Updated docker setup

.dockerignore

@@ -36,5 +36,3 @@ rnd/autogpt_builder/.env.example
 rnd/autogpt_builder/.env.local
 rnd/autogpt_server/.env
 rnd/autogpt_server/.venv/
-
-rnd/market/.env

docs/content/forge/components/built-in-components.md

@@ -69,8 +69,6 @@ Lets the agent execute non-interactive Shell commands and Python code. Python ex
 | `shell_denylist`         | List of prohibited shell commands                    | `List[str]`                 | `[]`              |
 | `docker_container_name`  | Name of the Docker container used for code execution | `str`                       | `"agent_sandbox"` |
 
-All shell command configurations are expected to be for convience only. This component is not secure and should not be used in production environments. It is recommended to use more appropriate sandboxing.
-
 ### CommandProvider
 
 - `execute_shell` execute shell command

docs/content/server/setup.md

@@ -73,7 +73,6 @@ Once you have installed Yarn and Poetry, you can run the following command to in
 
 ```bash
 cd rnd/autogpt_server
-cp .env.example .env
 poetry install
 ```
 
@@ -91,7 +90,7 @@ Once you have installed the dependencies, you can proceed to the next step.
 In order to setup the database, you need to run the following commands, in the same terminal you ran the `poetry install` command:
 
    ```sh
-   docker compose up postgres redis -d
+   docker compose up postgres -d
    poetry run prisma migrate dev
    ```
 After deploying the migration, to ensure that the database schema is correctly mapped to your codebase, allowing the application to interact with the database properly, you need to generate the Prisma database model:
@@ -102,15 +101,7 @@ poetry run prisma generate
 
 Without running this command, the necessary Python modules (prisma.models) won't be available, leading to a `ModuleNotFoundError`.
 
-### Running the server without Docker
-
-To run the server, you can run the following commands in the same terminal you ran the `poetry install` command:
-
-```bash
-poetry run app
-```
-
-### Running the server within Docker
+### Running the server
 
 To run the server, you can run the following commands in the same terminal you ran the `poetry install` command:
 
@@ -119,7 +110,7 @@ docker compose build
 docker compose up
 ```
 
-In the other terminal from autogpt_builder, you can run the following command to start the frontend:
+In the other terminal, you can run the following command to start the frontend:
 
 ```bash
 yarn dev
@@ -128,10 +119,3 @@ yarn dev
 ### Checking if the server is running
 
 You can check if the server is running by visiting [http://localhost:3000](http://localhost:3000) in your browser.
-
-### Notes: 
-By default the daemons for different services run on the following ports: 
-
-Execution Manager Daemon: 8002
-Execution Scheduler Daemon: 8003
-Rest Server Daemon: 8004

rnd/README.md

@@ -1,114 +1,36 @@
-# AutoGPT Platform
+This is a guide to setting up and running the AutoGPT Server and Builder. This tutorial will cover downloading the necessary files, setting up the server, and testing the system.
 
-Welcome to the AutoGPT Platform - a powerful system for creating and running AI agents to solve business problems. This platform enables you to harness the power of artificial intelligence to automate tasks, analyze data, and generate insights for your organization.
+https://github.com/user-attachments/assets/fd0d0f35-3155-4263-b575-ba3efb126cb4
 
-## Getting Started
+1. Navigate to the AutoGPT GitHub repository.
+2. Click the "Code" button, then select "Download ZIP".
+3. Once downloaded, extract the ZIP file to a folder of your choice.
 
-### Prerequisites
-
-- Docker
-- Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
-
-### Running the System
-
-To run the AutoGPT Platform, follow these steps:
-
-1. Clone this repository to your local machine.
-2. Navigate to the project directory.
-3. Run the following command:
-
-   ```
-   docker compose up -d
-   ```
-
-   This command will start all the necessary services defined in the `docker-compose.yml` file in detached mode.
-
-### Docker Compose Commands
-
-Here are some useful Docker Compose commands for managing your AutoGPT Platform:
-
-- `docker compose up -d`: Start the services in detached mode.
-- `docker compose stop`: Stop the running services without removing them.
-- `docker compose rm`: Remove stopped service containers.
-- `docker compose build`: Build or rebuild services.
-- `docker compose down`: Stop and remove containers, networks, and volumes.
-- `docker compose watch`: Watch for changes in your services and automatically update them.
-
-
-### Sample Scenarios
-
-Here are some common scenarios where you might use multiple Docker Compose commands:
-
-1. Updating and restarting a specific service:
-   ```
-   docker compose build api_srv
-   docker compose up -d --no-deps api_srv
-   ```
-   This rebuilds the `api_srv` service and restarts it without affecting other services.
-
-2. Viewing logs for troubleshooting:
-   ```
-   docker compose logs -f api_srv ws_srv
-   ```
-   This shows and follows the logs for both `api_srv` and `ws_srv` services.
-
-3. Scaling a service for increased load:
-   ```
-   docker compose up -d --scale executor=3
-   ```
-   This scales the `executor` service to 3 instances to handle increased load.
-
-4. Stopping the entire system for maintenance:
-   ```
-   docker compose stop
-   docker compose rm -f
-   docker compose pull
-   docker compose up -d
-   ```
-   This stops all services, removes containers, pulls the latest images, and restarts the system.
-
-5. Developing with live updates:
-   ```
-   docker compose watch
-   ```
-   This watches for changes in your code and automatically updates the relevant services.
-
-6. Checking the status of services:
-   ```
-   docker compose ps
-   ```
-   This shows the current status of all services defined in your docker-compose.yml file.
-
-These scenarios demonstrate how to use Docker Compose commands in combination to manage your AutoGPT Platform effectively.
-
-
-### Persisting Data
-
-To persist data for PostgreSQL and Redis, you can modify the `docker-compose.yml` file to add volumes. Here's how:
-
-1. Open the `docker-compose.yml` file in a text editor.
-2. Add volume configurations for PostgreSQL and Redis services:
-
-   ```yaml
-   services:
-     postgres:
-       # ... other configurations ...
-       volumes:
-         - postgres_data:/var/lib/postgresql/data
-
-     redis:
-       # ... other configurations ...
-       volumes:
-         - redis_data:/data
-
-   volumes:
-     postgres_data:
-     redis_data:
-   ```
-
-3. Save the file and run `docker compose up -d` to apply the changes.
-
-This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
+4. Open the extracted folder and navigate to the "rnd" directory.
+5. Enter the "AutoGPT server" folder.
+6. Open a terminal window in this directory.
+7. Locate and open the README file in the AutoGPT server folder: [doc](./autogpt_server/README.md#setup).
+8. Copy and paste each command from the setup section in the README into your terminal.
+   - Important: Wait for each command to finish before running the next one.
+9. If all commands run without errors, enter the final command: `poetry run app`
+10. You should now see the server running in your terminal.
 
+11. Navigate back to the "rnd" folder.
+12. Open the "AutoGPT builder" folder.
+13. Open the README file in this folder: [doc](./autogpt_builder/README.md#getting-started).
+14. In your terminal, run the following commands:
+    ```
+    npm install
+    ```
+    ```
+    npm run dev
+    ```
 
+15. Once the front-end is running, click the link to navigate to `localhost:3000`.
+16. Click on the "Build" option.
+17. Add a few blocks to test the functionality.
+18. Connect the blocks together.
+19. Click "Run".
+20. Check your terminal window - you should see that the server has received the request, is processing it, and has executed it.
 
+And there you have it! You've successfully set up and tested AutoGPT.

rnd/autogpt_builder/.gitignore

@@ -34,6 +34,3 @@ yarn-error.log*
 # typescript
 *.tsbuildinfo
 next-env.d.ts
-
-# Sentry Config File
-.env.sentry-build-plugin

rnd/autogpt_builder/.storybook/main.ts

@@ -0,0 +1,18 @@
+import type { StorybookConfig } from "@storybook/nextjs";
+
+const config: StorybookConfig = {
+  stories: ["../src/**/*.mdx", "../src/**/*.stories.@(js|jsx|mjs|ts|tsx)"],
+  addons: [
+    "@storybook/addon-onboarding",
+    "@storybook/addon-links",
+    "@storybook/addon-essentials",
+    "@chromatic-com/storybook",
+    "@storybook/addon-interactions",
+  ],
+  framework: {
+    name: "@storybook/nextjs",
+    options: {},
+  },
+  staticDirs: ["../public"],
+};
+export default config;

rnd/autogpt_builder/.storybook/preview.ts

@@ -0,0 +1,14 @@
+import type { Preview } from "@storybook/react";
+
+const preview: Preview = {
+  parameters: {
+    controls: {
+      matchers: {
+        color: /(background|color)$/i,
+        date: /Date$/i,
+      },
+    },
+  },
+};
+
+export default preview;

rnd/autogpt_builder/next.config.mjs

@@ -1,4 +1,3 @@
-import { withSentryConfig } from "@sentry/nextjs";
 import dotenv from "dotenv";
 
 // Load environment variables
@@ -29,56 +28,4 @@ const nextConfig = {
   },
 };
 
-export default withSentryConfig(nextConfig, {
-  // For all available options, see:
-  // https://github.com/getsentry/sentry-webpack-plugin#options
-
-  org: "significant-gravitas",
-  project: "builder",
-
-  // Only print logs for uploading source maps in CI
-  silent: !process.env.CI,
-
-  // For all available options, see:
-  // https://docs.sentry.io/platforms/javascript/guides/nextjs/manual-setup/
-
-  // Upload a larger set of source maps for prettier stack traces (increases build time)
-  widenClientFileUpload: true,
-
-  // Automatically annotate React components to show their full name in breadcrumbs and session replay
-  reactComponentAnnotation: {
-    enabled: true,
-  },
-
-  // Route browser requests to Sentry through a Next.js rewrite to circumvent ad-blockers.
-  // This can increase your server load as well as your hosting bill.
-  // Note: Check that the configured route will not match with your Next.js middleware, otherwise reporting of client-
-  // side errors will fail.
-  tunnelRoute: "/monitoring",
-
-  // Hides source maps from generated client bundles
-  hideSourceMaps: true,
-
-  // Automatically tree-shake Sentry logger statements to reduce bundle size
-  disableLogger: true,
-
-  // Enables automatic instrumentation of Vercel Cron Monitors. (Does not yet work with App Router route handlers.)
-  // See the following for more information:
-  // https://docs.sentry.io/product/crons/
-  // https://vercel.com/docs/cron-jobs
-  automaticVercelMonitors: true,
-
-  async headers() {
-    return [
-      {
-        source: "/:path*",
-        headers: [
-          {
-            key: "Document-Policy",
-            value: "js-profiling",
-          },
-        ],
-      },
-    ];
-  },
-});
+export default nextConfig;

rnd/autogpt_builder/package.json

@@ -27,7 +27,6 @@
     "@radix-ui/react-switch": "^1.1.0",
     "@radix-ui/react-toast": "^1.2.1",
     "@radix-ui/react-tooltip": "^1.1.2",
-    "@sentry/nextjs": "^8",
     "@supabase/ssr": "^0.4.0",
     "@supabase/supabase-js": "^2.45.0",
     "@tanstack/react-table": "^8.20.5",

rnd/autogpt_builder/sentry.client.config.ts

@@ -1,57 +0,0 @@
-// This file configures the initialization of Sentry on the client.
-// The config you add here will be used whenever a users loads a page in their browser.
-// https://docs.sentry.io/platforms/javascript/guides/nextjs/
-
-import * as Sentry from "@sentry/nextjs";
-
-Sentry.init({
-  dsn: "https://fe4e4aa4a283391808a5da396da20159@o4505260022104064.ingest.us.sentry.io/4507946746380288",
-
-  // Add optional integrations for additional features
-  integrations: [
-    Sentry.replayIntegration(),
-    Sentry.httpClientIntegration(),
-    Sentry.replayCanvasIntegration(),
-    Sentry.reportingObserverIntegration(),
-    Sentry.browserProfilingIntegration(),
-    // Sentry.feedbackIntegration({
-    //   // Additional SDK configuration goes in here, for example:
-    //   colorScheme: "system",
-    // }),
-  ],
-
-  // Define how likely traces are sampled. Adjust this value in production, or use tracesSampler for greater control.
-  tracesSampleRate: 1,
-
-  // Set `tracePropagationTargets` to control for which URLs trace propagation should be enabled
-  tracePropagationTargets: [
-    "localhost",
-    /^https:\/\/dev\-builder\.agpt\.co\/api/,
-  ],
-
-  beforeSend(event, hint) {
-    // Check if it is an exception, and if so, show the report dialog
-    if (event.exception && event.event_id) {
-      Sentry.showReportDialog({ eventId: event.event_id });
-    }
-    return event;
-  },
-
-  // Define how likely Replay events are sampled.
-  // This sets the sample rate to be 10%. You may want this to be 100% while
-  // in development and sample at a lower rate in production
-  replaysSessionSampleRate: 0.1,
-
-  // Define how likely Replay events are sampled when an error occurs.
-  replaysOnErrorSampleRate: 1.0,
-
-  // Setting this option to true will print useful information to the console while you're setting up Sentry.
-  debug: false,
-
-  // Set profilesSampleRate to 1.0 to profile every transaction.
-  // Since profilesSampleRate is relative to tracesSampleRate,
-  // the final profiling rate can be computed as tracesSampleRate * profilesSampleRate
-  // For example, a tracesSampleRate of 0.5 and profilesSampleRate of 0.5 would
-  // result in 25% of transactions being profiled (0.5*0.5=0.25)
-  profilesSampleRate: 1.0,
-});

rnd/autogpt_builder/sentry.edge.config.ts

@@ -1,16 +0,0 @@
-// This file configures the initialization of Sentry for edge features (middleware, edge routes, and so on).
-// The config you add here will be used whenever one of the edge features is loaded.
-// Note that this config is unrelated to the Vercel Edge Runtime and is also required when running locally.
-// https://docs.sentry.io/platforms/javascript/guides/nextjs/
-
-import * as Sentry from "@sentry/nextjs";
-
-Sentry.init({
-  dsn: "https://fe4e4aa4a283391808a5da396da20159@o4505260022104064.ingest.us.sentry.io/4507946746380288",
-
-  // Define how likely traces are sampled. Adjust this value in production, or use tracesSampler for greater control.
-  tracesSampleRate: 1,
-
-  // Setting this option to true will print useful information to the console while you're setting up Sentry.
-  debug: false,
-});

rnd/autogpt_builder/sentry.server.config.ts

@@ -1,23 +0,0 @@
-// This file configures the initialization of Sentry on the server.
-// The config you add here will be used whenever the server handles a request.
-// https://docs.sentry.io/platforms/javascript/guides/nextjs/
-
-import * as Sentry from "@sentry/nextjs";
-// import { NodeProfilingIntegration } from "@sentry/profiling-node";
-
-Sentry.init({
-  dsn: "https://fe4e4aa4a283391808a5da396da20159@o4505260022104064.ingest.us.sentry.io/4507946746380288",
-
-  // Define how likely traces are sampled. Adjust this value in production, or use tracesSampler for greater control.
-  tracesSampleRate: 1,
-
-  // Setting this option to true will print useful information to the console while you're setting up Sentry.
-  debug: false,
-
-  // Integrations
-  integrations: [
-    Sentry.anrIntegration(),
-    // NodeProfilingIntegration,
-    // Sentry.fsIntegration(),
-  ],
-});

rnd/autogpt_builder/src/app/error.tsx

@@ -26,8 +26,8 @@ export default function Error({
           Oops, something went wrong!
         </h1>
         <p className="mt-4 text-muted-foreground">
-          We&apos;re sorry, but an unexpected error has occurred. Please try
-          again later or contact support if the issue persists.
+          We're sorry, but an unexpected error has occurred. Please try again
+          later or contact support if the issue persists.
         </p>
         <div className="mt-6 flex flex-row justify-center gap-4">
           <Button onClick={reset} variant="outline">

rnd/autogpt_builder/src/app/global-error.tsx

@@ -1,27 +0,0 @@
-"use client";
-
-import * as Sentry from "@sentry/nextjs";
-import NextError from "next/error";
-import { useEffect } from "react";
-
-export default function GlobalError({
-  error,
-}: {
-  error: Error & { digest?: string };
-}) {
-  useEffect(() => {
-    Sentry.captureException(error);
-  }, [error]);
-
-  return (
-    <html>
-      <body>
-        {/* `NextError` is the default Next.js error page component. Its type
-        definition requires a `statusCode` prop. However, since the App Router
-        does not expose status codes for errors, we simply pass 0 to render a
-        generic error message. */}
-        <NextError statusCode={0} />
-      </body>
-    </html>
-  );
-}

rnd/autogpt_builder/src/app/login/actions.ts

@@ -3,7 +3,6 @@ import { revalidatePath } from "next/cache";
 import { redirect } from "next/navigation";
 import { createServerClient } from "@/lib/supabase/server";
 import { z } from "zod";
-import * as Sentry from "@sentry/nextjs";
 
 const loginFormSchema = z.object({
   email: z.string().email().min(2).max(64),
@@ -11,53 +10,45 @@ const loginFormSchema = z.object({
 });
 
 export async function login(values: z.infer<typeof loginFormSchema>) {
-  return await Sentry.withServerActionInstrumentation("login", {}, async () => {
-    const supabase = createServerClient();
+  const supabase = createServerClient();
 
-    if (!supabase) {
-      redirect("/error");
-    }
+  if (!supabase) {
+    redirect("/error");
+  }
 
-    // We are sure that the values are of the correct type because zod validates the form
-    const { data, error } = await supabase.auth.signInWithPassword(values);
+  // We are sure that the values are of the correct type because zod validates the form
+  const { data, error } = await supabase.auth.signInWithPassword(values);
 
-    if (error) {
-      return error.message;
-    }
+  if (error) {
+    return error.message;
+  }
 
-    if (data.session) {
-      await supabase.auth.setSession(data.session);
-    }
+  if (data.session) {
+    await supabase.auth.setSession(data.session);
+  }
 
-    revalidatePath("/", "layout");
-    redirect("/profile");
-  });
+  revalidatePath("/", "layout");
+  redirect("/profile");
 }
 
 export async function signup(values: z.infer<typeof loginFormSchema>) {
-  return await Sentry.withServerActionInstrumentation(
-    "signup",
-    {},
-    async () => {
-      const supabase = createServerClient();
+  const supabase = createServerClient();
 
-      if (!supabase) {
-        redirect("/error");
-      }
+  if (!supabase) {
+    redirect("/error");
+  }
 
-      // We are sure that the values are of the correct type because zod validates the form
-      const { data, error } = await supabase.auth.signUp(values);
+  // We are sure that the values are of the correct type because zod validates the form
+  const { data, error } = await supabase.auth.signUp(values);
 
-      if (error) {
-        return error.message;
-      }
+  if (error) {
+    return error.message;
+  }
 
-      if (data.session) {
-        await supabase.auth.setSession(data.session);
-      }
+  if (data.session) {
+    await supabase.auth.setSession(data.session);
+  }
 
-      revalidatePath("/", "layout");
-      redirect("/profile");
-    },
-  );
+  revalidatePath("/", "layout");
+  redirect("/profile");
 }

rnd/autogpt_builder/src/app/page.tsx

@@ -1,5 +1,5 @@
 "use client";
-import React, { useCallback, useEffect, useMemo, useState } from "react";
+import React, { useEffect, useState } from "react";
 
 import AutoGPTServerAPI, {
   GraphMeta,
@@ -22,57 +22,54 @@ const Monitor = () => {
   const [selectedFlow, setSelectedFlow] = useState<GraphMeta | null>(null);
   const [selectedRun, setSelectedRun] = useState<FlowRun | null>(null);
 
-  const api = useMemo(() => new AutoGPTServerAPI(), []);
+  const api = new AutoGPTServerAPI();
 
-  const refreshFlowRuns = useCallback(
-    (flowID: string) => {
-      // Fetch flow run IDs
-      api.listGraphRunIDs(flowID).then((runIDs) =>
-        runIDs.map((runID) => {
-          let run;
-          if (
-            (run = flowRuns.find((fr) => fr.id == runID)) &&
-            !["waiting", "running"].includes(run.status)
-          ) {
-            return;
-          }
-
-          // Fetch flow run
-          api.getGraphExecutionInfo(flowID, runID).then((execInfo) =>
-            setFlowRuns((flowRuns) => {
-              if (execInfo.length == 0) return flowRuns;
-
-              const flowRunIndex = flowRuns.findIndex((fr) => fr.id == runID);
-              const flowRun = flowRunFromNodeExecutionResults(execInfo);
-              if (flowRunIndex > -1) {
-                flowRuns.splice(flowRunIndex, 1, flowRun);
-              } else {
-                flowRuns.push(flowRun);
-              }
-              return [...flowRuns];
-            }),
-          );
-        }),
-      );
-    },
-    [api, flowRuns],
-  );
-
-  const fetchFlowsAndRuns = useCallback(() => {
-    api.listGraphs().then((flows) => {
-      setFlows(flows);
-      flows.map((flow) => refreshFlowRuns(flow.id));
-    });
-  }, [api, refreshFlowRuns]);
-
-  useEffect(() => fetchFlowsAndRuns(), [fetchFlowsAndRuns]);
+  useEffect(() => fetchFlowsAndRuns(), []);
   useEffect(() => {
     const intervalId = setInterval(
       () => flows.map((f) => refreshFlowRuns(f.id)),
       5000,
     );
     return () => clearInterval(intervalId);
-  }, [flows, refreshFlowRuns]);
+  }, []);
+
+  function fetchFlowsAndRuns() {
+    api.listGraphs().then((flows) => {
+      setFlows(flows);
+      flows.map((flow) => refreshFlowRuns(flow.id));
+    });
+  }
+
+  function refreshFlowRuns(flowID: string) {
+    // Fetch flow run IDs
+    api.listGraphRunIDs(flowID).then((runIDs) =>
+      runIDs.map((runID) => {
+        let run;
+        if (
+          (run = flowRuns.find((fr) => fr.id == runID)) &&
+          !["waiting", "running"].includes(run.status)
+        ) {
+          return;
+        }
+
+        // Fetch flow run
+        api.getGraphExecutionInfo(flowID, runID).then((execInfo) =>
+          setFlowRuns((flowRuns) => {
+            if (execInfo.length == 0) return flowRuns;
+
+            const flowRunIndex = flowRuns.findIndex((fr) => fr.id == runID);
+            const flowRun = flowRunFromNodeExecutionResults(execInfo);
+            if (flowRunIndex > -1) {
+              flowRuns.splice(flowRunIndex, 1, flowRun);
+            } else {
+              flowRuns.push(flowRun);
+            }
+            return [...flowRuns];
+          }),
+        );
+      }),
+    );
+  }
 
   const column1 = "md:col-span-2 xl:col-span-3 xxl:col-span-2";
   const column2 = "md:col-span-3 lg:col-span-2 xl:col-span-3 space-y-4";

rnd/autogpt_builder/src/components/CustomEdge.tsx

@@ -1,4 +1,4 @@
-import React, { useCallback, useContext, useEffect, useState } from "react";
+import React, { useContext, useEffect, useState } from "react";
 import {
   BaseEdge,
   EdgeLabelRenderer,
@@ -65,27 +65,24 @@ export function CustomEdge({
   const beadDiameter = 12;
   const deltaTime = 16;
 
-  const setTargetPositions = useCallback(
-    (beads: Bead[]) => {
-      const distanceBetween = Math.min(
-        (length - beadDiameter) / (beads.length + 1),
-        beadDiameter,
-      );
+  function setTargetPositions(beads: Bead[]) {
+    const distanceBetween = Math.min(
+      (length - beadDiameter) / (beads.length + 1),
+      beadDiameter,
+    );
 
-      return beads.map((bead, index) => {
-        const distanceFromEnd = beadDiameter * 1.35;
-        const targetPosition = distanceBetween * index + distanceFromEnd;
-        const t = getTForDistance(-targetPosition);
+    return beads.map((bead, index) => {
+      const distanceFromEnd = beadDiameter * 1.35;
+      const targetPosition = distanceBetween * index + distanceFromEnd;
+      const t = getTForDistance(-targetPosition);
 
-        return {
-          ...bead,
-          t: visualizeBeads === "animate" ? bead.t : t,
-          targetT: t,
-        } as Bead;
-      });
-    },
-    [getTForDistance, length, visualizeBeads],
-  );
+      return {
+        ...bead,
+        t: visualizeBeads === "animate" ? bead.t : t,
+        targetT: t,
+      } as Bead;
+    });
+  }
 
   useEffect(() => {
     if (data?.beadUp === 0 && data?.beadDown === 0) {
@@ -173,7 +170,7 @@ export function CustomEdge({
     }, deltaTime);
 
     return () => clearInterval(interval);
-  }, [data, setTargetPositions, visualizeBeads]);
+  }, [data]);
 
   const middle = getPointForT(0.5);
 

rnd/autogpt_builder/src/components/CustomNode.tsx

@@ -12,10 +12,8 @@ import InputModalComponent from "./InputModalComponent";
 import OutputModalComponent from "./OutputModalComponent";
 import {
   BlockIORootSchema,
-  BlockIOStringSubSchema,
   Category,
   NodeExecutionResult,
-  BlockUIType,
 } from "@/lib/autogpt-server-api/types";
 import { beautifyString, cn, setNestedProperty } from "@/lib/utils";
 import { Button } from "@/components/ui/button";
@@ -23,10 +21,7 @@ import { Switch } from "@/components/ui/switch";
 import { Copy, Trash2 } from "lucide-react";
 import { history } from "./history";
 import NodeHandle from "./NodeHandle";
-import {
-  NodeGenericInputField,
-  NodeTextBoxInput,
-} from "./node-input-components";
+import { NodeGenericInputField } from "./node-input-components";
 import SchemaTooltip from "./SchemaTooltip";
 import { getPrimaryCategoryColor } from "@/lib/utils";
 import { FlowContext } from "./Flow";
@@ -64,7 +59,6 @@ export type CustomNodeData = {
   backend_id?: string;
   errors?: { [key: string]: string };
   isOutputStatic?: boolean;
-  uiType: BlockUIType;
 };
 
 export type CustomNode = Node<CustomNodeData, "custom">;
@@ -102,7 +96,7 @@ export function CustomNode({ data, id, width, height }: NodeProps<CustomNode>) {
 
   useEffect(() => {
     setIsAnyModalOpen?.(isModalOpen || isOutputModalOpen);
-  }, [isModalOpen, isOutputModalOpen, data, setIsAnyModalOpen]);
+  }, [isModalOpen, isOutputModalOpen, data]);
 
   useEffect(() => {
     isInitialSetup.current = false;
@@ -124,16 +118,8 @@ export function CustomNode({ data, id, width, height }: NodeProps<CustomNode>) {
     setIsAdvancedOpen(checked);
   };
 
-  const generateOutputHandles = (
-    schema: BlockIORootSchema,
-    nodeType: BlockUIType,
-  ) => {
-    if (
-      !schema?.properties ||
-      nodeType === BlockUIType.OUTPUT ||
-      nodeType === BlockUIType.NOTE
-    )
-      return null;
+  const generateOutputHandles = (schema: BlockIORootSchema) => {
+    if (!schema?.properties) return null;
     const keys = Object.keys(schema.properties);
     return keys.map((key) => (
       <div key={key}>
@@ -147,137 +133,6 @@ export function CustomNode({ data, id, width, height }: NodeProps<CustomNode>) {
     ));
   };
 
-  const generateInputHandles = (
-    schema: BlockIORootSchema,
-    nodeType: BlockUIType,
-  ) => {
-    if (!schema?.properties) return null;
-    let keys = Object.entries(schema.properties);
-    switch (nodeType) {
-      case BlockUIType.INPUT:
-        // For INPUT blocks, dont include connection handles
-        return keys.map(([propKey, propSchema]) => {
-          const isRequired = data.inputSchema.required?.includes(propKey);
-          const isConnected = isHandleConnected(propKey);
-          const isAdvanced = propSchema.advanced;
-          return (
-            (isRequired || isAdvancedOpen || !isAdvanced) && (
-              <div key={propKey}>
-                <span className="text-m green -mb-1 text-gray-900">
-                  {propSchema.title || beautifyString(propKey)}
-                </span>
-                <div key={propKey} onMouseOver={() => {}}>
-                  {!isConnected && (
-                    <NodeGenericInputField
-                      className="mb-2 mt-1"
-                      propKey={propKey}
-                      propSchema={propSchema}
-                      currentValue={getValue(propKey)}
-                      connections={data.connections}
-                      handleInputChange={handleInputChange}
-                      handleInputClick={handleInputClick}
-                      errors={data.errors ?? {}}
-                      displayName={propSchema.title || beautifyString(propKey)}
-                    />
-                  )}
-                </div>
-              </div>
-            )
-          );
-        });
-
-      case BlockUIType.NOTE:
-        // For NOTE blocks, don't render any input handles
-        const [noteKey, noteSchema] = keys[0];
-        return (
-          <div key={noteKey}>
-            <NodeTextBoxInput
-              className=""
-              selfKey={noteKey}
-              schema={noteSchema as BlockIOStringSubSchema}
-              value={getValue(noteKey)}
-              handleInputChange={handleInputChange}
-              handleInputClick={handleInputClick}
-              error={data.errors?.[noteKey] ?? ""}
-              displayName={noteSchema.title || beautifyString(noteKey)}
-            />
-          </div>
-        );
-
-      case BlockUIType.OUTPUT:
-        // For OUTPUT blocks, only show the 'value' property
-        return keys.map(([propKey, propSchema]) => {
-          const isRequired = data.inputSchema.required?.includes(propKey);
-          const isConnected = isHandleConnected(propKey);
-          const isAdvanced = propSchema.advanced;
-          return (
-            (isRequired || isAdvancedOpen || !isAdvanced) && (
-              <div key={propKey} onMouseOver={() => {}}>
-                {propKey !== "value" ? (
-                  <span className="text-m green -mb-1 text-gray-900">
-                    {propSchema.title || beautifyString(propKey)}
-                  </span>
-                ) : (
-                  <NodeHandle
-                    keyName={propKey}
-                    isConnected={isConnected}
-                    isRequired={isRequired}
-                    schema={propSchema}
-                    side="left"
-                  />
-                )}
-                {!isConnected && (
-                  <NodeGenericInputField
-                    className="mb-2 mt-1"
-                    propKey={propKey}
-                    propSchema={propSchema}
-                    currentValue={getValue(propKey)}
-                    connections={data.connections}
-                    handleInputChange={handleInputChange}
-                    handleInputClick={handleInputClick}
-                    errors={data.errors ?? {}}
-                    displayName={propSchema.title || beautifyString(propKey)}
-                  />
-                )}
-              </div>
-            )
-          );
-        });
-
-      default:
-        return keys.map(([propKey, propSchema]) => {
-          const isRequired = data.inputSchema.required?.includes(propKey);
-          const isConnected = isHandleConnected(propKey);
-          const isAdvanced = propSchema.advanced;
-          return (
-            (isRequired || isAdvancedOpen || isConnected || !isAdvanced) && (
-              <div key={propKey} onMouseOver={() => {}}>
-                <NodeHandle
-                  keyName={propKey}
-                  isConnected={isConnected}
-                  isRequired={isRequired}
-                  schema={propSchema}
-                  side="left"
-                />
-                {!isConnected && (
-                  <NodeGenericInputField
-                    className="mb-2 mt-1"
-                    propKey={propKey}
-                    propSchema={propSchema}
-                    currentValue={getValue(propKey)}
-                    connections={data.connections}
-                    handleInputChange={handleInputChange}
-                    handleInputClick={handleInputClick}
-                    errors={data.errors ?? {}}
-                    displayName={propSchema.title || beautifyString(propKey)}
-                  />
-                )}
-              </div>
-            )
-          );
-        });
-    }
-  };
   const handleInputChange = (path: string, value: any) => {
     const keys = parseKeys(path);
     const newValues = JSON.parse(JSON.stringify(data.hardcodedValues));
@@ -523,13 +378,13 @@ export function CustomNode({ data, id, width, height }: NodeProps<CustomNode>) {
 
   return (
     <div
-      className={`${data.uiType === BlockUIType.NOTE ? "w-[300px]" : "w-[500px]"} ${blockClasses} ${errorClass} ${statusClass} ${data.uiType === BlockUIType.NOTE ? "bg-yellow-100" : "bg-white"}`}
+      className={`${blockClasses} ${errorClass} ${statusClass}`}
       onMouseEnter={handleHovered}
       onMouseLeave={handleMouseLeave}
       data-id={`custom-node-${id}`}
     >
       <div
-        className={`mb-2 p-3 ${data.uiType === BlockUIType.NOTE ? "bg-yellow-100" : getPrimaryCategoryColor(data.categories)} rounded-t-xl`}
+        className={`mb-2 p-3 ${getPrimaryCategoryColor(data.categories)} rounded-t-xl`}
       >
         <div className="flex items-center justify-between">
           <div className="font-roboto p-3 text-lg font-semibold">
@@ -562,24 +417,53 @@ export function CustomNode({ data, id, width, height }: NodeProps<CustomNode>) {
           )}
         </div>
       </div>
-      {data.uiType !== BlockUIType.NOTE ? (
-        <div className="flex items-start justify-between p-3">
-          <div>
-            {data.inputSchema &&
-              generateInputHandles(data.inputSchema, data.uiType)}
-          </div>
-          <div className="flex-none">
-            {data.outputSchema &&
-              generateOutputHandles(data.outputSchema, data.uiType)}
-          </div>
-        </div>
-      ) : (
+      <div className="flex items-start justify-between gap-2 p-3">
         <div>
           {data.inputSchema &&
-            generateInputHandles(data.inputSchema, data.uiType)}
+            Object.entries(data.inputSchema.properties).map(
+              ([propKey, propSchema]) => {
+                const isRequired = data.inputSchema.required?.includes(propKey);
+                const isConnected = isHandleConnected(propKey);
+                const isAdvanced = propSchema.advanced;
+                return (
+                  (isRequired ||
+                    isAdvancedOpen ||
+                    isConnected ||
+                    !isAdvanced) && (
+                    <div key={propKey} onMouseOver={() => {}}>
+                      <NodeHandle
+                        keyName={propKey}
+                        isConnected={isConnected}
+                        isRequired={isRequired}
+                        schema={propSchema}
+                        side="left"
+                      />
+                      {!isConnected && (
+                        <NodeGenericInputField
+                          className="mb-2 mt-1"
+                          propKey={propKey}
+                          propSchema={propSchema}
+                          currentValue={getValue(propKey)}
+                          connections={data.connections}
+                          handleInputChange={handleInputChange}
+                          handleInputClick={handleInputClick}
+                          errors={data.errors ?? {}}
+                          displayName={
+                            propSchema.title || beautifyString(propKey)
+                          }
+                        />
+                      )}
+                    </div>
+                  )
+                );
+              },
+            )}
         </div>
-      )}
-      {isOutputOpen && data.uiType !== BlockUIType.NOTE && (
+        <div className="flex-none">
+          {data.outputSchema && generateOutputHandles(data.outputSchema)}
+        </div>
+      </div>
+      {isOutputOpen && (
         <div
           data-id="latest-output"
           className="nodrag m-3 break-words rounded-md border-[1.5px] p-2"
@@ -602,27 +486,25 @@ export function CustomNode({ data, id, width, height }: NodeProps<CustomNode>) {
           )}
         </div>
       )}
-      {data.uiType !== BlockUIType.NOTE && (
-        <div className="mt-2.5 flex items-center pb-4 pl-4">
-          <Switch checked={isOutputOpen} onCheckedChange={toggleOutput} />
-          <span className="m-1 mr-4">Output</span>
-          {hasAdvancedFields && (
-            <>
-              <Switch onCheckedChange={toggleAdvancedSettings} />
-              <span className="m-1">Advanced</span>
-            </>
-          )}
-          {data.status && (
-            <Badge
-              variant="outline"
-              data-id={`badge-${id}-${data.status}`}
-              className={cn(data.status.toLowerCase(), "ml-auto mr-5")}
-            >
-              {data.status}
-            </Badge>
-          )}
-        </div>
-      )}
+      <div className="mt-2.5 flex items-center pb-4 pl-4">
+        <Switch checked={isOutputOpen} onCheckedChange={toggleOutput} />
+        <span className="m-1 mr-4">Output</span>
+        {hasAdvancedFields && (
+          <>
+            <Switch onCheckedChange={toggleAdvancedSettings} />
+            <span className="m-1">Advanced</span>
+          </>
+        )}
+        {data.status && (
+          <Badge
+            variant="outline"
+            data-id={`badge-${id}-${data.status}`}
+            className={cn(data.status.toLowerCase(), "ml-auto mr-5")}
+          >
+            {data.status}
+          </Badge>
+        )}
+      </div>
       <InputModalComponent
         title={activeKey ? `Enter ${beautifyString(activeKey)}` : undefined}
         isOpen={isModalOpen}

rnd/autogpt_builder/src/components/Flow.tsx

@@ -27,7 +27,7 @@ import "@xyflow/react/dist/style.css";
 import { CustomNode } from "./CustomNode";
 import "./flow.css";
 import { Link } from "@/lib/autogpt-server-api";
-import { getTypeColor, filterBlocksByType } from "@/lib/utils";
+import { getTypeColor } from "@/lib/utils";
 import { history } from "./history";
 import { CustomEdge } from "./CustomEdge";
 import ConnectionLine from "./ConnectionLine";
@@ -36,19 +36,14 @@ import { SaveControl } from "@/components/edit/control/SaveControl";
 import { BlocksControl } from "@/components/edit/control/BlocksControl";
 import {
   IconPlay,
-  IconUndo2,
   IconRedo2,
   IconSquare,
-  IconOutput,
+  IconUndo2,
 } from "@/components/ui/icons";
 import { startTutorial } from "./tutorial";
 import useAgentGraph from "@/hooks/useAgentGraph";
 import { v4 as uuidv4 } from "uuid";
 import { useRouter, usePathname, useSearchParams } from "next/navigation";
-import { LogOut } from "lucide-react";
-import RunnerUIWrapper, {
-  RunnerUIWrapperRef,
-} from "@/components/RunnerUIWrapper";
 
 // This is for the history, this is the minimum distance a block must move before it is logged
 // It helps to prevent spamming the history with small movements especially when pressing on a input in a block
@@ -106,8 +101,6 @@ const FlowEditor: React.FC<{
   // State to control if blocks menu should be pinned open
   const [pinBlocksPopover, setPinBlocksPopover] = useState(false);
 
-  const runnerUIRef = useRef<RunnerUIWrapperRef>(null);
-
   useEffect(() => {
     const params = new URLSearchParams(window.location.search);
 
@@ -128,7 +121,7 @@ const FlowEditor: React.FC<{
         localStorage.setItem("shepherd-tour", "yes");
       }
     }
-  }, [availableNodes, tutorialStarted, router, pathname]);
+  }, [availableNodes, tutorialStarted]);
 
   useEffect(() => {
     const handleKeyDown = (event: KeyboardEvent) => {
@@ -263,7 +256,7 @@ const FlowEditor: React.FC<{
       }
 
       const edgeColor = getTypeColor(
-        getOutputType(nodes, connection.source!, connection.sourceHandle!),
+        getOutputType(connection.source!, connection.sourceHandle!),
       );
       const sourceNode = getNode(connection.source!);
       const newEdge: CustomEdge = {
@@ -302,7 +295,6 @@ const FlowEditor: React.FC<{
       addEdges,
       deleteElements,
       clearNodesStatusAndOutput,
-      nodes,
       edges,
       formatEdgeID,
       getOutputType,
@@ -385,7 +377,7 @@ const FlowEditor: React.FC<{
         clearNodesStatusAndOutput();
       }
     },
-    [setNodes, clearNodesStatusAndOutput, setEdges],
+    [setNodes, clearNodesStatusAndOutput],
   );
 
   const getNextNodeId = useCallback(() => {
@@ -424,7 +416,6 @@ const FlowEditor: React.FC<{
           isOutputOpen: false,
           block_id: blockId,
           isOutputStatic: nodeSchema.staticOutput,
-          uiType: nodeSchema.uiType,
         },
       };
 
@@ -443,6 +434,7 @@ const FlowEditor: React.FC<{
       nodeId,
       availableNodes,
       addNodes,
+      setNodes,
       deleteElements,
       clearNodesStatusAndOutput,
       x,
@@ -557,21 +549,9 @@ const FlowEditor: React.FC<{
       onClick: handleRedo,
     },
     {
-      label: !savedAgent
-        ? "Please save the agent to run"
-        : !isRunning
-          ? "Run"
-          : "Stop",
+      label: !isRunning ? "Run" : "Stop",
       icon: !isRunning ? <IconPlay /> : <IconSquare />,
-      onClick: !isRunning
-        ? () => runnerUIRef.current?.runOrOpenInput()
-        : requestStopRun,
-      disabled: !savedAgent,
-    },
-    {
-      label: "Runner Output",
-      icon: <LogOut size={18} strokeWidth={1.8} />,
-      onClick: () => runnerUIRef.current?.openRunnerOutput(),
+      onClick: !isRunning ? requestSaveAndRun : requestStopRun,
     },
   ];
 
@@ -607,21 +587,12 @@ const FlowEditor: React.FC<{
             <SaveControl
               agentMeta={savedAgent}
               onSave={(isTemplate) => requestSave(isTemplate ?? false)}
-              agentDescription={agentDescription}
               onDescriptionChange={setAgentDescription}
-              agentName={agentName}
               onNameChange={setAgentName}
             />
           </ControlPanel>
         </ReactFlow>
       </div>
-      <RunnerUIWrapper
-        ref={runnerUIRef}
-        nodes={nodes}
-        setNodes={setNodes}
-        isRunning={isRunning}
-        requestSaveAndRun={requestSaveAndRun}
-      />
     </FlowContext.Provider>
   );
 };

rnd/autogpt_builder/src/components/NavBar.stories.tsx

@@ -0,0 +1,42 @@
+import type { Meta, StoryObj } from '@storybook/react';
+import { NavBar } from './NavBar';
+import { UserProvider } from '@/context/UserContext'; // You'll need to create this context
+
+const meta: Meta<typeof NavBar> = {
+  title: 'Components/NavBar',
+  component: NavBar,
+  parameters: {
+    layout: 'fullscreen',
+  },
+  decorators: [
+    (Story) => (
+      <UserProvider>
+        <Story />
+      </UserProvider>
+    ),
+  ],
+};
+
+export default meta;
+type Story = StoryObj<typeof NavBar>;
+
+export const Default: Story = {
+  render: () => <NavBar />,
+};
+
+// You might need to mock the server-side functionality
+// for the story to work properly in Storybook
+export const LoggedOut: Story = {
+  parameters: {
+    userContext: { user: null, isAvailable: true },
+  },
+};
+
+export const LoggedIn: Story = {
+  parameters: {
+    userContext: { 
+      user: { id: '1', name: 'John Doe', email: 'john@example.com' },
+      isAvailable: true
+    },
+  },
+};

rnd/autogpt_builder/src/components/RunnerUIWrapper.tsx

@@ -1,141 +0,0 @@
-import React, {
-  useState,
-  useCallback,
-  forwardRef,
-  useImperativeHandle,
-} from "react";
-import RunnerInputUI from "./runner-ui/RunnerInputUI";
-import RunnerOutputUI from "./runner-ui/RunnerOutputUI";
-import { Node } from "@xyflow/react";
-import { filterBlocksByType } from "@/lib/utils";
-import { BlockIORootSchema } from "@/lib/autogpt-server-api/types";
-
-interface RunnerUIWrapperProps {
-  nodes: Node[];
-  setNodes: React.Dispatch<React.SetStateAction<Node[]>>;
-  isRunning: boolean;
-  requestSaveAndRun: () => void;
-}
-
-export interface RunnerUIWrapperRef {
-  openRunnerInput: () => void;
-  openRunnerOutput: () => void;
-  runOrOpenInput: () => void;
-}
-
-const RunnerUIWrapper = forwardRef<RunnerUIWrapperRef, RunnerUIWrapperProps>(
-  ({ nodes, setNodes, isRunning, requestSaveAndRun }, ref) => {
-    const [isRunnerInputOpen, setIsRunnerInputOpen] = useState(false);
-    const [isRunnerOutputOpen, setIsRunnerOutputOpen] = useState(false);
-
-    const getBlockInputsAndOutputs = useCallback(() => {
-      const inputBlocks = filterBlocksByType(
-        nodes,
-        (node) => node.data.block_id === "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      );
-
-      const outputBlocks = filterBlocksByType(
-        nodes,
-        (node) => node.data.block_id === "363ae599-353e-4804-937e-b2ee3cef3da4",
-      );
-
-      const inputs = inputBlocks.map((node) => ({
-        id: node.id,
-        type: "input" as const,
-        inputSchema: node.data.inputSchema as BlockIORootSchema,
-        hardcodedValues: {
-          name: (node.data.hardcodedValues as any).name || "",
-          description: (node.data.hardcodedValues as any).description || "",
-          value: (node.data.hardcodedValues as any).value,
-          placeholder_values:
-            (node.data.hardcodedValues as any).placeholder_values || [],
-          limit_to_placeholder_values:
-            (node.data.hardcodedValues as any).limit_to_placeholder_values ||
-            false,
-        },
-      }));
-
-      const outputs = outputBlocks.map((node) => ({
-        id: node.id,
-        type: "output" as const,
-        outputSchema: node.data.outputSchema as BlockIORootSchema,
-        hardcodedValues: {
-          name: (node.data.hardcodedValues as any).name || "Output",
-          description:
-            (node.data.hardcodedValues as any).description ||
-            "Output from the agent",
-          value: (node.data.hardcodedValues as any).value,
-        },
-        result: (node.data.executionResults as any)?.at(-1)?.data?.output,
-      }));
-
-      return { inputs, outputs };
-    }, [nodes]);
-
-    const handleInputChange = useCallback(
-      (nodeId: string, field: string, value: string) => {
-        setNodes((nds) =>
-          nds.map((node) => {
-            if (node.id === nodeId) {
-              return {
-                ...node,
-                data: {
-                  ...node.data,
-                  hardcodedValues: {
-                    ...(node.data.hardcodedValues as any),
-                    [field]: value,
-                  },
-                },
-              };
-            }
-            return node;
-          }),
-        );
-      },
-      [setNodes],
-    );
-
-    const openRunnerInput = () => setIsRunnerInputOpen(true);
-    const openRunnerOutput = () => setIsRunnerOutputOpen(true);
-
-    const runOrOpenInput = () => {
-      const { inputs } = getBlockInputsAndOutputs();
-      if (inputs.length > 0) {
-        openRunnerInput();
-      } else {
-        requestSaveAndRun();
-      }
-    };
-
-    useImperativeHandle(ref, () => ({
-      openRunnerInput,
-      openRunnerOutput,
-      runOrOpenInput,
-    }));
-
-    return (
-      <>
-        <RunnerInputUI
-          isOpen={isRunnerInputOpen}
-          onClose={() => setIsRunnerInputOpen(false)}
-          blockInputs={getBlockInputsAndOutputs().inputs}
-          onInputChange={handleInputChange}
-          onRun={() => {
-            setIsRunnerInputOpen(false);
-            requestSaveAndRun();
-          }}
-          isRunning={isRunning}
-        />
-        <RunnerOutputUI
-          isOpen={isRunnerOutputOpen}
-          onClose={() => setIsRunnerOutputOpen(false)}
-          blockOutputs={getBlockInputsAndOutputs().outputs}
-        />
-      </>
-    );
-  },
-);
-
-RunnerUIWrapper.displayName = "RunnerUIWrapper";
-
-export default RunnerUIWrapper;

rnd/autogpt_builder/src/components/admin/marketplace/AdminFeaturedAgentsControl.tsx

@@ -9,7 +9,6 @@ import {
 import FeaturedAgentsTable from "./FeaturedAgentsTable";
 import { AdminAddFeaturedAgentDialog } from "./AdminAddFeaturedAgentDialog";
 import { revalidatePath } from "next/cache";
-import * as Sentry from "@sentry/nextjs";
 
 export default async function AdminFeaturedAgentsControl({
   className,
@@ -56,15 +55,9 @@ export default async function AdminFeaturedAgentsControl({
             component: <Button>Remove</Button>,
             action: async (rows) => {
               "use server";
-              return await Sentry.withServerActionInstrumentation(
-                "removeFeaturedAgent",
-                {},
-                async () => {
-                  const all = rows.map((row) => removeFeaturedAgent(row.id));
-                  await Promise.all(all);
-                  revalidatePath("/marketplace");
-                },
-              );
+              const all = rows.map((row) => removeFeaturedAgent(row.id));
+              await Promise.all(all);
+              revalidatePath("/marketplace");
             },
           },
         ]}

rnd/autogpt_builder/src/components/admin/marketplace/actions.ts

@@ -2,23 +2,16 @@
 import AutoGPTServerAPI from "@/lib/autogpt-server-api";
 import MarketplaceAPI from "@/lib/marketplace-api";
 import { revalidatePath } from "next/cache";
-import * as Sentry from "@sentry/nextjs";
 
 export async function approveAgent(
   agentId: string,
   version: number,
   comment: string,
 ) {
-  return await Sentry.withServerActionInstrumentation(
-    "approveAgent",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      await api.approveAgentSubmission(agentId, version, comment);
-      console.debug(`Approving agent ${agentId}`);
-      revalidatePath("/marketplace");
-    },
-  );
+  const api = new MarketplaceAPI();
+  await api.approveAgentSubmission(agentId, version, comment);
+  console.debug(`Approving agent ${agentId}`);
+  revalidatePath("/marketplace");
 }
 
 export async function rejectAgent(
@@ -26,117 +19,67 @@ export async function rejectAgent(
   version: number,
   comment: string,
 ) {
-  return await Sentry.withServerActionInstrumentation(
-    "rejectAgent",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      await api.rejectAgentSubmission(agentId, version, comment);
-      console.debug(`Rejecting agent ${agentId}`);
-      revalidatePath("/marketplace");
-    },
-  );
+  const api = new MarketplaceAPI();
+  await api.rejectAgentSubmission(agentId, version, comment);
+  console.debug(`Rejecting agent ${agentId}`);
+  revalidatePath("/marketplace");
 }
 
 export async function getReviewableAgents() {
-  return await Sentry.withServerActionInstrumentation(
-    "getReviewableAgents",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      return api.getAgentSubmissions();
-    },
-  );
+  const api = new MarketplaceAPI();
+  return api.getAgentSubmissions();
 }
 
 export async function getFeaturedAgents(
   page: number = 1,
   pageSize: number = 10,
 ) {
-  return await Sentry.withServerActionInstrumentation(
-    "getFeaturedAgents",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      const featured = await api.getFeaturedAgents(page, pageSize);
-      console.debug(`Getting featured agents ${featured.agents.length}`);
-      return featured;
-    },
-  );
+  const api = new MarketplaceAPI();
+  const featured = await api.getFeaturedAgents(page, pageSize);
+  console.debug(`Getting featured agents ${featured.agents.length}`);
+  return featured;
 }
 
 export async function getFeaturedAgent(agentId: string) {
-  return await Sentry.withServerActionInstrumentation(
-    "getFeaturedAgent",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      const featured = await api.getFeaturedAgent(agentId);
-      console.debug(`Getting featured agent ${featured.agentId}`);
-      return featured;
-    },
-  );
+  const api = new MarketplaceAPI();
+  const featured = await api.getFeaturedAgent(agentId);
+  console.debug(`Getting featured agent ${featured.agentId}`);
+  return featured;
 }
 
 export async function addFeaturedAgent(
   agentId: string,
   categories: string[] = ["featured"],
 ) {
-  return await Sentry.withServerActionInstrumentation(
-    "addFeaturedAgent",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      await api.addFeaturedAgent(agentId, categories);
-      console.debug(`Adding featured agent ${agentId}`);
-      revalidatePath("/marketplace");
-    },
-  );
+  const api = new MarketplaceAPI();
+  await api.addFeaturedAgent(agentId, categories);
+  console.debug(`Adding featured agent ${agentId}`);
+  revalidatePath("/marketplace");
 }
 
 export async function removeFeaturedAgent(
   agentId: string,
   categories: string[] = ["featured"],
 ) {
-  return await Sentry.withServerActionInstrumentation(
-    "removeFeaturedAgent",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      await api.removeFeaturedAgent(agentId, categories);
-      console.debug(`Removing featured agent ${agentId}`);
-      revalidatePath("/marketplace");
-    },
-  );
+  const api = new MarketplaceAPI();
+  await api.removeFeaturedAgent(agentId, categories);
+  console.debug(`Removing featured agent ${agentId}`);
+  revalidatePath("/marketplace");
 }
 
 export async function getCategories() {
-  return await Sentry.withServerActionInstrumentation(
-    "getCategories",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      const categories = await api.getCategories();
-      console.debug(
-        `Getting categories ${categories.unique_categories.length}`,
-      );
-      return categories;
-    },
-  );
+  const api = new MarketplaceAPI();
+  const categories = await api.getCategories();
+  console.debug(`Getting categories ${categories.unique_categories.length}`);
+  return categories;
 }
 
 export async function getNotFeaturedAgents(
   page: number = 1,
   pageSize: number = 100,
 ) {
-  return await Sentry.withServerActionInstrumentation(
-    "getNotFeaturedAgents",
-    {},
-    async () => {
-      const api = new MarketplaceAPI();
-      const agents = await api.getNotFeaturedAgents(page, pageSize);
-      console.debug(`Getting not featured agents ${agents.agents.length}`);
-      return agents;
-    },
-  );
+  const api = new MarketplaceAPI();
+  const agents = await api.getNotFeaturedAgents(page, pageSize);
+  console.debug(`Getting not featured agents ${agents.agents.length}`);
+  return agents;
 }

rnd/autogpt_builder/src/components/customnode.css

@@ -1,5 +1,6 @@
 .custom-node {
   color: #000000;
+  width: 500px;
   box-sizing: border-box;
   transition: border-color 0.3s ease-in-out;
 }

rnd/autogpt_builder/src/components/edit/control/ControlPanel.tsx

@@ -19,7 +19,6 @@ import React from "react";
 export type Control = {
   icon: React.ReactNode;
   label: string;
-  disabled?: boolean;
   onClick: () => void;
 };
 
@@ -51,18 +50,15 @@ export const ControlPanel = ({
           {controls.map((control, index) => (
             <Tooltip key={index} delayDuration={500}>
               <TooltipTrigger asChild>
-                <div>
-                  <Button
-                    variant="ghost"
-                    size="icon"
-                    onClick={() => control.onClick()}
-                    data-id={`control-button-${index}`}
-                    disabled={control.disabled || false}
-                  >
-                    {control.icon}
-                    <span className="sr-only">{control.label}</span>
-                  </Button>
-                </div>
+                <Button
+                  variant="ghost"
+                  size="icon"
+                  onClick={() => control.onClick()}
+                  data-id={`control-button-${index}`}
+                >
+                  {control.icon}
+                  <span className="sr-only">{control.label}</span>
+                </Button>
               </TooltipTrigger>
               <TooltipContent side="right">{control.label}</TooltipContent>
             </Tooltip>

rnd/autogpt_builder/src/components/edit/control/SaveControl.tsx

@@ -18,8 +18,6 @@ import {
 
 interface SaveControlProps {
   agentMeta: GraphMeta | null;
-  agentName: string;
-  agentDescription: string;
   onSave: (isTemplate: boolean | undefined) => void;
   onNameChange: (name: string) => void;
   onDescriptionChange: (description: string) => void;
@@ -37,9 +35,7 @@ interface SaveControlProps {
 export const SaveControl = ({
   agentMeta,
   onSave,
-  agentName,
   onNameChange,
-  agentDescription,
   onDescriptionChange,
 }: SaveControlProps) => {
   /**
@@ -79,7 +75,7 @@ export const SaveControl = ({
                 id="name"
                 placeholder="Enter your agent name"
                 className="col-span-3"
-                value={agentName}
+                defaultValue={agentMeta?.name || ""}
                 onChange={(e) => onNameChange(e.target.value)}
               />
               <Label htmlFor="description">Description</Label>
@@ -87,21 +83,9 @@ export const SaveControl = ({
                 id="description"
                 placeholder="Your agent description"
                 className="col-span-3"
-                value={agentDescription}
+                defaultValue={agentMeta?.description || ""}
                 onChange={(e) => onDescriptionChange(e.target.value)}
               />
-              {agentMeta?.version && (
-                <>
-                  <Label htmlFor="version">Version</Label>
-                  <Input
-                    id="version"
-                    placeholder="Version"
-                    className="col-span-3"
-                    value={agentMeta?.version || "-"}
-                    disabled
-                  />
-                </>
-              )}
             </div>
           </CardContent>
           <CardFooter className="flex flex-col items-stretch gap-2">

rnd/autogpt_builder/src/components/marketplace/actions.ts

@@ -1,16 +1,9 @@
 "use server";
 
-import * as Sentry from "@sentry/nextjs";
 import MarketplaceAPI, { AnalyticsEvent } from "@/lib/marketplace-api";
 
 export async function makeAnalyticsEvent(event: AnalyticsEvent) {
-  return await Sentry.withServerActionInstrumentation(
-    "makeAnalyticsEvent",
-    {},
-    async () => {
-      const apiUrl = process.env.AGPT_SERVER_API_URL;
-      const api = new MarketplaceAPI();
-      await api.makeAnalyticsEvent(event);
-    },
-  );
+  const apiUrl = process.env.AGPT_SERVER_API_URL;
+  const api = new MarketplaceAPI();
+  await api.makeAnalyticsEvent(event);
 }

rnd/autogpt_builder/src/components/monitor/AgentFlowList.tsx

@@ -1,5 +1,5 @@
 import AutoGPTServerAPI, { GraphMeta } from "@/lib/autogpt-server-api";
-import React, { useEffect, useMemo, useState } from "react";
+import React, { useEffect, useState } from "react";
 import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
 import { Button } from "@/components/ui/button";
 import Link from "next/link";
@@ -45,10 +45,10 @@ export const AgentFlowList = ({
   className?: string;
 }) => {
   const [templates, setTemplates] = useState<GraphMeta[]>([]);
-  const api = useMemo(() => new AutoGPTServerAPI(), []);
+  const api = new AutoGPTServerAPI();
   useEffect(() => {
     api.listTemplates().then((templates) => setTemplates(templates));
-  }, [api]);
+  }, []);
 
   return (
     <Card className={className}>

rnd/autogpt_builder/src/components/monitor/FlowInfo.tsx

@@ -1,4 +1,4 @@
-import React, { useEffect, useMemo, useState } from "react";
+import React, { useEffect, useState } from "react";
 import AutoGPTServerAPI, {
   Graph,
   GraphMeta,
@@ -28,7 +28,7 @@ export const FlowInfo: React.FC<
     flowVersion?: number | "all";
   }
 > = ({ flow, flowRuns, flowVersion, ...props }) => {
-  const api = useMemo(() => new AutoGPTServerAPI(), []);
+  const api = new AutoGPTServerAPI();
 
   const [flowVersions, setFlowVersions] = useState<Graph[] | null>(null);
   const [selectedVersion, setSelectedFlowVersion] = useState(
@@ -41,7 +41,7 @@ export const FlowInfo: React.FC<
 
   useEffect(() => {
     api.getGraphAllVersions(flow.id).then((result) => setFlowVersions(result));
-  }, [flow.id, api]);
+  }, [flow.id]);
 
   return (
     <Card {...props}>

rnd/autogpt_builder/src/components/node-input-components.tsx

@@ -10,7 +10,7 @@ import {
   BlockIONumberSubSchema,
   BlockIOBooleanSubSchema,
 } from "@/lib/autogpt-server-api/types";
-import React, { FC, useCallback, useEffect, useState } from "react";
+import { FC, useEffect, useState } from "react";
 import { Button } from "./ui/button";
 import { Switch } from "./ui/switch";
 import {
@@ -296,7 +296,7 @@ const NodeKeyValueInput: FC<{
   className,
   displayName,
 }) => {
-  const getPairValues = useCallback(() => {
+  const getPairValues = () => {
     let defaultEntries = new Map<string, any>();
 
     connections
@@ -311,7 +311,7 @@ const NodeKeyValueInput: FC<{
     });
 
     return Array.from(defaultEntries, ([key, value]) => ({ key, value }));
-  }, [connections, entries, schema.default, selfKey]);
+  };
 
   const [keyValuePairs, setKeyValuePairs] = useState<
     { key: string; value: string | number | null }[]
@@ -319,7 +319,7 @@ const NodeKeyValueInput: FC<{
 
   useEffect(
     () => setKeyValuePairs(getPairValues()),
-    [connections, entries, schema.default, getPairValues],
+    [connections, entries, schema.default],
   );
 
   function updateKeyValuePairs(newPairs: typeof keyValuePairs) {
@@ -380,7 +380,7 @@ const NodeKeyValueInput: FC<{
                 <Input
                   type="text"
                   placeholder="Value"
-                  defaultValue={value ?? ""}
+                  value={value ?? ""}
                   onBlur={(e) =>
                     updateKeyValuePairs(
                       keyValuePairs.toSpliced(index, 1, {
@@ -563,7 +563,7 @@ const NodeStringInput: FC<{
           <Input
             type="text"
             id={selfKey}
-            defaultValue={schema.secret && value ? "********" : value}
+            value={schema.secret && value ? "********" : value}
             readOnly={schema.secret}
             placeholder={
               schema?.placeholder || `Enter ${beautifyString(displayName)}`
@@ -587,52 +587,6 @@ const NodeStringInput: FC<{
   );
 };
 
-export const NodeTextBoxInput: FC<{
-  selfKey: string;
-  schema: BlockIOStringSubSchema;
-  value?: string;
-  error?: string;
-  handleInputChange: NodeObjectInputTreeProps["handleInputChange"];
-  handleInputClick: NodeObjectInputTreeProps["handleInputClick"];
-  className?: string;
-  displayName: string;
-}> = ({
-  selfKey,
-  schema,
-  value = "",
-  error,
-  handleInputChange,
-  handleInputClick,
-  className,
-  displayName,
-}) => {
-  return (
-    <div className={className}>
-      <div
-        className="nodrag relative m-0 h-[200px] w-full bg-yellow-100 p-4"
-        onClick={schema.secret ? () => handleInputClick(selfKey) : undefined}
-      >
-        <textarea
-          id={selfKey}
-          value={schema.secret && value ? "********" : value}
-          readOnly={schema.secret}
-          placeholder={
-            schema?.placeholder || `Enter ${beautifyString(displayName)}`
-          }
-          onChange={(e) => handleInputChange(selfKey, e.target.value)}
-          onBlur={(e) => handleInputChange(selfKey, e.target.value)}
-          className="h-full w-full resize-none overflow-hidden border-none bg-transparent text-lg text-black outline-none"
-          style={{
-            fontSize: "min(1em, 16px)",
-            lineHeight: "1.2",
-          }}
-        />
-      </div>
-      {error && <span className="error-message">{error}</span>}
-    </div>
-  );
-};
-
 const NodeNumberInput: FC<{
   selfKey: string;
   schema: BlockIONumberSubSchema;
@@ -658,7 +612,7 @@ const NodeNumberInput: FC<{
         <Input
           type="number"
           id={selfKey}
-          defaultValue={value}
+          value={value}
           onBlur={(e) => handleInputChange(selfKey, parseFloat(e.target.value))}
           placeholder={
             schema.placeholder || `Enter ${beautifyString(displayName)}`

rnd/autogpt_builder/src/components/runner-ui/RunnerInputBlock.tsx

@@ -1,61 +0,0 @@
-import React from "react";
-import { Input } from "@/components/ui/input";
-import {
-  Select,
-  SelectContent,
-  SelectItem,
-  SelectTrigger,
-  SelectValue,
-} from "@/components/ui/select";
-
-interface InputBlockProps {
-  id: string;
-  name: string;
-  description?: string;
-  value: string;
-  placeholder_values?: any[];
-  onInputChange: (id: string, field: string, value: string) => void;
-}
-
-export function InputBlock({
-  id,
-  name,
-  description,
-  value,
-  placeholder_values,
-  onInputChange,
-}: InputBlockProps) {
-  return (
-    <div className="space-y-1">
-      <h3 className="text-base font-semibold">{name || "Unnamed Input"}</h3>
-      {description && <p className="text-sm text-gray-600">{description}</p>}
-      <div>
-        {placeholder_values && placeholder_values.length > 1 ? (
-          <Select
-            onValueChange={(value) => onInputChange(id, "value", value)}
-            value={value}
-          >
-            <SelectTrigger className="w-full">
-              <SelectValue placeholder="Select a value" />
-            </SelectTrigger>
-            <SelectContent>
-              {placeholder_values.map((placeholder, index) => (
-                <SelectItem key={index} value={placeholder.toString()}>
-                  {placeholder.toString()}
-                </SelectItem>
-              ))}
-            </SelectContent>
-          </Select>
-        ) : (
-          <Input
-            id={`${id}-Value`}
-            value={value}
-            onChange={(e) => onInputChange(id, "value", e.target.value)}
-            placeholder={placeholder_values?.[0]?.toString() || "Enter value"}
-            className="w-full"
-          />
-        )}
-      </div>
-    </div>
-  );
-}

rnd/autogpt_builder/src/components/runner-ui/RunnerInputList.tsx

@@ -1,33 +0,0 @@
-import React from "react";
-import { ScrollArea } from "@/components/ui/scroll-area";
-import { InputBlock } from "./RunnerInputBlock";
-import { BlockInput } from "./RunnerInputUI";
-
-interface InputListProps {
-  blockInputs: BlockInput[];
-  onInputChange: (nodeId: string, field: string, value: string) => void;
-}
-
-export function InputList({ blockInputs, onInputChange }: InputListProps) {
-  return (
-    <ScrollArea className="h-[20vh] overflow-auto pr-4 sm:h-[30vh] md:h-[40vh] lg:h-[50vh]">
-      <div className="space-y-4">
-        {blockInputs && blockInputs.length > 0 ? (
-          blockInputs.map((block) => (
-            <InputBlock
-              key={block.id}
-              id={block.id}
-              name={block.hardcodedValues.name}
-              description={block.hardcodedValues.description}
-              value={block.hardcodedValues.value?.toString() || ""}
-              placeholder_values={block.hardcodedValues.placeholder_values}
-              onInputChange={onInputChange}
-            />
-          ))
-        ) : (
-          <p>No input blocks available.</p>
-        )}
-      </div>
-    </ScrollArea>
-  );
-}

rnd/autogpt_builder/src/components/runner-ui/RunnerInputUI.tsx

@@ -1,74 +0,0 @@
-import React from "react";
-import {
-  Dialog,
-  DialogContent,
-  DialogHeader,
-  DialogTitle,
-  DialogDescription,
-  DialogFooter,
-} from "@/components/ui/dialog";
-import { Button } from "@/components/ui/button";
-import { BlockIORootSchema } from "@/lib/autogpt-server-api/types";
-import { InputList } from "./RunnerInputList";
-
-export interface BlockInput {
-  id: string;
-  inputSchema: BlockIORootSchema;
-  hardcodedValues: {
-    name: string;
-    description: string;
-    value: any;
-    placeholder_values?: any[];
-    limit_to_placeholder_values?: boolean;
-  };
-}
-
-interface RunSettingsUiProps {
-  isOpen: boolean;
-  onClose: () => void;
-  blockInputs: BlockInput[];
-  onInputChange: (nodeId: string, field: string, value: string) => void;
-  onRun: () => void;
-  isRunning: boolean;
-}
-
-export function RunnerInputUI({
-  isOpen,
-  onClose,
-  blockInputs,
-  onInputChange,
-  onRun,
-  isRunning,
-}: RunSettingsUiProps) {
-  const handleRun = () => {
-    onRun();
-    onClose();
-  };
-
-  return (
-    <Dialog open={isOpen} onOpenChange={onClose}>
-      <DialogContent className="flex max-h-[80vh] flex-col overflow-hidden sm:max-w-[400px] md:max-w-[500px] lg:max-w-[600px]">
-        <DialogHeader className="px-4 py-4">
-          <DialogTitle className="text-2xl">Run Settings</DialogTitle>
-          <DialogDescription className="mt-2 text-sm">
-            Configure settings for running your agent.
-          </DialogDescription>
-        </DialogHeader>
-        <div className="flex-grow overflow-y-auto px-4 py-4">
-          <InputList blockInputs={blockInputs} onInputChange={onInputChange} />
-        </div>
-        <DialogFooter className="px-6 py-4">
-          <Button
-            onClick={handleRun}
-            className="px-8 py-2 text-lg"
-            disabled={isRunning}
-          >
-            {isRunning ? "Running..." : "Run"}
-          </Button>
-        </DialogFooter>
-      </DialogContent>
-    </Dialog>
-  );
-}
-
-export default RunnerInputUI;

rnd/autogpt_builder/src/components/runner-ui/RunnerOutputUI.tsx

@@ -1,94 +0,0 @@
-import React from "react";
-import {
-  Sheet,
-  SheetContent,
-  SheetHeader,
-  SheetTitle,
-  SheetDescription,
-} from "@/components/ui/sheet";
-import { ScrollArea } from "@/components/ui/scroll-area";
-import { BlockIORootSchema } from "@/lib/autogpt-server-api/types";
-import { Label } from "@/components/ui/label";
-import { Textarea } from "@/components/ui/textarea";
-
-interface BlockOutput {
-  id: string;
-  outputSchema: BlockIORootSchema;
-  hardcodedValues: {
-    name: string;
-    description: string;
-  };
-  result?: any;
-}
-
-interface OutputModalProps {
-  isOpen: boolean;
-  onClose: () => void;
-  blockOutputs: BlockOutput[];
-}
-
-const formatOutput = (output: any): string => {
-  if (typeof output === "object") {
-    try {
-      return JSON.stringify(output, null, 2);
-    } catch (error) {
-      return `Error formatting output: ${(error as Error).message}`;
-    }
-  }
-  return String(output);
-};
-
-export function RunnerOutputUI({
-  isOpen,
-  onClose,
-  blockOutputs,
-}: OutputModalProps) {
-  return (
-    <Sheet open={isOpen} onOpenChange={onClose}>
-      <SheetContent
-        side="right"
-        className="flex h-full w-full flex-col overflow-hidden sm:max-w-[500px]"
-      >
-        <SheetHeader className="px-2 py-2">
-          <SheetTitle className="text-xl">Run Outputs</SheetTitle>
-          <SheetDescription className="mt-1 text-sm">
-            View the outputs from your agent run.
-          </SheetDescription>
-        </SheetHeader>
-        <div className="flex-grow overflow-y-auto px-2 py-2">
-          <ScrollArea className="h-full overflow-auto pr-4">
-            <div className="space-y-4">
-              {blockOutputs && blockOutputs.length > 0 ? (
-                blockOutputs.map((block) => (
-                  <div key={block.id} className="space-y-1">
-                    <Label className="text-base font-semibold">
-                      {block.hardcodedValues.name || "Unnamed Output"}
-                    </Label>
-
-                    {block.hardcodedValues.description && (
-                      <Label className="block text-sm text-gray-600">
-                        {block.hardcodedValues.description}
-                      </Label>
-                    )}
-
-                    <div className="rounded-md bg-gray-100 p-2">
-                      <Textarea
-                        readOnly
-                        value={formatOutput(block.result ?? "No output yet")}
-                        className="resize-none whitespace-pre-wrap break-words border-none bg-transparent text-sm"
-                      />
-                    </div>
-                  </div>
-                ))
-              ) : (
-                <p>No output blocks available.</p>
-              )}
-            </div>
-          </ScrollArea>
-        </div>
-      </SheetContent>
-    </Sheet>
-  );
-}
-
-export default RunnerOutputUI;

rnd/autogpt_builder/src/components/ui/input.tsx

@@ -6,7 +6,20 @@ export interface InputProps
   extends React.InputHTMLAttributes<HTMLInputElement> {}
 
 const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
+  ({ className, type, value, ...props }, ref) => {
+    // This ref allows the `Input` component to be both controlled and uncontrolled.
+    // The HTMLvalue will only be updated if the value prop changes, but the user can still type in the input.
+    ref = ref || React.createRef<HTMLInputElement>();
+    React.useEffect(() => {
+      if (
+        ref &&
+        ref.current &&
+        ref.current.value !== value &&
+        type !== "file"
+      ) {
+        ref.current.value = value;
+      }
+    }, [value, type]);
     return (
       <input
         type={type}
@@ -16,6 +29,7 @@ const Input = React.forwardRef<HTMLInputElement, InputProps>(
           className,
         )}
         ref={ref}
+        defaultValue={type !== "file" ? value : undefined}
         {...props}
       />
     );

rnd/autogpt_builder/src/hooks/useAgentGraph.ts

@@ -16,7 +16,6 @@ import {
 import { Connection, MarkerType } from "@xyflow/react";
 import Ajv from "ajv";
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
-import { useRouter, useSearchParams, usePathname } from "next/navigation";
 
 const ajv = new Ajv({ strict: false, allErrors: true });
 
@@ -25,11 +24,6 @@ export default function useAgentGraph(
   template?: boolean,
   passDataToBeads?: boolean,
 ) {
-  const [router, searchParams, pathname] = [
-    useRouter(),
-    useSearchParams(),
-    usePathname(),
-  ];
   const [savedAgent, setSavedAgent] = useState<Graph | null>(null);
   const [agentDescription, setAgentDescription] = useState<string>("");
   const [agentName, setAgentName] = useState<string>("");
@@ -67,10 +61,8 @@ export default function useAgentGraph(
   const [nodes, setNodes] = useState<CustomNode[]>([]);
   const [edges, setEdges] = useState<CustomEdge[]>([]);
 
-  const api = useMemo(
-    () => new AutoGPTServerAPI(process.env.NEXT_PUBLIC_AGPT_SERVER_URL!),
-    [],
-  );
+  const apiUrl = process.env.NEXT_PUBLIC_AGPT_SERVER_URL!;
+  const api = useMemo(() => new AutoGPTServerAPI(apiUrl), [apiUrl]);
 
   // Connect to WebSocket
   useEffect(() => {
@@ -97,227 +89,16 @@ export default function useAgentGraph(
       .getBlocks()
       .then((blocks) => setAvailableNodes(blocks))
       .catch();
-  }, [api]);
-
-  //TODO to utils? repeated in Flow
-  const formatEdgeID = useCallback((conn: Link | Connection): string => {
-    if ("sink_id" in conn) {
-      return `${conn.source_id}_${conn.source_name}_${conn.sink_id}_${conn.sink_name}`;
-    } else {
-      return `${conn.source}_${conn.sourceHandle}_${conn.target}_${conn.targetHandle}`;
-    }
   }, []);
 
-  const getOutputType = useCallback(
-    (nodes: CustomNode[], nodeId: string, handleId: string) => {
-      const node = nodes.find((n) => n.id === nodeId);
-      if (!node) return "unknown";
-
-      const outputSchema = node.data.outputSchema;
-      if (!outputSchema) return "unknown";
-
-      const outputHandle = outputSchema.properties[handleId];
-      if (!("type" in outputHandle)) return "unknown";
-      return outputHandle.type;
-    },
-    [],
-  );
-
   // Load existing graph
-  const loadGraph = useCallback(
-    (graph: Graph) => {
-      setSavedAgent(graph);
-      setAgentName(graph.name);
-      setAgentDescription(graph.description);
-
-      setNodes(() => {
-        const newNodes = graph.nodes.map((node) => {
-          const block = availableNodes.find(
-            (block) => block.id === node.block_id,
-          )!;
-          const newNode: CustomNode = {
-            id: node.id,
-            type: "custom",
-            position: {
-              x: node?.metadata?.position?.x || 0,
-              y: node?.metadata?.position?.y || 0,
-            },
-            data: {
-              block_id: block.id,
-              blockType: block.name,
-              categories: block.categories,
-              description: block.description,
-              title: `${block.name} ${node.id}`,
-              inputSchema: block.inputSchema,
-              outputSchema: block.outputSchema,
-              hardcodedValues: node.input_default,
-              connections: graph.links
-                .filter((l) => [l.source_id, l.sink_id].includes(node.id))
-                .map((link) => ({
-                  edge_id: formatEdgeID(link),
-                  source: link.source_id,
-                  sourceHandle: link.source_name,
-                  target: link.sink_id,
-                  targetHandle: link.sink_name,
-                })),
-              isOutputOpen: false,
-            },
-          };
-          return newNode;
-        });
-        setEdges((_) =>
-          graph.links.map((link) => ({
-            id: formatEdgeID(link),
-            type: "custom",
-            data: {
-              edgeColor: getTypeColor(
-                getOutputType(newNodes, link.source_id, link.source_name!),
-              ),
-              sourcePos: newNodes.find((node) => node.id === link.source_id)
-                ?.position,
-              isStatic: link.is_static,
-              beadUp: 0,
-              beadDown: 0,
-              beadData: [],
-            },
-            markerEnd: {
-              type: MarkerType.ArrowClosed,
-              strokeWidth: 2,
-              color: getTypeColor(
-                getOutputType(newNodes, link.source_id, link.source_name!),
-              ),
-            },
-            source: link.source_id,
-            target: link.sink_id,
-            sourceHandle: link.source_name || undefined,
-            targetHandle: link.sink_name || undefined,
-          })),
-        );
-        return newNodes;
-      });
-    },
-    [availableNodes, formatEdgeID, getOutputType],
-  );
-
-  const getFrontendId = useCallback(
-    (backendId: string, nodes: CustomNode[]) => {
-      const node = nodes.find((node) => node.data.backend_id === backendId);
-      return node?.id;
-    },
-    [],
-  );
-
-  const updateEdgeBeads = useCallback(
-    (executionData: NodeExecutionResult) => {
-      setEdges((edges) => {
-        return edges.map((e) => {
-          const edge = { ...e, data: { ...e.data } } as CustomEdge;
-
-          if (executionData.status === "COMPLETED") {
-            // Produce output beads
-            for (let key in executionData.output_data) {
-              if (
-                edge.source !== getFrontendId(executionData.node_id, nodes) ||
-                edge.sourceHandle !== key
-              ) {
-                continue;
-              }
-              edge.data!.beadUp = (edge.data!.beadUp ?? 0) + 1;
-              // For static edges beadDown is always one less than beadUp
-              // Because there's no queueing and one bead is always at the connection point
-              if (edge.data?.isStatic) {
-                edge.data!.beadDown = (edge.data!.beadUp ?? 0) - 1;
-                edge.data!.beadData = edge.data!.beadData!.slice(0, -1);
-                continue;
-              }
-              //todo kcze this assumes output at key is always array with one element
-              edge.data!.beadData = [
-                executionData.output_data[key][0],
-                ...edge.data!.beadData!,
-              ];
-            }
-          } else if (executionData.status === "RUNNING") {
-            // Consume input beads
-            for (let key in executionData.input_data) {
-              if (
-                edge.target !== getFrontendId(executionData.node_id, nodes) ||
-                edge.targetHandle !== key
-              ) {
-                continue;
-              }
-              // Skip decreasing bead count if edge doesn't match or if it's static
-              if (
-                edge.data!.beadData![edge.data!.beadData!.length - 1] !==
-                  executionData.input_data[key] ||
-                edge.data?.isStatic
-              ) {
-                continue;
-              }
-              edge.data!.beadDown = (edge.data!.beadDown ?? 0) + 1;
-              edge.data!.beadData = edge.data!.beadData!.slice(0, -1);
-            }
-          }
-          return edge;
-        });
-      });
-    },
-    [getFrontendId, nodes],
-  );
-
-  const updateNodesWithExecutionData = useCallback(
-    (executionData: NodeExecutionResult) => {
-      if (passDataToBeads) {
-        updateEdgeBeads(executionData);
-      }
-      setNodes((nodes) => {
-        const nodeId = nodes.find(
-          (node) => node.data.backend_id === executionData.node_id,
-        )?.id;
-        if (!nodeId) {
-          console.error(
-            "Node not found for execution data:",
-            executionData,
-            "This shouldn't happen and means that the frontend and backend are out of sync.",
-          );
-          return nodes;
-        }
-        return nodes.map((node) =>
-          node.id === nodeId
-            ? {
-                ...node,
-                data: {
-                  ...node.data,
-                  status: executionData.status,
-                  executionResults:
-                    Object.keys(executionData.output_data).length > 0
-                      ? [
-                          ...(node.data.executionResults || []),
-                          {
-                            execId: executionData.node_exec_id,
-                            data: executionData.output_data,
-                          },
-                        ]
-                      : node.data.executionResults,
-                  isOutputOpen: true,
-                },
-              }
-            : node,
-        );
-      });
-    },
-    [passDataToBeads, updateEdgeBeads],
-  );
-
   useEffect(() => {
     if (!flowID || availableNodes.length == 0) return;
 
-    (template ? api.getTemplate(flowID) : api.getGraph(flowID)).then(
-      (graph) => {
-        console.debug("Loading graph");
-        loadGraph(graph);
-      },
+    (template ? api.getTemplate(flowID) : api.getGraph(flowID)).then((graph) =>
+      loadGraph(graph),
     );
-  }, [flowID, template, availableNodes, api, loadGraph]);
+  }, [flowID, template, availableNodes]);
 
   // Update nodes with execution data
   useEffect(() => {
@@ -338,68 +119,7 @@ export default function useAgentGraph(
       });
       return [];
     });
-  }, [updateQueue, nodesSyncedWithSavedAgent, updateNodesWithExecutionData]);
-
-  const validateNodes = useCallback((): boolean => {
-    let isValid = true;
-
-    nodes.forEach((node) => {
-      const validate = ajv.compile(node.data.inputSchema);
-      const errors = {} as { [key: string]: string };
-
-      // Validate values against schema using AJV
-      const valid = validate(node.data.hardcodedValues);
-      if (!valid) {
-        // Populate errors if validation fails
-        validate.errors?.forEach((error) => {
-          // Skip error if there's an edge connected
-          const path =
-            "dataPath" in error
-              ? (error.dataPath as string)
-              : error.instancePath;
-          const handle = path.split(/[\/.]/)[0];
-          if (
-            node.data.connections.some(
-              (conn) => conn.target === node.id || conn.targetHandle === handle,
-            )
-          ) {
-            return;
-          }
-          console.warn("Error", error);
-          isValid = false;
-          if (path && error.message) {
-            const key = path.slice(1);
-            console.log("Error", key, error.message);
-            setNestedProperty(
-              errors,
-              key,
-              error.message[0].toUpperCase() + error.message.slice(1),
-            );
-          } else if (error.keyword === "required") {
-            const key = error.params.missingProperty;
-            setNestedProperty(errors, key, "This field is required");
-          }
-        });
-      }
-      // Set errors
-      setNodes((nodes) => {
-        return nodes.map((n) => {
-          if (n.id === node.id) {
-            return {
-              ...n,
-              data: {
-                ...n.data,
-                errors,
-              },
-            };
-          }
-          return n;
-        });
-      });
-    });
-
-    return isValid;
-  }, [nodes]);
+  }, [updateQueue, nodesSyncedWithSavedAgent]);
 
   // Handle user requests
   useEffect(() => {
@@ -504,13 +224,7 @@ export default function useAgentGraph(
         .stopGraphExecution(savedAgent.id, saveRunRequest.activeExecutionID)
         .then(() => setSaveRunRequest({ request: "none", state: "none" }));
     }
-  }, [
-    api,
-    saveRunRequest,
-    savedAgent,
-    nodesSyncedWithSavedAgent,
-    validateNodes,
-  ]);
+  }, [saveRunRequest, savedAgent, nodesSyncedWithSavedAgent]);
 
   // Check if node ids are synced with saved agent
   useEffect(() => {
@@ -527,6 +241,275 @@ export default function useAgentGraph(
     setNodesSyncedWithSavedAgent(oneNodeSynced);
   }, [savedAgent, nodes]);
 
+  const validateNodes = useCallback((): boolean => {
+    let isValid = true;
+
+    nodes.forEach((node) => {
+      const validate = ajv.compile(node.data.inputSchema);
+      const errors = {} as { [key: string]: string };
+
+      // Validate values against schema using AJV
+      const valid = validate(node.data.hardcodedValues);
+      if (!valid) {
+        // Populate errors if validation fails
+        validate.errors?.forEach((error) => {
+          // Skip error if there's an edge connected
+          const path =
+            "dataPath" in error
+              ? (error.dataPath as string)
+              : error.instancePath;
+          const handle = path.split(/[\/.]/)[0];
+          if (
+            node.data.connections.some(
+              (conn) => conn.target === node.id || conn.targetHandle === handle,
+            )
+          ) {
+            return;
+          }
+          console.warn("Error", error);
+          isValid = false;
+          if (path && error.message) {
+            const key = path.slice(1);
+            console.log("Error", key, error.message);
+            setNestedProperty(
+              errors,
+              key,
+              error.message[0].toUpperCase() + error.message.slice(1),
+            );
+          } else if (error.keyword === "required") {
+            const key = error.params.missingProperty;
+            setNestedProperty(errors, key, "This field is required");
+          }
+        });
+      }
+      // Set errors
+      setNodes((nodes) => {
+        return nodes.map((n) => {
+          if (n.id === node.id) {
+            return {
+              ...n,
+              data: {
+                ...n.data,
+                errors,
+              },
+            };
+          }
+          return n;
+        });
+      });
+    });
+
+    return isValid;
+  }, [nodes]);
+
+  const getFrontendId = useCallback(
+    (backendId: string, nodes: CustomNode[]) => {
+      const node = nodes.find((node) => node.data.backend_id === backendId);
+      return node?.id;
+    },
+    [],
+  );
+
+  const updateEdgeBeads = useCallback(
+    (executionData: NodeExecutionResult) => {
+      setEdges((edges) => {
+        return edges.map((e) => {
+          const edge = { ...e, data: { ...e.data } } as CustomEdge;
+
+          if (executionData.status === "COMPLETED") {
+            // Produce output beads
+            for (let key in executionData.output_data) {
+              if (
+                edge.source !== getFrontendId(executionData.node_id, nodes) ||
+                edge.sourceHandle !== key
+              ) {
+                continue;
+              }
+              edge.data!.beadUp = (edge.data!.beadUp ?? 0) + 1;
+              // For static edges beadDown is always one less than beadUp
+              // Because there's no queueing and one bead is always at the connection point
+              if (edge.data?.isStatic) {
+                edge.data!.beadDown = (edge.data!.beadUp ?? 0) - 1;
+                edge.data!.beadData = edge.data!.beadData!.slice(0, -1);
+                continue;
+              }
+              //todo kcze this assumes output at key is always array with one element
+              edge.data!.beadData = [
+                executionData.output_data[key][0],
+                ...edge.data!.beadData!,
+              ];
+            }
+          } else if (executionData.status === "RUNNING") {
+            // Consume input beads
+            for (let key in executionData.input_data) {
+              if (
+                edge.target !== getFrontendId(executionData.node_id, nodes) ||
+                edge.targetHandle !== key
+              ) {
+                continue;
+              }
+              // Skip decreasing bead count if edge doesn't match or if it's static
+              if (
+                edge.data!.beadData![edge.data!.beadData!.length - 1] !==
+                  executionData.input_data[key] ||
+                edge.data?.isStatic
+              ) {
+                continue;
+              }
+              edge.data!.beadDown = (edge.data!.beadDown ?? 0) + 1;
+              edge.data!.beadData = edge.data!.beadData!.slice(0, -1);
+            }
+          }
+          return edge;
+        });
+      });
+    },
+    [edges],
+  );
+
+  const updateNodesWithExecutionData = useCallback(
+    (executionData: NodeExecutionResult) => {
+      if (passDataToBeads) {
+        updateEdgeBeads(executionData);
+      }
+      setNodes((nodes) => {
+        const nodeId = nodes.find(
+          (node) => node.data.backend_id === executionData.node_id,
+        )?.id;
+        if (!nodeId) {
+          console.error(
+            "Node not found for execution data:",
+            executionData,
+            "This shouldn't happen and means that the frontend and backend are out of sync.",
+          );
+          return nodes;
+        }
+        return nodes.map((node) =>
+          node.id === nodeId
+            ? {
+                ...node,
+                data: {
+                  ...node.data,
+                  status: executionData.status,
+                  executionResults:
+                    Object.keys(executionData.output_data).length > 0
+                      ? [
+                          ...(node.data.executionResults || []),
+                          {
+                            execId: executionData.node_exec_id,
+                            data: executionData.output_data,
+                          },
+                        ]
+                      : node.data.executionResults,
+                  isOutputOpen: true,
+                },
+              }
+            : node,
+        );
+      });
+    },
+    [nodes],
+  );
+
+  //TODO to utils? repeated in Flow
+  const formatEdgeID = useCallback((conn: Link | Connection): string => {
+    if ("sink_id" in conn) {
+      return `${conn.source_id}_${conn.source_name}_${conn.sink_id}_${conn.sink_name}`;
+    } else {
+      return `${conn.source}_${conn.sourceHandle}_${conn.target}_${conn.targetHandle}`;
+    }
+  }, []);
+
+  const getOutputType = useCallback(
+    (nodeId: string, handleId: string) => {
+      const node = nodes.find((n) => n.id === nodeId);
+      if (!node) return "unknown";
+
+      const outputSchema = node.data.outputSchema;
+      if (!outputSchema) return "unknown";
+
+      const outputHandle = outputSchema.properties[handleId];
+      if (!("type" in outputHandle)) return "unknown";
+      return outputHandle.type;
+    },
+    [nodes],
+  );
+
+  const loadGraph = useCallback(
+    (graph: Graph) => {
+      setSavedAgent(graph);
+      setAgentName(graph.name);
+      setAgentDescription(graph.description);
+
+      setNodes(() => {
+        const newNodes = graph.nodes.map((node) => {
+          const block = availableNodes.find(
+            (block) => block.id === node.block_id,
+          )!;
+          const newNode: CustomNode = {
+            id: node.id,
+            type: "custom",
+            position: {
+              x: node.metadata.position.x,
+              y: node.metadata.position.y,
+            },
+            data: {
+              block_id: block.id,
+              blockType: block.name,
+              categories: block.categories,
+              description: block.description,
+              title: `${block.name} ${node.id}`,
+              inputSchema: block.inputSchema,
+              outputSchema: block.outputSchema,
+              hardcodedValues: node.input_default,
+              connections: graph.links
+                .filter((l) => [l.source_id, l.sink_id].includes(node.id))
+                .map((link) => ({
+                  edge_id: formatEdgeID(link),
+                  source: link.source_id,
+                  sourceHandle: link.source_name,
+                  target: link.sink_id,
+                  targetHandle: link.sink_name,
+                })),
+              isOutputOpen: false,
+            },
+          };
+          return newNode;
+        });
+        setEdges((_) =>
+          graph.links.map((link) => ({
+            id: formatEdgeID(link),
+            type: "custom",
+            data: {
+              edgeColor: getTypeColor(
+                getOutputType(link.source_id, link.source_name!),
+              ),
+              sourcePos: nodes.find((node) => node.id === link.source_id)
+                ?.position,
+              isStatic: link.is_static,
+              beadUp: 0,
+              beadDown: 0,
+              beadData: [],
+            },
+            markerEnd: {
+              type: MarkerType.ArrowClosed,
+              strokeWidth: 2,
+              color: getTypeColor(
+                getOutputType(link.source_id, link.source_name!),
+              ),
+            },
+            source: link.source_id,
+            target: link.sink_id,
+            sourceHandle: link.source_name || undefined,
+            targetHandle: link.sink_name || undefined,
+          })),
+        );
+        return newNodes;
+      });
+    },
+    [availableNodes],
+  );
+
   const prepareNodeInputData = useCallback(
     (node: CustomNode) => {
       console.debug(
@@ -644,59 +627,31 @@ export default function useAgentGraph(
         links: links,
       };
 
-      // To avoid saving the same graph, we compare the payload with the saved agent.
-      // Differences in IDs are ignored.
-      const comparedPayload = {
-        ...(({ id, ...rest }) => rest)(payload),
-        nodes: payload.nodes.map(
-          ({ id, data, input_nodes, output_nodes, ...rest }) => rest,
-        ),
-        links: payload.links.map(({ source_id, sink_id, ...rest }) => rest),
-      };
-      const comparedSavedAgent = {
-        name: savedAgent?.name,
-        description: savedAgent?.description,
-        nodes: savedAgent?.nodes.map((v) => ({
-          block_id: v.block_id,
-          input_default: v.input_default,
-          metadata: v.metadata,
-        })),
-        links: savedAgent?.links.map((v) => ({
-          sink_name: v.sink_name,
-          source_name: v.source_name,
-        })),
-      };
-
-      let newSavedAgent = null;
-      if (savedAgent && deepEquals(comparedPayload, comparedSavedAgent)) {
-        console.warn("No need to save: Graph is the same as version on server");
-        newSavedAgent = savedAgent;
+      if (savedAgent && deepEquals(payload, savedAgent)) {
+        console.debug(
+          "No need to save: Graph is the same as version on server",
+        );
+        // Trigger state change
+        setSavedAgent(savedAgent);
+        return;
       } else {
         console.debug(
           "Saving new Graph version; old vs new:",
-          comparedPayload,
+          savedAgent,
           payload,
         );
-        setNodesSyncedWithSavedAgent(false);
-
-        newSavedAgent = savedAgent
-          ? await (savedAgent.is_template
-              ? api.updateTemplate(savedAgent.id, payload)
-              : api.updateGraph(savedAgent.id, payload))
-          : await (asTemplate
-              ? api.createTemplate(payload)
-              : api.createGraph(payload));
-
-        console.debug("Response from the API:", newSavedAgent);
       }
 
-      // Route the URL to the new flow ID if it's a new agent.
-      if (!savedAgent) {
-        const path = new URLSearchParams(searchParams);
-        path.set("flowID", newSavedAgent.id);
-        router.push(`${pathname}?${path.toString()}`);
-        return;
-      }
+      setNodesSyncedWithSavedAgent(false);
+
+      const newSavedAgent = savedAgent
+        ? await (savedAgent.is_template
+            ? api.updateTemplate(savedAgent.id, payload)
+            : api.updateGraph(savedAgent.id, payload))
+        : await (asTemplate
+            ? api.createTemplate(payload)
+            : api.createGraph(payload));
+      console.debug("Response from the API:", newSavedAgent);
 
       // Update the node IDs on the frontend
       setSavedAgent(newSavedAgent);
@@ -741,15 +696,7 @@ export default function useAgentGraph(
         }));
       });
     },
-    [
-      api,
-      nodes,
-      edges,
-      savedAgent,
-      agentName,
-      agentDescription,
-      prepareNodeInputData,
-    ],
+    [nodes, edges, savedAgent],
   );
 
   const requestSave = useCallback(

rnd/autogpt_builder/src/hooks/useBezierPath.ts

@@ -84,12 +84,12 @@ export function useBezierPath(
 
       return length;
     },
-    [getPointForT],
+    [path],
   );
 
   const length = useMemo(() => {
     return getArcLength(1);
-  }, [getArcLength]);
+  }, [path]);
 
   const getBezierDerivative = useCallback(
     (t: number) => {
@@ -131,7 +131,7 @@ export function useBezierPath(
 
       return t;
     },
-    [getArcLength, getBezierDerivative, length],
+    [path],
   );
 
   const getPointAtDistance = useCallback(
@@ -143,7 +143,7 @@ export function useBezierPath(
       const t = getTForDistance(distance);
       return getPointForT(t);
     },
-    [getTForDistance, getPointForT, length],
+    [path],
   );
 
   return {

rnd/autogpt_builder/src/instrumentation.ts

@@ -1,13 +0,0 @@
-import * as Sentry from "@sentry/nextjs";
-
-export async function register() {
-  if (process.env.NEXT_RUNTIME === "nodejs") {
-    await import("../sentry.server.config");
-  }
-
-  if (process.env.NEXT_RUNTIME === "edge") {
-    await import("../sentry.edge.config");
-  }
-}
-
-export const onRequestError = Sentry.captureRequestError;

rnd/autogpt_builder/src/lib/autogpt-server-api/types.ts

@@ -13,7 +13,6 @@ export type Block = {
   inputSchema: BlockIORootSchema;
   outputSchema: BlockIORootSchema;
   staticOutput: boolean;
-  uiType: BlockUIType;
 };
 
 export type BlockIORootSchema = {
@@ -183,10 +182,3 @@ export type User = {
   id: string;
   email: string;
 };
-
-export enum BlockUIType {
-  STANDARD = "Standard",
-  INPUT = "Input",
-  OUTPUT = "Output",
-  NOTE = "Note",
-}

rnd/autogpt_builder/src/lib/supabase/client.ts

@@ -7,7 +7,6 @@ export function createClient() {
       process.env.NEXT_PUBLIC_SUPABASE_ANON_KEY!,
     );
   } catch (error) {
-    console.error("error creating client", error);
     return null;
   }
 }

rnd/autogpt_builder/src/lib/utils.ts

@@ -24,16 +24,15 @@ export function deepEquals(x: any, y: any): boolean {
   const ok = Object.keys,
     tx = typeof x,
     ty = typeof y;
-
-  const res =
+  return (
     x &&
     y &&
     tx === ty &&
     (tx === "object"
       ? ok(x).length === ok(y).length &&
         ok(x).every((key) => deepEquals(x[key], y[key]))
-      : x === y);
-  return res;
+      : x === y)
+  );
 }
 
 /** Get tailwind text color class from type name */
@@ -185,7 +184,7 @@ export const categoryColorMap: Record<string, string> = {
   SEARCH: "bg-blue-300/[.7]",
   BASIC: "bg-purple-300/[.7]",
   INPUT: "bg-cyan-300/[.7]",
-  OUTPUT: "bg-red-300/[.7]",
+  OUTPUT: "bg-brown-300/[.7]",
   LOGIC: "bg-teal-300/[.7]",
 };
 
@@ -195,10 +194,3 @@ export function getPrimaryCategoryColor(categories: Category[]): string {
   }
   return categoryColorMap[categories[0].category] || "bg-gray-300/[.7]";
 }
-
-export function filterBlocksByType<T>(
-  blocks: T[],
-  predicate: (block: T) => boolean,
-): T[] {
-  return blocks.filter(predicate);
-}

rnd/autogpt_builder/src/lib/withRoleAccess.ts

@@ -1,23 +1,16 @@
 import { redirect } from "next/navigation";
 import getServerUser from "@/hooks/getServerUser";
 import React from "react";
-import * as Sentry from "@sentry/nextjs";
 
 export async function withRoleAccess(allowedRoles: string[]) {
   "use server";
-  return await Sentry.withServerActionInstrumentation(
-    "withRoleAccess",
-    {},
-    async () => {
-      return async function <T extends React.ComponentType<any>>(Component: T) {
-        const { user, role, error } = await getServerUser();
+  return async function <T extends React.ComponentType<any>>(Component: T) {
+    const { user, role, error } = await getServerUser();
 
-        if (error || !user || !role || !allowedRoles.includes(role)) {
-          redirect("/unauthorized");
-        }
+    if (error || !user || !role || !allowedRoles.includes(role)) {
+      redirect("/unauthorized");
+    }
 
-        return Component;
-      };
-    },
-  );
+    return Component;
+  };
 }

rnd/autogpt_builder/src/stories/Button.stories.ts

@@ -0,0 +1,59 @@
+import type { Meta, StoryObj } from '@storybook/react';
+import { fn } from '@storybook/test';
+import { Button } from './Button';
+
+// More on how to set up stories at: https://storybook.js.org/docs/writing-stories#default-export
+const meta = {
+  title: 'Example/Button',
+  component: Button,
+  parameters: {
+    // Optional parameter to center the component in the Canvas. More info: https://storybook.js.org/docs/configure/story-layout
+    layout: 'centered',
+  },
+  // This component will have an automatically generated Autodocs entry: https://storybook.js.org/docs/writing-docs/autodocs
+  tags: ['autodocs'],
+  // More on argTypes: https://storybook.js.org/docs/api/argtypes
+  argTypes: {
+    backgroundColor: { control: 'color' },
+  },
+  // Use `fn` to spy on the onClick arg, which will appear in the actions panel once invoked: https://storybook.js.org/docs/essentials/actions#action-args
+  args: { onClick: fn() },
+} satisfies Meta<typeof Button>;
+
+export default meta;
+type Story = StoryObj<typeof meta>;
+
+// More on writing stories with args: https://storybook.js.org/docs/writing-stories/args
+export const Primary: Story = {
+  args: {
+    primary: true,
+    label: 'Button',
+  },
+};
+
+export const Secondary: Story = {
+  args: {
+    label: 'Button',
+  },
+};
+
+export const Large: Story = {
+  args: {
+    size: 'large',
+    label: 'Button',
+  },
+};
+
+export const Small: Story = {
+  args: {
+    size: 'small',
+    label: 'Button',
+  },
+};
+
+export const MySampleStory: Story = {
+  args: {
+    primary: false,
+    label: "Button"
+  }
+};

rnd/autogpt_builder/src/stories/Button.tsx

@@ -0,0 +1,52 @@
+import React from 'react';
+import './button.css';
+
+export interface ButtonProps {
+  /**
+   * Is this the principal call to action on the page?
+   */
+  primary?: boolean;
+  /**
+   * What background color to use
+   */
+  backgroundColor?: string;
+  /**
+   * How large should the button be?
+   */
+  size?: 'small' | 'medium' | 'large';
+  /**
+   * Button contents
+   */
+  label: string;
+  /**
+   * Optional click handler
+   */
+  onClick?: () => void;
+}
+
+/**
+ * Primary UI component for user interaction
+ */
+export const Button = ({
+  primary = false,
+  size = 'medium',
+  backgroundColor,
+  label,
+  ...props
+}: ButtonProps) => {
+  const mode = primary ? 'storybook-button--primary' : 'storybook-button--secondary';
+  return (
+    <button
+      type="button"
+      className={['storybook-button', `storybook-button--${size}`, mode].join(' ')}
+      {...props}
+    >
+      {label}
+      <style jsx>{`
+        button {
+          background-color: ${backgroundColor};
+        }
+      `}</style>
+    </button>
+  );
+};

rnd/autogpt_builder/src/stories/Configure.mdx

@@ -0,0 +1,446 @@
+import { Meta } from "@storybook/blocks";
+import Image from "next/image";
+
+import Github from "./assets/github.svg";
+import Discord from "./assets/discord.svg";
+import Youtube from "./assets/youtube.svg";
+import Tutorials from "./assets/tutorials.svg";
+import Styling from "./assets/styling.png";
+import Context from "./assets/context.png";
+import Assets from "./assets/assets.png";
+import Docs from "./assets/docs.png";
+import Share from "./assets/share.png";
+import FigmaPlugin from "./assets/figma-plugin.png";
+import Testing from "./assets/testing.png";
+import Accessibility from "./assets/accessibility.png";
+import Theming from "./assets/theming.png";
+import AddonLibrary from "./assets/addon-library.png";
+
+export const RightArrow = () => <svg 
+    viewBox="0 0 14 14" 
+    width="8px" 
+    height="14px" 
+    style={{ 
+      marginLeft: '4px',
+      display: 'inline-block',
+      shapeRendering: 'inherit',
+      verticalAlign: 'middle',
+      fill: 'currentColor',
+      'path fill': 'currentColor'
+    }}
+>
+  <path d="m11.1 7.35-5.5 5.5a.5.5 0 0 1-.7-.7L10.04 7 4.9 1.85a.5.5 0 1 1 .7-.7l5.5 5.5c.2.2.2.5 0 .7Z" />
+</svg>
+
+<Meta title="Configure your project" />
+
+<div className="sb-container">
+  <div className='sb-section-title'>
+    # Configure your project
+
+    Because Storybook works separately from your app, you'll need to configure it for your specific stack and setup. Below, explore guides for configuring Storybook with popular frameworks and tools. If you get stuck, learn how you can ask for help from our community.
+  </div>
+  <div className="sb-section">
+    <div className="sb-section-item">
+      <Image
+        src={Styling}
+        alt="A wall of logos representing different styling technologies"
+        width={0}
+        height={0}
+        style={{ width: '100%', height: 'auto' }}
+      />
+      <h4 className="sb-section-item-heading">Add styling and CSS</h4>
+      <p className="sb-section-item-paragraph">Like with web applications, there are many ways to include CSS within Storybook. Learn more about setting up styling within Storybook.</p>
+      <a
+        href="https://storybook.js.org/docs/configure/styling-and-css/?renderer=react"
+        target="_blank"
+      >Learn more<RightArrow /></a>
+    </div>
+    <div className="sb-section-item">
+      <Image 
+        width={0}
+        height={0}
+        style={{ width: '100%', height: 'auto' }}
+        src={Context}
+        alt="An abstraction representing the composition of data for a component"
+      />
+      <h4 className="sb-section-item-heading">Provide context and mocking</h4>
+      <p className="sb-section-item-paragraph">Often when a story doesn't render, it's because your component is expecting a specific environment or context (like a theme provider) to be available.</p>
+      <a
+        href="https://storybook.js.org/docs/writing-stories/decorators/?renderer=react#context-for-mocking"
+        target="_blank"
+      >Learn more<RightArrow /></a>
+    </div>
+    <div className="sb-section-item">
+      <Image 
+        width={0}
+        height={0}
+        style={{ width: '100%', height: 'auto' }} 
+        src={Assets} 
+        alt="A representation of typography and image assets" 
+      />
+      <div>
+        <h4 className="sb-section-item-heading">Load assets and resources</h4>
+        <p className="sb-section-item-paragraph">To link static files (like fonts) to your projects and stories, use the
+        `staticDirs` configuration option to specify folders to load when
+        starting Storybook.</p>
+        <a
+          href="https://storybook.js.org/docs/configure/images-and-assets/?renderer=react"
+          target="_blank"
+        >Learn more<RightArrow /></a>
+      </div>
+    </div>
+  </div>
+</div>
+<div className="sb-container">
+  <div className='sb-section-title'>
+    # Do more with Storybook
+
+    Now that you know the basics, let's explore other parts of Storybook that will improve your experience. This list is just to get you started. You can customise Storybook in many ways to fit your needs.
+  </div>
+
+  <div className="sb-section">
+    <div className="sb-features-grid">
+      <div className="sb-grid-item">
+        <Image 
+          width={0}
+          height={0}
+          style={{ width: '100%', height: 'auto' }} 
+          src={Docs} 
+          alt="A screenshot showing the autodocs tag being set, pointing a docs page being generated" 
+        />
+        <h4 className="sb-section-item-heading">Autodocs</h4>
+        <p className="sb-section-item-paragraph">Auto-generate living,
+          interactive reference documentation from your components and stories.</p>
+        <a
+          href="https://storybook.js.org/docs/writing-docs/autodocs/?renderer=react"
+          target="_blank"
+        >Learn more<RightArrow /></a>
+      </div>
+      <div className="sb-grid-item">
+        <Image 
+          width={0}
+          height={0}
+          style={{ width: '100%', height: 'auto' }} 
+          src={Share} 
+          alt="A browser window showing a Storybook being published to a chromatic.com URL" 
+        />
+        <h4 className="sb-section-item-heading">Publish to Chromatic</h4>
+        <p className="sb-section-item-paragraph">Publish your Storybook to review and collaborate with your entire team.</p>
+        <a
+          href="https://storybook.js.org/docs/sharing/publish-storybook/?renderer=react#publish-storybook-with-chromatic"
+          target="_blank"
+        >Learn more<RightArrow /></a>
+      </div>
+      <div className="sb-grid-item">
+        <Image 
+          width={0}
+          height={0}
+          style={{ width: '100%', height: 'auto' }} 
+          src={FigmaPlugin} 
+          alt="Windows showing the Storybook plugin in Figma" 
+        />
+        <h4 className="sb-section-item-heading">Figma Plugin</h4>
+        <p className="sb-section-item-paragraph">Embed your stories into Figma to cross-reference the design and live
+          implementation in one place.</p>
+        <a
+          href="https://storybook.js.org/docs/sharing/design-integrations/?renderer=react#embed-storybook-in-figma-with-the-plugin"
+          target="_blank"
+        >Learn more<RightArrow /></a>
+      </div>
+      <div className="sb-grid-item">
+        <Image 
+          width={0}
+          height={0}
+          style={{ width: '100%', height: 'auto' }} 
+          src={Testing} 
+          alt="Screenshot of tests passing and failing" 
+        />
+        <h4 className="sb-section-item-heading">Testing</h4>
+        <p className="sb-section-item-paragraph">Use stories to test a component in all its variations, no matter how
+          complex.</p>
+        <a
+          href="https://storybook.js.org/docs/writing-tests/?renderer=react"
+          target="_blank"
+        >Learn more<RightArrow /></a>
+      </div>
+      <div className="sb-grid-item">
+        <Image 
+          width={0}
+          height={0}
+          style={{ width: '100%', height: 'auto' }} 
+          src={Accessibility} 
+          alt="Screenshot of accessibility tests passing and failing" 
+        />
+        <h4 className="sb-section-item-heading">Accessibility</h4>
+        <p className="sb-section-item-paragraph">Automatically test your components for a11y issues as you develop.</p>
+        <a
+          href="https://storybook.js.org/docs/writing-tests/accessibility-testing/?renderer=react"
+          target="_blank"
+        >Learn more<RightArrow /></a>
+      </div>
+      <div className="sb-grid-item">
+        <Image 
+          width={0}
+          height={0}
+          style={{ width: '100%', height: 'auto' }} 
+          src={Theming} 
+          alt="Screenshot of Storybook in light and dark mode" 
+        />
+        <h4 className="sb-section-item-heading">Theming</h4>
+        <p className="sb-section-item-paragraph">Theme Storybook's UI to personalize it to your project.</p>
+        <a
+          href="https://storybook.js.org/docs/configure/theming/?renderer=react"
+          target="_blank"
+        >Learn more<RightArrow /></a>
+      </div>
+    </div>
+  </div>
+</div>
+<div className='sb-addon'>
+  <div className='sb-addon-text'>
+    <h4>Addons</h4>
+    <p className="sb-section-item-paragraph">Integrate your tools with Storybook to connect workflows.</p>
+    <a
+        href="https://storybook.js.org/addons/"
+        target="_blank"
+      >Discover all addons<RightArrow /></a>
+  </div>
+  <div className='sb-addon-img'>
+    <Image 
+      width={650}
+      height={347}
+      src={AddonLibrary} 
+      alt="Integrate your tools with Storybook to connect workflows." 
+    />
+  </div>
+</div>
+
+<div className="sb-section sb-socials">
+    <div className="sb-section-item">
+      <Image 
+        width={32}
+        height={32}
+        layout="fixed"
+        src={Github} 
+        alt="Github logo" 
+        className="sb-explore-image"
+      />
+      Join our contributors building the future of UI development.
+
+      <a
+        href="https://github.com/storybookjs/storybook"
+        target="_blank"
+      >Star on GitHub<RightArrow /></a>
+    </div>
+    <div className="sb-section-item">
+      <Image 
+        width={33}
+        height={32}
+        layout="fixed"
+        src={Discord} 
+        alt="Discord logo" 
+        className="sb-explore-image"
+      />
+      <div>
+        Get support and chat with frontend developers.
+
+        <a
+          href="https://discord.gg/storybook"
+          target="_blank"
+        >Join Discord server<RightArrow /></a>
+      </div>
+    </div>
+    <div className="sb-section-item">
+      <Image 
+        width={32}
+        height={32}
+        layout="fixed"
+        src={Youtube} 
+        alt="Youtube logo" 
+        className="sb-explore-image"
+      />
+      <div>
+        Watch tutorials, feature previews and interviews.
+
+        <a
+          href="https://www.youtube.com/@chromaticui"
+          target="_blank"
+        >Watch on YouTube<RightArrow /></a>
+      </div>
+    </div>
+    <div className="sb-section-item">
+      <Image 
+        width={33}
+        height={32}
+        layout="fixed"
+        src={Tutorials} 
+        alt="A book" 
+        className="sb-explore-image"
+      />
+      <p>Follow guided walkthroughs on for key workflows.</p>
+
+      <a
+          href="https://storybook.js.org/tutorials/"
+          target="_blank"
+        >Discover tutorials<RightArrow /></a>
+    </div>
+</div>
+
+<style>
+  {`
+  .sb-container {
+    margin-bottom: 48px;
+  }
+
+  .sb-section {
+    width: 100%;
+    display: flex;
+    flex-direction: row;
+    gap: 20px;
+  }
+
+  img {
+    object-fit: cover;
+  }
+
+  .sb-section-title {
+    margin-bottom: 32px;
+  }
+
+  .sb-section a:not(h1 a, h2 a, h3 a) {
+    font-size: 14px;
+  }
+
+  .sb-section-item, .sb-grid-item {
+    flex: 1;
+    display: flex;
+    flex-direction: column;
+  }
+
+  .sb-section-item-heading {
+    padding-top: 20px !important;
+    padding-bottom: 5px !important;
+    margin: 0 !important;
+  }
+  .sb-section-item-paragraph {
+    margin: 0;
+    padding-bottom: 10px;
+  }
+
+  .sb-chevron {
+    margin-left: 5px;
+  }
+
+  .sb-features-grid {
+    display: grid;
+    grid-template-columns: repeat(2, 1fr);
+    grid-gap: 32px 20px;
+  }
+
+  .sb-socials {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+  }
+
+  .sb-socials p {
+    margin-bottom: 10px;
+  }
+
+  .sb-explore-image {
+    max-height: 32px;
+    align-self: flex-start;
+  }
+
+  .sb-addon {
+    width: 100%;
+    display: flex;
+    align-items: center;
+    position: relative;
+    background-color: #EEF3F8;
+    border-radius: 5px;
+    border: 1px solid rgba(0, 0, 0, 0.05);
+    background: #EEF3F8;
+    height: 180px;
+    margin-bottom: 48px;
+    overflow: hidden;
+  }
+
+  .sb-addon-text {
+    padding-left: 48px;
+    max-width: 240px;
+  }
+
+  .sb-addon-text h4 {
+    padding-top: 0px;
+  }
+
+  .sb-addon-img {
+    position: absolute;
+    left: 345px;
+    top: 0;
+    height: 100%;
+    width: 200%;
+    overflow: hidden;
+  }
+
+  .sb-addon-img img {
+    width: 650px;
+    transform: rotate(-15deg);
+    margin-left: 40px;
+    margin-top: -72px;
+    box-shadow: 0 0 1px rgba(255, 255, 255, 0);
+    backface-visibility: hidden;
+  }
+
+  @media screen and (max-width: 800px) {
+    .sb-addon-img {
+      left: 300px;
+    }
+  }
+
+  @media screen and (max-width: 600px) {
+    .sb-section {
+      flex-direction: column;
+    }
+
+    .sb-features-grid {
+      grid-template-columns: repeat(1, 1fr);
+    }
+
+    .sb-socials {
+      grid-template-columns: repeat(2, 1fr);
+    }
+
+    .sb-addon {
+      height: 280px;
+      align-items: flex-start;
+      padding-top: 32px;
+      overflow: hidden;
+    }
+
+    .sb-addon-text {
+      padding-left: 24px;
+    }
+
+    .sb-addon-img {
+      right: 0;
+      left: 0;
+      top: 130px;
+      bottom: 0;
+      overflow: hidden;
+      height: auto;
+      width: 124%;
+    }
+
+    .sb-addon-img img {
+      width: 1200px;
+      transform: rotate(-12deg);
+      margin-left: 0;
+      margin-top: 48px;
+      margin-bottom: -40px;
+      margin-left: -24px;
+    }
+  }
+  `}
+</style>

rnd/autogpt_builder/src/stories/Header.stories.ts

@@ -0,0 +1,32 @@
+import type { Meta, StoryObj } from '@storybook/react';
+import { fn } from '@storybook/test';
+import { Header } from './Header';
+
+const meta = {
+  title: 'Example/Header',
+  component: Header,
+  // This component will have an automatically generated Autodocs entry: https://storybook.js.org/docs/writing-docs/autodocs
+  tags: ['autodocs'],
+  parameters: {
+    // More on how to position stories at: https://storybook.js.org/docs/configure/story-layout
+    layout: 'fullscreen',
+  },
+  args: {
+    onLogin: fn(),
+    onLogout: fn(),
+    onCreateAccount: fn(),
+  },
+} satisfies Meta<typeof Header>;
+
+export default meta;
+type Story = StoryObj<typeof meta>;
+
+export const LoggedIn: Story = {
+  args: {
+    user: {
+      name: 'Jane Doe',
+    },
+  },
+};
+
+export const LoggedOut: Story = {};

rnd/autogpt_builder/src/stories/Header.tsx

@@ -0,0 +1,56 @@
+import React from 'react';
+
+import { Button } from './Button';
+import './header.css';
+
+type User = {
+  name: string;
+};
+
+export interface HeaderProps {
+  user?: User;
+  onLogin?: () => void;
+  onLogout?: () => void;
+  onCreateAccount?: () => void;
+}
+
+export const Header = ({ user, onLogin, onLogout, onCreateAccount }: HeaderProps) => (
+  <header>
+    <div className="storybook-header">
+      <div>
+        <svg width="32" height="32" viewBox="0 0 32 32" xmlns="http://www.w3.org/2000/svg">
+          <g fill="none" fillRule="evenodd">
+            <path
+              d="M10 0h12a10 10 0 0110 10v12a10 10 0 01-10 10H10A10 10 0 010 22V10A10 10 0 0110 0z"
+              fill="#FFF"
+            />
+            <path
+              d="M5.3 10.6l10.4 6v11.1l-10.4-6v-11zm11.4-6.2l9.7 5.5-9.7 5.6V4.4z"
+              fill="#555AB9"
+            />
+            <path
+              d="M27.2 10.6v11.2l-10.5 6V16.5l10.5-6zM15.7 4.4v11L6 10l9.7-5.5z"
+              fill="#91BAF8"
+            />
+          </g>
+        </svg>
+        <h1>Acme</h1>
+      </div>
+      <div>
+        {user ? (
+          <>
+            <span className="welcome">
+              Welcome, <b>{user.name}</b>!
+            </span>
+            <Button size="small" onClick={onLogout} label="Log out" />
+          </>
+        ) : (
+          <>
+            <Button size="small" onClick={onLogin} label="Log in" />
+            <Button primary size="small" onClick={onCreateAccount} label="Sign up" />
+          </>
+        )}
+      </div>
+    </div>
+  </header>
+);

rnd/autogpt_builder/src/stories/Page.stories.ts

@@ -0,0 +1,32 @@
+import type { Meta, StoryObj } from '@storybook/react';
+import { within, userEvent, expect } from '@storybook/test';
+
+import { Page } from './Page';
+
+const meta = {
+  title: 'Example/Page',
+  component: Page,
+  parameters: {
+    // More on how to position stories at: https://storybook.js.org/docs/configure/story-layout
+    layout: 'fullscreen',
+  },
+} satisfies Meta<typeof Page>;
+
+export default meta;
+type Story = StoryObj<typeof meta>;
+
+export const LoggedOut: Story = {};
+
+// More on interaction testing: https://storybook.js.org/docs/writing-tests/interaction-testing
+export const LoggedIn: Story = {
+  play: async ({ canvasElement }) => {
+    const canvas = within(canvasElement);
+    const loginButton = canvas.getByRole('button', { name: /Log in/i });
+    await expect(loginButton).toBeInTheDocument();
+    await userEvent.click(loginButton);
+    await expect(loginButton).not.toBeInTheDocument();
+
+    const logoutButton = canvas.getByRole('button', { name: /Log out/i });
+    await expect(logoutButton).toBeInTheDocument();
+  },
+};

rnd/autogpt_builder/src/stories/Page.tsx

@@ -0,0 +1,73 @@
+import React from 'react';
+
+import { Header } from './Header';
+import './page.css';
+
+type User = {
+  name: string;
+};
+
+export const Page: React.FC = () => {
+  const [user, setUser] = React.useState<User>();
+
+  return (
+    <article>
+      <Header
+        user={user}
+        onLogin={() => setUser({ name: 'Jane Doe' })}
+        onLogout={() => setUser(undefined)}
+        onCreateAccount={() => setUser({ name: 'Jane Doe' })}
+      />
+
+      <section className="storybook-page">
+        <h2>Pages in Storybook</h2>
+        <p>
+          We recommend building UIs with a{' '}
+          <a href="https://componentdriven.org" target="_blank" rel="noopener noreferrer">
+            <strong>component-driven</strong>
+          </a>{' '}
+          process starting with atomic components and ending with pages.
+        </p>
+        <p>
+          Render pages with mock data. This makes it easy to build and review page states without
+          needing to navigate to them in your app. Here are some handy patterns for managing page
+          data in Storybook:
+        </p>
+        <ul>
+          <li>
+            Use a higher-level connected component. Storybook helps you compose such data from the
+            "args" of child component stories
+          </li>
+          <li>
+            Assemble data in the page component from your services. You can mock these services out
+            using Storybook.
+          </li>
+        </ul>
+        <p>
+          Get a guided tutorial on component-driven development at{' '}
+          <a href="https://storybook.js.org/tutorials/" target="_blank" rel="noopener noreferrer">
+            Storybook tutorials
+          </a>
+          . Read more in the{' '}
+          <a href="https://storybook.js.org/docs" target="_blank" rel="noopener noreferrer">
+            docs
+          </a>
+          .
+        </p>
+        <div className="tip-wrapper">
+          <span className="tip">Tip</span> Adjust the width of the canvas with the{' '}
+          <svg width="10" height="10" viewBox="0 0 12 12" xmlns="http://www.w3.org/2000/svg">
+            <g fill="none" fillRule="evenodd">
+              <path
+                d="M1.5 5.2h4.8c.3 0 .5.2.5.4v5.1c-.1.2-.3.3-.4.3H1.4a.5.5 0 01-.5-.4V5.7c0-.3.2-.5.5-.5zm0-2.1h6.9c.3 0 .5.2.5.4v7a.5.5 0 01-1 0V4H1.5a.5.5 0 010-1zm0-2.1h9c.3 0 .5.2.5.4v9.1a.5.5 0 01-1 0V2H1.5a.5.5 0 010-1zm4.3 5.2H2V10h3.8V6.2z"
+                id="a"
+                fill="#999"
+              />
+            </g>
+          </svg>
+          Viewports addon in the toolbar
+        </div>
+      </section>
+    </article>
+  );
+};

rnd/autogpt_builder/src/stories/assets/accessibility.svg

@@ -0,0 +1 @@
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rnd/autogpt_builder/src/stories/assets/discord.svg

@@ -0,0 +1 @@
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rnd/autogpt_builder/src/stories/assets/github.svg

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+<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="none" viewBox="0 0 32 32"><path fill="#161614" d="M16.0001 0C7.16466 0 0 7.17472 0 16.0256C0 23.1061 4.58452 29.1131 10.9419 31.2322C11.7415 31.3805 12.0351 30.8845 12.0351 30.4613C12.0351 30.0791 12.0202 28.8167 12.0133 27.4776C7.56209 28.447 6.62283 25.5868 6.62283 25.5868C5.89499 23.7345 4.8463 23.2419 4.8463 23.2419C3.39461 22.2473 4.95573 22.2678 4.95573 22.2678C6.56242 22.3808 7.40842 23.9192 7.40842 23.9192C8.83547 26.3691 11.1514 25.6609 12.0645 25.2514C12.2081 24.2156 12.6227 23.5087 13.0803 23.1085C9.52648 22.7032 5.7906 21.3291 5.7906 15.1886C5.7906 13.4389 6.41563 12.0094 7.43916 10.8871C7.27303 10.4834 6.72537 8.85349 7.59415 6.64609C7.59415 6.64609 8.93774 6.21539 11.9953 8.28877C13.2716 7.9337 14.6404 7.75563 16.0001 7.74953C17.3599 7.75563 18.7297 7.9337 20.0084 8.28877C23.0623 6.21539 24.404 6.64609 24.404 6.64609C25.2749 8.85349 24.727 10.4834 24.5608 10.8871C25.5868 12.0094 26.2075 13.4389 26.2075 15.1886C26.2075 21.3437 22.4645 22.699 18.9017 23.0957C19.4756 23.593 19.9869 24.5683 19.9869 26.0634C19.9869 28.2077 19.9684 29.9334 19.9684 30.4613C19.9684 30.8877 20.2564 31.3874 21.0674 31.2301C27.4213 29.1086 32 23.1037 32 16.0256C32 7.17472 24.8364 0 16.0001 0ZM5.99257 22.8288C5.95733 22.9084 5.83227 22.9322 5.71834 22.8776C5.60229 22.8253 5.53711 22.7168 5.57474 22.6369C5.60918 22.5549 5.7345 22.5321 5.85029 22.587C5.9666 22.6393 6.03284 22.7489 5.99257 22.8288ZM6.7796 23.5321C6.70329 23.603 6.55412 23.5701 6.45291 23.4581C6.34825 23.3464 6.32864 23.197 6.40601 23.125C6.4847 23.0542 6.62937 23.0874 6.73429 23.1991C6.83895 23.3121 6.85935 23.4605 6.7796 23.5321ZM7.31953 24.4321C7.2215 24.5003 7.0612 24.4363 6.96211 24.2938C6.86407 24.1513 6.86407 23.9804 6.96422 23.9119C7.06358 23.8435 7.2215 23.905 7.32191 24.0465C7.41968 24.1914 7.41968 24.3623 7.31953 24.4321ZM8.23267 25.4743C8.14497 25.5712 7.95818 25.5452 7.82146 25.413C7.68156 25.2838 7.64261 25.1004 7.73058 25.0035C7.81934 24.9064 8.00719 24.9337 8.14497 25.0648C8.28381 25.1938 8.3262 25.3785 8.23267 25.4743ZM9.41281 25.8262C9.37413 25.9517 9.19423 26.0088 9.013 25.9554C8.83203 25.9005 8.7136 25.7535 8.75016 25.6266C8.78778 25.5003 8.96848 25.4408 9.15104 25.4979C9.33174 25.5526 9.45044 25.6985 9.41281 25.8262ZM10.7559 25.9754C10.7604 26.1076 10.6067 26.2172 10.4165 26.2196C10.2252 26.2238 10.0704 26.1169 10.0683 25.9868C10.0683 25.8534 10.2185 25.7448 10.4098 25.7416C10.6001 25.7379 10.7559 25.8441 10.7559 25.9754ZM12.0753 25.9248C12.0981 26.0537 11.9658 26.1862 11.7769 26.2215C11.5912 26.2554 11.4192 26.1758 11.3957 26.0479C11.3726 25.9157 11.5072 25.7833 11.6927 25.7491C11.8819 25.7162 12.0512 25.7937 12.0753 25.9248Z"/></svg>

rnd/autogpt_builder/src/stories/assets/tutorials.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="33" height="32" fill="none" viewBox="0 0 33 32"><g clip-path="url(#clip0_10031_177597)"><path fill="#B7F0EF" fill-rule="evenodd" d="M17 7.87059C17 6.48214 17.9812 5.28722 19.3431 5.01709L29.5249 2.99755C31.3238 2.64076 33 4.01717 33 5.85105V22.1344C33 23.5229 32.0188 24.7178 30.6569 24.9879L20.4751 27.0074C18.6762 27.3642 17 25.9878 17 24.1539L17 7.87059Z" clip-rule="evenodd" opacity=".7"/><path fill="#87E6E5" fill-rule="evenodd" d="M1 5.85245C1 4.01857 2.67623 2.64215 4.47507 2.99895L14.6569 5.01848C16.0188 5.28861 17 6.48354 17 7.87198V24.1553C17 25.9892 15.3238 27.3656 13.5249 27.0088L3.34311 24.9893C1.98119 24.7192 1 23.5242 1 22.1358V5.85245Z" clip-rule="evenodd"/><path fill="#61C1FD" fill-rule="evenodd" d="M15.543 5.71289C15.543 5.71289 16.8157 5.96289 17.4002 6.57653C17.9847 7.19016 18.4521 9.03107 18.4521 9.03107C18.4521 9.03107 18.4521 25.1106 18.4521 26.9629C18.4521 28.8152 19.3775 31.4174 19.3775 31.4174L17.4002 28.8947L16.2575 31.4174C16.2575 31.4174 15.543 29.0765 15.543 27.122C15.543 25.1674 15.543 5.71289 15.543 5.71289Z" clip-rule="evenodd"/></g><defs><clipPath id="clip0_10031_177597"><rect width="32" height="32" fill="#fff" transform="translate(0.5)"/></clipPath></defs></svg>

rnd/autogpt_builder/src/stories/assets/youtube.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" fill="none" viewBox="0 0 32 32"><path fill="#ED1D24" d="M31.3313 8.44657C30.9633 7.08998 29.8791 6.02172 28.5022 5.65916C26.0067 5.00026 16 5.00026 16 5.00026C16 5.00026 5.99333 5.00026 3.4978 5.65916C2.12102 6.02172 1.03665 7.08998 0.668678 8.44657C0 10.9053 0 16.0353 0 16.0353C0 16.0353 0 21.1652 0.668678 23.6242C1.03665 24.9806 2.12102 26.0489 3.4978 26.4116C5.99333 27.0703 16 27.0703 16 27.0703C16 27.0703 26.0067 27.0703 28.5022 26.4116C29.8791 26.0489 30.9633 24.9806 31.3313 23.6242C32 21.1652 32 16.0353 32 16.0353C32 16.0353 32 10.9053 31.3313 8.44657Z"/><path fill="#fff" d="M12.7266 20.6934L21.0902 16.036L12.7266 11.3781V20.6934Z"/></svg>

rnd/autogpt_builder/src/stories/button.css

@@ -0,0 +1,30 @@
+.storybook-button {
+  font-family: 'Nunito Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+  font-weight: 700;
+  border: 0;
+  border-radius: 3em;
+  cursor: pointer;
+  display: inline-block;
+  line-height: 1;
+}
+.storybook-button--primary {
+  color: white;
+  background-color: #1ea7fd;
+}
+.storybook-button--secondary {
+  color: #333;
+  background-color: transparent;
+  box-shadow: rgba(0, 0, 0, 0.15) 0px 0px 0px 1px inset;
+}
+.storybook-button--small {
+  font-size: 12px;
+  padding: 10px 16px;
+}
+.storybook-button--medium {
+  font-size: 14px;
+  padding: 11px 20px;
+}
+.storybook-button--large {
+  font-size: 16px;
+  padding: 12px 24px;
+}

rnd/autogpt_builder/src/stories/header.css

@@ -0,0 +1,32 @@
+.storybook-header {
+  font-family: 'Nunito Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+  border-bottom: 1px solid rgba(0, 0, 0, 0.1);
+  padding: 15px 20px;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+}
+
+.storybook-header svg {
+  display: inline-block;
+  vertical-align: top;
+}
+
+.storybook-header h1 {
+  font-weight: 700;
+  font-size: 20px;
+  line-height: 1;
+  margin: 6px 0 6px 10px;
+  display: inline-block;
+  vertical-align: top;
+}
+
+.storybook-header button + button {
+  margin-left: 10px;
+}
+
+.storybook-header .welcome {
+  color: #333;
+  font-size: 14px;
+  margin-right: 10px;
+}

rnd/autogpt_builder/src/stories/page.css

@@ -0,0 +1,69 @@
+.storybook-page {
+  font-family: 'Nunito Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
+  font-size: 14px;
+  line-height: 24px;
+  padding: 48px 20px;
+  margin: 0 auto;
+  max-width: 600px;
+  color: #333;
+}
+
+.storybook-page h2 {
+  font-weight: 700;
+  font-size: 32px;
+  line-height: 1;
+  margin: 0 0 4px;
+  display: inline-block;
+  vertical-align: top;
+}
+
+.storybook-page p {
+  margin: 1em 0;
+}
+
+.storybook-page a {
+  text-decoration: none;
+  color: #1ea7fd;
+}
+
+.storybook-page ul {
+  padding-left: 30px;
+  margin: 1em 0;
+}
+
+.storybook-page li {
+  margin-bottom: 8px;
+}
+
+.storybook-page .tip {
+  display: inline-block;
+  border-radius: 1em;
+  font-size: 11px;
+  line-height: 12px;
+  font-weight: 700;
+  background: #e7fdd8;
+  color: #66bf3c;
+  padding: 4px 12px;
+  margin-right: 10px;
+  vertical-align: top;
+}
+
+.storybook-page .tip-wrapper {
+  font-size: 13px;
+  line-height: 20px;
+  margin-top: 40px;
+  margin-bottom: 40px;
+}
+
+.storybook-page .tip-wrapper svg {
+  display: inline-block;
+  height: 12px;
+  width: 12px;
+  margin-right: 4px;
+  vertical-align: top;
+  margin-top: 3px;
+}
+
+.storybook-page .tip-wrapper svg path {
+  fill: #1ea7fd;
+}

rnd/autogpt_libs/autogpt_libs/auth/depends_tests.py

@@ -1,6 +1,6 @@
 import pytest
 
-from .depends import requires_admin_user, requires_user, verify_user
+from .depends import verify_user, requires_admin_user, requires_user
 
 
 def test_verify_user_no_payload():

rnd/autogpt_libs/autogpt_libs/logging/__init__.py

@@ -1,9 +0,0 @@
-from .config import configure_logging
-from .filters import BelowLevelFilter
-from .formatters import FancyConsoleFormatter
-
-__all__ = [
-    "configure_logging",
-    "BelowLevelFilter",
-    "FancyConsoleFormatter",
-]

rnd/autogpt_libs/autogpt_libs/logging/config.py

@@ -1,166 +0,0 @@
-"""Logging module for Auto-GPT."""
-
-import logging
-import sys
-from pathlib import Path
-
-from pydantic import Field, field_validator
-from pydantic_settings import BaseSettings, SettingsConfigDict
-from .filters import BelowLevelFilter
-from .formatters import AGPTFormatter, StructuredLoggingFormatter
-
-LOG_DIR = Path(__file__).parent.parent.parent.parent / "logs"
-LOG_FILE = "activity.log"
-DEBUG_LOG_FILE = "debug.log"
-ERROR_LOG_FILE = "error.log"
-
-SIMPLE_LOG_FORMAT = "%(asctime)s %(levelname)s  %(title)s%(message)s"
-
-DEBUG_LOG_FORMAT = (
-    "%(asctime)s %(levelname)s %(filename)s:%(lineno)d" "  %(title)s%(message)s"
-)
-
-
-class LoggingConfig(BaseSettings):
-
-    level: str = Field(
-        default="INFO",
-        description="Logging level",
-        validation_alias="LOG_LEVEL",
-    )
-
-    enable_cloud_logging: bool = Field(
-        default=False,
-        description="Enable logging to Google Cloud Logging",
-    )
-
-    enable_file_logging: bool = Field(
-        default=False,
-        description="Enable logging to file",
-    )
-    # File output
-    log_dir: Path = Field(
-        default=LOG_DIR,
-        description="Log directory",
-    )
-
-    model_config = SettingsConfigDict(
-        env_prefix="",
-        env_file=".env",
-        env_file_encoding="utf-8",
-        extra="ignore",
-    )
-
-    @field_validator("level", mode="before")
-    @classmethod
-    def parse_log_level(cls, v):
-        if isinstance(v, str):
-            v = v.upper()
-            if v not in ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]:
-                raise ValueError(f"Invalid log level: {v}")
-            return v
-        return v
-
-
-def configure_logging(force_cloud_logging: bool = False) -> None:
-    """Configure the native logging module based on the LoggingConfig settings.
-
-    This function sets up logging handlers and formatters according to the
-    configuration specified in the LoggingConfig object. It supports various
-    logging outputs including console, file, cloud, and JSON logging.
-
-    The function uses the LoggingConfig object to determine which logging
-    features to enable and how to configure them. This includes setting
-    log levels, log formats, and output destinations.
-
-    No arguments are required as the function creates its own LoggingConfig
-    instance internally.
-
-    Note: This function is typically called at the start of the application
-    to set up the logging infrastructure.
-    """
-
-    config = LoggingConfig()
-
-    log_handlers: list[logging.Handler] = []
-
-    # Cloud logging setup
-    if config.enable_cloud_logging or force_cloud_logging:
-        import google.cloud.logging
-        from google.cloud.logging.handlers import CloudLoggingHandler
-        from google.cloud.logging_v2.handlers.transports.sync import SyncTransport
-
-        client = google.cloud.logging.Client()
-        cloud_handler = CloudLoggingHandler(
-            client,
-            name="autogpt_logs",
-            transport=SyncTransport,
-        )
-        cloud_handler.setLevel(config.level)
-        cloud_handler.setFormatter(StructuredLoggingFormatter())
-        log_handlers.append(cloud_handler)
-        print("Cloud logging enabled")
-    else:
-        # Console output handlers
-        stdout = logging.StreamHandler(stream=sys.stdout)
-        stdout.setLevel(config.level)
-        stdout.addFilter(BelowLevelFilter(logging.WARNING))
-        if config.level == logging.DEBUG:
-            stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
-
-        stderr = logging.StreamHandler()
-        stderr.setLevel(logging.WARNING)
-        if config.level == logging.DEBUG:
-            stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
-
-        log_handlers += [stdout, stderr]
-        print("Console logging enabled")
-
-    # File logging setup
-    if config.enable_file_logging:
-        # create log directory if it doesn't exist
-        if not config.log_dir.exists():
-            config.log_dir.mkdir(parents=True, exist_ok=True)
-
-        print(f"Log directory: {config.log_dir}")
-
-        # Activity log handler (INFO and above)
-        activity_log_handler = logging.FileHandler(
-            config.log_dir / LOG_FILE, "a", "utf-8"
-        )
-        activity_log_handler.setLevel(config.level)
-        activity_log_handler.setFormatter(
-            AGPTFormatter(SIMPLE_LOG_FORMAT, no_color=True)
-        )
-        log_handlers.append(activity_log_handler)
-
-        if config.level == logging.DEBUG:
-            # Debug log handler (all levels)
-            debug_log_handler = logging.FileHandler(
-                config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
-            )
-            debug_log_handler.setLevel(logging.DEBUG)
-            debug_log_handler.setFormatter(
-                AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True)
-            )
-            log_handlers.append(debug_log_handler)
-
-        # Error log handler (ERROR and above)
-        error_log_handler = logging.FileHandler(
-            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
-        )
-        error_log_handler.setLevel(logging.ERROR)
-        error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))
-        log_handlers.append(error_log_handler)
-        print("File logging enabled")
-
-    # Configure the root logger
-    logging.basicConfig(
-        format=DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT,
-        level=config.level,
-        handlers=log_handlers,
-    )

rnd/autogpt_libs/autogpt_libs/logging/filters.py

@@ -1,12 +0,0 @@
-import logging
-
-
-class BelowLevelFilter(logging.Filter):
-    """Filter for logging levels below a certain threshold."""
-
-    def __init__(self, below_level: int):
-        super().__init__()
-        self.below_level = below_level
-
-    def filter(self, record: logging.LogRecord):
-        return record.levelno < self.below_level

rnd/autogpt_libs/autogpt_libs/logging/formatters.py

@@ -1,95 +0,0 @@
-import logging
-
-from colorama import Fore, Style
-from google.cloud.logging_v2.handlers import CloudLoggingFilter, StructuredLogHandler
-
-from .utils import remove_color_codes
-
-
-class FancyConsoleFormatter(logging.Formatter):
-    """
-    A custom logging formatter designed for console output.
-
-    This formatter enhances the standard logging output with color coding. The color
-    coding is based on the level of the log message, making it easier to distinguish
-    between different types of messages in the console output.
-
-    The color for each level is defined in the LEVEL_COLOR_MAP class attribute.
-    """
-
-    # level -> (level & text color, title color)
-    LEVEL_COLOR_MAP = {
-        logging.DEBUG: Fore.LIGHTBLACK_EX,
-        logging.INFO: Fore.BLUE,
-        logging.WARNING: Fore.YELLOW,
-        logging.ERROR: Fore.RED,
-        logging.CRITICAL: Fore.RED + Style.BRIGHT,
-    }
-
-    def format(self, record: logging.LogRecord) -> str:
-        # Make sure `msg` is a string
-        if not hasattr(record, "msg"):
-            record.msg = ""
-        elif type(record.msg) is not str:
-            record.msg = str(record.msg)
-
-        # Determine default color based on error level
-        level_color = ""
-        if record.levelno in self.LEVEL_COLOR_MAP:
-            level_color = self.LEVEL_COLOR_MAP[record.levelno]
-            record.levelname = f"{level_color}{record.levelname}{Style.RESET_ALL}"
-
-        # Determine color for message
-        color = getattr(record, "color", level_color)
-        color_is_specified = hasattr(record, "color")
-
-        # Don't color INFO messages unless the color is explicitly specified.
-        if color and (record.levelno != logging.INFO or color_is_specified):
-            record.msg = f"{color}{record.msg}{Style.RESET_ALL}"
-
-        return super().format(record)
-
-
-class AGPTFormatter(FancyConsoleFormatter):
-    def __init__(self, *args, no_color: bool = False, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.no_color = no_color
-
-    def format(self, record: logging.LogRecord) -> str:
-        # Make sure `msg` is a string
-        if not hasattr(record, "msg"):
-            record.msg = ""
-        elif type(record.msg) is not str:
-            record.msg = str(record.msg)
-
-        # Strip color from the message to prevent color spoofing
-        if record.msg and not getattr(record, "preserve_color", False):
-            record.msg = remove_color_codes(record.msg)
-
-        # Determine color for title
-        title = getattr(record, "title", "")
-        title_color = getattr(record, "title_color", "") or self.LEVEL_COLOR_MAP.get(
-            record.levelno, ""
-        )
-        if title and title_color:
-            title = f"{title_color + Style.BRIGHT}{title}{Style.RESET_ALL}"
-        # Make sure record.title is set, and padded with a space if not empty
-        record.title = f"{title} " if title else ""
-
-        if self.no_color:
-            return remove_color_codes(super().format(record))
-        else:
-            return super().format(record)
-
-
-class StructuredLoggingFormatter(StructuredLogHandler, logging.Formatter):
-    def __init__(self):
-        # Set up CloudLoggingFilter to add diagnostic info to the log records
-        self.cloud_logging_filter = CloudLoggingFilter()
-
-        # Init StructuredLogHandler
-        super().__init__()
-
-    def format(self, record: logging.LogRecord) -> str:
-        self.cloud_logging_filter.filter(record)
-        return super().format(record)

rnd/autogpt_libs/autogpt_libs/logging/handlers.py

@@ -1,14 +0,0 @@
-from __future__ import annotations
-
-import json
-import logging
-
-
-class JsonFileHandler(logging.FileHandler):
-    def format(self, record: logging.LogRecord) -> str:
-        record.json_data = json.loads(record.getMessage())
-        return json.dumps(getattr(record, "json_data"), ensure_ascii=False, indent=4)
-
-    def emit(self, record: logging.LogRecord) -> None:
-        with open(self.baseFilename, "w", encoding="utf-8") as f:
-            f.write(self.format(record))

rnd/autogpt_libs/autogpt_libs/logging/test_utils.py

@@ -1,36 +0,0 @@
-import pytest
-
-from .utils import remove_color_codes
-
-
-@pytest.mark.parametrize(
-    "raw_text, clean_text",
-    [
-        (
-            "COMMAND = \x1b[36mbrowse_website\x1b[0m  "
-            "ARGUMENTS = \x1b[36m{'url': 'https://www.google.com',"
-            " 'question': 'What is the capital of France?'}\x1b[0m",
-            "COMMAND = browse_website  "
-            "ARGUMENTS = {'url': 'https://www.google.com',"
-            " 'question': 'What is the capital of France?'}",
-        ),
-        (
-            "{'Schaue dir meine Projekte auf github () an, als auch meine Webseiten': "
-            "'https://github.com/Significant-Gravitas/AutoGPT,"
-            " https://discord.gg/autogpt und https://twitter.com/Auto_GPT'}",
-            "{'Schaue dir meine Projekte auf github () an, als auch meine Webseiten': "
-            "'https://github.com/Significant-Gravitas/AutoGPT,"
-            " https://discord.gg/autogpt und https://twitter.com/Auto_GPT'}",
-        ),
-        ("", ""),
-        ("hello", "hello"),
-        ("hello\x1B[31m world", "hello world"),
-        ("\x1B[36mHello,\x1B[32m World!", "Hello, World!"),
-        (
-            "\x1B[1m\x1B[31mError:\x1B[0m\x1B[31m file not found",
-            "Error: file not found",
-        ),
-    ],
-)
-def test_remove_color_codes(raw_text, clean_text):
-    assert remove_color_codes(raw_text) == clean_text

rnd/autogpt_libs/autogpt_libs/logging/utils.py

@@ -1,27 +0,0 @@
-import logging
-import re
-from typing import Any
-
-from colorama import Fore
-
-
-def remove_color_codes(s: str) -> str:
-    return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
-
-
-def fmt_kwargs(kwargs: dict) -> str:
-    return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
-
-
-def print_attribute(
-    title: str, value: Any, title_color: str = Fore.GREEN, value_color: str = ""
-) -> None:
-    logger = logging.getLogger()
-    logger.info(
-        str(value),
-        extra={
-            "title": f"{title.rstrip(':')}:",
-            "title_color": title_color,
-            "color": value_color,
-        },
-    )

rnd/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/store.py

@@ -1,21 +1,13 @@
-import secrets
-from datetime import datetime, timedelta, timezone
 from typing import cast
 
-from supabase import Client
+from supabase import Client, create_client
 
-from .types import (
-    Credentials,
-    OAuth2Credentials,
-    OAuthState,
-    UserMetadata,
-    UserMetadataRaw,
-)
+from .types import Credentials, OAuth2Credentials, UserMetadata, UserMetadataRaw
 
 
 class SupabaseIntegrationCredentialsStore:
-    def __init__(self, supabase: Client):
-        self.supabase = supabase
+    def __init__(self, url: str, key: str):
+        self.supabase: Client = create_client(url, key)
 
     def add_creds(self, user_id: str, credentials: Credentials) -> None:
         if self.get_creds_by_id(user_id, credentials.id):
@@ -81,52 +73,6 @@ class SupabaseIntegrationCredentialsStore:
         ]
         self._set_user_integration_creds(user_id, filtered_credentials)
 
-    async def store_state_token(self, user_id: str, provider: str) -> str:
-        token = secrets.token_urlsafe(32)
-        expires_at = datetime.now(timezone.utc) + timedelta(minutes=10)
-
-        state = OAuthState(
-            token=token, provider=provider, expires_at=int(expires_at.timestamp())
-        )
-
-        user_metadata = self._get_user_metadata(user_id)
-        oauth_states = user_metadata.get("integration_oauth_states", [])
-        oauth_states.append(state.model_dump())
-        user_metadata["integration_oauth_states"] = oauth_states
-
-        self.supabase.auth.admin.update_user_by_id(
-            user_id, {"user_metadata": user_metadata}
-        )
-
-        return token
-
-    async def verify_state_token(self, user_id: str, token: str, provider: str) -> bool:
-        user_metadata = self._get_user_metadata(user_id)
-        oauth_states = user_metadata.get("integration_oauth_states", [])
-
-        now = datetime.now(timezone.utc)
-        valid_state = next(
-            (
-                state
-                for state in oauth_states
-                if state["token"] == token
-                and state["provider"] == provider
-                and state["expires_at"] > now.timestamp()
-            ),
-            None,
-        )
-
-        if valid_state:
-            # Remove the used state
-            oauth_states.remove(valid_state)
-            user_metadata["integration_oauth_states"] = oauth_states
-            self.supabase.auth.admin.update_user_by_id(
-                user_id, {"user_metadata": user_metadata}
-            )
-            return True
-
-        return False
-
     def _set_user_integration_creds(
         self, user_id: str, credentials: list[Credentials]
     ) -> None:

rnd/autogpt_libs/autogpt_libs/supabase_integration_credentials_store/types.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel, Field, SecretStr, field_serializer
 class _BaseCredentials(BaseModel):
     id: str = Field(default_factory=lambda: str(uuid4()))
     provider: str
-    title: Optional[str]
+    title: str
 
     @field_serializer("*")
     def dump_secret_strings(value: Any, _info):
@@ -18,14 +18,10 @@ class _BaseCredentials(BaseModel):
 
 class OAuth2Credentials(_BaseCredentials):
     type: Literal["oauth2"] = "oauth2"
-    username: Optional[str]
-    """Username of the third-party service user that these credentials belong to"""
     access_token: SecretStr
-    access_token_expires_at: Optional[int]
-    """Unix timestamp (seconds) indicating when the access token expires (if at all)"""
+    access_token_expires_at: Optional[int]  # seconds
     refresh_token: Optional[SecretStr]
-    refresh_token_expires_at: Optional[int]
-    """Unix timestamp (seconds) indicating when the refresh token expires (if at all)"""
+    refresh_token_expires_at: Optional[int]  # seconds
     scopes: list[str]
     metadata: dict[str, Any] = Field(default_factory=dict)
 
@@ -33,8 +29,7 @@ class OAuth2Credentials(_BaseCredentials):
 class APIKeyCredentials(_BaseCredentials):
     type: Literal["api_key"] = "api_key"
     api_key: SecretStr
-    expires_at: Optional[int]
-    """Unix timestamp (seconds) indicating when the API key expires (if at all)"""
+    expires_at: Optional[int]  # seconds
 
 
 Credentials = Annotated[
@@ -43,18 +38,9 @@ Credentials = Annotated[
 ]
 
 
-class OAuthState(BaseModel):
-    token: str
-    provider: str
-    expires_at: int
-    """Unix timestamp (seconds) indicating when this OAuth state expires"""
-
-
 class UserMetadata(BaseModel):
     integration_credentials: list[Credentials] = Field(default_factory=list)
-    integration_oauth_states: list[OAuthState] = Field(default_factory=list)
 
 
 class UserMetadataRaw(TypedDict, total=False):
     integration_credentials: list[dict]
-    integration_oauth_states: list[dict]

rnd/autogpt_libs/pyproject.toml

@@ -1,21 +1,19 @@
 [tool.poetry]
 name = "autogpt-libs"
-version = "0.2.0"
+version = "0.1.0"
 description = "Shared libraries across NextGen AutoGPT"
 authors = ["Aarushi <aarushik93@gmail.com>"]
 readme = "README.md"
 packages = [{ include = "autogpt_libs" }]
 
 [tool.poetry.dependencies]
-colorama = "^0.4.6"
-google-cloud-logging = "^3.8.0"
-pydantic = "^2.8.2"
-pydantic-settings = "^2.5.2"
-pyjwt = "^2.8.0"
 python = ">=3.10,<4.0"
+pydantic = "^2.8.2"
+pyjwt = "^2.8.0"
 python-dotenv = "^1.0.1"
 supabase = "^2.7.2"
 
+
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"

rnd/autogpt_server/.env.example

@@ -9,8 +9,7 @@ REDIS_HOST=localhost
 REDIS_PORT=6379
 REDIS_PASSWORD=password
 
-ENABLE_AUTH=false
-ENABLE_CREDIT=false
+AUTH_ENABLED=false
 APP_ENV="local"
 PYRO_HOST=localhost
 SENTRY_DSN=
@@ -52,11 +51,3 @@ SMTP_PASSWORD=
 # Medium
 MEDIUM_API_KEY=
 MEDIUM_AUTHOR_ID=
-
-
-# Logging Configuration
-LOG_LEVEL=INFO
-ENABLE_CLOUD_LOGGING=false
-ENABLE_FILE_LOGGING=false
-# Use to manually set the log directory
-# LOG_DIR=./logs

rnd/autogpt_server/Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.11-slim-buster AS builder
+FROM python:3.11-slim-buster AS server_base
 
 # Set environment variables
 ENV PYTHONDONTWRITEBYTECODE 1
@@ -6,74 +6,49 @@ ENV PYTHONUNBUFFERED 1
 
 WORKDIR /app
 
-# Install build dependencies
 RUN apt-get update \
-    && apt-get install -y build-essential curl ffmpeg wget libcurl4-gnutls-dev libexpat1-dev gettext libz-dev libssl-dev postgresql-client git \
+    && apt-get install -y build-essential curl ffmpeg wget libcurl4-gnutls-dev libexpat1-dev gettext libz-dev libssl-dev \
     && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
+    && rm -rf /var/lib/apt/lists/* \
+    && wget https://github.com/git/git/archive/v2.28.0.tar.gz -O git.tar.gz \
+    && tar -zxf git.tar.gz \
+    && cd git-* \
+    && make prefix=/usr all \
+    && make prefix=/usr install
 
 ENV POETRY_VERSION=1.8.3 \
     POETRY_HOME="/opt/poetry" \
     POETRY_NO_INTERACTION=1 \
     POETRY_VIRTUALENVS_CREATE=false \
     PATH="$POETRY_HOME/bin:$PATH"
-
-# Upgrade pip and setuptools to fix security vulnerabilities
-RUN pip3 install --upgrade pip setuptools
-
 RUN pip3 install poetry
 
-# Copy and install dependencies
-COPY rnd/autogpt_libs /app/rnd/autogpt_libs
-COPY rnd/autogpt_server/poetry.lock rnd/autogpt_server/pyproject.toml /app/rnd/autogpt_server/
-WORKDIR /app/rnd/autogpt_server
-RUN poetry config virtualenvs.create false \
-    && poetry install --no-interaction --no-ansi
-
-# Generate Prisma client
-COPY rnd/autogpt_server/schema.prisma ./
-RUN poetry config virtualenvs.create false \
-    && poetry run prisma generate
-
-FROM python:3.11-slim-buster AS server_dependencies
-
-WORKDIR /app
-
-ENV POETRY_VERSION=1.8.3 \
-    POETRY_HOME="/opt/poetry" \
-    POETRY_NO_INTERACTION=1 \
-    POETRY_VIRTUALENVS_CREATE=false \
-    PATH="$POETRY_HOME/bin:$PATH"
-
-
-# Upgrade pip and setuptools to fix security vulnerabilities
-RUN pip3 install --upgrade pip setuptools
-
-# Copy only necessary files from builder
-COPY --from=builder /app /app
-COPY --from=builder /usr/local/lib/python3.11 /usr/local/lib/python3.11
-COPY --from=builder /usr/local/bin /usr/local/bin
-# Copy Prisma binaries
-COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
-
-
-ENV PATH="/app/.venv/bin:$PATH"
+FROM server_base AS server_dependencies
 
+RUN mkdir -p /app/autogpt
+RUN mkdir -p /app/forge
 RUN mkdir -p /app/rnd/autogpt_libs
 RUN mkdir -p /app/rnd/autogpt_server
 
+COPY autogpt /app/autogpt
+COPY forge /app/forge
 COPY rnd/autogpt_libs /app/rnd/autogpt_libs
 
 COPY rnd/autogpt_server/poetry.lock rnd/autogpt_server/pyproject.toml /app/rnd/autogpt_server/
 
 WORKDIR /app/rnd/autogpt_server
 
-FROM server_dependencies AS server
+RUN poetry install --no-interaction --no-ansi
+
+FROM server_dependencies AS server_prisma
+
+COPY rnd/autogpt_server/schema.prisma ./
+RUN poetry run prisma generate
+
+FROM server_prisma AS server
 
 COPY rnd/autogpt_server /app/rnd/autogpt_server
 
 ENV DATABASE_URL=""
 ENV PORT=8000
 
-CMD ["poetry", "run", "rest"]
-

rnd/autogpt_server/README.md

@@ -101,7 +101,7 @@ docker compose down
 If you run into issues with dangling orphans, try:
 
 ```sh
-docker compose down --volumes --remove-orphans && docker-compose up --force-recreate --renew-anon-volumes --remove-orphans  
+docker-compose down --volumes --remove-orphans && docker-compose up --force-recreate --renew-anon-volumes --remove-orphans  
 ```
 
 ## Testing

rnd/autogpt_server/autogpt_server/app.py

@@ -36,5 +36,12 @@ def main(**kwargs):
     )
 
 
+def execution_manager(**kwargs):
+
+    from autogpt_server.executor import ExecutionManager
+
+    run_processes(ExecutionManager(), **kwargs)
+
+
 if __name__ == "__main__":
     main()

rnd/autogpt_server/autogpt_server/blocks/__init__.py

@@ -55,15 +55,15 @@ for cls in all_subclasses(Block):
         raise ValueError(f"Block ID {block.name} error: {block.id} is already in use")
 
     # Prevent duplicate field name in input_schema and output_schema
-    duplicate_field_names = set(block.input_schema.model_fields.keys()) & set(
-        block.output_schema.model_fields.keys()
+    duplicate_field_names = set(block.input_schema.__fields__.keys()) & set(
+        block.output_schema.__fields__.keys()
     )
     if duplicate_field_names:
         raise ValueError(
             f"{block.name} has duplicate field names in input_schema and output_schema: {duplicate_field_names}"
         )
 
-    for field in block.input_schema.model_fields.values():
+    for field in block.input_schema.__fields__.values():
         if field.annotation is bool and field.default not in (True, False):
             raise ValueError(f"{block.name} has a boolean field with no default value")
 

rnd/autogpt_server/autogpt_server/blocks/agent.py

@@ -0,0 +1,190 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, Iterator
+
+from autogpt.agents.agent import Agent, AgentSettings
+from autogpt.app.config import ConfigBuilder
+from forge.agent.components import AgentComponent
+from forge.agent.protocols import CommandProvider
+from forge.command import command
+from forge.command.command import Command
+from forge.file_storage import FileStorageBackendName, get_storage
+from forge.file_storage.base import FileStorage
+from forge.llm.providers import MultiProvider
+from forge.llm.providers.openai import OpenAICredentials, OpenAIProvider
+from forge.llm.providers.schema import ModelProviderName
+from forge.models.json_schema import JSONSchema
+from pydantic import Field, SecretStr
+
+from autogpt_server.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from autogpt_server.data.model import BlockSecret, SchemaField, SecretField
+
+if TYPE_CHECKING:
+    from autogpt.app.config import AppConfig
+
+logger = logging.getLogger(__name__)
+
+
+class BlockAgentSettings(AgentSettings):
+    enabled_components: list[str] = Field(default_factory=list)
+
+
+class OutputComponent(CommandProvider):
+    def get_commands(self) -> Iterator[Command]:
+        yield self.output
+
+    @command(
+        parameters={
+            "output": JSONSchema(
+                type=JSONSchema.Type.STRING,
+                description="Output data to be returned.",
+                required=True,
+            ),
+        },
+    )
+    def output(self, output: str) -> str:
+        """Use this to output the result."""
+        return output
+
+
+class BlockAgent(Agent):
+    def __init__(
+        self,
+        settings: BlockAgentSettings,
+        llm_provider: MultiProvider,
+        file_storage: FileStorage,
+        app_config: AppConfig,
+    ):
+        super().__init__(settings, llm_provider, file_storage, app_config)
+
+        self.output = OutputComponent()
+
+        # Disable components
+        for attr_name in list(self.__dict__.keys()):
+            attr_value = getattr(self, attr_name)
+            if not isinstance(attr_value, AgentComponent):
+                continue
+            component_name = type(attr_value).__name__
+            if (
+                component_name != "SystemComponent"
+                and component_name not in settings.enabled_components
+            ):
+                delattr(self, attr_name)
+
+
+class AutoGPTAgentBlock(Block):
+    class Input(BlockSchema):
+        task: str = SchemaField(
+            description="Task description for the agent.",
+            placeholder="Calculate and use Output command",
+        )
+        input: str = SchemaField(
+            description="Input data for the task",
+            placeholder="8 + 5",
+        )
+        openai_api_key: BlockSecret = SecretField(
+            key="openai_api_key", description="OpenAI API key"
+        )
+        enabled_components: list[str] = Field(
+            default_factory=lambda: [OutputComponent.__name__],
+            description="List of [AgentComponents](https://docs.agpt.co/forge/components/built-in-components/) enabled for the agent.",
+        )
+        disabled_commands: list[str] = Field(
+            default_factory=list,
+            description="List of commands from enabled components to disable.",
+        )
+        fast_mode: bool = Field(
+            False,
+            description="If true uses fast llm, otherwise uses smart and slow llm.",
+        )
+
+    class Output(BlockSchema):
+        result: str
+
+    def __init__(self):
+        super().__init__(
+            id="d2e2ecd2-9ae6-422d-8dfe-ceca500ce6a6",
+            description="AutoGPT agent, it utilizes a Large Language Model and enabled components/tools to perform a task.",
+            categories={BlockCategory.AI},
+            input_schema=AutoGPTAgentBlock.Input,
+            output_schema=AutoGPTAgentBlock.Output,
+            test_input={
+                "task": "Make calculations and use output command to output the result",
+                "input": "5 + 3",
+                "openai_api_key": "openai_api_key",
+                "enabled_components": [OutputComponent.__name__],
+                "disabled_commands": ["finish"],
+                "fast_mode": True,
+            },
+            test_output=[
+                ("result", "8"),
+            ],
+            test_mock={
+                "get_provider": lambda _: MultiProvider(),
+                "get_result": lambda _: "8",
+            },
+        )
+
+    @staticmethod
+    def get_provider(openai_api_key: str) -> MultiProvider:
+        # LLM provider
+        settings = OpenAIProvider.default_settings.model_copy()
+        settings.credentials = OpenAICredentials(api_key=SecretStr(openai_api_key))
+        openai_provider = OpenAIProvider(settings=settings)
+
+        multi_provider = MultiProvider()
+        # HACK: Add OpenAI provider to the multi provider with api key
+        multi_provider._provider_instances[ModelProviderName.OPENAI] = openai_provider
+
+        return multi_provider
+
+    @staticmethod
+    def get_result(agent: BlockAgent) -> str:
+        error: Exception | None = None
+
+        for tries in range(3):
+            try:
+                proposal = asyncio.run(agent.propose_action())
+                result = asyncio.run(agent.execute(proposal))
+                return str(result)
+            except Exception as e:
+                error = e
+
+        raise error or Exception("Failed to get result")
+
+    def run(self, input_data: Input) -> BlockOutput:
+        # Set up configuration
+        config = ConfigBuilder.build_config_from_env()
+        # Disable commands
+        config.disabled_commands.extend(input_data.disabled_commands)
+
+        # Storage
+        local = config.file_storage_backend == FileStorageBackendName.LOCAL
+        restrict_to_root = not local or config.restrict_to_workspace
+        file_storage = get_storage(
+            config.file_storage_backend,
+            root_path=Path("data"),
+            restrict_to_root=restrict_to_root,
+        )
+        file_storage.initialize()
+
+        # State
+        state = BlockAgentSettings(
+            agent_id="TemporaryAgentID",
+            name="WrappedAgent",
+            description="Wrapped agent for the Agent Server.",
+            task=f"Your task: {input_data.task}\n" f"Input data: {input_data.input}",
+            enabled_components=input_data.enabled_components,
+        )
+        # Switch big brain mode
+        state.config.big_brain = not input_data.fast_mode
+        provider = self.get_provider(input_data.openai_api_key.get_secret_value())
+
+        agent = BlockAgent(state, provider, file_storage, config)
+
+        result = self.get_result(agent)
+
+        yield "result", result

rnd/autogpt_server/autogpt_server/blocks/basic.py

@@ -1,7 +1,5 @@
-import re
 from typing import Any, List
 
-from jinja2 import BaseLoader, Environment
 from pydantic import Field
 
 from autogpt_server.data.block import (
@@ -14,8 +12,6 @@ from autogpt_server.data.block import (
 from autogpt_server.data.model import SchemaField
 from autogpt_server.util.mock import MockObject
 
-jinja = Environment(loader=BaseLoader())
-
 
 class StoreValueBlock(Block):
     """
@@ -140,7 +136,7 @@ class FindInDictionaryBlock(Block):
             yield "missing", input_data.input
 
 
-class AgentInputBlock(Block):
+class InputBlock(Block):
     """
     This block is used to provide input to the graph.
 
@@ -152,20 +148,13 @@ class AgentInputBlock(Block):
     class Input(BlockSchema):
         value: Any = SchemaField(description="The value to be passed as input.")
         name: str = SchemaField(description="The name of the input.")
-        description: str = SchemaField(
-            description="The description of the input.",
-            default="",
-            advanced=True,
-        )
+        description: str = SchemaField(description="The description of the input.")
         placeholder_values: List[Any] = SchemaField(
-            description="The placeholder values to be passed as input.",
-            default=[],
-            advanced=True,
+            description="The placeholder values to be passed as input."
         )
         limit_to_placeholder_values: bool = SchemaField(
             description="Whether to limit the selection to placeholder values.",
             default=False,
-            advanced=True,
         )
 
     class Output(BlockSchema):
@@ -175,8 +164,8 @@ class AgentInputBlock(Block):
         super().__init__(
             id="c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
             description="This block is used to provide input to the graph.",
-            input_schema=AgentInputBlock.Input,
-            output_schema=AgentInputBlock.Output,
+            input_schema=InputBlock.Input,
+            output_schema=InputBlock.Output,
             test_input=[
                 {
                     "value": "Hello, World!",
@@ -205,7 +194,7 @@ class AgentInputBlock(Block):
         yield "result", input_data.value
 
 
-class AgentOutputBlock(Block):
+class OutputBlock(Block):
     """
     Records the output of the graph for users to see.
 
@@ -226,17 +215,13 @@ class AgentOutputBlock(Block):
     """
 
     class Input(BlockSchema):
-        value: Any = SchemaField(description="The value to be recorded as output.")
-        name: str = SchemaField(description="The name of the output.")
-        description: str = SchemaField(
-            description="The description of the output.",
-            default="",
-            advanced=True,
+        recorded_value: Any = SchemaField(
+            description="The value to be recorded as output."
         )
-        format: str = SchemaField(
-            description="The format string to be used to format the recorded_value.",
-            default="",
-            advanced=True,
+        name: str = SchemaField(description="The name of the output.")
+        description: str = SchemaField(description="The description of the output.")
+        fmt_string: str = SchemaField(
+            description="The format string to be used to format the recorded_value."
         )
 
     class Output(BlockSchema):
@@ -253,31 +238,31 @@ class AgentOutputBlock(Block):
                 "This block is key for capturing and presenting final results or "
                 "important intermediate outputs of the graph execution."
             ),
-            input_schema=AgentOutputBlock.Input,
-            output_schema=AgentOutputBlock.Output,
+            input_schema=OutputBlock.Input,
+            output_schema=OutputBlock.Output,
             test_input=[
                 {
-                    "value": "Hello, World!",
+                    "recorded_value": "Hello, World!",
                     "name": "output_1",
                     "description": "This is a test output.",
-                    "format": "{{ output_1 }}!!",
+                    "fmt_string": "{value}",
                 },
                 {
-                    "value": "42",
+                    "recorded_value": 42,
                     "name": "output_2",
                     "description": "This is another test output.",
-                    "format": "{{ output_2 }}",
+                    "fmt_string": "{value}",
                 },
                 {
-                    "value": MockObject(value="!!", key="key"),
+                    "recorded_value": MockObject(value="!!", key="key"),
                     "name": "output_3",
                     "description": "This is a test output with a mock object.",
-                    "format": "{{ output_3 }}",
+                    "fmt_string": "{value}",
                 },
             ],
             test_output=[
-                ("output", "Hello, World!!!"),
-                ("output", "42"),
+                ("output", "Hello, World!"),
+                ("output", 42),
                 ("output", MockObject(value="!!", key="key")),
             ],
             categories={BlockCategory.OUTPUT, BlockCategory.BASIC},
@@ -289,15 +274,13 @@ class AgentOutputBlock(Block):
         Attempts to format the recorded_value using the fmt_string if provided.
         If formatting fails or no fmt_string is given, returns the original recorded_value.
         """
-        if input_data.format:
+        if input_data.fmt_string:
             try:
-                fmt = re.sub(r"(?<!{){[ a-zA-Z0-9_]+}", r"{\g<0>}", input_data.format)
-                template = jinja.from_string(fmt)
-                yield "output", template.render({input_data.name: input_data.value})
-            except Exception as e:
-                yield "output", f"Error: {e}, {input_data.value}"
+                yield "output", input_data.fmt_string.format(input_data.recorded_value)
+            except Exception:
+                yield "output", input_data.recorded_value
         else:
-            yield "output", input_data.value
+            yield "output", input_data.recorded_value
 
 
 class AddToDictionaryBlock(Block):
@@ -439,8 +422,7 @@ class NoteBlock(Block):
     class Input(BlockSchema):
         text: str = SchemaField(description="The text to display in the sticky note.")
 
-    class Output(BlockSchema):
-        output: str = SchemaField(description="The text to display in the sticky note.")
+    class Output(BlockSchema): ...
 
     def __init__(self):
         super().__init__(
@@ -450,11 +432,8 @@ class NoteBlock(Block):
             input_schema=NoteBlock.Input,
             output_schema=NoteBlock.Output,
             test_input={"text": "Hello, World!"},
-            test_output=[
-                ("output", "Hello, World!"),
-            ],
+            test_output=None,
             ui_type=BlockUIType.NOTE,
         )
 
-    def run(self, input_data: Input) -> BlockOutput:
-        yield "output", input_data.text
+    def run(self, input_data: Input) -> BlockOutput: ...

rnd/autogpt_server/autogpt_server/blocks/csv.py

@@ -14,8 +14,7 @@ class ReadCsvBlock(Block):
         skip_columns: list[str] = []
 
     class Output(BlockSchema):
-        row: dict[str, str]
-        all_data: list[dict[str, str]]
+        data: dict[str, str]
 
     def __init__(self):
         super().__init__(
@@ -28,15 +27,8 @@ class ReadCsvBlock(Block):
                 "contents": "a, b, c\n1,2,3\n4,5,6",
             },
             test_output=[
-                ("row", {"a": "1", "b": "2", "c": "3"}),
-                ("row", {"a": "4", "b": "5", "c": "6"}),
-                (
-                    "all_data",
-                    [
-                        {"a": "1", "b": "2", "c": "3"},
-                        {"a": "4", "b": "5", "c": "6"},
-                    ],
-                ),
+                ("data", {"a": "1", "b": "2", "c": "3"}),
+                ("data", {"a": "4", "b": "5", "c": "6"}),
             ],
         )
 
@@ -61,7 +53,8 @@ class ReadCsvBlock(Block):
         for _ in range(input_data.skip_rows):
             next(reader)
 
-        def process_row(row):
+        # join the data with the header
+        for row in reader:
             data = {}
             for i, value in enumerate(row):
                 if i not in input_data.skip_columns:
@@ -69,12 +62,4 @@ class ReadCsvBlock(Block):
                         data[header[i]] = value.strip() if input_data.strip else value
                     else:
                         data[str(i)] = value.strip() if input_data.strip else value
-            return data
-
-        all_data = []
-        for row in reader:
-            processed_row = process_row(row)
-            all_data.append(processed_row)
-            yield "row", processed_row
-
-        yield "all_data", all_data
+            yield "data", data

rnd/autogpt_server/autogpt_server/blocks/llm.py

@@ -1,7 +1,6 @@
 import logging
 from enum import Enum
-from json import JSONDecodeError
-from typing import Any, List, NamedTuple
+from typing import List, NamedTuple
 
 import anthropic
 import ollama
@@ -25,7 +24,6 @@ LlmApiKeys = {
 class ModelMetadata(NamedTuple):
     provider: str
     context_window: int
-    cost_factor: int
 
 
 class LlmModel(str, Enum):
@@ -57,29 +55,26 @@ class LlmModel(str, Enum):
 
 
 MODEL_METADATA = {
-    LlmModel.GPT4O_MINI: ModelMetadata("openai", 128000, cost_factor=10),
-    LlmModel.GPT4O: ModelMetadata("openai", 128000, cost_factor=12),
-    LlmModel.GPT4_TURBO: ModelMetadata("openai", 128000, cost_factor=11),
-    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385, cost_factor=8),
-    LlmModel.CLAUDE_3_5_SONNET: ModelMetadata("anthropic", 200000, cost_factor=14),
-    LlmModel.CLAUDE_3_HAIKU: ModelMetadata("anthropic", 200000, cost_factor=13),
-    LlmModel.LLAMA3_8B: ModelMetadata("groq", 8192, cost_factor=6),
-    LlmModel.LLAMA3_70B: ModelMetadata("groq", 8192, cost_factor=9),
-    LlmModel.MIXTRAL_8X7B: ModelMetadata("groq", 32768, cost_factor=7),
-    LlmModel.GEMMA_7B: ModelMetadata("groq", 8192, cost_factor=6),
-    LlmModel.GEMMA2_9B: ModelMetadata("groq", 8192, cost_factor=7),
-    LlmModel.LLAMA3_1_405B: ModelMetadata("groq", 8192, cost_factor=10),
-    # Limited to 16k during preview
-    LlmModel.LLAMA3_1_70B: ModelMetadata("groq", 131072, cost_factor=15),
-    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 131072, cost_factor=13),
-    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192, cost_factor=7),
-    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192, cost_factor=11),
+    LlmModel.GPT4O_MINI: ModelMetadata("openai", 128000),
+    LlmModel.GPT4O: ModelMetadata("openai", 128000),
+    LlmModel.GPT4_TURBO: ModelMetadata("openai", 128000),
+    LlmModel.GPT3_5_TURBO: ModelMetadata("openai", 16385),
+    LlmModel.CLAUDE_3_5_SONNET: ModelMetadata("anthropic", 200000),
+    LlmModel.CLAUDE_3_HAIKU: ModelMetadata("anthropic", 200000),
+    LlmModel.LLAMA3_8B: ModelMetadata("groq", 8192),
+    LlmModel.LLAMA3_70B: ModelMetadata("groq", 8192),
+    LlmModel.MIXTRAL_8X7B: ModelMetadata("groq", 32768),
+    LlmModel.GEMMA_7B: ModelMetadata("groq", 8192),
+    LlmModel.GEMMA2_9B: ModelMetadata("groq", 8192),
+    LlmModel.LLAMA3_1_405B: ModelMetadata(
+        "groq", 8192
+    ),  # Limited to 16k during preview
+    LlmModel.LLAMA3_1_70B: ModelMetadata("groq", 131072),
+    LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 131072),
+    LlmModel.OLLAMA_LLAMA3_8B: ModelMetadata("ollama", 8192),
+    LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192),
 }
 
-for model in LlmModel:
-    if model not in MODEL_METADATA:
-        raise ValueError(f"Missing MODEL_METADATA metadata for model: {model}")
-
 
 class AIStructuredResponseGeneratorBlock(Block):
     class Input(BlockSchema):
@@ -94,7 +89,7 @@ class AIStructuredResponseGeneratorBlock(Block):
         )
 
     class Output(BlockSchema):
-        response: dict[str, Any]
+        response: dict[str, str]
         error: str
 
     def __init__(self):
@@ -140,33 +135,16 @@ class AIStructuredResponseGeneratorBlock(Block):
             )
             return response.choices[0].message.content or ""
         elif provider == "anthropic":
-            system_messages = [p["content"] for p in prompt if p["role"] == "system"]
-            sysprompt = " ".join(system_messages)
-
-            messages = []
-            last_role = None
-            for p in prompt:
-                if p["role"] in ["user", "assistant"]:
-                    if p["role"] != last_role:
-                        messages.append({"role": p["role"], "content": p["content"]})
-                        last_role = p["role"]
-                    else:
-                        # If the role is the same as the last one, combine the content
-                        messages[-1]["content"] += "\n" + p["content"]
-
+            sysprompt = "".join([p["content"] for p in prompt if p["role"] == "system"])
+            usrprompt = [p for p in prompt if p["role"] == "user"]
             client = anthropic.Anthropic(api_key=api_key)
-            try:
-                response = client.messages.create(
-                    model=model.value,
-                    max_tokens=4096,
-                    system=sysprompt,
-                    messages=messages,
-                )
-                return response.content[0].text if response.content else ""
-            except anthropic.APIError as e:
-                error_message = f"Anthropic API error: {str(e)}"
-                logger.error(error_message)
-                raise ValueError(error_message)
+            response = client.messages.create(
+                model=model.value,
+                max_tokens=4096,
+                system=sysprompt,
+                messages=usrprompt,  # type: ignore
+            )
+            return response.content[0].text if response.content else ""
         elif provider == "groq":
             client = Groq(api_key=api_key)
             response_format = {"type": "json_object"} if json_format else None
@@ -217,16 +195,14 @@ class AIStructuredResponseGeneratorBlock(Block):
 
         prompt.append({"role": "user", "content": input_data.prompt})
 
-        def parse_response(resp: str) -> tuple[dict[str, Any], str | None]:
+        def parse_response(resp: str) -> tuple[dict[str, str], str | None]:
             try:
                 parsed = json.loads(resp)
-                if not isinstance(parsed, dict):
-                    return {}, f"Expected a dictionary, but got {type(parsed)}"
                 miss_keys = set(input_data.expected_format.keys()) - set(parsed.keys())
                 if miss_keys:
                     return parsed, f"Missing keys: {miss_keys}"
                 return parsed, None
-            except JSONDecodeError as e:
+            except Exception as e:
                 return {}, f"JSON decode error: {e}"
 
         logger.info(f"LLM request: {prompt}")
@@ -250,16 +226,7 @@ class AIStructuredResponseGeneratorBlock(Block):
                 if input_data.expected_format:
                     parsed_dict, parsed_error = parse_response(response_text)
                     if not parsed_error:
-                        yield "response", {
-                            k: (
-                                json.loads(v)
-                                if isinstance(v, str)
-                                and v.startswith("[")
-                                and v.endswith("]")
-                                else (", ".join(v) if isinstance(v, list) else v)
-                            )
-                            for k, v in parsed_dict.items()
-                        }
+                        yield "response", {k: str(v) for k, v in parsed_dict.items()}
                         return
                 else:
                     yield "response", {"response": response_text}
@@ -334,7 +301,7 @@ class AITextGeneratorBlock(Block):
             yield "error", str(e)
 
 
-class AITextSummarizerBlock(Block):
+class TextSummarizerBlock(Block):
     class Input(BlockSchema):
         text: str
         model: LlmModel = LlmModel.GPT4_TURBO
@@ -352,8 +319,8 @@ class AITextSummarizerBlock(Block):
             id="c3d4e5f6-7g8h-9i0j-1k2l-m3n4o5p6q7r8",
             description="Utilize a Large Language Model (LLM) to summarize a long text.",
             categories={BlockCategory.AI, BlockCategory.TEXT},
-            input_schema=AITextSummarizerBlock.Input,
-            output_schema=AITextSummarizerBlock.Output,
+            input_schema=TextSummarizerBlock.Input,
+            output_schema=TextSummarizerBlock.Output,
             test_input={"text": "Lorem ipsum..." * 100},
             test_output=("summary", "Final summary of a long text"),
             test_mock={
@@ -445,7 +412,7 @@ class AITextSummarizerBlock(Block):
         else:
             # If combined summaries are still too long, recursively summarize
             return self._run(
-                AITextSummarizerBlock.Input(
+                TextSummarizerBlock.Input(
                     text=combined_text,
                     api_key=input_data.api_key,
                     model=input_data.model,
@@ -471,7 +438,7 @@ class Message(BlockSchema):
 class AIConversationBlock(Block):
     class Input(BlockSchema):
         messages: List[Message] = SchemaField(
-            description="List of messages in the conversation.", min_length=1
+            description="List of messages in the conversation.", min_items=1
         )
         model: LlmModel = SchemaField(
             default=LlmModel.GPT4_TURBO,

rnd/autogpt_server/autogpt_server/blocks/sampling.py

@@ -1,264 +0,0 @@
-import random
-from collections import defaultdict
-from enum import Enum
-from typing import Any, Dict, List, Optional, Union
-
-from autogpt_server.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from autogpt_server.data.model import SchemaField
-
-
-class SamplingMethod(str, Enum):
-    RANDOM = "random"
-    SYSTEMATIC = "systematic"
-    TOP = "top"
-    BOTTOM = "bottom"
-    STRATIFIED = "stratified"
-    WEIGHTED = "weighted"
-    RESERVOIR = "reservoir"
-    CLUSTER = "cluster"
-
-
-class DataSamplingBlock(Block):
-    class Input(BlockSchema):
-        data: Union[Dict[str, Any], List[Union[dict, List[Any]]]] = SchemaField(
-            description="The dataset to sample from. Can be a single dictionary, a list of dictionaries, or a list of lists.",
-            placeholder="{'id': 1, 'value': 'a'} or [{'id': 1, 'value': 'a'}, {'id': 2, 'value': 'b'}, ...]",
-        )
-        sample_size: int = SchemaField(
-            description="The number of samples to take from the dataset.",
-            placeholder="10",
-            default=10,
-        )
-        sampling_method: SamplingMethod = SchemaField(
-            description="The method to use for sampling.",
-            default=SamplingMethod.RANDOM,
-        )
-        accumulate: bool = SchemaField(
-            description="Whether to accumulate data before sampling.",
-            default=False,
-        )
-        random_seed: Optional[int] = SchemaField(
-            description="Seed for random number generator (optional).",
-            default=None,
-        )
-        stratify_key: Optional[str] = SchemaField(
-            description="Key to use for stratified sampling (required for stratified sampling).",
-            default=None,
-        )
-        weight_key: Optional[str] = SchemaField(
-            description="Key to use for weighted sampling (required for weighted sampling).",
-            default=None,
-        )
-        cluster_key: Optional[str] = SchemaField(
-            description="Key to use for cluster sampling (required for cluster sampling).",
-            default=None,
-        )
-
-    class Output(BlockSchema):
-        sampled_data: List[Union[dict, List[Any]]] = SchemaField(
-            description="The sampled subset of the input data."
-        )
-        sample_indices: List[int] = SchemaField(
-            description="The indices of the sampled data in the original dataset."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="4a448883-71fa-49cf-91cf-70d793bd7d87",
-            description="This block samples data from a given dataset using various sampling methods.",
-            categories={BlockCategory.LOGIC},
-            input_schema=DataSamplingBlock.Input,
-            output_schema=DataSamplingBlock.Output,
-            test_input={
-                "data": [
-                    {"id": i, "value": chr(97 + i), "group": i % 3} for i in range(10)
-                ],
-                "sample_size": 3,
-                "sampling_method": SamplingMethod.STRATIFIED,
-                "accumulate": False,
-                "random_seed": 42,
-                "stratify_key": "group",
-            },
-            test_output=[
-                (
-                    "sampled_data",
-                    [
-                        {"id": 0, "value": "a", "group": 0},
-                        {"id": 1, "value": "b", "group": 1},
-                        {"id": 8, "value": "i", "group": 2},
-                    ],
-                ),
-                ("sample_indices", [0, 1, 8]),
-            ],
-        )
-        self.accumulated_data = []
-
-    def run(self, input_data: Input) -> BlockOutput:
-        if input_data.accumulate:
-            if isinstance(input_data.data, dict):
-                self.accumulated_data.append(input_data.data)
-            elif isinstance(input_data.data, list):
-                self.accumulated_data.extend(input_data.data)
-            else:
-                raise ValueError(f"Unsupported data type: {type(input_data.data)}")
-
-            # If we don't have enough data yet, return without sampling
-            if len(self.accumulated_data) < input_data.sample_size:
-                return
-
-            data_to_sample = self.accumulated_data
-        else:
-            # If not accumulating, use the input data directly
-            data_to_sample = (
-                input_data.data
-                if isinstance(input_data.data, list)
-                else [input_data.data]
-            )
-
-        if input_data.random_seed is not None:
-            random.seed(input_data.random_seed)
-
-        data_size = len(data_to_sample)
-
-        if input_data.sample_size > data_size:
-            raise ValueError(
-                f"Sample size ({input_data.sample_size}) cannot be larger than the dataset size ({data_size})."
-            )
-
-        indices = []
-
-        if input_data.sampling_method == SamplingMethod.RANDOM:
-            indices = random.sample(range(data_size), input_data.sample_size)
-        elif input_data.sampling_method == SamplingMethod.SYSTEMATIC:
-            step = data_size // input_data.sample_size
-            start = random.randint(0, step - 1)
-            indices = list(range(start, data_size, step))[: input_data.sample_size]
-        elif input_data.sampling_method == SamplingMethod.TOP:
-            indices = list(range(input_data.sample_size))
-        elif input_data.sampling_method == SamplingMethod.BOTTOM:
-            indices = list(range(data_size - input_data.sample_size, data_size))
-        elif input_data.sampling_method == SamplingMethod.STRATIFIED:
-            if not input_data.stratify_key:
-                raise ValueError(
-                    "Stratify key must be provided for stratified sampling."
-                )
-            strata = defaultdict(list)
-            for i, item in enumerate(data_to_sample):
-                if isinstance(item, dict):
-                    strata_value = item.get(input_data.stratify_key)
-                elif hasattr(item, input_data.stratify_key):
-                    strata_value = getattr(item, input_data.stratify_key)
-                else:
-                    raise ValueError(
-                        f"Stratify key '{input_data.stratify_key}' not found in item {item}"
-                    )
-
-                if strata_value is None:
-                    raise ValueError(
-                        f"Stratify value for key '{input_data.stratify_key}' is None"
-                    )
-
-                strata[str(strata_value)].append(i)
-
-            # Calculate the number of samples to take from each stratum
-            stratum_sizes = {
-                k: max(1, int(len(v) / data_size * input_data.sample_size))
-                for k, v in strata.items()
-            }
-
-            # Adjust sizes to ensure we get exactly sample_size samples
-            while sum(stratum_sizes.values()) != input_data.sample_size:
-                if sum(stratum_sizes.values()) < input_data.sample_size:
-                    stratum_sizes[
-                        max(stratum_sizes, key=lambda k: stratum_sizes[k])
-                    ] += 1
-                else:
-                    stratum_sizes[
-                        max(stratum_sizes, key=lambda k: stratum_sizes[k])
-                    ] -= 1
-
-            for stratum, size in stratum_sizes.items():
-                indices.extend(random.sample(strata[stratum], size))
-        elif input_data.sampling_method == SamplingMethod.WEIGHTED:
-            if not input_data.weight_key:
-                raise ValueError("Weight key must be provided for weighted sampling.")
-            weights = []
-            for item in data_to_sample:
-                if isinstance(item, dict):
-                    weight = item.get(input_data.weight_key)
-                elif hasattr(item, input_data.weight_key):
-                    weight = getattr(item, input_data.weight_key)
-                else:
-                    raise ValueError(
-                        f"Weight key '{input_data.weight_key}' not found in item {item}"
-                    )
-
-                if weight is None:
-                    raise ValueError(
-                        f"Weight value for key '{input_data.weight_key}' is None"
-                    )
-                try:
-                    weights.append(float(weight))
-                except ValueError:
-                    raise ValueError(
-                        f"Weight value '{weight}' cannot be converted to a number"
-                    )
-
-            if not weights:
-                raise ValueError(
-                    f"No valid weights found using key '{input_data.weight_key}'"
-                )
-
-            indices = random.choices(
-                range(data_size), weights=weights, k=input_data.sample_size
-            )
-        elif input_data.sampling_method == SamplingMethod.RESERVOIR:
-            indices = list(range(input_data.sample_size))
-            for i in range(input_data.sample_size, data_size):
-                j = random.randint(0, i)
-                if j < input_data.sample_size:
-                    indices[j] = i
-        elif input_data.sampling_method == SamplingMethod.CLUSTER:
-            if not input_data.cluster_key:
-                raise ValueError("Cluster key must be provided for cluster sampling.")
-            clusters = defaultdict(list)
-            for i, item in enumerate(data_to_sample):
-                if isinstance(item, dict):
-                    cluster_value = item.get(input_data.cluster_key)
-                elif hasattr(item, input_data.cluster_key):
-                    cluster_value = getattr(item, input_data.cluster_key)
-                else:
-                    raise TypeError(
-                        f"Item {item} does not have the cluster key '{input_data.cluster_key}'"
-                    )
-
-                clusters[str(cluster_value)].append(i)
-
-            # Randomly select clusters until we have enough samples
-            selected_clusters = []
-            while (
-                sum(len(clusters[c]) for c in selected_clusters)
-                < input_data.sample_size
-            ):
-                available_clusters = [c for c in clusters if c not in selected_clusters]
-                if not available_clusters:
-                    break
-                selected_clusters.append(random.choice(available_clusters))
-
-            for cluster in selected_clusters:
-                indices.extend(clusters[cluster])
-
-            # If we have more samples than needed, randomly remove some
-            if len(indices) > input_data.sample_size:
-                indices = random.sample(indices, input_data.sample_size)
-        else:
-            raise ValueError(f"Unknown sampling method: {input_data.sampling_method}")
-
-        sampled_data = [data_to_sample[i] for i in indices]
-
-        # Clear accumulated data after sampling if accumulation is enabled
-        if input_data.accumulate:
-            self.accumulated_data = []
-
-        yield "sampled_data", sampled_data
-        yield "sample_indices", indices

rnd/autogpt_server/autogpt_server/data/credit.py

@@ -1,263 +0,0 @@
-from abc import ABC, abstractmethod
-from datetime import datetime, timezone
-from enum import Enum
-from typing import Any, Optional, Type
-
-import prisma.errors
-from prisma import Json
-from prisma.enums import UserBlockCreditType
-from prisma.models import UserBlockCredit
-from pydantic import BaseModel
-
-from autogpt_server.blocks.llm import (
-    MODEL_METADATA,
-    AIConversationBlock,
-    AIStructuredResponseGeneratorBlock,
-    AITextGeneratorBlock,
-    AITextSummarizerBlock,
-)
-from autogpt_server.blocks.talking_head import CreateTalkingAvatarVideoBlock
-from autogpt_server.data.block import Block, BlockInput
-from autogpt_server.util.settings import Config
-
-
-class BlockCostType(str, Enum):
-    RUN = "run"  # cost X credits per run
-    BYTE = "byte"  # cost X credits per byte
-    SECOND = "second"  # cost X credits per second
-
-
-class BlockCost(BaseModel):
-    cost_amount: int
-    cost_filter: BlockInput
-    cost_type: BlockCostType
-
-    def __init__(
-        self,
-        cost_amount: int,
-        cost_type: BlockCostType = BlockCostType.RUN,
-        cost_filter: Optional[BlockInput] = None,
-        **data: Any,
-    ) -> None:
-        super().__init__(
-            cost_amount=cost_amount,
-            cost_filter=cost_filter or {},
-            cost_type=cost_type,
-            **data,
-        )
-
-
-llm_cost = [
-    BlockCost(
-        cost_type=BlockCostType.RUN,
-        cost_filter={
-            "model": model,
-            "api_key": None,  # Running LLM with user own API key is free.
-        },
-        cost_amount=metadata.cost_factor,
-    )
-    for model, metadata in MODEL_METADATA.items()
-]
-
-BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
-    AIConversationBlock: llm_cost,
-    AITextGeneratorBlock: llm_cost,
-    AIStructuredResponseGeneratorBlock: llm_cost,
-    AITextSummarizerBlock: llm_cost,
-    CreateTalkingAvatarVideoBlock: [
-        BlockCost(cost_amount=15, cost_filter={"api_key": None})
-    ],
-}
-
-
-class UserCreditBase(ABC):
-    def __init__(self, num_user_credits_refill: int):
-        self.num_user_credits_refill = num_user_credits_refill
-
-    @abstractmethod
-    async def get_or_refill_credit(self, user_id: str) -> int:
-        """
-        Get the current credit for the user and refill if no transaction has been made in the current cycle.
-
-        Returns:
-            int: The current credit for the user.
-        """
-        pass
-
-    @abstractmethod
-    async def spend_credits(
-        self,
-        user_id: str,
-        user_credit: int,
-        block: Block,
-        input_data: BlockInput,
-        data_size: float,
-        run_time: float,
-    ) -> int:
-        """
-        Spend the credits for the user based on the block usage.
-
-        Args:
-            user_id (str): The user ID.
-            user_credit (int): The current credit for the user.
-            block (Block): The block that is being used.
-            input_data (BlockInput): The input data for the block.
-            data_size (float): The size of the data being processed.
-            run_time (float): The time taken to run the block.
-
-        Returns:
-            int: amount of credit spent
-        """
-        pass
-
-    @abstractmethod
-    async def top_up_credits(self, user_id: str, amount: int):
-        """
-        Top up the credits for the user.
-
-        Args:
-            user_id (str): The user ID.
-            amount (int): The amount to top up.
-        """
-        pass
-
-
-class UserCredit(UserCreditBase):
-    async def get_or_refill_credit(self, user_id: str) -> int:
-        cur_time = self.time_now()
-        cur_month = cur_time.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
-        nxt_month = cur_month.replace(month=cur_month.month + 1)
-
-        user_credit = await UserBlockCredit.prisma().group_by(
-            by=["userId"],
-            sum={"amount": True},
-            where={
-                "userId": user_id,
-                "createdAt": {"gte": cur_month, "lt": nxt_month},
-                "isActive": True,
-            },
-        )
-
-        if user_credit:
-            credit_sum = user_credit[0].get("_sum") or {}
-            return credit_sum.get("amount", 0)
-
-        key = f"MONTHLY-CREDIT-TOP-UP-{cur_month}"
-
-        try:
-            await UserBlockCredit.prisma().create(
-                data={
-                    "amount": self.num_user_credits_refill,
-                    "type": UserBlockCreditType.TOP_UP,
-                    "userId": user_id,
-                    "transactionKey": key,
-                    "createdAt": self.time_now(),
-                }
-            )
-        except prisma.errors.UniqueViolationError:
-            pass  # Already refilled this month
-
-        return self.num_user_credits_refill
-
-    @staticmethod
-    def time_now():
-        return datetime.now(timezone.utc)
-
-    @staticmethod
-    def _block_usage_cost(
-        block: Block,
-        input_data: BlockInput,
-        data_size: float,
-        run_time: float,
-    ) -> tuple[int, BlockInput]:
-        block_costs = BLOCK_COSTS.get(type(block))
-        if not block_costs:
-            return 0, {}
-
-        for block_cost in block_costs:
-            if all(input_data.get(k) == b for k, b in block_cost.cost_filter.items()):
-                if block_cost.cost_type == BlockCostType.RUN:
-                    return block_cost.cost_amount, block_cost.cost_filter
-
-                if block_cost.cost_type == BlockCostType.SECOND:
-                    return (
-                        int(run_time * block_cost.cost_amount),
-                        block_cost.cost_filter,
-                    )
-
-                if block_cost.cost_type == BlockCostType.BYTE:
-                    return (
-                        int(data_size * block_cost.cost_amount),
-                        block_cost.cost_filter,
-                    )
-
-        return 0, {}
-
-    async def spend_credits(
-        self,
-        user_id: str,
-        user_credit: int,
-        block: Block,
-        input_data: BlockInput,
-        data_size: float,
-        run_time: float,
-        validate_balance: bool = True,
-    ) -> int:
-        cost, matching_filter = self._block_usage_cost(
-            block=block, input_data=input_data, data_size=data_size, run_time=run_time
-        )
-        if cost <= 0:
-            return 0
-
-        if validate_balance and user_credit < cost:
-            raise ValueError(f"Insufficient credit: {user_credit} < {cost}")
-
-        await UserBlockCredit.prisma().create(
-            data={
-                "userId": user_id,
-                "amount": -cost,
-                "type": UserBlockCreditType.USAGE,
-                "blockId": block.id,
-                "metadata": Json(
-                    {
-                        "block": block.name,
-                        "input": matching_filter,
-                    }
-                ),
-                "createdAt": self.time_now(),
-            }
-        )
-        return cost
-
-    async def top_up_credits(self, user_id: str, amount: int):
-        await UserBlockCredit.prisma().create(
-            data={
-                "userId": user_id,
-                "amount": amount,
-                "type": UserBlockCreditType.TOP_UP,
-                "createdAt": self.time_now(),
-            }
-        )
-
-
-class DisabledUserCredit(UserCreditBase):
-    async def get_or_refill_credit(self, *args, **kwargs) -> int:
-        return 0
-
-    async def spend_credits(self, *args, **kwargs) -> int:
-        return 0
-
-    async def top_up_credits(self, *args, **kwargs):
-        pass
-
-
-def get_user_credit_model() -> UserCreditBase:
-    config = Config()
-    if config.enable_credit.lower() == "true":
-        return UserCredit(config.num_user_credits_refill)
-    else:
-        return DisabledUserCredit(0)
-
-
-def get_block_costs() -> dict[str, list[BlockCost]]:
-    return {block().id: costs for block, costs in BLOCK_COSTS.items()}

rnd/autogpt_server/autogpt_server/data/db.py

@@ -31,7 +31,7 @@ async def connect(call_count=0):
     except Exception as e:
         if call_count <= 5:
             logger.info(f"[Prisma-{conn_id}] Connection failed: {e}. Retrying now..")
-            await asyncio.sleep(2**call_count)
+            await asyncio.sleep(call_count)
             await connect(call_count + 1)
         else:
             raise e

rnd/autogpt_server/autogpt_server/data/execution.py

@@ -1,9 +1,9 @@
 from collections import defaultdict
 from datetime import datetime, timezone
+from enum import Enum
 from multiprocessing import Manager
 from typing import Any, Generic, TypeVar
 
-from prisma.enums import AgentExecutionStatus
 from prisma.models import (
     AgentGraphExecution,
     AgentNodeExecution,
@@ -21,14 +21,12 @@ from autogpt_server.util import json, mock
 
 
 class GraphExecution(BaseModel):
-    user_id: str
     graph_exec_id: str
-    graph_id: str
     start_node_execs: list["NodeExecution"]
+    graph_id: str
 
 
 class NodeExecution(BaseModel):
-    user_id: str
     graph_exec_id: str
     graph_id: str
     node_exec_id: str
@@ -36,7 +34,13 @@ class NodeExecution(BaseModel):
     data: BlockInput
 
 
-ExecutionStatus = AgentExecutionStatus
+class ExecutionStatus(str, Enum):
+    INCOMPLETE = "INCOMPLETE"
+    QUEUED = "QUEUED"
+    RUNNING = "RUNNING"
+    COMPLETED = "COMPLETED"
+    FAILED = "FAILED"
+
 
 T = TypeVar("T")
 
@@ -144,7 +148,6 @@ async def create_graph_execution(
         data={
             "agentGraphId": graph_id,
             "agentGraphVersion": graph_version,
-            "executionStatus": ExecutionStatus.QUEUED,
             "AgentNodeExecutions": {
                 "create": [  # type: ignore
                     {
@@ -256,20 +259,10 @@ async def upsert_execution_output(
     )
 
 
-async def update_graph_execution_start_time(graph_exec_id: str):
-    await AgentGraphExecution.prisma().update(
-        where={"id": graph_exec_id},
-        data={
-            "executionStatus": ExecutionStatus.RUNNING,
-            "startedAt": datetime.now(tz=timezone.utc),
-        },
-    )
-
-
 async def update_graph_execution_stats(graph_exec_id: str, stats: dict[str, Any]):
     await AgentGraphExecution.prisma().update(
         where={"id": graph_exec_id},
-        data={"executionStatus": ExecutionStatus.COMPLETED, "stats": json.dumps(stats)},
+        data={"stats": json.dumps(stats)},
     )
 
 

rnd/autogpt_server/autogpt_server/data/graph.py

@@ -9,7 +9,7 @@ from prisma.models import AgentGraph, AgentNode, AgentNodeLink
 from pydantic import BaseModel, PrivateAttr
 from pydantic_core import PydanticUndefinedType
 
-from autogpt_server.blocks.basic import AgentInputBlock, AgentOutputBlock
+from autogpt_server.blocks.basic import InputBlock, OutputBlock
 from autogpt_server.data.block import BlockInput, get_block, get_blocks
 from autogpt_server.data.db import BaseDbModel, transaction
 from autogpt_server.data.user import DEFAULT_USER_ID
@@ -106,9 +106,7 @@ class Graph(GraphMeta):
     def starting_nodes(self) -> list[Node]:
         outbound_nodes = {link.sink_id for link in self.links}
         input_nodes = {
-            v.id
-            for v in self.nodes
-            if isinstance(get_block(v.block_id), AgentInputBlock)
+            v.id for v in self.nodes if isinstance(get_block(v.block_id), InputBlock)
         }
         return [
             node
@@ -118,9 +116,7 @@ class Graph(GraphMeta):
 
     @property
     def ending_nodes(self) -> list[Node]:
-        return [
-            v for v in self.nodes if isinstance(get_block(v.block_id), AgentOutputBlock)
-        ]
+        return [v for v in self.nodes if isinstance(get_block(v.block_id), OutputBlock)]
 
     @property
     def subgraph_map(self) -> dict[str, str]:
@@ -183,9 +179,7 @@ class Graph(GraphMeta):
                 + [sanitize(link.sink_name) for link in node.input_links]
             )
             for name in block.input_schema.get_required_fields():
-                if name not in provided_inputs and not isinstance(
-                    block, AgentInputBlock
-                ):
+                if name not in provided_inputs and not isinstance(block, InputBlock):
                     raise ValueError(
                         f"Node {block.name} #{node.id} required input missing: `{name}`"
                     )
@@ -199,7 +193,7 @@ class Graph(GraphMeta):
         def is_input_output_block(nid: str) -> bool:
             bid = node_map[nid].block_id
             b = get_block(bid)
-            return isinstance(b, AgentInputBlock) or isinstance(b, AgentOutputBlock)
+            return isinstance(b, InputBlock) or isinstance(b, OutputBlock)
 
         # subgraphs: all nodes in subgraph must be present in the graph.
         for subgraph_id, node_ids in self.subgraphs.items():

rnd/autogpt_server/autogpt_server/exec.py

@@ -1,15 +0,0 @@
-from autogpt_server.app import run_processes
-from autogpt_server.executor import ExecutionManager
-
-
-def main():
-    """
-    Run all the processes required for the AutoGPT-server REST API.
-    """
-    run_processes(
-        ExecutionManager(),
-    )
-
-
-if __name__ == "__main__":
-    main()

rnd/autogpt_server/autogpt_server/executor/manager.py

@@ -14,13 +14,11 @@ from typing import TYPE_CHECKING, Any, Coroutine, Generator, TypeVar
 if TYPE_CHECKING:
     from autogpt_server.server.rest_api import AgentServer
 
-from autogpt_server.blocks.basic import AgentInputBlock
+from autogpt_server.blocks.basic import InputBlock
 from autogpt_server.data import db
 from autogpt_server.data.block import Block, BlockData, BlockInput, get_block
-from autogpt_server.data.credit import get_user_credit_model
 from autogpt_server.data.execution import (
     ExecutionQueue,
-    ExecutionResult,
     ExecutionStatus,
     GraphExecution,
     NodeExecution,
@@ -47,41 +45,21 @@ from autogpt_server.util.type import convert
 logger = logging.getLogger(__name__)
 
 
-class LogMetadata:
-    def __init__(
-        self,
-        user_id: str,
-        graph_eid: str,
-        graph_id: str,
-        node_eid: str,
-        node_id: str,
-        block_name: str,
-    ):
-        self.metadata = {
-            "component": "ExecutionManager",
-            "user_id": user_id,
-            "graph_eid": graph_eid,
-            "graph_id": graph_id,
-            "node_eid": node_eid,
-            "node_id": node_id,
-            "block_name": block_name,
-        }
-        self.prefix = f"[ExecutionManager|uid:{user_id}|gid:{graph_id}|nid:{node_id}]|geid:{graph_eid}|nid:{node_eid}|{block_name}]"
-
-    def info(self, msg: str, **extra):
-        logger.info(msg, extra={"json_fields": {**self.metadata, **extra}})
-
-    def warning(self, msg: str, **extra):
-        logger.warning(msg, extra={"json_fields": {**self.metadata, **extra}})
-
-    def error(self, msg: str, **extra):
-        logger.error(msg, extra={"json_fields": {**self.metadata, **extra}})
-
-    def debug(self, msg: str, **extra):
-        logger.debug(msg, extra={"json_fields": {**self.metadata, **extra}})
-
-    def exception(self, msg: str, **extra):
-        logger.exception(msg, extra={"json_fields": {**self.metadata, **extra}})
+def get_log_metadata(
+    graph_eid: str,
+    graph_id: str,
+    node_eid: str,
+    node_id: str,
+    block_name: str,
+) -> dict:
+    return {
+        "component": "ExecutionManager",
+        "graph_eid": graph_eid,
+        "graph_id": graph_id,
+        "node_eid": node_eid,
+        "node_id": node_id,
+        "block_name": block_name,
+    }
 
 
 T = TypeVar("T")
@@ -107,7 +85,6 @@ def execute_node(
     Returns:
         The subsequent node to be enqueued, or None if there is no subsequent node.
     """
-    user_id = data.user_id
     graph_exec_id = data.graph_exec_id
     graph_id = data.graph_id
     node_exec_id = data.node_exec_id
@@ -118,10 +95,9 @@ def execute_node(
     def wait(f: Coroutine[Any, Any, T]) -> T:
         return loop.run_until_complete(f)
 
-    def update_execution(status: ExecutionStatus) -> ExecutionResult:
+    def update_execution(status: ExecutionStatus):
         exec_update = wait(update_execution_status(node_exec_id, status))
         api_client.send_execution_update(exec_update.model_dump())
-        return exec_update
 
     node = wait(get_node(node_id))
 
@@ -131,8 +107,7 @@ def execute_node(
         return
 
     # Sanity check: validate the execution input.
-    log_metadata = LogMetadata(
-        user_id=user_id,
+    log_metadata = get_log_metadata(
         graph_eid=graph_exec_id,
         graph_id=graph_id,
         node_eid=node_exec_id,
@@ -141,25 +116,29 @@ def execute_node(
     )
     input_data, error = validate_exec(node, data.data, resolve_input=False)
     if input_data is None:
-        log_metadata.error(f"Skip execution, input validation error: {error}")
+        logger.error(
+            "Skip execution, input validation error",
+            extra={"json_fields": {**log_metadata, "error": error}},
+        )
         return
 
     # Execute the node
     input_data_str = json.dumps(input_data)
     input_size = len(input_data_str)
-    log_metadata.info("Executed node with input", input=input_data_str)
+    logger.info(
+        "Executed node with input",
+        extra={"json_fields": {**log_metadata, "input": input_data_str}},
+    )
     update_execution(ExecutionStatus.RUNNING)
-    user_credit = get_user_credit_model()
 
     output_size = 0
     try:
-        credit = wait(user_credit.get_or_refill_credit(user_id))
-        if credit < 0:
-            raise ValueError(f"Insufficient credit: {credit}")
-
         for output_name, output_data in node_block.execute(input_data):
             output_size += len(json.dumps(output_data))
-            log_metadata.info("Node produced output", output_name=output_data)
+            logger.info(
+                "Node produced output",
+                extra={"json_fields": {**log_metadata, output_name: output_data}},
+            )
             wait(upsert_execution_output(node_exec_id, output_name, output_data))
 
             for execution in _enqueue_next_nodes(
@@ -167,25 +146,20 @@ def execute_node(
                 loop=loop,
                 node=node,
                 output=(output_name, output_data),
-                user_id=user_id,
                 graph_exec_id=graph_exec_id,
                 graph_id=graph_id,
                 log_metadata=log_metadata,
             ):
                 yield execution
 
-        r = update_execution(ExecutionStatus.COMPLETED)
-        s = input_size + output_size
-        t = (
-            (r.end_time - r.start_time).total_seconds()
-            if r.end_time and r.start_time
-            else 0
-        )
-        wait(user_credit.spend_credits(user_id, credit, node_block, input_data, s, t))
+        update_execution(ExecutionStatus.COMPLETED)
 
     except Exception as e:
-        error_msg = str(e)
-        log_metadata.exception(f"Node execution failed with error {error_msg}")
+        error_msg = f"{e.__class__.__name__}: {e}"
+        logger.exception(
+            "Node execution failed with error",
+            extra={"json_fields": {**log_metadata, error: error_msg}},
+        )
         wait(upsert_execution_output(node_exec_id, "error", error_msg))
         update_execution(ExecutionStatus.FAILED)
 
@@ -211,10 +185,9 @@ def _enqueue_next_nodes(
     loop: asyncio.AbstractEventLoop,
     node: Node,
     output: BlockData,
-    user_id: str,
     graph_exec_id: str,
     graph_id: str,
-    log_metadata: LogMetadata,
+    log_metadata: dict,
 ) -> list[NodeExecution]:
     def wait(f: Coroutine[Any, Any, T]) -> T:
         return loop.run_until_complete(f)
@@ -227,7 +200,6 @@ def _enqueue_next_nodes(
         )
         api_client.send_execution_update(exec_update.model_dump())
         return NodeExecution(
-            user_id=user_id,
             graph_exec_id=graph_exec_id,
             graph_id=graph_id,
             node_exec_id=node_exec_id,
@@ -281,11 +253,17 @@ def _enqueue_next_nodes(
 
             # Incomplete input data, skip queueing the execution.
             if not next_node_input:
-                log_metadata.warning(f"Skipped queueing {suffix}")
+                logger.warning(
+                    f"Skipped queueing {suffix}",
+                    extra={"json_fields": {**log_metadata}},
+                )
                 return enqueued_executions
 
             # Input is complete, enqueue the execution.
-            log_metadata.info(f"Enqueued {suffix}")
+            logger.info(
+                f"Enqueued {suffix}",
+                extra={"json_fields": {**log_metadata}},
+            )
             enqueued_executions.append(
                 add_enqueued_execution(next_node_exec_id, next_node_id, next_node_input)
             )
@@ -311,9 +289,11 @@ def _enqueue_next_nodes(
                 idata, msg = validate_exec(next_node, idata)
                 suffix = f"{next_output_name}>{next_input_name}~{ineid}:{msg}"
                 if not idata:
-                    log_metadata.info(f"Enqueueing static-link skipped: {suffix}")
+                    logger.info(
+                        f"{log_metadata} Enqueueing static-link skipped: {suffix}"
+                    )
                     continue
-                log_metadata.info(f"Enqueueing static-link execution {suffix}")
+                logger.info(f"{log_metadata} Enqueueing static-link execution {suffix}")
                 enqueued_executions.append(
                     add_enqueued_execution(iexec.node_exec_id, next_node_id, idata)
                 )
@@ -454,8 +434,7 @@ class Executor:
     def on_node_execution(
         cls, q: ExecutionQueue[NodeExecution], node_exec: NodeExecution
     ):
-        log_metadata = LogMetadata(
-            user_id=node_exec.user_id,
+        log_metadata = get_log_metadata(
             graph_eid=node_exec.graph_exec_id,
             graph_id=node_exec.graph_id,
             node_eid=node_exec.node_exec_id,
@@ -480,19 +459,28 @@ class Executor:
         cls,
         q: ExecutionQueue[NodeExecution],
         node_exec: NodeExecution,
-        log_metadata: LogMetadata,
+        log_metadata: dict,
         stats: dict[str, Any] | None = None,
     ):
         try:
-            log_metadata.info(f"Start node execution {node_exec.node_exec_id}")
+            logger.info(
+                f"Start node execution {node_exec.node_exec_id}",
+                extra={"json_fields": {**log_metadata}},
+            )
             for execution in execute_node(
                 cls.loop, cls.agent_server_client, node_exec, stats
             ):
                 q.add(execution)
-            log_metadata.info(f"Finished node execution {node_exec.node_exec_id}")
+            logger.info(
+                f"Finished node execution {node_exec.node_exec_id}",
+                extra={"json_fields": {**log_metadata}},
+            )
         except Exception as e:
-            log_metadata.exception(
-                f"Failed node execution {node_exec.node_exec_id}: {e}"
+            logger.exception(
+                f"Failed node execution {node_exec.node_exec_id}: {e}",
+                extra={
+                    **log_metadata,
+                },
             )
 
     @classmethod
@@ -514,12 +502,10 @@ class Executor:
 
     @classmethod
     def on_graph_executor_stop(cls):
-        prefix = f"[on_graph_executor_stop {cls.pid}]"
-        logger.info(f"{prefix} ⏳ Disconnecting DB...")
-        cls.loop.run_until_complete(db.disconnect())
-        logger.info(f"{prefix} ⏳ Terminating node executor pool...")
+        logger.info(
+            f"[on_graph_executor_stop {cls.pid}] ⏳ Terminating node executor pool..."
+        )
         cls.executor.terminate()
-        logger.info(f"{prefix} ✅ Finished cleanup")
 
     @classmethod
     def _init_node_executor_pool(cls):
@@ -531,8 +517,7 @@ class Executor:
     @classmethod
     @error_logged
     def on_graph_execution(cls, graph_exec: GraphExecution, cancel: threading.Event):
-        log_metadata = LogMetadata(
-            user_id=graph_exec.user_id,
+        log_metadata = get_log_metadata(
             graph_eid=graph_exec.graph_exec_id,
             graph_id=graph_exec.graph_id,
             node_id="*",
@@ -557,12 +542,12 @@ class Executor:
     @classmethod
     @time_measured
     def _on_graph_execution(
-        cls,
-        graph_exec: GraphExecution,
-        cancel: threading.Event,
-        log_metadata: LogMetadata,
+        cls, graph_exec: GraphExecution, cancel: threading.Event, log_metadata: dict
     ) -> int:
-        log_metadata.info(f"Start graph execution {graph_exec.graph_exec_id}")
+        logger.info(
+            f"Start graph execution {graph_exec.graph_exec_id}",
+            extra={"json_fields": {**log_metadata}},
+        )
         n_node_executions = 0
         finished = False
 
@@ -572,7 +557,10 @@ class Executor:
             if finished:
                 return
             cls.executor.terminate()
-            log_metadata.info(f"Terminated graph execution {graph_exec.graph_exec_id}")
+            logger.info(
+                f"Terminated graph execution {graph_exec.graph_exec_id}",
+                extra={"json_fields": {**log_metadata}},
+            )
             cls._init_node_executor_pool()
 
         cancel_thread = threading.Thread(target=cancel_handler)
@@ -610,9 +598,10 @@ class Executor:
                     #   Re-enqueueing the data back to the queue will disrupt the order.
                     execution.wait()
 
-                log_metadata.debug(
+                logger.debug(
                     f"Dispatching node execution {exec_data.node_exec_id} "
                     f"for node {exec_data.node_id}",
+                    extra={**log_metadata},
                 )
                 running_executions[exec_data.node_id] = cls.executor.apply_async(
                     cls.on_node_execution,
@@ -622,8 +611,10 @@ class Executor:
 
                 # Avoid terminating graph execution when some nodes are still running.
                 while queue.empty() and running_executions:
-                    log_metadata.debug(
-                        f"Queue empty; running nodes: {list(running_executions.keys())}"
+                    logger.debug(
+                        "Queue empty; running nodes: "
+                        f"{list(running_executions.keys())}",
+                        extra={"json_fields": {**log_metadata}},
                     )
                     for node_id, execution in list(running_executions.items()):
                         if cancel.is_set():
@@ -632,13 +623,20 @@ class Executor:
                         if not queue.empty():
                             break  # yield to parent loop to execute new queue items
 
-                        log_metadata.debug(f"Waiting on execution of node {node_id}")
+                        logger.debug(
+                            f"Waiting on execution of node {node_id}",
+                            extra={"json_fields": {**log_metadata}},
+                        )
                         execution.wait(3)
 
-            log_metadata.info(f"Finished graph execution {graph_exec.graph_exec_id}")
+            logger.info(
+                f"Finished graph execution {graph_exec.graph_exec_id}",
+                extra={"json_fields": {**log_metadata}},
+            )
         except Exception as e:
-            log_metadata.exception(
-                f"Failed graph execution {graph_exec.graph_exec_id}: {e}"
+            logger.exception(
+                f"Failed graph execution {graph_exec.graph_exec_id}: {e}",
+                extra={"json_fields": {**log_metadata}},
             )
         finally:
             if not cancel.is_set():
@@ -701,7 +699,7 @@ class ExecutionManager(AppService):
         nodes_input = []
         for node in graph.starting_nodes:
             input_data = {}
-            if isinstance(get_block(node.block_id), AgentInputBlock):
+            if isinstance(get_block(node.block_id), InputBlock):
                 name = node.input_default.get("name")
                 if name and name in data:
                     input_data = {"value": data[name]}
@@ -725,7 +723,6 @@ class ExecutionManager(AppService):
         for node_exec in node_execs:
             starting_node_execs.append(
                 NodeExecution(
-                    user_id=user_id,
                     graph_exec_id=node_exec.graph_exec_id,
                     graph_id=node_exec.graph_id,
                     node_exec_id=node_exec.node_exec_id,
@@ -741,7 +738,6 @@ class ExecutionManager(AppService):
             self.agent_server_client.send_execution_update(exec_update.model_dump())
 
         graph_exec = GraphExecution(
-            user_id=user_id,
             graph_id=graph_id,
             graph_exec_id=graph_exec_id,
             start_node_execs=starting_node_execs,

rnd/autogpt_server/autogpt_server/integrations/github.py

@@ -5,7 +5,7 @@ from urllib.parse import urlencode
 import requests
 from autogpt_libs.supabase_integration_credentials_store import OAuth2Credentials
 
-from .base import BaseOAuthHandler
+from autogpt_server.integrations.oauth import BaseOAuthHandler
 
 
 class GitHubOAuthHandler(BaseOAuthHandler):
@@ -23,7 +23,6 @@ class GitHubOAuthHandler(BaseOAuthHandler):
     """  # noqa
 
     PROVIDER_NAME = "github"
-    EMAIL_ENDPOINT = "https://api.github.com/user/emails"
 
     def __init__(self, client_id: str, client_secret: str, redirect_uri: str):
         self.client_id = client_id
@@ -70,13 +69,10 @@ class GitHubOAuthHandler(BaseOAuthHandler):
         response.raise_for_status()
         token_data: dict = response.json()
 
-        username = self._request_username(token_data["access_token"])
-
         now = int(time.time())
         new_credentials = OAuth2Credentials(
             provider=self.PROVIDER_NAME,
-            title=current_credentials.title if current_credentials else None,
-            username=username,
+            title=current_credentials.title if current_credentials else "GitHub",
             access_token=token_data["access_token"],
             # Token refresh responses have an empty `scope` property (see docs),
             # so we have to get the scope from the existing credentials object.
@@ -101,19 +97,3 @@ class GitHubOAuthHandler(BaseOAuthHandler):
         if current_credentials:
             new_credentials.id = current_credentials.id
         return new_credentials
-
-    def _request_username(self, access_token: str) -> str | None:
-        url = "https://api.github.com/user"
-        headers = {
-            "Accept": "application/vnd.github+json",
-            "Authorization": f"Bearer {access_token}",
-            "X-GitHub-Api-Version": "2022-11-28",
-        }
-
-        response = requests.get(url, headers=headers)
-
-        if not response.ok:
-            return None
-
-        # Get the login (username)
-        return response.json().get("login")

rnd/autogpt_server/autogpt_server/integrations/google.py

@@ -1,13 +1,10 @@
 from autogpt_libs.supabase_integration_credentials_store import OAuth2Credentials
-from google.auth.external_account_authorized_user import (
-    Credentials as ExternalAccountCredentials,
-)
-from google.auth.transport.requests import AuthorizedSession, Request
+from google.auth.transport.requests import Request
 from google.oauth2.credentials import Credentials
 from google_auth_oauthlib.flow import Flow
 from pydantic import SecretStr
 
-from .base import BaseOAuthHandler
+from .oauth import BaseOAuthHandler
 
 
 class GoogleOAuthHandler(BaseOAuthHandler):
@@ -16,7 +13,6 @@ class GoogleOAuthHandler(BaseOAuthHandler):
     """  # noqa
 
     PROVIDER_NAME = "google"
-    EMAIL_ENDPOINT = "https://www.googleapis.com/oauth2/v2/userinfo"
 
     def __init__(self, client_id: str, client_secret: str, redirect_uri: str):
         self.client_id = client_id
@@ -41,8 +37,6 @@ class GoogleOAuthHandler(BaseOAuthHandler):
         flow.fetch_token(code=code)
 
         google_creds = flow.credentials
-        username = self._request_email(google_creds)
-
         # Google's OAuth library is poorly typed so we need some of these:
         assert google_creds.token
         assert google_creds.refresh_token
@@ -50,8 +44,7 @@ class GoogleOAuthHandler(BaseOAuthHandler):
         assert google_creds.scopes
         return OAuth2Credentials(
             provider=self.PROVIDER_NAME,
-            title=None,
-            username=username,
+            title="Google",
             access_token=SecretStr(google_creds.token),
             refresh_token=SecretStr(google_creds.refresh_token),
             access_token_expires_at=int(google_creds.expiry.timestamp()),
@@ -59,15 +52,6 @@ class GoogleOAuthHandler(BaseOAuthHandler):
             scopes=google_creds.scopes,
         )
 
-    def _request_email(
-        self, creds: Credentials | ExternalAccountCredentials
-    ) -> str | None:
-        session = AuthorizedSession(creds)
-        response = session.get(self.EMAIL_ENDPOINT)
-        if not response.ok:
-            return None
-        return response.json()["email"]
-
     def _refresh_tokens(self, credentials: OAuth2Credentials) -> OAuth2Credentials:
         # Google credentials should ALWAYS have a refresh token
         assert credentials.refresh_token
@@ -88,10 +72,9 @@ class GoogleOAuthHandler(BaseOAuthHandler):
         assert google_creds.expiry
 
         return OAuth2Credentials(
-            provider=self.PROVIDER_NAME,
             id=credentials.id,
+            provider=self.PROVIDER_NAME,
             title=credentials.title,
-            username=credentials.username,
             access_token=SecretStr(google_creds.token),
             refresh_token=SecretStr(google_creds.refresh_token),
             access_token_expires_at=int(google_creds.expiry.timestamp()),

rnd/autogpt_server/autogpt_server/integrations/notion.py

@@ -4,7 +4,7 @@ from urllib.parse import urlencode
 import requests
 from autogpt_libs.supabase_integration_credentials_store import OAuth2Credentials
 
-from .base import BaseOAuthHandler
+from autogpt_server.integrations.oauth import BaseOAuthHandler
 
 
 class NotionOAuthHandler(BaseOAuthHandler):
@@ -49,18 +49,10 @@ class NotionOAuthHandler(BaseOAuthHandler):
         response = requests.post(self.token_url, json=request_body, headers=headers)
         response.raise_for_status()
         token_data = response.json()
-        # Email is only available for non-bot users
-        email = (
-            token_data["owner"]["person"]["email"]
-            if "person" in token_data["owner"]
-            and "email" in token_data["owner"]["person"]
-            else None
-        )
 
         return OAuth2Credentials(
             provider=self.PROVIDER_NAME,
-            title=token_data.get("workspace_name"),
-            username=email,
+            title=token_data.get("workspace_name", "Notion"),
             access_token=token_data["access_token"],
             refresh_token=None,
             access_token_expires_at=None,  # Notion tokens don't expire