378d256b58
## Summary This PR addresses the recurring job validation failures by adding graph validation before scheduling jobs. Previously, validation errors only occurred at runtime during job execution, making it difficult to communicate errors to users for scheduled recurring jobs. ### Changes 🏗️ - **Extract validation logic**: Created `validate_and_construct_node_execution_input` wrapper function that centralizes graph fetching, credential mapping, and validation logic - **Add pre-scheduling validation**: Modified `add_graph_execution_schedule` to validate graphs before creating scheduled jobs - **Make construct function private**: Renamed `construct_node_execution_input` to `_construct_node_execution_input` to prevent direct usage and encourage use of the wrapper - **Reduce code duplication**: Eliminated duplicate validation logic between scheduler and execution paths - **Improve scheduler lifecycle management**: - Enhanced cleanup process with proper event loop shutdown sequence - Added graceful event loop thread termination with timeout - Fixed thread lifecycle management to prevent resource leaks - **Add helper utilities**: - Created `run_async` helper to reduce `asyncio.run_coroutine_threadsafe` boilerplate - Added `SCHEDULER_OPERATION_TIMEOUT_SECONDS` constant for consistent timeout handling across all scheduler operations ### Technical Details **Validation Flow:** The validation now happens in `add_graph_execution_schedule` before calling `scheduler.add_job()`, ensuring that: 1. Graph exists and is accessible to the user 2. All credentials are valid and available 3. Graph structure and node configurations are valid 4. Starting nodes are present and properly configured This uses the same validation logic as runtime execution, guaranteeing consistency. **Scheduler Lifecycle Improvements:** - **Proper cleanup sequence**: Event loop is stopped before thread termination - **Thread management**: Added global tracking of event loop thread for proper cleanup - **Timeout consistency**: All scheduler operations now use the same 300-second timeout - **Resource management**: Prevents potential memory leaks from unclosed event loops **Code Quality Improvements:** - **DRY principle**: `run_async` helper eliminates repeated `asyncio.run_coroutine_threadsafe` patterns - **Single source of truth**: All timeout values use `SCHEDULER_OPERATION_TIMEOUT_SECONDS` constant - **Cleaner abstractions**: Direct utility function calls instead of unnecessary wrapper methods ### 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] Verified imports work correctly for both scheduler and utils modules - [x] Confirmed code passes all linting and type checking - [x] Validated that existing functionality remains intact - [x] Tested that validation logic is properly extracted and reused - [x] Verified scheduler cleanup process works correctly - [x] Confirmed thread lifecycle management improvements #### For configuration changes: - [x] `.env.example` is updated or already compatible with my changes - [x] `docker-compose.yml` is updated or already compatible with my changes - [x] I have included a list of my configuration changes in the PR description (under **Changes**) *Note: No configuration changes were required for this fix.* ## Impact - **Prevents runtime failures**: Invalid graphs are caught before scheduling instead of failing silently during execution - **Better error communication**: Validation errors surface immediately when scheduling - **Improved resource management**: Proper event loop and thread cleanup prevents memory leaks - **Enhanced maintainability**: Single source of truth for validation logic and consistent timeout handling - **Reduced code duplication**: Eliminated ~30+ lines of duplicate code across validation and async execution patterns - **Better developer experience**: Cleaner code with helper functions and consistent patterns Resolves the TODO comment: "We need to communicate this error to the user somehow" in scheduler.py:107 Co-authored-by: Claude <noreply@anthropic.com>
AutoGPT: Build, Deploy, and Run AI Agents
AutoGPT is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows.
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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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