Implement automatic cache invalidation based on file modification time to ensure memory data consistency across Gateway API and agent prompts. The cache now automatically reloads when the memory file is updated externally.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implement automatic cache invalidation based on file modification time to ensure memory data consistency across Gateway API and agent prompts. The cache now automatically reloads when the memory file is updated externally.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Add memory API endpoints for retrieving memory data:
- GET /api/memory - get current memory data
- POST /api/memory/reload - reload from file
- GET /api/memory/config - get memory configuration
- GET /api/memory/status - get config and data together
- Optimize MemoryMiddleware to only use user inputs and final
assistant responses, filtering out intermediate tool calls
- Add memory configuration example to config.example.yaml
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add memory API endpoints for retrieving memory data:
- GET /api/memory - get current memory data
- POST /api/memory/reload - reload from file
- GET /api/memory/config - get memory configuration
- GET /api/memory/status - get config and data together
- Optimize MemoryMiddleware to only use user inputs and final
assistant responses, filtering out intermediate tool calls
- Add memory configuration example to config.example.yaml
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement a memory system that stores user context and conversation history
in memory.json, uses LLM to summarize conversations, and injects relevant
context into system prompts for personalized responses.
Key components:
- MemoryConfig for configuration management
- MemoryUpdateQueue with debounce for batch processing
- MemoryUpdater for LLM-based memory extraction
- MemoryMiddleware to queue conversations after agent execution
- Memory injection into lead agent system prompt
Note: Add memory section to config.yaml to enable (see config.example.yaml)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implement a memory system that stores user context and conversation history
in memory.json, uses LLM to summarize conversations, and injects relevant
context into system prompts for personalized responses.
Key components:
- MemoryConfig for configuration management
- MemoryUpdateQueue with debounce for batch processing
- MemoryUpdater for LLM-based memory extraction
- MemoryMiddleware to queue conversations after agent execution
- Memory injection into lead agent system prompt
Note: Add memory section to config.yaml to enable (see config.example.yaml)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add README.md with project overview, quick start, and API reference
- Add CONTRIBUTING.md with development setup and contribution guidelines
- Add docs/ARCHITECTURE.md with detailed system architecture diagrams
- Add docs/API.md with complete API reference for LangGraph and Gateway
- Add docs/README.md as documentation index
- Update CLAUDE.md with improved structure and new features
- Update docs/TODO.md to reflect current status
- Update pyproject.toml description
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add README.md with project overview, quick start, and API reference
- Add CONTRIBUTING.md with development setup and contribution guidelines
- Add docs/ARCHITECTURE.md with detailed system architecture diagrams
- Add docs/API.md with complete API reference for LangGraph and Gateway
- Add docs/README.md as documentation index
- Update CLAUDE.md with improved structure and new features
- Update docs/TODO.md to reflect current status
- Update pyproject.toml description
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
