Files
AutoGPT/autogpt_platform
Zamil Majdy a78145505b fix(copilot): merge split assistant messages to prevent Anthropic API errors (#12062)
## Summary
- When the copilot model responds with both text content AND a
long-running tool call (e.g., `create_agent`), the streaming code
created two separate consecutive assistant messages — one with text, one
with `tool_calls`. This caused Anthropic's API to reject with
`"unexpected tool_use_id found in tool_result blocks"` because the
`tool_result` couldn't find a matching `tool_use` in the immediately
preceding assistant message.
- Added a defensive merge of consecutive assistant messages in
`to_openai_messages()` (fixes existing corrupt sessions too)
- Fixed `_yield_tool_call` to add tool_calls to the existing
current-turn assistant message instead of creating a new one
- Changed `accumulated_tool_calls` assignment to use `extend` to prevent
overwriting tool_calls added by long-running tool flow

## Test plan
- [x] All 23 chat feature tests pass (`backend/api/features/chat/`)
- [x] All 44 prompt utility tests pass (`backend/util/prompt_test.py`)
- [x] All pre-commit hooks pass (ruff, isort, black, pyright)
- [ ] Manual test: create an agent via copilot, then ask a follow-up
question — should no longer get 400 error

<!-- greptile_comment -->

<h2>Greptile Overview</h2>

<details><summary><h3>Greptile Summary</h3></summary>

Fixes a critical bug where long-running tool calls (like `create_agent`)
caused Anthropic API 400 errors due to split assistant messages. The fix
ensures tool calls are added to the existing assistant message instead
of creating new ones, and adds a defensive merge function to repair any
existing corrupt sessions.

**Key changes:**
- Added `_merge_consecutive_assistant_messages()` to defensively merge
split assistant messages in `to_openai_messages()`
- Modified `_yield_tool_call()` to append tool calls to the current-turn
assistant message instead of creating a new one
- Changed `accumulated_tool_calls` from assignment to `extend` to
preserve tool calls already added by long-running tool flow

**Impact:** Resolves the issue where users received 400 errors after
creating agents via copilot and asking follow-up questions.
</details>


<details><summary><h3>Confidence Score: 4/5</h3></summary>

- Safe to merge with minor verification recommended
- The changes are well-targeted and solve a real API compatibility
issue. The logic is sound: searching backwards for the current assistant
message is correct, and using `extend` instead of assignment prevents
overwriting. The defensive merge in `to_openai_messages()` also fixes
existing corrupt sessions. All existing tests pass according to the PR
description.
- No files require special attention - changes are localized and
defensive
</details>


<details><summary><h3>Sequence Diagram</h3></summary>

```mermaid
sequenceDiagram
    participant User
    participant StreamAPI as stream_chat_completion
    participant Chunks as _stream_chat_chunks
    participant ToolCall as _yield_tool_call
    participant Session as ChatSession
    
    User->>StreamAPI: Send message
    StreamAPI->>Chunks: Stream chat chunks
    
    alt Text + Long-running tool call
        Chunks->>StreamAPI: Text delta (content)
        StreamAPI->>Session: Append assistant message with content
        Chunks->>ToolCall: Tool call detected
        
        Note over ToolCall: OLD: Created new assistant message<br/>NEW: Appends to existing assistant
        
        ToolCall->>Session: Search backwards for current assistant
        ToolCall->>Session: Append tool_call to existing message
        ToolCall->>Session: Add pending tool result
    end
    
    StreamAPI->>StreamAPI: Merge accumulated_tool_calls
    Note over StreamAPI: Use extend (not assign)<br/>to preserve existing tool_calls
    
    StreamAPI->>Session: to_openai_messages()
    Session->>Session: _merge_consecutive_assistant_messages()
    Note over Session: Defensive: Merges any split<br/>assistant messages
    Session-->>StreamAPI: Merged messages
    
    StreamAPI->>User: Return response
```
</details>


<!-- greptile_other_comments_section -->

<!-- /greptile_comment -->
2026-02-12 01:52:17 +00:00
..

AutoGPT Platform

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Getting Started

Prerequisites

  • Docker
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Running the System

To run the AutoGPT Platform, follow these steps:

  1. Clone this repository to your local machine and navigate to the autogpt_platform directory within the repository:

    git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git>
    cd AutoGPT/autogpt_platform
    
  2. Run the following command:

    cp .env.default .env
    

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  3. Run the following command:

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  4. After all the services are in ready state, open your browser and navigate to http://localhost:3000 to 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
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make logs-core

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make format

# Run migrations for backend database
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# Run backend server
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# Run frontend development server
make run-frontend

Docker Compose Commands

Here are some useful Docker Compose commands for managing your AutoGPT Platform:

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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
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  2. Viewing logs for troubleshooting:

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  4. Stopping the entire system for maintenance:

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  5. Developing with live updates:

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  6. Checking the status of services:

    docker compose ps
    

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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:

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    volumes:
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      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.

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Manual API Client Updates

If you need to update the API client after making changes to the backend API:

  1. Ensure the backend services are running:

    docker compose up -d
    
  2. Generate the updated API client:

    pnpm generate:api
    

This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.