## Changes 🏗️ <img width="800" height="621" alt="Screenshot 2026-02-11 at 19 32 39" src="https://github.com/user-attachments/assets/e97be1a7-972e-4ae0-8dfa-6ade63cf287b" /> When the BE API has an error, prevent it from leaking into the stream and instead handle it gracefully via toast. ## Checklist 📋 ### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Run the app locally and trust the changes <!-- greptile_comment --> <h2>Greptile Overview</h2> <details><summary><h3>Greptile Summary</h3></summary> This PR fixes an issue where backend API stream errors were leaking into the chat prompt instead of being handled gracefully. The fix involves both backend and frontend changes to ensure error events conform to the AI SDK's strict schema. **Key Changes:** - **Backend (`response_model.py`)**: Added custom `to_sse()` method for `StreamError` that only emits `type` and `errorText` fields, stripping extra fields like `code` and `details` that cause AI SDK validation failures - **Backend (`prompt.py`)**: Added validation step after context compression to remove orphaned tool responses without matching tool calls, preventing "unexpected tool_use_id" API errors - **Frontend (`route.ts`)**: Implemented SSE stream normalization with `normalizeSSEStream()` and `normalizeSSEEvent()` functions to strip non-conforming fields from error events before they reach the AI SDK - **Frontend (`ChatMessagesContainer.tsx`)**: Added toast notifications for errors and improved error display UI with deduplication logic The changes ensure a clean separation between internal error metadata (useful for logging/debugging) and the strict schema required by the AI SDK on the frontend. </details> <details><summary><h3>Confidence Score: 4/5</h3></summary> - This PR is safe to merge with low risk - The changes are well-structured and address a specific bug with proper error handling. The dual-layer approach (backend filtering in `to_sse()` + frontend normalization) provides defense-in-depth. However, the lack of automated tests for the new error normalization logic and the potential for edge cases in SSE parsing prevent a perfect score. - Pay close attention to `autogpt_platform/frontend/src/app/api/chat/sessions/[sessionId]/stream/route.ts` - the SSE normalization logic should be tested with various error scenarios </details> <details><summary><h3>Sequence Diagram</h3></summary> ```mermaid sequenceDiagram participant User participant Frontend as ChatMessagesContainer participant Proxy as /api/chat/.../stream participant Backend as Backend API participant AISDK as AI SDK User->>Frontend: Send message Frontend->>Proxy: POST with message Proxy->>Backend: Forward request with auth Backend->>Backend: Process message alt Success Path Backend->>Proxy: SSE stream (text-delta, etc.) Proxy->>Proxy: normalizeSSEStream (pass through) Proxy->>AISDK: Forward SSE events AISDK->>Frontend: Update messages Frontend->>User: Display response else Error Path Backend->>Backend: StreamError.to_sse() Note over Backend: Only emit {type, errorText} Backend->>Proxy: SSE error event Proxy->>Proxy: normalizeSSEEvent() Note over Proxy: Strip extra fields (code, details) Proxy->>AISDK: {type: "error", errorText: "..."} AISDK->>Frontend: error state updated Frontend->>Frontend: Toast notification (deduplicated) Frontend->>User: Show error UI + toast end ``` </details> <!-- greptile_other_comments_section --> <!-- /greptile_comment --> --------- Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com> Co-authored-by: Otto-AGPT <otto@agpt.co>
AutoGPT Platform
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.
Getting Started
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:
-
Clone this repository to your local machine and navigate to the
autogpt_platformdirectory within the repository:git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git> cd AutoGPT/autogpt_platform -
Run the following command:
cp .env.default .envThis command will copy the
.env.defaultfile to.env. You can modify the.envfile to add your own environment variables. -
Run the following command:
docker compose up -dThis command will start all the necessary backend services defined in the
docker-compose.ymlfile in detached mode. -
After all the services are in ready state, open your browser and navigate to
http://localhost:3000to access the AutoGPT Platform frontend.
Running Just Core services
You can now run the following to enable just the core services.
# For help
make help
# Run just Supabase + Redis + RabbitMQ
make start-core
# Stop core services
make stop-core
# View logs from core services
make logs-core
# Run formatting and linting for backend and frontend
make format
# Run migrations for backend database
make migrate
# Run backend server
make run-backend
# Run frontend development server
make run-frontend
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:
-
Updating and restarting a specific service:
docker compose build api_srv docker compose up -d --no-deps api_srvThis rebuilds the
api_srvservice and restarts it without affecting other services. -
Viewing logs for troubleshooting:
docker compose logs -f api_srv ws_srvThis shows and follows the logs for both
api_srvandws_srvservices. -
Scaling a service for increased load:
docker compose up -d --scale executor=3This scales the
executorservice to 3 instances to handle increased load. -
Stopping the entire system for maintenance:
docker compose stop docker compose rm -f docker compose pull docker compose up -dThis stops all services, removes containers, pulls the latest images, and restarts the system.
-
Developing with live updates:
docker compose watchThis watches for changes in your code and automatically updates the relevant services.
-
Checking the status of services:
docker compose psThis 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:
-
Open the
docker-compose.ymlfile in a text editor. -
Add volume configurations for PostgreSQL and Redis services:
services: postgres: # ... other configurations ... volumes: - postgres_data:/var/lib/postgresql/data redis: # ... other configurations ... volumes: - redis_data:/data volumes: postgres_data: redis_data: -
Save the file and run
docker compose up -dto apply the changes.
This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
API Client Generation
The platform includes scripts for generating and managing the API client:
pnpm fetch:openapi: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)pnpm generate:api-client: Generates the TypeScript API client from the OpenAPI specification using Orvalpnpm generate:api: Runs both fetch and generate commands in sequence
Manual API Client Updates
If you need to update the API client after making changes to the backend API:
-
Ensure the backend services are running:
docker compose up -d -
Generate the updated API client:
pnpm generate:api
This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.