fc64f83331
## Summary
Fixes multiple reliability issues in the copilot's Claude Agent SDK
streaming pipeline — tool outputs getting stuck, parallel tool calls
flushing prematurely, messages lost on page refresh, and SSE
reconnection failures.
## Changes
### Backend: Streaming loop rewrite (`sdk/service.py`)
- **Non-cancelling heartbeat pattern**: Replace `asyncio.timeout()` with
`asyncio.wait()` for SDK message iteration. The old approach corrupted
the SDK's internal anyio memory stream when timeouts fired
mid-`__anext__()`, causing `StopAsyncIteration` on the next call and
silently dropping all in-flight tool results.
- **Hook synchronization**: Add `wait_for_stash()` before
`convert_message()` — the SDK fires PostToolUse hooks via `start_soon()`
(fire-and-forget), so the next message can arrive before the hook
stashes its output. The new asyncio.Event-based mechanism bridges this
gap without arbitrary sleeps.
- **Error handling**: Add `asyncio.CancelledError` handling at both
inner (streaming loop) and outer (session) levels, plus pending task
cleanup in `finally` block to prevent leaked coroutines. Catch
`Exception` from `done.pop().result()` for SDK error messages.
- **Safety-net flush**: After streaming loop ends, flush any remaining
unresolved tool calls so the frontend stops showing spinners even if the
stream drops unexpectedly.
- **StreamFinish fallback**: Emit `StreamFinishStep` + `StreamFinish`
when stream ends without `ResultMessage` (StopAsyncIteration) so the
frontend transitions to "ready" state.
- **Incremental session saves**: Save session to PostgreSQL after each
tool input/output event (not just at stream end), so page refresh and
other devices see recent messages.
- **Enhanced logging**: All log lines now include `session_id[:12]`
prefix and tool call resolution state (unresolved/current/resolved
counts).
### Backend: Response adapter (`sdk/response_adapter.py`)
- **Parallel tool call support**: Skip `_flush_unresolved_tool_calls()`
when an AssistantMessage contains only ToolUseBlocks (parallel
continuation) — the prior tools are still executing concurrently and
haven't finished yet.
- **Duplicate output prevention**: Skip already-resolved tool results in
both UserMessage (ToolResultBlock) and parent_tool_use_id handling to
prevent duplicate `StreamToolOutputAvailable` events.
- **`has_unresolved_tool_calls` property**: Used by the streaming loop
to decide whether to wait for PostToolUse hooks.
- **`session_id` parameter**: Passed through for structured logging.
### Backend: Hook synchronization (`sdk/tool_adapter.py`)
- **`_stash_event` ContextVar**: asyncio.Event signaled by
`stash_pending_tool_output()` whenever a PostToolUse hook stashes
output.
- **`wait_for_stash()`**: Awaits the event with configurable timeout —
replaces the racy "hope the hook finished" approach.
### Backend: Security hooks (`sdk/security_hooks.py`)
- Enhanced logging in `post_tool_use_hook` — log whether tool is
built-in, preview of stashed output, warning when `tool_response` is
None.
### Backend: Incremental save optimization (`model.py`)
- **`existing_message_count` parameter** on `upsert_chat_session`: Skip
the DB query to count existing messages when the caller already tracks
this (streaming loop).
- **`skip_existence_check` parameter** on `_save_session_to_db`: Skip
the `get_chat_session` existence query when we know the session row
already exists. Reduces from 4 DB round trips to 2 per incremental save.
### Backend: SDK version bump (`pyproject.toml`, `poetry.lock`)
- Bump `claude-agent-sdk` from `^0.1.0` to `^0.1.39`.
### Backend: New tests
- **`sdk_compat_test.py`** (new file): SDK compatibility tests — verify
the installed SDK exposes every class, attribute, and method the copilot
integration relies on. Catches SDK upgrade breakage immediately.
- **`response_adapter_test.py`**: 9 new tests covering
flush-at-ResultMessage, flush-at-next-AssistantMessage, stashed output
flush, wait_for_stash signaling/timeout/fast-path, parallel tool call
non-premature-flush, text-message flush of prior tools, and
already-resolved tool skip in UserMessage.
### Frontend: Session hydration (`convertChatSessionToUiMessages.ts`)
- **`isComplete` option**: When session has no active stream, mark
dangling tool calls (no output in DB) as `output-available` with empty
output — stops stale spinners after page refresh.
### Frontend: Chat session hook (`useChatSession.ts`)
- Reorder `hasActiveStream` memo before `hydratedMessages` so
`isComplete` flag is available.
- Pass `{ isComplete: !hasActiveStream }` to
`convertChatSessionMessagesToUiMessages`.
### Frontend: Copilot page hook (`useCopilotPage.ts`)
- **Cache invalidation on stream end**: Invalidate React Query session
cache when stream transitions active → idle, so next hydration fetches
fresh messages from backend (staleTime: Infinity otherwise keeps stale
data).
- **Resume ref reset**: Reset `hasResumedRef` on stream end to allow
re-resume if SSE drops but backend task is still running.
- **Remove old `resolveInProgressTools` effect**: Replaced by
backend-side safety-net flush + hydration-time `isComplete` marking.
## Test plan
- [ ] Existing copilot tests pass (`pytest backend/copilot/ -x -q`)
- [ ] SDK compat tests pass (`pytest
backend/copilot/sdk/sdk_compat_test.py -v`)
- [ ] Tool outputs (bash_exec, web_fetch, WebSearch) appear in the UI
instead of getting stuck
- [ ] Parallel tool calls (e.g. multiple WebSearch) complete and display
results without premature flush
- [ ] Page refresh during active stream reconnects and recovers messages
- [ ] Opening session from another device shows recent tool results
- [ ] SSE drop → automatic reconnection without losing messages
- [ ] Long-running tools (create_agent) still delegate to background
infrastructure
AutoGPT: Build, Deploy, and Run AI Agents
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How to Self-Host the AutoGPT Platform
Note
Setting up and hosting the AutoGPT Platform yourself is a technical process. If you'd rather something that just works, we recommend joining the waitlist for the cloud-hosted beta.
System Requirements
Before proceeding with the installation, ensure your system meets the following requirements:
Hardware Requirements
- CPU: 4+ cores recommended
- RAM: Minimum 8GB, 16GB recommended
- Storage: At least 10GB of free space
Software Requirements
- Operating Systems:
- Linux (Ubuntu 20.04 or newer recommended)
- macOS (10.15 or newer)
- Windows 10/11 with WSL2
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- Git (2.30 or newer)
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License Overview:
🛡️ Polyform Shield License:
All code and content within the autogpt_platform folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents.
Read more about this effort
🦉 MIT License:
All other portions of the AutoGPT repository (i.e., everything outside the autogpt_platform folder) are licensed under the MIT License. This includes the original stand-alone AutoGPT Agent, along with projects such as Forge, agbenchmark and the AutoGPT Classic GUI.
We also publish additional work under the MIT Licence in other repositories, such as GravitasML which is developed for and used in the AutoGPT Platform. See also our MIT Licenced Code Ability project.
Mission
Our mission is to provide the tools, so that you can focus on what matters:
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📖 Documentation | 🚀 Contributing
🤖 AutoGPT Classic
Below is information about the classic version of AutoGPT.
🛠️ Build your own Agent - Quickstart
🏗️ Forge
Forge your own agent! – Forge is a ready-to-go toolkit to build your own agent application. It handles most of the boilerplate code, letting you channel all your creativity into the things that set your agent apart. All tutorials are located here. Components from forge can also be used individually to speed up development and reduce boilerplate in your agent project.
🚀 Getting Started with Forge – This guide will walk you through the process of creating your own agent and using the benchmark and user interface.
📘 Learn More about Forge
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Measure your agent's performance! The agbenchmark can be used with any agent that supports the agent protocol, and the integration with the project's CLI makes it even easier to use with AutoGPT and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.
📦 agbenchmark on Pypi
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📘 Learn More about the Frontend
⌨️ CLI
To make it as easy as possible to use all of the tools offered by the repository, a CLI is included at the root of the repo:
$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Options:
--help Show this message and exit.
Commands:
agent Commands to create, start and stop agents
benchmark Commands to start the benchmark and list tests and categories
setup Installs dependencies needed for your system.
Just clone the repo, install dependencies with ./run setup, and you should be good to go!
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🤝 Sister projects
🔄 Agent Protocol
To maintain a uniform standard and ensure seamless compatibility with many current and future applications, AutoGPT employs the agent protocol standard by the AI Engineer Foundation. This standardizes the communication pathways from your agent to the frontend and benchmark.
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