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Merge branch 'dev' into feature/video-editing-blocks

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This commit is contained in:
Nicholas Tindle
2026-02-05 08:42:38 -06:00
committed by GitHub
parent e30873e6b6 b121030c94
commit 5f432054e0
13 changed files with 293 additions and 20 deletions
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3
autogpt_platform/backend/.gitignore vendored
View File

@@ -19,3 +19,6 @@ load-tests/*.json
load-tests/*.log
load-tests/node_modules/*
migrations/*/rollback*.sql
# Workspace files
workspaces/

17
autogpt_platform/backend/backend/api/features/chat/service.py
View File

@@ -33,7 +33,7 @@ from backend.data.understanding import (
get_business_understanding,
)
from backend.util.exceptions import NotFoundError
from backend.util.settings import Settings
from backend.util.settings import AppEnvironment, Settings
from . import db as chat_db
from . import stream_registry
@@ -222,8 +222,18 @@ async def _get_system_prompt_template(context: str) -> str:
try:
# cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
# Use asyncio.to_thread to avoid blocking the event loop
# In non-production environments, fetch the latest prompt version
# instead of the production-labeled version for easier testing
label = (
None
if settings.config.app_env == AppEnvironment.PRODUCTION
else "latest"
)
prompt = await asyncio.to_thread(
langfuse.get_prompt, config.langfuse_prompt_name, cache_ttl_seconds=0
langfuse.get_prompt,
config.langfuse_prompt_name,
label=label,
cache_ttl_seconds=0,
)
return prompt.compile(users_information=context)
except Exception as e:
@@ -618,6 +628,9 @@ async def stream_chat_completion(
total_tokens=chunk.totalTokens,
)
)
elif isinstance(chunk, StreamHeartbeat):
# Pass through heartbeat to keep SSE connection alive
yield chunk
else:
logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)

8
autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py
View File

@@ -206,9 +206,9 @@ async def search_agents(
]
)
no_results_msg = (
f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
f"No agents found matching '{query}'. Let the user know they can try different keywords or browse the marketplace. Also let them know you can create a custom agent for them based on their needs."
if source == "marketplace"
else f"No agents matching '{query}' found in your library."
else f"No agents matching '{query}' found in your library. Let the user know you can create a custom agent for them based on their needs."
)
return NoResultsResponse(
message=no_results_msg, session_id=session_id, suggestions=suggestions
@@ -224,10 +224,10 @@ async def search_agents(
message = (
"Now you have found some options for the user to choose from. "
"You can add a link to a recommended agent at: /marketplace/agent/agent_id "
"Please ask the user if they would like to use any of these agents."
"Please ask the user if they would like to use any of these agents. Let the user know we can create a custom agent for them based on their needs."
if source == "marketplace"
else "Found agents in the user's library. You can provide a link to view an agent at: "
"/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
"/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute. Let the user know we can create a custom agent for them based on their needs."
)
return AgentsFoundResponse(

15
autogpt_platform/backend/backend/blocks/youtube.py
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@@ -165,10 +165,13 @@ class TranscribeYoutubeVideoBlock(Block):
credentials: WebshareProxyCredentials,
**kwargs,
) -> BlockOutput:
video_id = self.extract_video_id(input_data.youtube_url)
yield "video_id", video_id
try:
video_id = self.extract_video_id(input_data.youtube_url)
transcript = self.get_transcript(video_id, credentials)
transcript_text = self.format_transcript(transcript=transcript)
transcript = self.get_transcript(video_id, credentials)
transcript_text = self.format_transcript(transcript=transcript)
yield "transcript", transcript_text
# Only yield after all operations succeed
yield "video_id", video_id
yield "transcript", transcript_text
except Exception as e:
yield "error", str(e)

12
autogpt_platform/backend/backend/data/credit_test.py
View File

@@ -132,9 +132,15 @@ async def test_block_credit_reset(server: SpinTestServer):
month1 = datetime(2025, 1, 15, 12, 0, 0, tzinfo=timezone.utc)
user_credit.time_now = lambda: month1
# Reset transactions AFTER setting time_now so the old date calculation
# uses a date that's definitely in a previous month relative to month1
await disable_test_user_transactions()
# IMPORTANT: Set updatedAt to December of previous year to ensure it's
# in a different month than month1 (January). This fixes a timing bug
# where if the test runs in early February, 35 days ago would be January,
# matching the mocked month1 and preventing the refill from triggering.
dec_previous_year = month1.replace(year=month1.year - 1, month=12, day=15)
await UserBalance.prisma().update(
where={"userId": DEFAULT_USER_ID},
data={"updatedAt": dec_previous_year},
)
# First call in month 1 should trigger refill
balance = await user_credit.get_credits(DEFAULT_USER_ID)

1
autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx
View File

@@ -346,6 +346,7 @@ export function ChatMessage({
toolId={message.toolId}
toolName={message.toolName}
result={message.result}
onSendMessage={onSendMessage}
/>
</div>
);

1
autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx
View File

@@ -73,6 +73,7 @@ export function MessageList({
key={index}
message={message}
prevMessage={messages[index - 1]}
onSendMessage={onSendMessage}
/>
);
}

3
autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/components/LastToolResponse/LastToolResponse.tsx
View File

@@ -5,11 +5,13 @@ import { shouldSkipAgentOutput } from "../../helpers";
export interface LastToolResponseProps {
message: ChatMessageData;
prevMessage: ChatMessageData | undefined;
onSendMessage?: (content: string) => void;
}
export function LastToolResponse({
message,
prevMessage,
onSendMessage,
}: LastToolResponseProps) {
if (message.type !== "tool_response") return null;
@@ -21,6 +23,7 @@ export function LastToolResponse({
toolId={message.toolId}
toolName={message.toolName}
result={message.result}
onSendMessage={onSendMessage}
/>
</div>
);

22
autogpt_platform/frontend/src/components/contextual/Chat/components/ThinkingMessage/ThinkingMessage.tsx
View File

@@ -1,6 +1,8 @@
import { Progress } from "@/components/atoms/Progress/Progress";
import { cn } from "@/lib/utils";
import { useEffect, useRef, useState } from "react";
import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
import { useAsymptoticProgress } from "../ToolCallMessage/useAsymptoticProgress";
export interface ThinkingMessageProps {
className?: string;
@@ -11,18 +13,19 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
const [showCoffeeMessage, setShowCoffeeMessage] = useState(false);
const timerRef = useRef<NodeJS.Timeout | null>(null);
const coffeeTimerRef = useRef<NodeJS.Timeout | null>(null);
const progress = useAsymptoticProgress(showCoffeeMessage);
useEffect(() => {
if (timerRef.current === null) {
timerRef.current = setTimeout(() => {
setShowSlowLoader(true);
}, 8000);
}, 3000);
}
if (coffeeTimerRef.current === null) {
coffeeTimerRef.current = setTimeout(() => {
setShowCoffeeMessage(true);
}, 10000);
}, 8000);
}
return () => {
@@ -49,9 +52,18 @@ export function ThinkingMessage({ className }: ThinkingMessageProps) {
<AIChatBubble>
<div className="transition-all duration-500 ease-in-out">
{showCoffeeMessage ? (
<span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
This could take a few minutes, grab a coffee ☕️
</span>
<div className="flex flex-col items-center gap-3">
<div className="flex w-full max-w-[280px] flex-col gap-1.5">
<div className="flex items-center justify-between text-xs text-neutral-500">
<span>Working on it...</span>
<span>{Math.round(progress)}%</span>
</div>
<Progress value={progress} className="h-2 w-full" />
</div>
<span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
This could take a few minutes, grab a coffee ☕️
</span>
</div>
) : showSlowLoader ? (
<span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
Taking a bit more time...

50
autogpt_platform/frontend/src/components/contextual/Chat/components/ToolCallMessage/useAsymptoticProgress.ts Normal file
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@@ -0,0 +1,50 @@
import { useEffect, useRef, useState } from "react";
/**
* Hook that returns a progress value that starts fast and slows down,
* asymptotically approaching but never reaching the max value.
*
* Uses a half-life formula: progress = max * (1 - 0.5^(time/halfLife))
* This creates the "game loading bar" effect where:
* - 50% is reached at halfLifeSeconds
* - 75% is reached at 2 * halfLifeSeconds
* - 87.5% is reached at 3 * halfLifeSeconds
* - and so on...
*
* @param isActive - Whether the progress should be animating
* @param halfLifeSeconds - Time in seconds to reach 50% progress (default: 30)
* @param maxProgress - Maximum progress value to approach (default: 100)
* @param intervalMs - Update interval in milliseconds (default: 100)
* @returns Current progress value (0-maxProgress)
*/
export function useAsymptoticProgress(
isActive: boolean,
halfLifeSeconds = 30,
maxProgress = 100,
intervalMs = 100,
) {
const [progress, setProgress] = useState(0);
const elapsedTimeRef = useRef(0);
useEffect(() => {
if (!isActive) {
setProgress(0);
elapsedTimeRef.current = 0;
return;
}
const interval = setInterval(() => {
elapsedTimeRef.current += intervalMs / 1000;
// Half-life approach: progress = max * (1 - 0.5^(time/halfLife))
// At t=halfLife: 50%, at t=2*halfLife: 75%, at t=3*halfLife: 87.5%, etc.
const newProgress =
maxProgress *
(1 - Math.pow(0.5, elapsedTimeRef.current / halfLifeSeconds));
setProgress(newProgress);
}, intervalMs);
return () => clearInterval(interval);
}, [isActive, halfLifeSeconds, maxProgress, intervalMs]);
return progress;
}

128
autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/AgentCreatedPrompt.tsx Normal file
View File

@@ -0,0 +1,128 @@
"use client";
import { useGetV2GetLibraryAgent } from "@/app/api/__generated__/endpoints/library/library";
import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
import { RunAgentModal } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/RunAgentModal";
import { Button } from "@/components/atoms/Button/Button";
import { Text } from "@/components/atoms/Text/Text";
import {
CheckCircleIcon,
PencilLineIcon,
PlayIcon,
} from "@phosphor-icons/react";
import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
interface Props {
agentName: string;
libraryAgentId: string;
onSendMessage?: (content: string) => void;
}
export function AgentCreatedPrompt({
agentName,
libraryAgentId,
onSendMessage,
}: Props) {
// Fetch library agent eagerly so modal is ready when user clicks
const { data: libraryAgentResponse, isLoading } = useGetV2GetLibraryAgent(
libraryAgentId,
{
query: {
enabled: !!libraryAgentId,
},
},
);
const libraryAgent =
libraryAgentResponse?.status === 200 ? libraryAgentResponse.data : null;
function handleRunWithPlaceholders() {
onSendMessage?.(
`Run the agent "${agentName}" with placeholder/example values so I can test it.`,
);
}
function handleRunCreated(execution: GraphExecutionMeta) {
onSendMessage?.(
`I've started the agent "${agentName}". The execution ID is ${execution.id}. Please monitor its progress and let me know when it completes.`,
);
}
function handleScheduleCreated(schedule: GraphExecutionJobInfo) {
const scheduleInfo = schedule.cron
? `with cron schedule "${schedule.cron}"`
: "to run on the specified schedule";
onSendMessage?.(
`I've scheduled the agent "${agentName}" ${scheduleInfo}. The schedule ID is ${schedule.id}.`,
);
}
return (
<AIChatBubble>
<div className="flex flex-col gap-4">
<div className="flex items-center gap-2">
<div className="flex h-8 w-8 items-center justify-center rounded-full bg-green-100">
<CheckCircleIcon
size={18}
weight="fill"
className="text-green-600"
/>
</div>
<div>
<Text variant="body-medium" className="text-neutral-900">
Agent Created Successfully
</Text>
<Text variant="small" className="text-neutral-500">
&quot;{agentName}&quot; is ready to test
</Text>
</div>
</div>
<div className="flex flex-col gap-2">
<Text variant="small-medium" className="text-neutral-700">
Ready to test?
</Text>
<div className="flex flex-wrap gap-2">
<Button
variant="outline"
size="small"
onClick={handleRunWithPlaceholders}
className="gap-2"
>
<PlayIcon size={16} />
Run with example values
</Button>
{libraryAgent ? (
<RunAgentModal
triggerSlot={
<Button variant="outline" size="small" className="gap-2">
<PencilLineIcon size={16} />
Run with my inputs
</Button>
}
agent={libraryAgent}
onRunCreated={handleRunCreated}
onScheduleCreated={handleScheduleCreated}
/>
) : (
<Button
variant="outline"
size="small"
loading={isLoading}
disabled
className="gap-2"
>
<PencilLineIcon size={16} />
Run with my inputs
</Button>
)}
</div>
<Text variant="small" className="text-neutral-500">
or just ask me
</Text>
</div>
</div>
</AIChatBubble>
);
}

16
autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/ToolResponseMessage.tsx
View File

@@ -2,11 +2,13 @@ import { Text } from "@/components/atoms/Text/Text";
import { cn } from "@/lib/utils";
import type { ToolResult } from "@/types/chat";
import { WarningCircleIcon } from "@phosphor-icons/react";
import { AgentCreatedPrompt } from "./AgentCreatedPrompt";
import { AIChatBubble } from "../AIChatBubble/AIChatBubble";
import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
import {
formatToolResponse,
getErrorMessage,
isAgentSavedResponse,
isErrorResponse,
} from "./helpers";
@@ -16,6 +18,7 @@ export interface ToolResponseMessageProps {
result?: ToolResult;
success?: boolean;
className?: string;
onSendMessage?: (content: string) => void;
}
export function ToolResponseMessage({
@@ -24,6 +27,7 @@ export function ToolResponseMessage({
result,
success: _success,
className,
onSendMessage,
}: ToolResponseMessageProps) {
if (isErrorResponse(result)) {
const errorMessage = getErrorMessage(result);
@@ -43,6 +47,18 @@ export function ToolResponseMessage({
);
}
// Check for agent_saved response - show special prompt
const agentSavedData = isAgentSavedResponse(result);
if (agentSavedData.isSaved) {
return (
<AgentCreatedPrompt
agentName={agentSavedData.agentName}
libraryAgentId={agentSavedData.libraryAgentId}
onSendMessage={onSendMessage}
/>
);
}
const formattedText = formatToolResponse(result, toolName);
return (

37
autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/helpers.ts
View File

@@ -6,6 +6,43 @@ function stripInternalReasoning(content: string): string {
.trim();
}
export interface AgentSavedData {
isSaved: boolean;
agentName: string;
agentId: string;
libraryAgentId: string;
libraryAgentLink: string;
}
export function isAgentSavedResponse(result: unknown): AgentSavedData {
if (typeof result !== "object" || result === null) {
return {
isSaved: false,
agentName: "",
agentId: "",
libraryAgentId: "",
libraryAgentLink: "",
};
}
const response = result as Record<string, unknown>;
if (response.type === "agent_saved") {
return {
isSaved: true,
agentName: (response.agent_name as string) || "Agent",
agentId: (response.agent_id as string) || "",
libraryAgentId: (response.library_agent_id as string) || "",
libraryAgentLink: (response.library_agent_link as string) || "",
};
}
return {
isSaved: false,
agentName: "",
agentId: "",
libraryAgentId: "",
libraryAgentLink: "",
};
}
export function isErrorResponse(result: unknown): boolean {
if (typeof result === "string") {
const lower = result.toLowerCase();