_user_id), +) -> dict: + """ + Get the status of a long-running task. + + Args: + task_id: The task ID to check. + user_id: Authenticated user ID for ownership validation. + + Returns: + dict: Task status including task_id, status, tool_name, and operation_id. + + Raises: + NotFoundError: If task_id is not found or user doesn't have access. + """ + task = await stream_registry.get_task(task_id) + + if task is None: + raise NotFoundError(f"Task {task_id} not found.") + + # Validate ownership - if task has an owner, requester must match + if task.user_id and user_id != task.user_id: + raise NotFoundError(f"Task {task_id} not found.") + + return { + "task_id": task.task_id, + "session_id": task.session_id, + "status": task.status, + "tool_name": task.tool_name, + "operation_id": task.operation_id, + "created_at": task.created_at.isoformat(), + } + + +# ========== External Completion Webhook ========== + + +@router.post( + "/operations/{operation_id}/complete", + status_code=200, +) +async def complete_operation( + operation_id: str, + request: OperationCompleteRequest, + x_api_key: str | None = Header(default=None), +) -> dict: + """ + External completion webhook for long-running operations. + + Called by Agent Generator (or other services) when an operation completes. + This triggers the stream registry to publish completion and continue LLM generation. + + Args: + operation_id: The operation ID to complete. + request: Completion payload with success status and result/error. + x_api_key: Internal API key for authentication. + + Returns: + dict: Status of the completion. + + Raises: + HTTPException: If API key is invalid or operation not found. + """ + # Validate internal API key - reject if not configured or invalid + if not config.internal_api_key: + logger.error( + "Operation complete webhook rejected: CHAT_INTERNAL_API_KEY not configured" + ) + raise HTTPException( + status_code=503, + detail="Webhook not available: internal API key not configured", + ) + if x_api_key != config.internal_api_key: + raise HTTPException(status_code=401, detail="Invalid API key") + + # Find task by operation_id + task = await stream_registry.find_task_by_operation_id(operation_id) + if task is None: + raise HTTPException( + status_code=404, + detail=f"Operation {operation_id} not found", + ) + + logger.info( + f"Received completion webhook for operation {operation_id} " + f"(task_id={task.task_id}, success={request.success})" + ) + + if request.success: + await process_operation_success(task, request.result) + else: + await process_operation_failure(task, request.error) + + return {"status": "ok", "task_id": task.task_id} + + +# ========== Configuration ========== + + +@router.get("/config/ttl", status_code=200) +async def get_ttl_config() -> dict: + """ + Get the stream TTL configuration. + + Returns the Time-To-Live settings for chat streams, which determines + how long clients can reconnect to an active stream. + + Returns: + dict: TTL configuration with seconds and milliseconds values. + """ + return { + "stream_ttl_seconds": config.stream_ttl, + "stream_ttl_ms": config.stream_ttl * 1000, + } + + # ========== Health Check ========== diff --git a/autogpt_platform/backend/backend/api/features/chat/service.py b/autogpt_platform/backend/backend/api/features/chat/service.py index 6336d1c5af..218575085b 100644 --- a/autogpt_platform/backend/backend/api/features/chat/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/service.py @@ -36,6 +36,7 @@ from backend.util.exceptions import NotFoundError from backend.util.settings import Settings from . import db as chat_db +from . import stream_registry from .config import ChatConfig from .model import ( ChatMessage, @@ -1184,8 +1185,9 @@ async def _yield_tool_call( ) return - # Generate operation ID + # Generate operation ID and task ID operation_id = str(uuid_module.uuid4()) + task_id = str(uuid_module.uuid4()) # Build a user-friendly message based on tool and arguments if tool_name == "create_agent": @@ -1228,6 +1230,16 @@ async def _yield_tool_call( # Wrap session save and task creation in try-except to release lock on failure try: + # Create task in stream registry for SSE reconnection support + await stream_registry.create_task( + task_id=task_id, + session_id=session.session_id, + user_id=session.user_id, + tool_call_id=tool_call_id, + tool_name=tool_name, + operation_id=operation_id, + ) + # Save assistant message with tool_call FIRST (required by LLM) assistant_message = ChatMessage( role="assistant", @@ -1249,23 +1261,27 @@ async def _yield_tool_call( session.messages.append(pending_message) await upsert_chat_session(session) logger.info( - f"Saved pending operation {operation_id} for tool {tool_name} " - f"in session {session.session_id}" + f"Saved pending operation {operation_id} (task_id={task_id}) " + f"for tool {tool_name} in session {session.session_id}" ) # Store task reference in module-level set to prevent GC before completion - task = asyncio.create_task( - _execute_long_running_tool( + bg_task = asyncio.create_task( + _execute_long_running_tool_with_streaming( tool_name=tool_name, parameters=arguments, tool_call_id=tool_call_id, operation_id=operation_id, + task_id=task_id, session_id=session.session_id, user_id=session.user_id, ) ) - _background_tasks.add(task) - task.add_done_callback(_background_tasks.discard) + _background_tasks.add(bg_task) + bg_task.add_done_callback(_background_tasks.discard) + + # Associate the asyncio task with the stream registry task + await stream_registry.set_task_asyncio_task(task_id, bg_task) except Exception as e: # Roll back appended messages to prevent data corruption on subsequent saves if ( @@ -1283,6 +1299,11 @@ async def _yield_tool_call( # Release the Redis lock since the background task won't be spawned await _mark_operation_completed(tool_call_id) + # Mark stream registry task as failed if it was created + try: + await stream_registry.mark_task_completed(task_id, status="failed") + except Exception: + pass logger.error( f"Failed to setup long-running tool {tool_name}: {e}", exc_info=True ) @@ -1296,6 +1317,7 @@ async def _yield_tool_call( message=started_msg, operation_id=operation_id, tool_name=tool_name, + task_id=task_id, # Include task_id for SSE reconnection ).model_dump_json(), success=True, ) @@ -1365,6 +1387,9 @@ async def _execute_long_running_tool( This function runs independently of the SSE connection, so the operation survives if the user closes their browser tab. + + NOTE: This is the legacy function without stream registry support. + Use _execute_long_running_tool_with_streaming for new implementations. """ try: # Load fresh session (not stale reference) @@ -1417,6 +1442,133 @@ async def _execute_long_running_tool( await _mark_operation_completed(tool_call_id) +async def _execute_long_running_tool_with_streaming( + tool_name: str, + parameters: dict[str, Any], + tool_call_id: str, + operation_id: str, + task_id: str, + session_id: str, + user_id: str | None, +) -> None: + """Execute a long-running tool with stream registry support for SSE reconnection. + + This function runs independently of the SSE connection, publishes progress + to the stream registry, and survives if the user closes their browser tab. + Clients can reconnect via GET /chat/tasks/{task_id}/stream to resume streaming. + + If the external service returns a 202 Accepted (async), this function exits + early and lets the Redis Streams completion consumer handle the rest. + """ + # Track whether we delegated to async processing - if so, the Redis Streams + # completion consumer (stream_registry / completion_consumer) will handle cleanup, not us + delegated_to_async = False + + try: + # Load fresh session (not stale reference) + session = await get_chat_session(session_id, user_id) + if not session: + logger.error(f"Session {session_id} not found for background tool") + await stream_registry.mark_task_completed(task_id, status="failed") + return + + # Pass operation_id and task_id to the tool for async processing + enriched_parameters = { + **parameters, + "_operation_id": operation_id, + "_task_id": task_id, + } + + # Execute the actual tool + result = await execute_tool( + tool_name=tool_name, + parameters=enriched_parameters, + tool_call_id=tool_call_id, + user_id=user_id, + session=session, + ) + + # Check if the tool result indicates async processing + # (e.g., Agent Generator returned 202 Accepted) + try: + if isinstance(result.output, dict): + result_data = result.output + elif result.output: + result_data = orjson.loads(result.output) + else: + result_data = {} + if result_data.get("status") == "accepted": + logger.info( + f"Tool {tool_name} delegated to async processing " + f"(operation_id={operation_id}, task_id={task_id}). " + f"Redis Streams completion consumer will handle the rest." + ) + # Don't publish result, don't continue with LLM, and don't cleanup + # The Redis Streams consumer (completion_consumer) will handle + # everything when the external service completes via webhook + delegated_to_async = True + return + except (orjson.JSONDecodeError, TypeError): + pass # Not JSON or not async - continue normally + + # Publish tool result to stream registry + await stream_registry.publish_chunk(task_id, result) + + # Update the pending message with result + result_str = ( + result.output + if isinstance(result.output, str) + else orjson.dumps(result.output).decode("utf-8") + ) + await _update_pending_operation( + session_id=session_id, + tool_call_id=tool_call_id, + result=result_str, + ) + + logger.info( + f"Background tool {tool_name} completed for session {session_id} " + f"(task_id={task_id})" + ) + + # Generate LLM continuation and stream chunks to registry + await _generate_llm_continuation_with_streaming( + session_id=session_id, + user_id=user_id, + task_id=task_id, + ) + + # Mark task as completed in stream registry + await stream_registry.mark_task_completed(task_id, status="completed") + + except Exception as e: + logger.error(f"Background tool {tool_name} failed: {e}", exc_info=True) + error_response = ErrorResponse( + message=f"Tool {tool_name} failed: {str(e)}", + ) + + # Publish error to stream registry followed by finish event + await stream_registry.publish_chunk( + task_id, + StreamError(errorText=str(e)), + ) + await stream_registry.publish_chunk(task_id, StreamFinish()) + + await _update_pending_operation( + session_id=session_id, + tool_call_id=tool_call_id, + result=error_response.model_dump_json(), + ) + + # Mark task as failed in stream registry + await stream_registry.mark_task_completed(task_id, status="failed") + finally: + # Only cleanup if we didn't delegate to async processing + # For async path, the Redis Streams completion consumer handles cleanup + if not delegated_to_async: + await _mark_operation_completed(tool_call_id) + + async def _update_pending_operation( session_id: str, tool_call_id: str, @@ -1597,3 +1749,128 @@ async def _generate_llm_continuation( except Exception as e: logger.error(f"Failed to generate LLM continuation: {e}", exc_info=True) + + +async def _generate_llm_continuation_with_streaming( + session_id: str, + user_id: str | None, + task_id: str, +) -> None: + """Generate an LLM response with streaming to the stream registry. + + This is called by background tasks to continue the conversation + after a tool result is saved. Chunks are published to the stream registry + so reconnecting clients can receive them. + """ + import uuid as uuid_module + + try: + # Load fresh session from DB (bypass cache to get the updated tool result) + await invalidate_session_cache(session_id) + session = await get_chat_session(session_id, user_id) + if not session: + logger.error(f"Session {session_id} not found for LLM continuation") + return + + # Build system prompt + system_prompt, _ = await _build_system_prompt(user_id) + + # Build messages in OpenAI format + messages = session.to_openai_messages() + if system_prompt: + from openai.types.chat import ChatCompletionSystemMessageParam + + system_message = ChatCompletionSystemMessageParam( + role="system", + content=system_prompt, + ) + messages = [system_message] + messages + + # Build extra_body for tracing + extra_body: dict[str, Any] = { + "posthogProperties": { + "environment": settings.config.app_env.value, + }, + } + if user_id: + extra_body["user"] = user_id[:128] + extra_body["posthogDistinctId"] = user_id + if session_id: + extra_body["session_id"] = session_id[:128] + + # Make streaming LLM call (no tools - just text response) + from typing import cast + + from openai.types.chat import ChatCompletionMessageParam + + # Generate unique IDs for AI SDK protocol + message_id = str(uuid_module.uuid4()) + text_block_id = str(uuid_module.uuid4()) + + # Publish start event + await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id)) + await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id)) + + # Stream the response + stream = await client.chat.completions.create( + model=config.model, + messages=cast(list[ChatCompletionMessageParam], messages), + extra_body=extra_body, + stream=True, + ) + + assistant_content = "" + async for chunk in stream: + if chunk.choices and chunk.choices[0].delta.content: + delta = chunk.choices[0].delta.content + assistant_content += delta + # Publish delta to stream registry + await stream_registry.publish_chunk( + task_id, + StreamTextDelta(id=text_block_id, delta=delta), + ) + + # Publish end events + await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id)) + + if assistant_content: + # Reload session from DB to avoid race condition with user messages + fresh_session = await get_chat_session(session_id, user_id) + if not fresh_session: + logger.error( + f"Session {session_id} disappeared during LLM continuation" + ) + return + + # Save assistant message to database + assistant_message = ChatMessage( + role="assistant", + content=assistant_content, + ) + fresh_session.messages.append(assistant_message) + + # Save to database (not cache) to persist the response + await upsert_chat_session(fresh_session) + + # Invalidate cache so next poll/refresh gets fresh data + await invalidate_session_cache(session_id) + + logger.info( + f"Generated streaming LLM continuation for session {session_id} " + f"(task_id={task_id}), response length: {len(assistant_content)}" + ) + else: + logger.warning( + f"Streaming LLM continuation returned empty response for {session_id}" + ) + + except Exception as e: + logger.error( + f"Failed to generate streaming LLM continuation: {e}", exc_info=True + ) + # Publish error to stream registry followed by finish event + await stream_registry.publish_chunk( + task_id, + StreamError(errorText=f"Failed to generate response: {e}"), + ) + await stream_registry.publish_chunk(task_id, StreamFinish()) diff --git a/autogpt_platform/backend/backend/api/features/chat/stream_registry.py b/autogpt_platform/backend/backend/api/features/chat/stream_registry.py new file mode 100644 index 0000000000..88a5023e2b --- /dev/null +++ b/autogpt_platform/backend/backend/api/features/chat/stream_registry.py @@ -0,0 +1,704 @@ +"""Stream registry for managing reconnectable SSE streams. + +This module provides a registry for tracking active streaming tasks and their +messages. It uses Redis for all state management (no in-memory state), making +pods stateless and horizontally scalable. + +Architecture: +- Redis Stream: Persists all messages for replay and real-time delivery +- Redis Hash: Task metadata (status, session_id, etc.) + +Subscribers: +1. Replay missed messages from Redis Stream (XREAD) +2. Listen for live updates via blocking XREAD +3. No in-memory state required on the subscribing pod +""" + +import asyncio +import logging +from dataclasses import dataclass, field +from datetime import datetime, timezone +from typing import Any, Literal + +import orjson + +from backend.data.redis_client import get_redis_async + +from .config import ChatConfig +from .response_model import StreamBaseResponse, StreamError, StreamFinish + +logger = logging.getLogger(__name__) +config = ChatConfig() + +# Track background tasks for this pod (just the asyncio.Task reference, not subscribers) +_local_tasks: dict[str, asyncio.Task] = {} + +# Track listener tasks per subscriber queue for cleanup +# Maps queue id() to (task_id, asyncio.Task) for proper cleanup on unsubscribe +_listener_tasks: dict[int, tuple[str, asyncio.Task]] = {} + +# Timeout for putting chunks into subscriber queues (seconds) +# If the queue is full and doesn't drain within this time, send an overflow error +QUEUE_PUT_TIMEOUT = 5.0 + +# Lua script for atomic compare-and-swap status update (idempotent completion) +# Returns 1 if status was updated, 0 if already completed/failed +COMPLETE_TASK_SCRIPT = """ +local current = redis.call("HGET", KEYS[1], "status") +if current == "running" then + redis.call("HSET", KEYS[1], "status", ARGV[1]) + return 1 +end +return 0 +""" + + +@dataclass +class ActiveTask: + """Represents an active streaming task (metadata only, no in-memory queues).""" + + task_id: str + session_id: str + user_id: str | None + tool_call_id: str + tool_name: str + operation_id: str + status: Literal["running", "completed", "failed"] = "running" + created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc)) + asyncio_task: asyncio.Task | None = None + + +def _get_task_meta_key(task_id: str) -> str: + """Get Redis key for task metadata.""" + return f"{config.task_meta_prefix}{task_id}" + + +def _get_task_stream_key(task_id: str) -> str: + """Get Redis key for task message stream.""" + return f"{config.task_stream_prefix}{task_id}" + + +def _get_operation_mapping_key(operation_id: str) -> str: + """Get Redis key for operation_id to task_id mapping.""" + return f"{config.task_op_prefix}{operation_id}" + + +async def create_task( + task_id: str, + session_id: str, + user_id: str | None, + tool_call_id: str, + tool_name: str, + operation_id: str, +) -> ActiveTask: + """Create a new streaming task in Redis. + + Args: + task_id: Unique identifier for the task + session_id: Chat session ID + user_id: User ID (may be None for anonymous) + tool_call_id: Tool call ID from the LLM + tool_name: Name of the tool being executed + operation_id: Operation ID for webhook callbacks + + Returns: + The created ActiveTask instance (metadata only) + """ + task = ActiveTask( + task_id=task_id, + session_id=session_id, + user_id=user_id, + tool_call_id=tool_call_id, + tool_name=tool_name, + operation_id=operation_id, + ) + + # Store metadata in Redis + redis = await get_redis_async() + meta_key = _get_task_meta_key(task_id) + op_key = _get_operation_mapping_key(operation_id) + + await redis.hset( # type: ignore[misc] + meta_key, + mapping={ + "task_id": task_id, + "session_id": session_id, + "user_id": user_id or "", + "tool_call_id": tool_call_id, + "tool_name": tool_name, + "operation_id": operation_id, + "status": task.status, + "created_at": task.created_at.isoformat(), + }, + ) + await redis.expire(meta_key, config.stream_ttl) + + # Create operation_id -> task_id mapping for webhook lookups + await redis.set(op_key, task_id, ex=config.stream_ttl) + + logger.debug(f"Created task {task_id} for session {session_id}") + + return task + + +async def publish_chunk( + task_id: str, + chunk: StreamBaseResponse, +) -> str: + """Publish a chunk to Redis Stream. + + All delivery is via Redis Streams - no in-memory state. + + Args: + task_id: Task ID to publish to + chunk: The stream response chunk to publish + + Returns: + The Redis Stream message ID + """ + chunk_json = chunk.model_dump_json() + message_id = "0-0" + + try: + redis = await get_redis_async() + stream_key = _get_task_stream_key(task_id) + + # Write to Redis Stream for persistence and real-time delivery + raw_id = await redis.xadd( + stream_key, + {"data": chunk_json}, + maxlen=config.stream_max_length, + ) + message_id = raw_id if isinstance(raw_id, str) else raw_id.decode() + + # Set TTL on stream to match task metadata TTL + await redis.expire(stream_key, config.stream_ttl) + except Exception as e: + logger.error( + f"Failed to publish chunk for task {task_id}: {e}", + exc_info=True, + ) + + return message_id + + +async def subscribe_to_task( + task_id: str, + user_id: str | None, + last_message_id: str = "0-0", +) -> asyncio.Queue[StreamBaseResponse] | None: + """Subscribe to a task's stream with replay of missed messages. + + This is fully stateless - uses Redis Stream for replay and pub/sub for live updates. + + Args: + task_id: Task ID to subscribe to + user_id: User ID for ownership validation + last_message_id: Last Redis Stream message ID received ("0-0" for full replay) + + Returns: + An asyncio Queue that will receive stream chunks, or None if task not found + or user doesn't have access + """ + redis = await get_redis_async() + meta_key = _get_task_meta_key(task_id) + meta: dict[Any, Any] = await redis.hgetall(meta_key) # type: ignore[misc] + + if not meta: + logger.debug(f"Task {task_id} not found in Redis") + return None + + # Note: Redis client uses decode_responses=True, so keys are strings + task_status = meta.get("status", "") + task_user_id = meta.get("user_id", "") or None + + # Validate ownership - if task has an owner, requester must match + if task_user_id: + if user_id != task_user_id: + logger.warning( + f"User {user_id} denied access to task {task_id} " + f"owned by {task_user_id}" + ) + return None + + subscriber_queue: asyncio.Queue[StreamBaseResponse] = asyncio.Queue() + stream_key = _get_task_stream_key(task_id) + + # Step 1: Replay messages from Redis Stream + messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000) + + replayed_count = 0 + replay_last_id = last_message_id + if messages: + for _stream_name, stream_messages in messages: + for msg_id, msg_data in stream_messages: + replay_last_id = msg_id if isinstance(msg_id, str) else msg_id.decode() + # Note: Redis client uses decode_responses=True, so keys are strings + if "data" in msg_data: + try: + chunk_data = orjson.loads(msg_data["data"]) + chunk = _reconstruct_chunk(chunk_data) + if chunk: + await subscriber_queue.put(chunk) + replayed_count += 1 + except Exception as e: + logger.warning(f"Failed to replay message: {e}") + + logger.debug(f"Task {task_id}: replayed {replayed_count} messages") + + # Step 2: If task is still running, start stream listener for live updates + if task_status == "running": + listener_task = asyncio.create_task( + _stream_listener(task_id, subscriber_queue, replay_last_id) + ) + # Track listener task for cleanup on unsubscribe + _listener_tasks[id(subscriber_queue)] = (task_id, listener_task) + else: + # Task is completed/failed - add finish marker + await subscriber_queue.put(StreamFinish()) + + return subscriber_queue + + +async def _stream_listener( + task_id: str, + subscriber_queue: asyncio.Queue[StreamBaseResponse], + last_replayed_id: str, +) -> None: + """Listen to Redis Stream for new messages using blocking XREAD. + + This approach avoids the duplicate message issue that can occur with pub/sub + when messages are published during the gap between replay and subscription. + + Args: + task_id: Task ID to listen for + subscriber_queue: Queue to deliver messages to + last_replayed_id: Last message ID from replay (continue from here) + """ + queue_id = id(subscriber_queue) + # Track the last successfully delivered message ID for recovery hints + last_delivered_id = last_replayed_id + + try: + redis = await get_redis_async() + stream_key = _get_task_stream_key(task_id) + current_id = last_replayed_id + + while True: + # Block for up to 30 seconds waiting for new messages + # This allows periodic checking if task is still running + messages = await redis.xread( + {stream_key: current_id}, block=30000, count=100 + ) + + if not messages: + # Timeout - check if task is still running + meta_key = _get_task_meta_key(task_id) + status = await redis.hget(meta_key, "status") # type: ignore[misc] + if status and status != "running": + try: + await asyncio.wait_for( + subscriber_queue.put(StreamFinish()), + timeout=QUEUE_PUT_TIMEOUT, + ) + except asyncio.TimeoutError: + logger.warning( + f"Timeout delivering finish event for task {task_id}" + ) + break + continue + + for _stream_name, stream_messages in messages: + for msg_id, msg_data in stream_messages: + current_id = msg_id if isinstance(msg_id, str) else msg_id.decode() + + if "data" not in msg_data: + continue + + try: + chunk_data = orjson.loads(msg_data["data"]) + chunk = _reconstruct_chunk(chunk_data) + if chunk: + try: + await asyncio.wait_for( + subscriber_queue.put(chunk), + timeout=QUEUE_PUT_TIMEOUT, + ) + # Update last delivered ID on successful delivery + last_delivered_id = current_id + except asyncio.TimeoutError: + logger.warning( + f"Subscriber queue full for task {task_id}, " + f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s" + ) + # Send overflow error with recovery info + try: + overflow_error = StreamError( + errorText="Message delivery timeout - some messages may have been missed", + code="QUEUE_OVERFLOW", + details={ + "last_delivered_id": last_delivered_id, + "recovery_hint": f"Reconnect with last_message_id={last_delivered_id}", + }, + ) + subscriber_queue.put_nowait(overflow_error) + except asyncio.QueueFull: + # Queue is completely stuck, nothing more we can do + logger.error( + f"Cannot deliver overflow error for task {task_id}, " + "queue completely blocked" + ) + + # Stop listening on finish + if isinstance(chunk, StreamFinish): + return + except Exception as e: + logger.warning(f"Error processing stream message: {e}") + + except asyncio.CancelledError: + logger.debug(f"Stream listener cancelled for task {task_id}") + raise # Re-raise to propagate cancellation + except Exception as e: + logger.error(f"Stream listener error for task {task_id}: {e}") + # On error, send finish to unblock subscriber + try: + await asyncio.wait_for( + subscriber_queue.put(StreamFinish()), + timeout=QUEUE_PUT_TIMEOUT, + ) + except (asyncio.TimeoutError, asyncio.QueueFull): + logger.warning( + f"Could not deliver finish event for task {task_id} after error" + ) + finally: + # Clean up listener task mapping on exit + _listener_tasks.pop(queue_id, None) + + +async def mark_task_completed( + task_id: str, + status: Literal["completed", "failed"] = "completed", +) -> bool: + """Mark a task as completed and publish finish event. + + This is idempotent - calling multiple times with the same task_id is safe. + Uses atomic compare-and-swap via Lua script to prevent race conditions. + Status is updated first (source of truth), then finish event is published (best-effort). + + Args: + task_id: Task ID to mark as completed + status: Final status ("completed" or "failed") + + Returns: + True if task was newly marked completed, False if already completed/failed + """ + redis = await get_redis_async() + meta_key = _get_task_meta_key(task_id) + + # Atomic compare-and-swap: only update if status is "running" + # This prevents race conditions when multiple callers try to complete simultaneously + result = await redis.eval(COMPLETE_TASK_SCRIPT, 1, meta_key, status) # type: ignore[misc] + + if result == 0: + logger.debug(f"Task {task_id} already completed/failed, skipping") + return False + + # THEN publish finish event (best-effort - listeners can detect via status polling) + try: + await publish_chunk(task_id, StreamFinish()) + except Exception as e: + logger.error( + f"Failed to publish finish event for task {task_id}: {e}. " + "Listeners will detect completion via status polling." + ) + + # Clean up local task reference if exists + _local_tasks.pop(task_id, None) + return True + + +async def find_task_by_operation_id(operation_id: str) -> ActiveTask | None: + """Find a task by its operation ID. + + Used by webhook callbacks to locate the task to update. + + Args: + operation_id: Operation ID to search for + + Returns: + ActiveTask if found, None otherwise + """ + redis = await get_redis_async() + op_key = _get_operation_mapping_key(operation_id) + task_id = await redis.get(op_key) + + if not task_id: + return None + + task_id_str = task_id.decode() if isinstance(task_id, bytes) else task_id + return await get_task(task_id_str) + + +async def get_task(task_id: str) -> ActiveTask | None: + """Get a task by its ID from Redis. + + Args: + task_id: Task ID to look up + + Returns: + ActiveTask if found, None otherwise + """ + redis = await get_redis_async() + meta_key = _get_task_meta_key(task_id) + meta: dict[Any, Any] = await redis.hgetall(meta_key) # type: ignore[misc] + + if not meta: + return None + + # Note: Redis client uses decode_responses=True, so keys/values are strings + return ActiveTask( + task_id=meta.get("task_id", ""), + session_id=meta.get("session_id", ""), + user_id=meta.get("user_id", "") or None, + tool_call_id=meta.get("tool_call_id", ""), + tool_name=meta.get("tool_name", ""), + operation_id=meta.get("operation_id", ""), + status=meta.get("status", "running"), # type: ignore[arg-type] + ) + + +async def get_task_with_expiry_info( + task_id: str, +) -> tuple[ActiveTask | None, str | None]: + """Get a task by its ID with expiration detection. + + Returns (task, error_code) where error_code is: + - None if task found + - "TASK_EXPIRED" if stream exists but metadata is gone (TTL expired) + - "TASK_NOT_FOUND" if neither exists + + Args: + task_id: Task ID to look up + + Returns: + Tuple of (ActiveTask or None, error_code or None) + """ + redis = await get_redis_async() + meta_key = _get_task_meta_key(task_id) + stream_key = _get_task_stream_key(task_id) + + meta: dict[Any, Any] = await redis.hgetall(meta_key) # type: ignore[misc] + + if not meta: + # Check if stream still has data (metadata expired but stream hasn't) + stream_len = await redis.xlen(stream_key) + if stream_len > 0: + return None, "TASK_EXPIRED" + return None, "TASK_NOT_FOUND" + + # Note: Redis client uses decode_responses=True, so keys/values are strings + return ( + ActiveTask( + task_id=meta.get("task_id", ""), + session_id=meta.get("session_id", ""), + user_id=meta.get("user_id", "") or None, + tool_call_id=meta.get("tool_call_id", ""), + tool_name=meta.get("tool_name", ""), + operation_id=meta.get("operation_id", ""), + status=meta.get("status", "running"), # type: ignore[arg-type] + ), + None, + ) + + +async def get_active_task_for_session( + session_id: str, + user_id: str | None = None, +) -> tuple[ActiveTask | None, str]: + """Get the active (running) task for a session, if any. + + Scans Redis for tasks matching the session_id with status="running". + + Args: + session_id: Session ID to look up + user_id: User ID for ownership validation (optional) + + Returns: + Tuple of (ActiveTask if found and running, last_message_id from Redis Stream) + """ + + redis = await get_redis_async() + + # Scan Redis for task metadata keys + cursor = 0 + tasks_checked = 0 + + while True: + cursor, keys = await redis.scan( + cursor, match=f"{config.task_meta_prefix}*", count=100 + ) + + for key in keys: + tasks_checked += 1 + meta: dict[Any, Any] = await redis.hgetall(key) # type: ignore[misc] + if not meta: + continue + + # Note: Redis client uses decode_responses=True, so keys/values are strings + task_session_id = meta.get("session_id", "") + task_status = meta.get("status", "") + task_user_id = meta.get("user_id", "") or None + task_id = meta.get("task_id", "") + + if task_session_id == session_id and task_status == "running": + # Validate ownership - if task has an owner, requester must match + if task_user_id and user_id != task_user_id: + continue + + # Get the last message ID from Redis Stream + stream_key = _get_task_stream_key(task_id) + last_id = "0-0" + try: + messages = await redis.xrevrange(stream_key, count=1) + if messages: + msg_id = messages[0][0] + last_id = msg_id if isinstance(msg_id, str) else msg_id.decode() + except Exception as e: + logger.warning(f"Failed to get last message ID: {e}") + + return ( + ActiveTask( + task_id=task_id, + session_id=task_session_id, + user_id=task_user_id, + tool_call_id=meta.get("tool_call_id", ""), + tool_name=meta.get("tool_name", ""), + operation_id=meta.get("operation_id", ""), + status="running", + ), + last_id, + ) + + if cursor == 0: + break + + return None, "0-0" + + +def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None: + """Reconstruct a StreamBaseResponse from JSON data. + + Args: + chunk_data: Parsed JSON data from Redis + + Returns: + Reconstructed response object, or None if unknown type + """ + from .response_model import ( + ResponseType, + StreamError, + StreamFinish, + StreamHeartbeat, + StreamStart, + StreamTextDelta, + StreamTextEnd, + StreamTextStart, + StreamToolInputAvailable, + StreamToolInputStart, + StreamToolOutputAvailable, + StreamUsage, + ) + + # Map response types to their corresponding classes + type_to_class: dict[str, type[StreamBaseResponse]] = { + ResponseType.START.value: StreamStart, + ResponseType.FINISH.value: StreamFinish, + ResponseType.TEXT_START.value: StreamTextStart, + ResponseType.TEXT_DELTA.value: StreamTextDelta, + ResponseType.TEXT_END.value: StreamTextEnd, + ResponseType.TOOL_INPUT_START.value: StreamToolInputStart, + ResponseType.TOOL_INPUT_AVAILABLE.value: StreamToolInputAvailable, + ResponseType.TOOL_OUTPUT_AVAILABLE.value: StreamToolOutputAvailable, + ResponseType.ERROR.value: StreamError, + ResponseType.USAGE.value: StreamUsage, + ResponseType.HEARTBEAT.value: StreamHeartbeat, + } + + chunk_type = chunk_data.get("type") + chunk_class = type_to_class.get(chunk_type) # type: ignore[arg-type] + + if chunk_class is None: + logger.warning(f"Unknown chunk type: {chunk_type}") + return None + + try: + return chunk_class(**chunk_data) + except Exception as e: + logger.warning(f"Failed to reconstruct chunk of type {chunk_type}: {e}") + return None + + +async def set_task_asyncio_task(task_id: str, asyncio_task: asyncio.Task) -> None: + """Track the asyncio.Task for a task (local reference only). + + This is just for cleanup purposes - the task state is in Redis. + + Args: + task_id: Task ID + asyncio_task: The asyncio Task to track + """ + _local_tasks[task_id] = asyncio_task + + +async def unsubscribe_from_task( + task_id: str, + subscriber_queue: asyncio.Queue[StreamBaseResponse], +) -> None: + """Clean up when a subscriber disconnects. + + Cancels the XREAD-based listener task associated with this subscriber queue + to prevent resource leaks. + + Args: + task_id: Task ID + subscriber_queue: The subscriber's queue used to look up the listener task + """ + queue_id = id(subscriber_queue) + listener_entry = _listener_tasks.pop(queue_id, None) + + if listener_entry is None: + logger.debug( + f"No listener task found for task {task_id} queue {queue_id} " + "(may have already completed)" + ) + return + + stored_task_id, listener_task = listener_entry + + if stored_task_id != task_id: + logger.warning( + f"Task ID mismatch in unsubscribe: expected {task_id}, " + f"found {stored_task_id}" + ) + + if listener_task.done(): + logger.debug(f"Listener task for task {task_id} already completed") + return + + # Cancel the listener task + listener_task.cancel() + + try: + # Wait for the task to be cancelled with a timeout + await asyncio.wait_for(listener_task, timeout=5.0) + except asyncio.CancelledError: + # Expected - the task was successfully cancelled + pass + except asyncio.TimeoutError: + logger.warning( + f"Timeout waiting for listener task cancellation for task {task_id}" + ) + except Exception as e: + logger.error(f"Error during listener task cancellation for task {task_id}: {e}") + + logger.debug(f"Successfully unsubscribed from task {task_id}") diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py index 5b40091bbb..b88b9b2924 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py @@ -550,15 +550,21 @@ async def decompose_goal( async def generate_agent( instructions: DecompositionResult | dict[str, Any], library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None, + operation_id: str | None = None, + task_id: str | None = None, ) -> dict[str, Any] | None: """Generate agent JSON from instructions. Args: instructions: Structured instructions from decompose_goal library_agents: User's library agents available for sub-agent composition + operation_id: Operation ID for async processing (enables Redis Streams + completion notification) + task_id: Task ID for async processing (enables Redis Streams persistence + and SSE delivery) Returns: - Agent JSON dict, error dict {"type": "error", ...}, or None on error + Agent JSON dict, {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error Raises: AgentGeneratorNotConfiguredError: If the external service is not configured. @@ -566,8 +572,13 @@ async def generate_agent( _check_service_configured() logger.info("Calling external Agent Generator service for generate_agent") result = await generate_agent_external( - dict(instructions), _to_dict_list(library_agents) + dict(instructions), _to_dict_list(library_agents), operation_id, task_id ) + + # Don't modify async response + if result and result.get("status") == "accepted": + return result + if result: if isinstance(result, dict) and result.get("type") == "error": return result @@ -819,6 +830,8 @@ async def generate_agent_patch( update_request: str, current_agent: dict[str, Any], library_agents: list[AgentSummary] | None = None, + operation_id: str | None = None, + task_id: str | None = None, ) -> dict[str, Any] | None: """Update an existing agent using natural language. @@ -831,10 +844,12 @@ async def generate_agent_patch( update_request: Natural language description of changes current_agent: Current agent JSON library_agents: User's library agents available for sub-agent composition + operation_id: Operation ID for async processing (enables Redis Streams callback) + task_id: Task ID for async processing (enables Redis Streams callback) Returns: Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...}, - error dict {"type": "error", ...}, or None on unexpected error + {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error Raises: AgentGeneratorNotConfiguredError: If the external service is not configured. @@ -842,7 +857,11 @@ async def generate_agent_patch( _check_service_configured() logger.info("Calling external Agent Generator service for generate_agent_patch") return await generate_agent_patch_external( - update_request, current_agent, _to_dict_list(library_agents) + update_request, + current_agent, + _to_dict_list(library_agents), + operation_id, + task_id, ) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py index 780247a776..62411b4e1b 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py @@ -212,24 +212,45 @@ async def decompose_goal_external( async def generate_agent_external( instructions: dict[str, Any], library_agents: list[dict[str, Any]] | None = None, + operation_id: str | None = None, + task_id: str | None = None, ) -> dict[str, Any] | None: """Call the external service to generate an agent from instructions. Args: instructions: Structured instructions from decompose_goal library_agents: User's library agents available for sub-agent composition + operation_id: Operation ID for async processing (enables Redis Streams callback) + task_id: Task ID for async processing (enables Redis Streams callback) Returns: - Agent JSON dict on success, or error dict {"type": "error", ...} on error + Agent JSON dict, {"status": "accepted"} for async, or error dict {"type": "error", ...} on error """ client = _get_client() + # Build request payload payload: dict[str, Any] = {"instructions": instructions} if library_agents: payload["library_agents"] = library_agents + if operation_id and task_id: + payload["operation_id"] = operation_id + payload["task_id"] = task_id try: response = await client.post("/api/generate-agent", json=payload) + + # Handle 202 Accepted for async processing + if response.status_code == 202: + logger.info( + f"Agent Generator accepted async request " + f"(operation_id={operation_id}, task_id={task_id})" + ) + return { + "status": "accepted", + "operation_id": operation_id, + "task_id": task_id, + } + response.raise_for_status() data = response.json() @@ -261,6 +282,8 @@ async def generate_agent_patch_external( update_request: str, current_agent: dict[str, Any], library_agents: list[dict[str, Any]] | None = None, + operation_id: str | None = None, + task_id: str | None = None, ) -> dict[str, Any] | None: """Call the external service to generate a patch for an existing agent. @@ -268,21 +291,40 @@ async def generate_agent_patch_external( update_request: Natural language description of changes current_agent: Current agent JSON library_agents: User's library agents available for sub-agent composition + operation_id: Operation ID for async processing (enables Redis Streams callback) + task_id: Task ID for async processing (enables Redis Streams callback) Returns: - Updated agent JSON, clarifying questions dict, or error dict on error + Updated agent JSON, clarifying questions dict, {"status": "accepted"} for async, or error dict on error """ client = _get_client() + # Build request payload payload: dict[str, Any] = { "update_request": update_request, "current_agent_json": current_agent, } if library_agents: payload["library_agents"] = library_agents + if operation_id and task_id: + payload["operation_id"] = operation_id + payload["task_id"] = task_id try: response = await client.post("/api/update-agent", json=payload) + + # Handle 202 Accepted for async processing + if response.status_code == 202: + logger.info( + f"Agent Generator accepted async update request " + f"(operation_id={operation_id}, task_id={task_id})" + ) + return { + "status": "accepted", + "operation_id": operation_id, + "task_id": task_id, + } + response.raise_for_status() data = response.json() diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py b/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py index adb2c78fce..7333851a5b 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py @@ -18,6 +18,7 @@ from .base import BaseTool from .models import ( AgentPreviewResponse, AgentSavedResponse, + AsyncProcessingResponse, ClarificationNeededResponse, ClarifyingQuestion, ErrorResponse, @@ -98,6 +99,10 @@ class CreateAgentTool(BaseTool): save = kwargs.get("save", True) session_id = session.session_id if session else None + # Extract async processing params (passed by long-running tool handler) + operation_id = kwargs.get("_operation_id") + task_id = kwargs.get("_task_id") + if not description: return ErrorResponse( message="Please provide a description of what the agent should do.", @@ -219,7 +224,12 @@ class CreateAgentTool(BaseTool): logger.warning(f"Failed to enrich library agents from steps: {e}") try: - agent_json = await generate_agent(decomposition_result, library_agents) + agent_json = await generate_agent( + decomposition_result, + library_agents, + operation_id=operation_id, + task_id=task_id, + ) except AgentGeneratorNotConfiguredError: return ErrorResponse( message=( @@ -263,6 +273,19 @@ class CreateAgentTool(BaseTool): session_id=session_id, ) + # Check if Agent Generator accepted for async processing + if agent_json.get("status") == "accepted": + logger.info( + f"Agent generation delegated to async processing " + f"(operation_id={operation_id}, task_id={task_id})" + ) + return AsyncProcessingResponse( + message="Agent generation started. You'll be notified when it's complete.", + operation_id=operation_id, + task_id=task_id, + session_id=session_id, + ) + agent_name = agent_json.get("name", "Generated Agent") agent_description = agent_json.get("description", "") node_count = len(agent_json.get("nodes", [])) diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py b/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py index 2c2c48226b..3ae56407a7 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py @@ -17,6 +17,7 @@ from .base import BaseTool from .models import ( AgentPreviewResponse, AgentSavedResponse, + AsyncProcessingResponse, ClarificationNeededResponse, ClarifyingQuestion, ErrorResponse, @@ -104,6 +105,10 @@ class EditAgentTool(BaseTool): save = kwargs.get("save", True) session_id = session.session_id if session else None + # Extract async processing params (passed by long-running tool handler) + operation_id = kwargs.get("_operation_id") + task_id = kwargs.get("_task_id") + if not agent_id: return ErrorResponse( message="Please provide the agent ID to edit.", @@ -149,7 +154,11 @@ class EditAgentTool(BaseTool): try: result = await generate_agent_patch( - update_request, current_agent, library_agents + update_request, + current_agent, + library_agents, + operation_id=operation_id, + task_id=task_id, ) except AgentGeneratorNotConfiguredError: return ErrorResponse( @@ -169,6 +178,20 @@ class EditAgentTool(BaseTool): session_id=session_id, ) + # Check if Agent Generator accepted for async processing + if result.get("status") == "accepted": + logger.info( + f"Agent edit delegated to async processing " + f"(operation_id={operation_id}, task_id={task_id})" + ) + return AsyncProcessingResponse( + message="Agent edit started. You'll be notified when it's complete.", + operation_id=operation_id, + task_id=task_id, + session_id=session_id, + ) + + # Check if the result is an error from the external service if isinstance(result, dict) and result.get("type") == "error": error_msg = result.get("error", "Unknown error") error_type = result.get("error_type", "unknown") diff --git a/autogpt_platform/backend/backend/api/features/chat/tools/models.py b/autogpt_platform/backend/backend/api/features/chat/tools/models.py index 5ff8190c31..69c8c6c684 100644 --- a/autogpt_platform/backend/backend/api/features/chat/tools/models.py +++ b/autogpt_platform/backend/backend/api/features/chat/tools/models.py @@ -372,11 +372,15 @@ class OperationStartedResponse(ToolResponseBase): This is returned immediately to the client while the operation continues to execute. The user can close the tab and check back later. + + The task_id can be used to reconnect to the SSE stream via + GET /chat/tasks/{task_id}/stream?last_idx=0 """ type: ResponseType = ResponseType.OPERATION_STARTED operation_id: str tool_name: str + task_id: str | None = None # For SSE reconnection class OperationPendingResponse(ToolResponseBase): @@ -400,3 +404,20 @@ class OperationInProgressResponse(ToolResponseBase): type: ResponseType = ResponseType.OPERATION_IN_PROGRESS tool_call_id: str + + +class AsyncProcessingResponse(ToolResponseBase): + """Response when an operation has been delegated to async processing. + + This is returned by tools when the external service accepts the request + for async processing (HTTP 202 Accepted). The Redis Streams completion + consumer will handle the result when the external service completes. + + The status field is specifically "accepted" to allow the long-running tool + handler to detect this response and skip LLM continuation. + """ + + type: ResponseType = ResponseType.OPERATION_STARTED + status: str = "accepted" # Must be "accepted" for detection + operation_id: str | None = None + task_id: str | None = None diff --git a/autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py b/autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py index bae5b97cd6..86af457f50 100644 --- a/autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py +++ b/autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py @@ -454,6 +454,9 @@ async def test_unified_hybrid_search_pagination( cleanup_embeddings: list, ): """Test unified search pagination works correctly.""" + # Use a unique search term to avoid matching other test data + unique_term = f"xyzpagtest{uuid.uuid4().hex[:8]}" + # Create multiple items content_ids = [] for i in range(5): @@ -465,14 +468,14 @@ async def test_unified_hybrid_search_pagination( content_type=ContentType.BLOCK, content_id=content_id, embedding=mock_embedding, - searchable_text=f"pagination test item number {i}", + searchable_text=f"{unique_term} item number {i}", metadata={"index": i}, user_id=None, ) # Get first page page1_results, total1 = await unified_hybrid_search( - query="pagination test", + query=unique_term, content_types=[ContentType.BLOCK], page=1, page_size=2, @@ -480,7 +483,7 @@ async def test_unified_hybrid_search_pagination( # Get second page page2_results, total2 = await unified_hybrid_search( - query="pagination test", + query=unique_term, content_types=[ContentType.BLOCK], page=2, page_size=2, diff --git a/autogpt_platform/backend/backend/api/rest_api.py b/autogpt_platform/backend/backend/api/rest_api.py index b936312ce1..0eef76193e 100644 --- a/autogpt_platform/backend/backend/api/rest_api.py +++ b/autogpt_platform/backend/backend/api/rest_api.py @@ -40,6 +40,10 @@ import backend.data.user import backend.integrations.webhooks.utils import backend.util.service import backend.util.settings +from backend.api.features.chat.completion_consumer import ( + start_completion_consumer, + stop_completion_consumer, +) from backend.blocks.llm import DEFAULT_LLM_MODEL from backend.data.model import Credentials from backend.integrations.providers import ProviderName @@ -118,9 +122,21 @@ async def lifespan_context(app: fastapi.FastAPI): await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL) await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs() + # Start chat completion consumer for Redis Streams notifications + try: + await start_completion_consumer() + except Exception as e: + logger.warning(f"Could not start chat completion consumer: {e}") + with launch_darkly_context(): yield + # Stop chat completion consumer + try: + await stop_completion_consumer() + except Exception as e: + logger.warning(f"Error stopping chat completion consumer: {e}") + try: await shutdown_cloud_storage_handler() except Exception as e: diff --git a/autogpt_platform/backend/test/agent_generator/test_core_integration.py b/autogpt_platform/backend/test/agent_generator/test_core_integration.py index 05ce4a3aff..528763e751 100644 --- a/autogpt_platform/backend/test/agent_generator/test_core_integration.py +++ b/autogpt_platform/backend/test/agent_generator/test_core_integration.py @@ -111,9 +111,7 @@ class TestGenerateAgent: instructions = {"type": "instructions", "steps": ["Step 1"]} result = await core.generate_agent(instructions) - # library_agents defaults to None - mock_external.assert_called_once_with(instructions, None) - # Result should have id, version, is_active added if not present + mock_external.assert_called_once_with(instructions, None, None, None) assert result is not None assert result["name"] == "Test Agent" assert "id" in result @@ -177,8 +175,9 @@ class TestGenerateAgentPatch: current_agent = {"nodes": [], "links": []} result = await core.generate_agent_patch("Add a node", current_agent) - # library_agents defaults to None - mock_external.assert_called_once_with("Add a node", current_agent, None) + mock_external.assert_called_once_with( + "Add a node", current_agent, None, None, None + ) assert result == expected_result @pytest.mark.asyncio diff --git a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts index 74fd663ab2..913c4d7ded 100644 --- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts +++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts @@ -11,7 +11,6 @@ import { useBreakpoint } from "@/lib/hooks/useBreakpoint"; import { useSupabase } from "@/lib/supabase/hooks/useSupabase"; import { useQueryClient } from "@tanstack/react-query"; import { usePathname, useSearchParams } from "next/navigation"; -import { useRef } from "react"; import { useCopilotStore } from "../../copilot-page-store"; import { useCopilotSessionId } from "../../useCopilotSessionId"; import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer"; @@ -70,41 +69,16 @@ export function useCopilotShell() { }); const stopStream = useChatStore((s) => s.stopStream); - const onStreamComplete = useChatStore((s) => s.onStreamComplete); - const isStreaming = useCopilotStore((s) => s.isStreaming); const isCreatingSession = useCopilotStore((s) => s.isCreatingSession); - const setIsSwitchingSession = useCopilotStore((s) => s.setIsSwitchingSession); - const openInterruptModal = useCopilotStore((s) => s.openInterruptModal); - const pendingActionRef = useRef<(() => void) | null>(null); - - async function stopCurrentStream() { - if (!currentSessionId) return; - - setIsSwitchingSession(true); - await new Promise((resolve) => { - const unsubscribe = onStreamComplete((completedId) => { - if (completedId === currentSessionId) { - clearTimeout(timeout); - unsubscribe(); - resolve(); - } - }); - const timeout = setTimeout(() => { - unsubscribe(); - resolve(); - }, 3000); - stopStream(currentSessionId); - }); - - queryClient.invalidateQueries({ - queryKey: getGetV2GetSessionQueryKey(currentSessionId), - }); - setIsSwitchingSession(false); - } - - function selectSession(sessionId: string) { + function handleSessionClick(sessionId: string) { if (sessionId === currentSessionId) return; + + // Stop current stream - SSE reconnection allows resuming later + if (currentSessionId) { + stopStream(currentSessionId); + } + if (recentlyCreatedSessionsRef.current.has(sessionId)) { queryClient.invalidateQueries({ queryKey: getGetV2GetSessionQueryKey(sessionId), @@ -114,7 +88,12 @@ export function useCopilotShell() { if (isMobile) handleCloseDrawer(); } - function startNewChat() { + function handleNewChatClick() { + // Stop current stream - SSE reconnection allows resuming later + if (currentSessionId) { + stopStream(currentSessionId); + } + resetPagination(); queryClient.invalidateQueries({ queryKey: getGetV2ListSessionsQueryKey(), @@ -123,32 +102,6 @@ export function useCopilotShell() { if (isMobile) handleCloseDrawer(); } - function handleSessionClick(sessionId: string) { - if (sessionId === currentSessionId) return; - - if (isStreaming) { - pendingActionRef.current = async () => { - await stopCurrentStream(); - selectSession(sessionId); - }; - openInterruptModal(pendingActionRef.current); - } else { - selectSession(sessionId); - } - } - - function handleNewChatClick() { - if (isStreaming) { - pendingActionRef.current = async () => { - await stopCurrentStream(); - startNewChat(); - }; - openInterruptModal(pendingActionRef.current); - } else { - startNewChat(); - } - } - return { isMobile, isDrawerOpen, diff --git a/autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts b/autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts new file mode 100644 index 0000000000..336786bfdb --- /dev/null +++ b/autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts @@ -0,0 +1,81 @@ +import { environment } from "@/services/environment"; +import { getServerAuthToken } from "@/lib/autogpt-server-api/helpers"; +import { NextRequest } from "next/server"; + +/** + * SSE Proxy for task stream reconnection. + * + * This endpoint allows clients to reconnect to an ongoing or recently completed + * background task's stream. It replays missed messages from Redis Streams and + * subscribes to live updates if the task is still running. + * + * Client contract: + * 1. When receiving an operation_started event, store the task_id + * 2. To reconnect: GET /api/chat/tasks/{taskId}/stream?last_message_id={idx} + * 3. Messages are replayed from the last_message_id position + * 4. Stream ends when "finish" event is received + */ +export async function GET( + request: NextRequest, + { params }: { params: Promise<{ taskId: string }> }, +) { + const { taskId } = await params; + const searchParams = request.nextUrl.searchParams; + const lastMessageId = searchParams.get("last_message_id") || "0-0"; + + try { + // Get auth token from server-side session + const token = await getServerAuthToken(); + + // Build backend URL + const backendUrl = environment.getAGPTServerBaseUrl(); + const streamUrl = new URL(`/api/chat/tasks/${taskId}/stream`, backendUrl); + streamUrl.searchParams.set("last_message_id", lastMessageId); + + // Forward request to backend with auth header + const headers: Record = { + Accept: "text/event-stream", + "Cache-Control": "no-cache", + Connection: "keep-alive", + }; + + if (token) { + headers["Authorization"] = `Bearer ${token}`; + } + + const response = await fetch(streamUrl.toString(), { + method: "GET", + headers, + }); + + if (!response.ok) { + const error = await response.text(); + return new Response(error, { + status: response.status, + headers: { "Content-Type": "application/json" }, + }); + } + + // Return the SSE stream directly + return new Response(response.body, { + headers: { + "Content-Type": "text/event-stream", + "Cache-Control": "no-cache, no-transform", + Connection: "keep-alive", + "X-Accel-Buffering": "no", + }, + }); + } catch (error) { + console.error("Task stream proxy error:", error); + return new Response( + JSON.stringify({ + error: "Failed to connect to task stream", + detail: error instanceof Error ? error.message : String(error), + }), + { + status: 500, + headers: { "Content-Type": "application/json" }, + }, + ); + } +} diff --git a/autogpt_platform/frontend/src/app/api/openapi.json b/autogpt_platform/frontend/src/app/api/openapi.json index aa4c49b1a2..5ed449829d 100644 --- a/autogpt_platform/frontend/src/app/api/openapi.json +++ b/autogpt_platform/frontend/src/app/api/openapi.json @@ -917,6 +917,28 @@ "security": [{ "HTTPBearerJWT": [] }] } }, + "/api/chat/config/ttl": { + "get": { + "tags": ["v2", "chat", "chat"], + "summary": "Get Ttl Config", + "description": "Get the stream TTL configuration.\n\nReturns the Time-To-Live settings for chat streams, which determines\nhow long clients can reconnect to an active stream.\n\nReturns:\n dict: TTL configuration with seconds and milliseconds values.", + "operationId": "getV2GetTtlConfig", + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "additionalProperties": true, + "type": "object", + "title": "Response Getv2Getttlconfig" + } + } + } + } + } + } + }, "/api/chat/health": { "get": { "tags": ["v2", "chat", "chat"], @@ -939,6 +961,63 @@ } } }, + "/api/chat/operations/{operation_id}/complete": { + "post": { + "tags": ["v2", "chat", "chat"], + "summary": "Complete Operation", + "description": "External completion webhook for long-running operations.\n\nCalled by Agent Generator (or other services) when an operation completes.\nThis triggers the stream registry to publish completion and continue LLM generation.\n\nArgs:\n operation_id: The operation ID to complete.\n request: Completion payload with success status and result/error.\n x_api_key: Internal API key for authentication.\n\nReturns:\n dict: Status of the completion.\n\nRaises:\n HTTPException: If API key is invalid or operation not found.", + "operationId": "postV2CompleteOperation", + "parameters": [ + { + "name": "operation_id", + "in": "path", + "required": true, + "schema": { "type": "string", "title": "Operation Id" } + }, + { + "name": "x-api-key", + "in": "header", + "required": false, + "schema": { + "anyOf": [{ "type": "string" }, { "type": "null" }], + "title": "X-Api-Key" + } + } + ], + "requestBody": { + "required": true, + "content": { + "application/json": { + "schema": { + "$ref": "#/components/schemas/OperationCompleteRequest" + } + } + } + }, + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "object", + "additionalProperties": true, + "title": "Response Postv2Completeoperation" + } + } + } + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { "$ref": "#/components/schemas/HTTPValidationError" } + } + } + } + } + } + }, "/api/chat/sessions": { "get": { "tags": ["v2", "chat", "chat"], @@ -1022,7 +1101,7 @@ "get": { "tags": ["v2", "chat", "chat"], "summary": "Get Session", - "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\n\nArgs:\n session_id: The unique identifier for the desired chat session.\n user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n SessionDetailResponse: Details for the requested session, or None if not found.", + "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\nIf there's an active stream for this session, returns the task_id for reconnection.\n\nArgs:\n session_id: The unique identifier for the desired chat session.\n user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n SessionDetailResponse: Details for the requested session, including active_stream info if applicable.", "operationId": "getV2GetSession", "security": [{ "HTTPBearerJWT": [] }], "parameters": [ @@ -1157,7 +1236,7 @@ "post": { "tags": ["v2", "chat", "chat"], "summary": "Stream Chat Post", - "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n - Text fragments as they are generated\n - Tool call UI elements (if invoked)\n - Tool execution results\n\nArgs:\n session_id: The chat session identifier to associate with the streamed messages.\n request: Request body containing message, is_user_message, and optional context.\n user_id: Optional authenticated user ID.\nReturns:\n StreamingResponse: SSE-formatted response chunks.", + "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n - Text fragments as they are generated\n - Tool call UI elements (if invoked)\n - Tool execution results\n\nThe AI generation runs in a background task that continues even if the client disconnects.\nAll chunks are written to Redis for reconnection support. If the client disconnects,\nthey can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.\n\nArgs:\n session_id: The chat session identifier to associate with the streamed messages.\n request: Request body containing message, is_user_message, and optional context.\n user_id: Optional authenticated user ID.\nReturns:\n StreamingResponse: SSE-formatted response chunks. First chunk is a \"start\" event\n containing the task_id for reconnection.", "operationId": "postV2StreamChatPost", "security": [{ "HTTPBearerJWT": [] }], "parameters": [ @@ -1195,6 +1274,94 @@ } } }, + "/api/chat/tasks/{task_id}": { + "get": { + "tags": ["v2", "chat", "chat"], + "summary": "Get Task Status", + "description": "Get the status of a long-running task.\n\nArgs:\n task_id: The task ID to check.\n user_id: Authenticated user ID for ownership validation.\n\nReturns:\n dict: Task status including task_id, status, tool_name, and operation_id.\n\nRaises:\n NotFoundError: If task_id is not found or user doesn't have access.", + "operationId": "getV2GetTaskStatus", + "security": [{ "HTTPBearerJWT": [] }], + "parameters": [ + { + "name": "task_id", + "in": "path", + "required": true, + "schema": { "type": "string", "title": "Task Id" } + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { + "application/json": { + "schema": { + "type": "object", + "additionalProperties": true, + "title": "Response Getv2Gettaskstatus" + } + } + } + }, + "401": { + "$ref": "#/components/responses/HTTP401NotAuthenticatedError" + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { "$ref": "#/components/schemas/HTTPValidationError" } + } + } + } + } + } + }, + "/api/chat/tasks/{task_id}/stream": { + "get": { + "tags": ["v2", "chat", "chat"], + "summary": "Stream Task", + "description": "Reconnect to a long-running task's SSE stream.\n\nWhen a long-running operation (like agent generation) starts, the client\nreceives a task_id. If the connection drops, the client can reconnect\nusing this endpoint to resume receiving updates.\n\nArgs:\n task_id: The task ID from the operation_started response.\n user_id: Authenticated user ID for ownership validation.\n last_message_id: Last Redis Stream message ID received (\"0-0\" for full replay).\n\nReturns:\n StreamingResponse: SSE-formatted response chunks starting after last_message_id.\n\nRaises:\n HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.", + "operationId": "getV2StreamTask", + "security": [{ "HTTPBearerJWT": [] }], + "parameters": [ + { + "name": "task_id", + "in": "path", + "required": true, + "schema": { "type": "string", "title": "Task Id" } + }, + { + "name": "last_message_id", + "in": "query", + "required": false, + "schema": { + "type": "string", + "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.", + "default": "0-0", + "title": "Last Message Id" + }, + "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay." + } + ], + "responses": { + "200": { + "description": "Successful Response", + "content": { "application/json": { "schema": {} } } + }, + "401": { + "$ref": "#/components/responses/HTTP401NotAuthenticatedError" + }, + "422": { + "description": "Validation Error", + "content": { + "application/json": { + "schema": { "$ref": "#/components/schemas/HTTPValidationError" } + } + } + } + } + } + }, "/api/credits": { "get": { "tags": ["v1", "credits"], @@ -6168,6 +6335,18 @@ "title": "AccuracyTrendsResponse", "description": "Response model for accuracy trends and alerts." }, + "ActiveStreamInfo": { + "properties": { + "task_id": { "type": "string", "title": "Task Id" }, + "last_message_id": { "type": "string", "title": "Last Message Id" }, + "operation_id": { "type": "string", "title": "Operation Id" }, + "tool_name": { "type": "string", "title": "Tool Name" } + }, + "type": "object", + "required": ["task_id", "last_message_id", "operation_id", "tool_name"], + "title": "ActiveStreamInfo", + "description": "Information about an active stream for reconnection." + }, "AddUserCreditsResponse": { "properties": { "new_balance": { "type": "integer", "title": "New Balance" }, @@ -8823,6 +9002,27 @@ ], "title": "OnboardingStep" }, + "OperationCompleteRequest": { + "properties": { + "success": { "type": "boolean", "title": "Success" }, + "result": { + "anyOf": [ + { "additionalProperties": true, "type": "object" }, + { "type": "string" }, + { "type": "null" } + ], + "title": "Result" + }, + "error": { + "anyOf": [{ "type": "string" }, { "type": "null" }], + "title": "Error" + } + }, + "type": "object", + "required": ["success"], + "title": "OperationCompleteRequest", + "description": "Request model for external completion webhook." + }, "Pagination": { "properties": { "total_items": { @@ -9678,6 +9878,12 @@ "items": { "additionalProperties": true, "type": "object" }, "type": "array", "title": "Messages" + }, + "active_stream": { + "anyOf": [ + { "$ref": "#/components/schemas/ActiveStreamInfo" }, + { "type": "null" } + ] } }, "type": "object", diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx index ada8c26231..da454150bf 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx @@ -1,7 +1,6 @@ "use client"; import { useCopilotSessionId } from "@/app/(platform)/copilot/useCopilotSessionId"; -import { useCopilotStore } from "@/app/(platform)/copilot/copilot-page-store"; import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner"; import { Text } from "@/components/atoms/Text/Text"; import { cn } from "@/lib/utils"; @@ -25,8 +24,8 @@ export function Chat({ }: ChatProps) { const { urlSessionId } = useCopilotSessionId(); const hasHandledNotFoundRef = useRef(false); - const isSwitchingSession = useCopilotStore((s) => s.isSwitchingSession); const { + session, messages, isLoading, isCreating, @@ -38,6 +37,18 @@ export function Chat({ startPollingForOperation, } = useChat({ urlSessionId }); + // Extract active stream info for reconnection + const activeStream = ( + session as { + active_stream?: { + task_id: string; + last_message_id: string; + operation_id: string; + tool_name: string; + }; + } + )?.active_stream; + useEffect(() => { if (!onSessionNotFound) return; if (!urlSessionId) return; @@ -53,8 +64,7 @@ export function Chat({ isCreating, ]); - const shouldShowLoader = - (showLoader && (isLoading || isCreating)) || isSwitchingSession; + const shouldShowLoader = showLoader && (isLoading || isCreating); return (
@@ -66,21 +76,19 @@ export function Chat({
- {isSwitchingSession - ? "Switching chat..." - : "Loading your chat..."} + Loading your chat...
)} {/* Error State */} - {error && !isLoading && !isSwitchingSession && ( + {error && !isLoading && ( )} {/* Session Content */} - {sessionId && !isLoading && !error && !isSwitchingSession && ( + {sessionId && !isLoading && !error && ( )} diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/SSE_RECONNECTION.md b/autogpt_platform/frontend/src/components/contextual/Chat/SSE_RECONNECTION.md new file mode 100644 index 0000000000..9e78679f4e --- /dev/null +++ b/autogpt_platform/frontend/src/components/contextual/Chat/SSE_RECONNECTION.md @@ -0,0 +1,159 @@ +# SSE Reconnection Contract for Long-Running Operations + +This document describes the client-side contract for handling SSE (Server-Sent Events) disconnections and reconnecting to long-running background tasks. + +## Overview + +When a user triggers a long-running operation (like agent generation), the backend: + +1. Spawns a background task that survives SSE disconnections +2. Returns an `operation_started` response with a `task_id` +3. Stores stream messages in Redis Streams for replay + +Clients can reconnect to the task stream at any time to receive missed messages. + +## Client-Side Flow + +### 1. Receiving Operation Started + +When you receive an `operation_started` tool response: + +```typescript +// The response includes a task_id for reconnection +{ + type: "operation_started", + tool_name: "generate_agent", + operation_id: "uuid-...", + task_id: "task-uuid-...", // <-- Store this for reconnection + message: "Operation started. You can close this tab." +} +``` + +### 2. Storing Task Info + +Use the chat store to track the active task: + +```typescript +import { useChatStore } from "./chat-store"; + +// When operation_started is received: +useChatStore.getState().setActiveTask(sessionId, { + taskId: response.task_id, + operationId: response.operation_id, + toolName: response.tool_name, + lastMessageId: "0", +}); +``` + +### 3. Reconnecting to a Task + +To reconnect (e.g., after page refresh or tab reopen): + +```typescript +const { reconnectToTask, getActiveTask } = useChatStore.getState(); + +// Check if there's an active task for this session +const activeTask = getActiveTask(sessionId); + +if (activeTask) { + // Reconnect to the task stream + await reconnectToTask( + sessionId, + activeTask.taskId, + activeTask.lastMessageId, // Resume from last position + (chunk) => { + // Handle incoming chunks + console.log("Received chunk:", chunk); + }, + ); +} +``` + +### 4. Tracking Message Position + +To enable precise replay, update the last message ID as chunks arrive: + +```typescript +const { updateTaskLastMessageId } = useChatStore.getState(); + +function handleChunk(chunk: StreamChunk) { + // If chunk has an index/id, track it + if (chunk.idx !== undefined) { + updateTaskLastMessageId(sessionId, String(chunk.idx)); + } +} +``` + +## API Endpoints + +### Task Stream Reconnection + +``` +GET /api/chat/tasks/{taskId}/stream?last_message_id={idx} +``` + +- `taskId`: The task ID from `operation_started` +- `last_message_id`: Last received message index (default: "0" for full replay) + +Returns: SSE stream of missed messages + live updates + +## Chunk Types + +The reconnected stream follows the same Vercel AI SDK protocol: + +| Type | Description | +| ----------------------- | ----------------------- | +| `start` | Message lifecycle start | +| `text-delta` | Streaming text content | +| `text-end` | Text block completed | +| `tool-output-available` | Tool result available | +| `finish` | Stream completed | +| `error` | Error occurred | + +## Error Handling + +If reconnection fails: + +1. Check if task still exists (may have expired - default TTL: 1 hour) +2. Fall back to polling the session for final state +3. Show appropriate UI message to user + +## Persistence Considerations + +For robust reconnection across browser restarts: + +```typescript +// Store in localStorage/sessionStorage +const ACTIVE_TASKS_KEY = "chat_active_tasks"; + +function persistActiveTask(sessionId: string, task: ActiveTaskInfo) { + const tasks = JSON.parse(localStorage.getItem(ACTIVE_TASKS_KEY) || "{}"); + tasks[sessionId] = task; + localStorage.setItem(ACTIVE_TASKS_KEY, JSON.stringify(tasks)); +} + +function loadPersistedTasks(): Record { + return JSON.parse(localStorage.getItem(ACTIVE_TASKS_KEY) || "{}"); +} +``` + +## Backend Configuration + +The following backend settings affect reconnection behavior: + +| Setting | Default | Description | +| ------------------- | ------- | ---------------------------------- | +| `stream_ttl` | 3600s | How long streams are kept in Redis | +| `stream_max_length` | 1000 | Max messages per stream | + +## Testing + +To test reconnection locally: + +1. Start a long-running operation (e.g., agent generation) +2. Note the `task_id` from the `operation_started` response +3. Close the browser tab +4. Reopen and call `reconnectToTask` with the saved `task_id` +5. Verify that missed messages are replayed + +See the main README for full local development setup. diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/chat-constants.ts b/autogpt_platform/frontend/src/components/contextual/Chat/chat-constants.ts new file mode 100644 index 0000000000..8802de2155 --- /dev/null +++ b/autogpt_platform/frontend/src/components/contextual/Chat/chat-constants.ts @@ -0,0 +1,16 @@ +/** + * Constants for the chat system. + * + * Centralizes magic strings and values used across chat components. + */ + +// LocalStorage keys +export const STORAGE_KEY_ACTIVE_TASKS = "chat_active_tasks"; + +// Redis Stream IDs +export const INITIAL_MESSAGE_ID = "0"; +export const INITIAL_STREAM_ID = "0-0"; + +// TTL values (in milliseconds) +export const COMPLETED_STREAM_TTL_MS = 5 * 60 * 1000; // 5 minutes +export const ACTIVE_TASK_TTL_MS = 60 * 60 * 1000; // 1 hour diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/chat-store.ts b/autogpt_platform/frontend/src/components/contextual/Chat/chat-store.ts index 8229630e5d..3083f65d2c 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/chat-store.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/chat-store.ts @@ -1,6 +1,12 @@ "use client"; import { create } from "zustand"; +import { + ACTIVE_TASK_TTL_MS, + COMPLETED_STREAM_TTL_MS, + INITIAL_STREAM_ID, + STORAGE_KEY_ACTIVE_TASKS, +} from "./chat-constants"; import type { ActiveStream, StreamChunk, @@ -8,15 +14,59 @@ import type { StreamResult, StreamStatus, } from "./chat-types"; -import { executeStream } from "./stream-executor"; +import { executeStream, executeTaskReconnect } from "./stream-executor"; -const COMPLETED_STREAM_TTL = 5 * 60 * 1000; // 5 minutes +export interface ActiveTaskInfo { + taskId: string; + sessionId: string; + operationId: string; + toolName: string; + lastMessageId: string; + startedAt: number; +} + +/** Load active tasks from localStorage */ +function loadPersistedTasks(): Map { + if (typeof window === "undefined") return new Map(); + try { + const stored = localStorage.getItem(STORAGE_KEY_ACTIVE_TASKS); + if (!stored) return new Map(); + const parsed = JSON.parse(stored) as Record; + const now = Date.now(); + const tasks = new Map(); + // Filter out expired tasks + for (const [sessionId, task] of Object.entries(parsed)) { + if (now - task.startedAt < ACTIVE_TASK_TTL_MS) { + tasks.set(sessionId, task); + } + } + return tasks; + } catch { + return new Map(); + } +} + +/** Save active tasks to localStorage */ +function persistTasks(tasks: Map): void { + if (typeof window === "undefined") return; + try { + const obj: Record = {}; + for (const [sessionId, task] of tasks) { + obj[sessionId] = task; + } + localStorage.setItem(STORAGE_KEY_ACTIVE_TASKS, JSON.stringify(obj)); + } catch { + // Ignore storage errors + } +} interface ChatStoreState { activeStreams: Map; completedStreams: Map; activeSessions: Set; streamCompleteCallbacks: Set; + /** Active tasks for SSE reconnection - keyed by sessionId */ + activeTasks: Map; } interface ChatStoreActions { @@ -41,6 +91,24 @@ interface ChatStoreActions { unregisterActiveSession: (sessionId: string) => void; isSessionActive: (sessionId: string) => boolean; onStreamComplete: (callback: StreamCompleteCallback) => () => void; + /** Track active task for SSE reconnection */ + setActiveTask: ( + sessionId: string, + taskInfo: Omit, + ) => void; + /** Get active task for a session */ + getActiveTask: (sessionId: string) => ActiveTaskInfo | undefined; + /** Clear active task when operation completes */ + clearActiveTask: (sessionId: string) => void; + /** Reconnect to an existing task stream */ + reconnectToTask: ( + sessionId: string, + taskId: string, + lastMessageId?: string, + onChunk?: (chunk: StreamChunk) => void, + ) => Promise; + /** Update last message ID for a task (for tracking replay position) */ + updateTaskLastMessageId: (sessionId: string, lastMessageId: string) => void; } type ChatStore = ChatStoreState & ChatStoreActions; @@ -64,18 +132,126 @@ function cleanupExpiredStreams( const now = Date.now(); const cleaned = new Map(completedStreams); for (const [sessionId, result] of cleaned) { - if (now - result.completedAt > COMPLETED_STREAM_TTL) { + if (now - result.completedAt > COMPLETED_STREAM_TTL_MS) { cleaned.delete(sessionId); } } return cleaned; } +/** + * Finalize a stream by moving it from activeStreams to completedStreams. + * Also handles cleanup and notifications. + */ +function finalizeStream( + sessionId: string, + stream: ActiveStream, + onChunk: ((chunk: StreamChunk) => void) | undefined, + get: () => ChatStoreState & ChatStoreActions, + set: (state: Partial) => void, +): void { + if (onChunk) stream.onChunkCallbacks.delete(onChunk); + + if (stream.status !== "streaming") { + const currentState = get(); + const finalActiveStreams = new Map(currentState.activeStreams); + let finalCompletedStreams = new Map(currentState.completedStreams); + + const storedStream = finalActiveStreams.get(sessionId); + if (storedStream === stream) { + const result: StreamResult = { + sessionId, + status: stream.status, + chunks: stream.chunks, + completedAt: Date.now(), + error: stream.error, + }; + finalCompletedStreams.set(sessionId, result); + finalActiveStreams.delete(sessionId); + finalCompletedStreams = cleanupExpiredStreams(finalCompletedStreams); + set({ + activeStreams: finalActiveStreams, + completedStreams: finalCompletedStreams, + }); + + if (stream.status === "completed" || stream.status === "error") { + notifyStreamComplete(currentState.streamCompleteCallbacks, sessionId); + } + } + } +} + +/** + * Clean up an existing stream for a session and move it to completed streams. + * Returns updated maps for both active and completed streams. + */ +function cleanupExistingStream( + sessionId: string, + activeStreams: Map, + completedStreams: Map, + callbacks: Set, +): { + activeStreams: Map; + completedStreams: Map; +} { + const newActiveStreams = new Map(activeStreams); + let newCompletedStreams = new Map(completedStreams); + + const existingStream = newActiveStreams.get(sessionId); + if (existingStream) { + existingStream.abortController.abort(); + const normalizedStatus = + existingStream.status === "streaming" + ? "completed" + : existingStream.status; + const result: StreamResult = { + sessionId, + status: normalizedStatus, + chunks: existingStream.chunks, + completedAt: Date.now(), + error: existingStream.error, + }; + newCompletedStreams.set(sessionId, result); + newActiveStreams.delete(sessionId); + newCompletedStreams = cleanupExpiredStreams(newCompletedStreams); + if (normalizedStatus === "completed" || normalizedStatus === "error") { + notifyStreamComplete(callbacks, sessionId); + } + } + + return { + activeStreams: newActiveStreams, + completedStreams: newCompletedStreams, + }; +} + +/** + * Create a new active stream with initial state. + */ +function createActiveStream( + sessionId: string, + onChunk?: (chunk: StreamChunk) => void, +): ActiveStream { + const abortController = new AbortController(); + const initialCallbacks = new Set<(chunk: StreamChunk) => void>(); + if (onChunk) initialCallbacks.add(onChunk); + + return { + sessionId, + abortController, + status: "streaming", + startedAt: Date.now(), + chunks: [], + onChunkCallbacks: initialCallbacks, + }; +} + export const useChatStore = create((set, get) => ({ activeStreams: new Map(), completedStreams: new Map(), activeSessions: new Set(), streamCompleteCallbacks: new Set(), + activeTasks: loadPersistedTasks(), startStream: async function startStream( sessionId, @@ -85,45 +261,21 @@ export const useChatStore = create((set, get) => ({ onChunk, ) { const state = get(); - const newActiveStreams = new Map(state.activeStreams); - let newCompletedStreams = new Map(state.completedStreams); const callbacks = state.streamCompleteCallbacks; - const existingStream = newActiveStreams.get(sessionId); - if (existingStream) { - existingStream.abortController.abort(); - const normalizedStatus = - existingStream.status === "streaming" - ? "completed" - : existingStream.status; - const result: StreamResult = { - sessionId, - status: normalizedStatus, - chunks: existingStream.chunks, - completedAt: Date.now(), - error: existingStream.error, - }; - newCompletedStreams.set(sessionId, result); - newActiveStreams.delete(sessionId); - newCompletedStreams = cleanupExpiredStreams(newCompletedStreams); - if (normalizedStatus === "completed" || normalizedStatus === "error") { - notifyStreamComplete(callbacks, sessionId); - } - } - - const abortController = new AbortController(); - const initialCallbacks = new Set<(chunk: StreamChunk) => void>(); - if (onChunk) initialCallbacks.add(onChunk); - - const stream: ActiveStream = { + // Clean up any existing stream for this session + const { + activeStreams: newActiveStreams, + completedStreams: newCompletedStreams, + } = cleanupExistingStream( sessionId, - abortController, - status: "streaming", - startedAt: Date.now(), - chunks: [], - onChunkCallbacks: initialCallbacks, - }; + state.activeStreams, + state.completedStreams, + callbacks, + ); + // Create new stream + const stream = createActiveStream(sessionId, onChunk); newActiveStreams.set(sessionId, stream); set({ activeStreams: newActiveStreams, @@ -133,36 +285,7 @@ export const useChatStore = create((set, get) => ({ try { await executeStream(stream, message, isUserMessage, context); } finally { - if (onChunk) stream.onChunkCallbacks.delete(onChunk); - if (stream.status !== "streaming") { - const currentState = get(); - const finalActiveStreams = new Map(currentState.activeStreams); - let finalCompletedStreams = new Map(currentState.completedStreams); - - const storedStream = finalActiveStreams.get(sessionId); - if (storedStream === stream) { - const result: StreamResult = { - sessionId, - status: stream.status, - chunks: stream.chunks, - completedAt: Date.now(), - error: stream.error, - }; - finalCompletedStreams.set(sessionId, result); - finalActiveStreams.delete(sessionId); - finalCompletedStreams = cleanupExpiredStreams(finalCompletedStreams); - set({ - activeStreams: finalActiveStreams, - completedStreams: finalCompletedStreams, - }); - if (stream.status === "completed" || stream.status === "error") { - notifyStreamComplete( - currentState.streamCompleteCallbacks, - sessionId, - ); - } - } - } + finalizeStream(sessionId, stream, onChunk, get, set); } }, @@ -286,4 +409,93 @@ export const useChatStore = create((set, get) => ({ set({ streamCompleteCallbacks: cleanedCallbacks }); }; }, + + setActiveTask: function setActiveTask(sessionId, taskInfo) { + const state = get(); + const newActiveTasks = new Map(state.activeTasks); + newActiveTasks.set(sessionId, { + ...taskInfo, + sessionId, + startedAt: Date.now(), + }); + set({ activeTasks: newActiveTasks }); + persistTasks(newActiveTasks); + }, + + getActiveTask: function getActiveTask(sessionId) { + return get().activeTasks.get(sessionId); + }, + + clearActiveTask: function clearActiveTask(sessionId) { + const state = get(); + if (!state.activeTasks.has(sessionId)) return; + + const newActiveTasks = new Map(state.activeTasks); + newActiveTasks.delete(sessionId); + set({ activeTasks: newActiveTasks }); + persistTasks(newActiveTasks); + }, + + reconnectToTask: async function reconnectToTask( + sessionId, + taskId, + lastMessageId = INITIAL_STREAM_ID, + onChunk, + ) { + const state = get(); + const callbacks = state.streamCompleteCallbacks; + + // Clean up any existing stream for this session + const { + activeStreams: newActiveStreams, + completedStreams: newCompletedStreams, + } = cleanupExistingStream( + sessionId, + state.activeStreams, + state.completedStreams, + callbacks, + ); + + // Create new stream for reconnection + const stream = createActiveStream(sessionId, onChunk); + newActiveStreams.set(sessionId, stream); + set({ + activeStreams: newActiveStreams, + completedStreams: newCompletedStreams, + }); + + try { + await executeTaskReconnect(stream, taskId, lastMessageId); + } finally { + finalizeStream(sessionId, stream, onChunk, get, set); + + // Clear active task on completion + if (stream.status === "completed" || stream.status === "error") { + const taskState = get(); + if (taskState.activeTasks.has(sessionId)) { + const newActiveTasks = new Map(taskState.activeTasks); + newActiveTasks.delete(sessionId); + set({ activeTasks: newActiveTasks }); + persistTasks(newActiveTasks); + } + } + } + }, + + updateTaskLastMessageId: function updateTaskLastMessageId( + sessionId, + lastMessageId, + ) { + const state = get(); + const task = state.activeTasks.get(sessionId); + if (!task) return; + + const newActiveTasks = new Map(state.activeTasks); + newActiveTasks.set(sessionId, { + ...task, + lastMessageId, + }); + set({ activeTasks: newActiveTasks }); + persistTasks(newActiveTasks); + }, })); diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/chat-types.ts b/autogpt_platform/frontend/src/components/contextual/Chat/chat-types.ts index 8c8aa7b704..34813e17fe 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/chat-types.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/chat-types.ts @@ -4,6 +4,7 @@ export type StreamStatus = "idle" | "streaming" | "completed" | "error"; export interface StreamChunk { type: + | "stream_start" | "text_chunk" | "text_ended" | "tool_call" @@ -15,6 +16,7 @@ export interface StreamChunk { | "error" | "usage" | "stream_end"; + taskId?: string; timestamp?: string; content?: string; message?: string; @@ -41,7 +43,7 @@ export interface StreamChunk { } export type VercelStreamChunk = - | { type: "start"; messageId: string } + | { type: "start"; messageId: string; taskId?: string } | { type: "finish" } | { type: "text-start"; id: string } | { type: "text-delta"; id: string; delta: string } @@ -92,3 +94,70 @@ export interface StreamResult { } export type StreamCompleteCallback = (sessionId: string) => void; + +// Type guards for message types + +/** + * Check if a message has a toolId property. + */ +export function hasToolId( + msg: T, +): msg is T & { toolId: string } { + return ( + "toolId" in msg && + typeof (msg as Record).toolId === "string" + ); +} + +/** + * Check if a message has an operationId property. + */ +export function hasOperationId( + msg: T, +): msg is T & { operationId: string } { + return ( + "operationId" in msg && + typeof (msg as Record).operationId === "string" + ); +} + +/** + * Check if a message has a toolCallId property. + */ +export function hasToolCallId( + msg: T, +): msg is T & { toolCallId: string } { + return ( + "toolCallId" in msg && + typeof (msg as Record).toolCallId === "string" + ); +} + +/** + * Check if a message is an operation message type. + */ +export function isOperationMessage( + msg: T, +): msg is T & { + type: "operation_started" | "operation_pending" | "operation_in_progress"; +} { + return ( + msg.type === "operation_started" || + msg.type === "operation_pending" || + msg.type === "operation_in_progress" + ); +} + +/** + * Get the tool ID from a message if available. + * Checks toolId, operationId, and toolCallId properties. + */ +export function getToolIdFromMessage( + msg: T, +): string | undefined { + const record = msg as Record; + if (typeof record.toolId === "string") return record.toolId; + if (typeof record.operationId === "string") return record.operationId; + if (typeof record.toolCallId === "string") return record.toolCallId; + return undefined; +} diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx index dec221338a..5df9944f47 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx @@ -17,6 +17,13 @@ export interface ChatContainerProps { className?: string; onStreamingChange?: (isStreaming: boolean) => void; onOperationStarted?: () => void; + /** Active stream info from the server for reconnection */ + activeStream?: { + taskId: string; + lastMessageId: string; + operationId: string; + toolName: string; + }; } export function ChatContainer({ @@ -26,6 +33,7 @@ export function ChatContainer({ className, onStreamingChange, onOperationStarted, + activeStream, }: ChatContainerProps) { const { messages, @@ -41,6 +49,7 @@ export function ChatContainer({ initialMessages, initialPrompt, onOperationStarted, + activeStream, }); useEffect(() => { diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/createStreamEventDispatcher.ts b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/createStreamEventDispatcher.ts index 82e9b05e88..af3b3329b7 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/createStreamEventDispatcher.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/createStreamEventDispatcher.ts @@ -2,6 +2,7 @@ import { toast } from "sonner"; import type { StreamChunk } from "../../chat-types"; import type { HandlerDependencies } from "./handlers"; import { + getErrorDisplayMessage, handleError, handleLoginNeeded, handleStreamEnd, @@ -24,16 +25,22 @@ export function createStreamEventDispatcher( chunk.type === "need_login" || chunk.type === "error" ) { - if (!deps.hasResponseRef.current) { - console.info("[ChatStream] First response chunk:", { - type: chunk.type, - sessionId: deps.sessionId, - }); - } deps.hasResponseRef.current = true; } switch (chunk.type) { + case "stream_start": + // Store task ID for SSE reconnection + if (chunk.taskId && deps.onActiveTaskStarted) { + deps.onActiveTaskStarted({ + taskId: chunk.taskId, + operationId: chunk.taskId, + toolName: "chat", + toolCallId: "chat_stream", + }); + } + break; + case "text_chunk": handleTextChunk(chunk, deps); break; @@ -56,11 +63,7 @@ export function createStreamEventDispatcher( break; case "stream_end": - console.info("[ChatStream] Stream ended:", { - sessionId: deps.sessionId, - hasResponse: deps.hasResponseRef.current, - chunkCount: deps.streamingChunksRef.current.length, - }); + // Note: "finish" type from backend gets normalized to "stream_end" by normalizeStreamChunk handleStreamEnd(chunk, deps); break; @@ -70,7 +73,7 @@ export function createStreamEventDispatcher( // Show toast at dispatcher level to avoid circular dependencies if (!isRegionBlocked) { toast.error("Chat Error", { - description: chunk.message || chunk.content || "An error occurred", + description: getErrorDisplayMessage(chunk), }); } break; diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts index f3cac01f96..5aec5b9818 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts @@ -18,11 +18,19 @@ export interface HandlerDependencies { setStreamingChunks: Dispatch>; streamingChunksRef: MutableRefObject; hasResponseRef: MutableRefObject; + textFinalizedRef: MutableRefObject; + streamEndedRef: MutableRefObject; setMessages: Dispatch>; setIsStreamingInitiated: Dispatch>; setIsRegionBlockedModalOpen: Dispatch>; sessionId: string; onOperationStarted?: () => void; + onActiveTaskStarted?: (taskInfo: { + taskId: string; + operationId: string; + toolName: string; + toolCallId: string; + }) => void; } export function isRegionBlockedError(chunk: StreamChunk): boolean { @@ -32,6 +40,25 @@ export function isRegionBlockedError(chunk: StreamChunk): boolean { return message.toLowerCase().includes("not available in your region"); } +export function getUserFriendlyErrorMessage( + code: string | undefined, +): string | undefined { + switch (code) { + case "TASK_EXPIRED": + return "This operation has expired. Please try again."; + case "TASK_NOT_FOUND": + return "Could not find the requested operation."; + case "ACCESS_DENIED": + return "You do not have access to this operation."; + case "QUEUE_OVERFLOW": + return "Connection was interrupted. Please refresh to continue."; + case "MODEL_NOT_AVAILABLE_REGION": + return "This model is not available in your region."; + default: + return undefined; + } +} + export function handleTextChunk(chunk: StreamChunk, deps: HandlerDependencies) { if (!chunk.content) return; deps.setHasTextChunks(true); @@ -46,10 +73,15 @@ export function handleTextEnded( _chunk: StreamChunk, deps: HandlerDependencies, ) { + if (deps.textFinalizedRef.current) { + return; + } + const completedText = deps.streamingChunksRef.current.join(""); if (completedText.trim()) { + deps.textFinalizedRef.current = true; + deps.setMessages((prev) => { - // Check if this exact message already exists to prevent duplicates const exists = prev.some( (msg) => msg.type === "message" && @@ -76,9 +108,14 @@ export function handleToolCallStart( chunk: StreamChunk, deps: HandlerDependencies, ) { + // Use deterministic fallback instead of Date.now() to ensure same ID on replay + const toolId = + chunk.tool_id || + `tool-${deps.sessionId}-${chunk.idx ?? "unknown"}-${chunk.tool_name || "unknown"}`; + const toolCallMessage: Extract = { type: "tool_call", - toolId: chunk.tool_id || `tool-${Date.now()}-${chunk.idx || 0}`, + toolId, toolName: chunk.tool_name || "Executing", arguments: chunk.arguments || {}, timestamp: new Date(), @@ -111,6 +148,29 @@ export function handleToolCallStart( deps.setMessages(updateToolCallMessages); } +const TOOL_RESPONSE_TYPES = new Set([ + "tool_response", + "operation_started", + "operation_pending", + "operation_in_progress", + "execution_started", + "agent_carousel", + "clarification_needed", +]); + +function hasResponseForTool( + messages: ChatMessageData[], + toolId: string, +): boolean { + return messages.some((msg) => { + if (!TOOL_RESPONSE_TYPES.has(msg.type)) return false; + const msgToolId = + (msg as { toolId?: string }).toolId || + (msg as { toolCallId?: string }).toolCallId; + return msgToolId === toolId; + }); +} + export function handleToolResponse( chunk: StreamChunk, deps: HandlerDependencies, @@ -152,31 +212,49 @@ export function handleToolResponse( ) { const inputsMessage = extractInputsNeeded(parsedResult, chunk.tool_name); if (inputsMessage) { - deps.setMessages((prev) => [...prev, inputsMessage]); + deps.setMessages((prev) => { + // Check for duplicate inputs_needed message + const exists = prev.some((msg) => msg.type === "inputs_needed"); + if (exists) return prev; + return [...prev, inputsMessage]; + }); } const credentialsMessage = extractCredentialsNeeded( parsedResult, chunk.tool_name, ); if (credentialsMessage) { - deps.setMessages((prev) => [...prev, credentialsMessage]); + deps.setMessages((prev) => { + // Check for duplicate credentials_needed message + const exists = prev.some((msg) => msg.type === "credentials_needed"); + if (exists) return prev; + return [...prev, credentialsMessage]; + }); } } return; } - // Trigger polling when operation_started is received if (responseMessage.type === "operation_started") { deps.onOperationStarted?.(); + const taskId = (responseMessage as { taskId?: string }).taskId; + if (taskId && deps.onActiveTaskStarted) { + deps.onActiveTaskStarted({ + taskId, + operationId: + (responseMessage as { operationId?: string }).operationId || "", + toolName: (responseMessage as { toolName?: string }).toolName || "", + toolCallId: (responseMessage as { toolId?: string }).toolId || "", + }); + } } deps.setMessages((prev) => { const toolCallIndex = prev.findIndex( (msg) => msg.type === "tool_call" && msg.toolId === chunk.tool_id, ); - const hasResponse = prev.some( - (msg) => msg.type === "tool_response" && msg.toolId === chunk.tool_id, - ); - if (hasResponse) return prev; + if (hasResponseForTool(prev, chunk.tool_id!)) { + return prev; + } if (toolCallIndex !== -1) { const newMessages = [...prev]; newMessages.splice(toolCallIndex + 1, 0, responseMessage); @@ -198,28 +276,48 @@ export function handleLoginNeeded( agentInfo: chunk.agent_info, timestamp: new Date(), }; - deps.setMessages((prev) => [...prev, loginNeededMessage]); + deps.setMessages((prev) => { + // Check for duplicate login_needed message + const exists = prev.some((msg) => msg.type === "login_needed"); + if (exists) return prev; + return [...prev, loginNeededMessage]; + }); } export function handleStreamEnd( _chunk: StreamChunk, deps: HandlerDependencies, ) { + if (deps.streamEndedRef.current) { + return; + } + deps.streamEndedRef.current = true; + const completedContent = deps.streamingChunksRef.current.join(""); if (!completedContent.trim() && !deps.hasResponseRef.current) { - deps.setMessages((prev) => [ - ...prev, - { - type: "message", - role: "assistant", - content: "No response received. Please try again.", - timestamp: new Date(), - }, - ]); - } - if (completedContent.trim()) { deps.setMessages((prev) => { - // Check if this exact message already exists to prevent duplicates + const exists = prev.some( + (msg) => + msg.type === "message" && + msg.role === "assistant" && + msg.content === "No response received. Please try again.", + ); + if (exists) return prev; + return [ + ...prev, + { + type: "message", + role: "assistant", + content: "No response received. Please try again.", + timestamp: new Date(), + }, + ]; + }); + } + if (completedContent.trim() && !deps.textFinalizedRef.current) { + deps.textFinalizedRef.current = true; + + deps.setMessages((prev) => { const exists = prev.some( (msg) => msg.type === "message" && @@ -244,8 +342,6 @@ export function handleStreamEnd( } export function handleError(chunk: StreamChunk, deps: HandlerDependencies) { - const errorMessage = chunk.message || chunk.content || "An error occurred"; - console.error("Stream error:", errorMessage); if (isRegionBlockedError(chunk)) { deps.setIsRegionBlockedModalOpen(true); } @@ -253,4 +349,14 @@ export function handleError(chunk: StreamChunk, deps: HandlerDependencies) { deps.setHasTextChunks(false); deps.setStreamingChunks([]); deps.streamingChunksRef.current = []; + deps.textFinalizedRef.current = false; + deps.streamEndedRef.current = true; +} + +export function getErrorDisplayMessage(chunk: StreamChunk): string { + const friendlyMessage = getUserFriendlyErrorMessage(chunk.code); + if (friendlyMessage) { + return friendlyMessage; + } + return chunk.message || chunk.content || "An error occurred"; } diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/helpers.ts b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/helpers.ts index e744c9bc34..f1e94cea17 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/helpers.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/helpers.ts @@ -349,6 +349,7 @@ export function parseToolResponse( toolName: (parsedResult.tool_name as string) || toolName, toolId, operationId: (parsedResult.operation_id as string) || "", + taskId: (parsedResult.task_id as string) || undefined, // For SSE reconnection message: (parsedResult.message as string) || "Operation started. You can close this tab.", diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts index 46f384d055..248383df42 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts @@ -1,10 +1,17 @@ import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse"; import { useEffect, useMemo, useRef, useState } from "react"; +import { INITIAL_STREAM_ID } from "../../chat-constants"; import { useChatStore } from "../../chat-store"; import { toast } from "sonner"; import { useChatStream } from "../../useChatStream"; import { usePageContext } from "../../usePageContext"; import type { ChatMessageData } from "../ChatMessage/useChatMessage"; +import { + getToolIdFromMessage, + hasToolId, + isOperationMessage, + type StreamChunk, +} from "../../chat-types"; import { createStreamEventDispatcher } from "./createStreamEventDispatcher"; import { createUserMessage, @@ -14,6 +21,13 @@ import { processInitialMessages, } from "./helpers"; +const TOOL_RESULT_TYPES = new Set([ + "tool_response", + "agent_carousel", + "execution_started", + "clarification_needed", +]); + // Helper to generate deduplication key for a message function getMessageKey(msg: ChatMessageData): string { if (msg.type === "message") { @@ -23,14 +37,18 @@ function getMessageKey(msg: ChatMessageData): string { return `msg:${msg.role}:${msg.content}`; } else if (msg.type === "tool_call") { return `toolcall:${msg.toolId}`; - } else if (msg.type === "tool_response") { - return `toolresponse:${(msg as any).toolId}`; - } else if ( - msg.type === "operation_started" || - msg.type === "operation_pending" || - msg.type === "operation_in_progress" - ) { - return `op:${(msg as any).toolId || (msg as any).operationId || (msg as any).toolCallId || ""}:${msg.toolName}`; + } else if (TOOL_RESULT_TYPES.has(msg.type)) { + // Unified key for all tool result types - same toolId with different types + // (tool_response vs agent_carousel) should deduplicate to the same key + const toolId = getToolIdFromMessage(msg); + // If no toolId, fall back to content-based key to avoid empty key collisions + if (!toolId) { + return `toolresult:content:${JSON.stringify(msg).slice(0, 200)}`; + } + return `toolresult:${toolId}`; + } else if (isOperationMessage(msg)) { + const toolId = getToolIdFromMessage(msg) || ""; + return `op:${toolId}:${msg.toolName}`; } else { return `${msg.type}:${JSON.stringify(msg).slice(0, 100)}`; } @@ -41,6 +59,13 @@ interface Args { initialMessages: SessionDetailResponse["messages"]; initialPrompt?: string; onOperationStarted?: () => void; + /** Active stream info from the server for reconnection */ + activeStream?: { + taskId: string; + lastMessageId: string; + operationId: string; + toolName: string; + }; } export function useChatContainer({ @@ -48,6 +73,7 @@ export function useChatContainer({ initialMessages, initialPrompt, onOperationStarted, + activeStream, }: Args) { const [messages, setMessages] = useState([]); const [streamingChunks, setStreamingChunks] = useState([]); @@ -57,6 +83,8 @@ export function useChatContainer({ useState(false); const hasResponseRef = useRef(false); const streamingChunksRef = useRef([]); + const textFinalizedRef = useRef(false); + const streamEndedRef = useRef(false); const previousSessionIdRef = useRef(null); const { error, @@ -65,44 +93,182 @@ export function useChatContainer({ } = useChatStream(); const activeStreams = useChatStore((s) => s.activeStreams); const subscribeToStream = useChatStore((s) => s.subscribeToStream); + const setActiveTask = useChatStore((s) => s.setActiveTask); + const getActiveTask = useChatStore((s) => s.getActiveTask); + const reconnectToTask = useChatStore((s) => s.reconnectToTask); const isStreaming = isStreamingInitiated || hasTextChunks; + // Track whether we've already connected to this activeStream to avoid duplicate connections + const connectedActiveStreamRef = useRef(null); + // Track if component is mounted to prevent state updates after unmount + const isMountedRef = useRef(true); + // Track current dispatcher to prevent multiple dispatchers from adding messages + const currentDispatcherIdRef = useRef(0); + + // Set mounted flag - reset on every mount, cleanup on unmount + useEffect(function trackMountedState() { + isMountedRef.current = true; + return function cleanup() { + isMountedRef.current = false; + }; + }, []); + + // Callback to store active task info for SSE reconnection + function handleActiveTaskStarted(taskInfo: { + taskId: string; + operationId: string; + toolName: string; + toolCallId: string; + }) { + if (!sessionId) return; + setActiveTask(sessionId, { + taskId: taskInfo.taskId, + operationId: taskInfo.operationId, + toolName: taskInfo.toolName, + lastMessageId: INITIAL_STREAM_ID, + }); + } + + // Create dispatcher for stream events - stable reference for current sessionId + // Each dispatcher gets a unique ID to prevent stale dispatchers from updating state + function createDispatcher() { + if (!sessionId) return () => {}; + // Increment dispatcher ID - only the most recent dispatcher should update state + const dispatcherId = ++currentDispatcherIdRef.current; + + const baseDispatcher = createStreamEventDispatcher({ + setHasTextChunks, + setStreamingChunks, + streamingChunksRef, + hasResponseRef, + textFinalizedRef, + streamEndedRef, + setMessages, + setIsRegionBlockedModalOpen, + sessionId, + setIsStreamingInitiated, + onOperationStarted, + onActiveTaskStarted: handleActiveTaskStarted, + }); + + // Wrap dispatcher to check if it's still the current one + return function guardedDispatcher(chunk: StreamChunk) { + // Skip if component unmounted or this is a stale dispatcher + if (!isMountedRef.current) { + return; + } + if (dispatcherId !== currentDispatcherIdRef.current) { + return; + } + baseDispatcher(chunk); + }; + } useEffect( function handleSessionChange() { - if (sessionId === previousSessionIdRef.current) return; + const isSessionChange = sessionId !== previousSessionIdRef.current; - const prevSession = previousSessionIdRef.current; - if (prevSession) { - stopStreaming(prevSession); + // Handle session change - reset state + if (isSessionChange) { + const prevSession = previousSessionIdRef.current; + if (prevSession) { + stopStreaming(prevSession); + } + previousSessionIdRef.current = sessionId; + connectedActiveStreamRef.current = null; + setMessages([]); + setStreamingChunks([]); + streamingChunksRef.current = []; + setHasTextChunks(false); + setIsStreamingInitiated(false); + hasResponseRef.current = false; + textFinalizedRef.current = false; + streamEndedRef.current = false; } - previousSessionIdRef.current = sessionId; - setMessages([]); - setStreamingChunks([]); - streamingChunksRef.current = []; - setHasTextChunks(false); - setIsStreamingInitiated(false); - hasResponseRef.current = false; if (!sessionId) return; - const activeStream = activeStreams.get(sessionId); - if (!activeStream || activeStream.status !== "streaming") return; + // Priority 1: Check if server told us there's an active stream (most authoritative) + if (activeStream) { + const streamKey = `${sessionId}:${activeStream.taskId}`; - const dispatcher = createStreamEventDispatcher({ - setHasTextChunks, - setStreamingChunks, - streamingChunksRef, - hasResponseRef, - setMessages, - setIsRegionBlockedModalOpen, - sessionId, - setIsStreamingInitiated, - onOperationStarted, - }); + if (connectedActiveStreamRef.current === streamKey) { + return; + } + + // Skip if there's already an active stream for this session in the store + const existingStream = activeStreams.get(sessionId); + if (existingStream && existingStream.status === "streaming") { + connectedActiveStreamRef.current = streamKey; + return; + } + + connectedActiveStreamRef.current = streamKey; + + // Clear all state before reconnection to prevent duplicates + // Server's initialMessages is authoritative; local state will be rebuilt from SSE replay + setMessages([]); + setStreamingChunks([]); + streamingChunksRef.current = []; + setHasTextChunks(false); + textFinalizedRef.current = false; + streamEndedRef.current = false; + hasResponseRef.current = false; + + setIsStreamingInitiated(true); + setActiveTask(sessionId, { + taskId: activeStream.taskId, + operationId: activeStream.operationId, + toolName: activeStream.toolName, + lastMessageId: activeStream.lastMessageId, + }); + reconnectToTask( + sessionId, + activeStream.taskId, + activeStream.lastMessageId, + createDispatcher(), + ); + // Don't return cleanup here - the guarded dispatcher handles stale events + // and the stream will complete naturally. Cleanup would prematurely stop + // the stream when effect re-runs due to activeStreams changing. + return; + } + + // Only check localStorage/in-memory on session change + if (!isSessionChange) return; + + // Priority 2: Check localStorage for active task + const activeTask = getActiveTask(sessionId); + if (activeTask) { + // Clear all state before reconnection to prevent duplicates + // Server's initialMessages is authoritative; local state will be rebuilt from SSE replay + setMessages([]); + setStreamingChunks([]); + streamingChunksRef.current = []; + setHasTextChunks(false); + textFinalizedRef.current = false; + streamEndedRef.current = false; + hasResponseRef.current = false; + + setIsStreamingInitiated(true); + reconnectToTask( + sessionId, + activeTask.taskId, + activeTask.lastMessageId, + createDispatcher(), + ); + // Don't return cleanup here - the guarded dispatcher handles stale events + return; + } + + // Priority 3: Check for an in-memory active stream (same-tab scenario) + const inMemoryStream = activeStreams.get(sessionId); + if (!inMemoryStream || inMemoryStream.status !== "streaming") { + return; + } setIsStreamingInitiated(true); const skipReplay = initialMessages.length > 0; - return subscribeToStream(sessionId, dispatcher, skipReplay); + return subscribeToStream(sessionId, createDispatcher(), skipReplay); }, [ sessionId, @@ -110,6 +276,10 @@ export function useChatContainer({ activeStreams, subscribeToStream, onOperationStarted, + getActiveTask, + reconnectToTask, + activeStream, + setActiveTask, ], ); @@ -124,7 +294,7 @@ export function useChatContainer({ msg.type === "agent_carousel" || msg.type === "execution_started" ) { - const toolId = (msg as any).toolId; + const toolId = hasToolId(msg) ? msg.toolId : undefined; if (toolId) { ids.add(toolId); } @@ -141,12 +311,8 @@ export function useChatContainer({ setMessages((prev) => { const filtered = prev.filter((msg) => { - if ( - msg.type === "operation_started" || - msg.type === "operation_pending" || - msg.type === "operation_in_progress" - ) { - const toolId = (msg as any).toolId || (msg as any).toolCallId; + if (isOperationMessage(msg)) { + const toolId = getToolIdFromMessage(msg); if (toolId && completedToolIds.has(toolId)) { return false; // Remove - operation completed } @@ -174,12 +340,8 @@ export function useChatContainer({ // Filter local messages: remove duplicates and completed operation messages const newLocalMessages = messages.filter((msg) => { // Remove operation messages for completed tools - if ( - msg.type === "operation_started" || - msg.type === "operation_pending" || - msg.type === "operation_in_progress" - ) { - const toolId = (msg as any).toolId || (msg as any).toolCallId; + if (isOperationMessage(msg)) { + const toolId = getToolIdFromMessage(msg); if (toolId && completedToolIds.has(toolId)) { return false; } @@ -190,7 +352,70 @@ export function useChatContainer({ }); // Server messages first (correct order), then new local messages - return [...processedInitial, ...newLocalMessages]; + const combined = [...processedInitial, ...newLocalMessages]; + + // Post-processing: Remove duplicate assistant messages that can occur during + // race conditions (e.g., rapid screen switching during SSE reconnection). + // Two assistant messages are considered duplicates if: + // - They are both text messages with role "assistant" + // - One message's content starts with the other's content (partial vs complete) + // - Or they have very similar content (>80% overlap at the start) + const deduplicated: ChatMessageData[] = []; + for (let i = 0; i < combined.length; i++) { + const current = combined[i]; + + // Check if this is an assistant text message + if (current.type !== "message" || current.role !== "assistant") { + deduplicated.push(current); + continue; + } + + // Look for duplicate assistant messages in the rest of the array + let dominated = false; + for (let j = 0; j < combined.length; j++) { + if (i === j) continue; + const other = combined[j]; + if (other.type !== "message" || other.role !== "assistant") continue; + + const currentContent = current.content || ""; + const otherContent = other.content || ""; + + // Skip empty messages + if (!currentContent.trim() || !otherContent.trim()) continue; + + // Check if current is a prefix of other (current is incomplete version) + if ( + otherContent.length > currentContent.length && + otherContent.startsWith(currentContent.slice(0, 100)) + ) { + // Current is a shorter/incomplete version of other - skip it + dominated = true; + break; + } + + // Check if messages are nearly identical (within a small difference) + // This catches cases where content differs only slightly + const minLen = Math.min(currentContent.length, otherContent.length); + const compareLen = Math.min(minLen, 200); // Compare first 200 chars + if ( + compareLen > 50 && + currentContent.slice(0, compareLen) === + otherContent.slice(0, compareLen) + ) { + // Same prefix - keep the longer one + if (otherContent.length > currentContent.length) { + dominated = true; + break; + } + } + } + + if (!dominated) { + deduplicated.push(current); + } + } + + return deduplicated; }, [initialMessages, messages, completedToolIds]); async function sendMessage( @@ -198,10 +423,8 @@ export function useChatContainer({ isUserMessage: boolean = true, context?: { url: string; content: string }, ) { - if (!sessionId) { - console.error("[useChatContainer] Cannot send message: no session ID"); - return; - } + if (!sessionId) return; + setIsRegionBlockedModalOpen(false); if (isUserMessage) { const userMessage = createUserMessage(content); @@ -214,31 +437,19 @@ export function useChatContainer({ setHasTextChunks(false); setIsStreamingInitiated(true); hasResponseRef.current = false; - - const dispatcher = createStreamEventDispatcher({ - setHasTextChunks, - setStreamingChunks, - streamingChunksRef, - hasResponseRef, - setMessages, - setIsRegionBlockedModalOpen, - sessionId, - setIsStreamingInitiated, - onOperationStarted, - }); + textFinalizedRef.current = false; + streamEndedRef.current = false; try { await sendStreamMessage( sessionId, content, - dispatcher, + createDispatcher(), isUserMessage, context, ); } catch (err) { - console.error("[useChatContainer] Failed to send message:", err); setIsStreamingInitiated(false); - if (err instanceof Error && err.name === "AbortError") return; const errorMessage = diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/useChatMessage.ts b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/useChatMessage.ts index d6526c78ab..6809497a93 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/useChatMessage.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/useChatMessage.ts @@ -111,6 +111,7 @@ export type ChatMessageData = toolName: string; toolId: string; operationId: string; + taskId?: string; // For SSE reconnection message: string; timestamp?: string | Date; } diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx index 84f31f9d20..01d107c64e 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx @@ -31,11 +31,6 @@ export function MessageList({ isStreaming, }); - /** - * Keeps this for debugging purposes šŸ’†šŸ½ - */ - console.log(messages); - return (
{/* Top fade shadow */} diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/stream-executor.ts b/autogpt_platform/frontend/src/components/contextual/Chat/stream-executor.ts index b0d970c286..8f4c8f9fec 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/stream-executor.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/stream-executor.ts @@ -1,3 +1,4 @@ +import { INITIAL_STREAM_ID } from "./chat-constants"; import type { ActiveStream, StreamChunk, @@ -10,8 +11,14 @@ import { parseSSELine, } from "./stream-utils"; -function notifySubscribers(stream: ActiveStream, chunk: StreamChunk) { - stream.chunks.push(chunk); +function notifySubscribers( + stream: ActiveStream, + chunk: StreamChunk, + skipStore = false, +) { + if (!skipStore) { + stream.chunks.push(chunk); + } for (const callback of stream.onChunkCallbacks) { try { callback(chunk); @@ -21,36 +28,114 @@ function notifySubscribers(stream: ActiveStream, chunk: StreamChunk) { } } -export async function executeStream( - stream: ActiveStream, - message: string, - isUserMessage: boolean, - context?: { url: string; content: string }, - retryCount: number = 0, +interface StreamExecutionOptions { + stream: ActiveStream; + mode: "new" | "reconnect"; + message?: string; + isUserMessage?: boolean; + context?: { url: string; content: string }; + taskId?: string; + lastMessageId?: string; + retryCount?: number; +} + +async function executeStreamInternal( + options: StreamExecutionOptions, ): Promise { + const { + stream, + mode, + message, + isUserMessage, + context, + taskId, + lastMessageId = INITIAL_STREAM_ID, + retryCount = 0, + } = options; + const { sessionId, abortController } = stream; + const isReconnect = mode === "reconnect"; + + if (isReconnect) { + if (!taskId) { + throw new Error("taskId is required for reconnect mode"); + } + if (lastMessageId === null || lastMessageId === undefined) { + throw new Error("lastMessageId is required for reconnect mode"); + } + } else { + if (!message) { + throw new Error("message is required for new stream mode"); + } + if (isUserMessage === undefined) { + throw new Error("isUserMessage is required for new stream mode"); + } + } try { - const url = `/api/chat/sessions/${sessionId}/stream`; - const body = JSON.stringify({ - message, - is_user_message: isUserMessage, - context: context || null, - }); + let url: string; + let fetchOptions: RequestInit; - const response = await fetch(url, { - method: "POST", - headers: { - "Content-Type": "application/json", - Accept: "text/event-stream", - }, - body, - signal: abortController.signal, - }); + if (isReconnect) { + url = `/api/chat/tasks/${taskId}/stream?last_message_id=${encodeURIComponent(lastMessageId)}`; + fetchOptions = { + method: "GET", + headers: { + Accept: "text/event-stream", + }, + signal: abortController.signal, + }; + } else { + url = `/api/chat/sessions/${sessionId}/stream`; + fetchOptions = { + method: "POST", + headers: { + "Content-Type": "application/json", + Accept: "text/event-stream", + }, + body: JSON.stringify({ + message, + is_user_message: isUserMessage, + context: context || null, + }), + signal: abortController.signal, + }; + } + + const response = await fetch(url, fetchOptions); if (!response.ok) { const errorText = await response.text(); - throw new Error(errorText || `HTTP ${response.status}`); + let errorCode: string | undefined; + let errorMessage = errorText || `HTTP ${response.status}`; + try { + const parsed = JSON.parse(errorText); + if (parsed.detail) { + const detail = + typeof parsed.detail === "string" + ? parsed.detail + : parsed.detail.message || JSON.stringify(parsed.detail); + errorMessage = detail; + errorCode = + typeof parsed.detail === "object" ? parsed.detail.code : undefined; + } + } catch {} + + const isPermanentError = + isReconnect && + (response.status === 404 || + response.status === 403 || + response.status === 410); + + const error = new Error(errorMessage) as Error & { + status?: number; + isPermanent?: boolean; + taskErrorCode?: string; + }; + error.status = response.status; + error.isPermanent = isPermanentError; + error.taskErrorCode = errorCode; + throw error; } if (!response.body) { @@ -104,9 +189,7 @@ export async function executeStream( ); return; } - } catch (err) { - console.warn("[StreamExecutor] Failed to parse SSE chunk:", err); - } + } catch {} } } } @@ -117,19 +200,17 @@ export async function executeStream( return; } - if (retryCount < MAX_RETRIES) { + const isPermanentError = + err instanceof Error && + (err as Error & { isPermanent?: boolean }).isPermanent; + + if (!isPermanentError && retryCount < MAX_RETRIES) { const retryDelay = INITIAL_RETRY_DELAY * Math.pow(2, retryCount); - console.log( - `[StreamExecutor] Retrying in ${retryDelay}ms (attempt ${retryCount + 1}/${MAX_RETRIES})`, - ); await new Promise((resolve) => setTimeout(resolve, retryDelay)); - return executeStream( - stream, - message, - isUserMessage, - context, - retryCount + 1, - ); + return executeStreamInternal({ + ...options, + retryCount: retryCount + 1, + }); } stream.status = "error"; @@ -140,3 +221,35 @@ export async function executeStream( }); } } + +export async function executeStream( + stream: ActiveStream, + message: string, + isUserMessage: boolean, + context?: { url: string; content: string }, + retryCount: number = 0, +): Promise { + return executeStreamInternal({ + stream, + mode: "new", + message, + isUserMessage, + context, + retryCount, + }); +} + +export async function executeTaskReconnect( + stream: ActiveStream, + taskId: string, + lastMessageId: string = INITIAL_STREAM_ID, + retryCount: number = 0, +): Promise { + return executeStreamInternal({ + stream, + mode: "reconnect", + taskId, + lastMessageId, + retryCount, + }); +} diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/stream-utils.ts b/autogpt_platform/frontend/src/components/contextual/Chat/stream-utils.ts index 4100926e79..253e47b874 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/stream-utils.ts +++ b/autogpt_platform/frontend/src/components/contextual/Chat/stream-utils.ts @@ -28,6 +28,7 @@ export function normalizeStreamChunk( switch (chunk.type) { case "text-delta": + // Vercel AI SDK sends "delta" for text content return { type: "text_chunk", content: chunk.delta }; case "text-end": return { type: "text_ended" }; @@ -63,6 +64,10 @@ export function normalizeStreamChunk( case "finish": return { type: "stream_end" }; case "start": + // Start event with optional taskId for reconnection + return chunk.taskId + ? { type: "stream_start", taskId: chunk.taskId } + : null; case "text-start": return null; case "tool-input-start": From 1eabc604842fa876c09d69af43d2d1e8fb9b8eb9 Mon Sep 17 00:00:00 2001 From: Nicholas Tindle Date: Tue, 3 Feb 2026 11:16:57 -0600 Subject: [PATCH 26/31] Merge commit from fork Fixes GHSA-rc89-6g7g-v5v7 / CVE-2026-22038 The logger.info() calls were explicitly logging API keys via get_secret_value(), exposing credentials in plaintext logs. Changes: - Replace info-level credential logging with debug-level provider logging - Remove all explicit secret value logging from observe/act/extract blocks Co-authored-by: Otto --- .../backend/backend/blocks/stagehand/blocks.py | 15 +++------------ 1 file changed, 3 insertions(+), 12 deletions(-) diff --git a/autogpt_platform/backend/backend/blocks/stagehand/blocks.py b/autogpt_platform/backend/backend/blocks/stagehand/blocks.py index 4d5d6bf4f3..91c096ffe4 100644 --- a/autogpt_platform/backend/backend/blocks/stagehand/blocks.py +++ b/autogpt_platform/backend/backend/blocks/stagehand/blocks.py @@ -182,10 +182,7 @@ class StagehandObserveBlock(Block): **kwargs, ) -> BlockOutput: - logger.info(f"OBSERVE: Stagehand credentials: {stagehand_credentials}") - logger.info( - f"OBSERVE: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}" - ) + logger.debug(f"OBSERVE: Using model provider {model_credentials.provider}") with disable_signal_handling(): stagehand = Stagehand( @@ -282,10 +279,7 @@ class StagehandActBlock(Block): **kwargs, ) -> BlockOutput: - logger.info(f"ACT: Stagehand credentials: {stagehand_credentials}") - logger.info( - f"ACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}" - ) + logger.debug(f"ACT: Using model provider {model_credentials.provider}") with disable_signal_handling(): stagehand = Stagehand( @@ -370,10 +364,7 @@ class StagehandExtractBlock(Block): **kwargs, ) -> BlockOutput: - logger.info(f"EXTRACT: Stagehand credentials: {stagehand_credentials}") - logger.info( - f"EXTRACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}" - ) + logger.debug(f"EXTRACT: Using model provider {model_credentials.provider}") with disable_signal_handling(): stagehand = Stagehand( From c0264850237e90365dfd722bf87f7c3cd2339e61 Mon Sep 17 00:00:00 2001 From: Krzysztof Czerwinski <34861343+kcze@users.noreply.github.com> Date: Wed, 4 Feb 2026 15:11:41 +0900 Subject: [PATCH 27/31] feat(frontend): Disable auto-opening wallet (#11961) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ### Changes šŸ—ļø - Disable auto-opening Wallet for first time user and on credit increase - Remove no longer needed `lastSeenCredits` state and storage ### Checklist šŸ“‹ #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Wallet doesn't open automatically --- .../Navbar/components/Wallet/Wallet.tsx | 48 +------------------ 1 file changed, 1 insertion(+), 47 deletions(-) diff --git a/autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/Wallet.tsx b/autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/Wallet.tsx index 4a25c84f92..5a5bb514f3 100644 --- a/autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/Wallet.tsx +++ b/autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/Wallet.tsx @@ -15,7 +15,6 @@ import { import { cn } from "@/lib/utils"; import { useOnboarding } from "@/providers/onboarding/onboarding-provider"; import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag"; -import { storage, Key as StorageKey } from "@/services/storage/local-storage"; import { WalletIcon } from "@phosphor-icons/react"; import { PopoverClose } from "@radix-ui/react-popover"; import { X } from "lucide-react"; @@ -175,7 +174,6 @@ export function Wallet() { const [prevCredits, setPrevCredits] = useState(credits); const [flash, setFlash] = useState(false); const [walletOpen, setWalletOpen] = useState(false); - const [lastSeenCredits, setLastSeenCredits] = useState(null); const totalCount = useMemo(() => { return groups.reduce((acc, group) => acc + group.tasks.length, 0); @@ -200,38 +198,6 @@ export function Wallet() { setCompletedCount(completed); }, [groups, state?.completedSteps]); - // Load last seen credits from localStorage once on mount - useEffect(() => { - const stored = storage.get(StorageKey.WALLET_LAST_SEEN_CREDITS); - if (stored !== undefined && stored !== null) { - const parsed = parseFloat(stored); - if (!Number.isNaN(parsed)) setLastSeenCredits(parsed); - else setLastSeenCredits(0); - } else { - setLastSeenCredits(0); - } - }, []); - - // Auto-open once if never shown, otherwise open only when credits increase beyond last seen - useEffect(() => { - if (typeof credits !== "number") return; - // Open once for first-time users - if (state && state.walletShown === false) { - requestAnimationFrame(() => setWalletOpen(true)); - // Mark as shown so it won't reopen on every reload - updateState({ walletShown: true }); - return; - } - // Open if user gained more credits than last acknowledged - if ( - lastSeenCredits !== null && - credits > lastSeenCredits && - walletOpen === false - ) { - requestAnimationFrame(() => setWalletOpen(true)); - } - }, [credits, lastSeenCredits, state?.walletShown, updateState, walletOpen]); - const onWalletOpen = useCallback(async () => { if (!state?.walletShown) { updateState({ walletShown: true }); @@ -324,19 +290,7 @@ export function Wallet() { if (credits === null || !state) return null; return ( - { - setWalletOpen(open); - if (!open) { - // Persist the latest acknowledged credits so we only auto-open on future gains - if (typeof credits === "number") { - storage.set(StorageKey.WALLET_LAST_SEEN_CREDITS, String(credits)); - setLastSeenCredits(credits); - } - } - }} - > + setWalletOpen(open)}>
diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx index 5df9944f47..fbf2d5d143 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx @@ -2,7 +2,6 @@ import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessi import { Button } from "@/components/atoms/Button/Button"; import { Text } from "@/components/atoms/Text/Text"; import { Dialog } from "@/components/molecules/Dialog/Dialog"; -import { useBreakpoint } from "@/lib/hooks/useBreakpoint"; import { cn } from "@/lib/utils"; import { GlobeHemisphereEastIcon } from "@phosphor-icons/react"; import { useEffect } from "react"; @@ -56,10 +55,6 @@ export function ChatContainer({ onStreamingChange?.(isStreaming); }, [isStreaming, onStreamingChange]); - const breakpoint = useBreakpoint(); - const isMobile = - breakpoint === "base" || breakpoint === "sm" || breakpoint === "md"; - return (
diff --git a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx index beb4678e73..bac004f6ed 100644 --- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx +++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx @@ -74,19 +74,20 @@ export function ChatInput({ hasMultipleLines ? "rounded-xlarge" : "rounded-full", )} > + {!value && !isRecording && ( + + )}