GraphFusion/GraphFusionAI-Lite

AI Summary: Implement persistent storage and retrieval of agent state, including capabilities, memory, task queues, and status history, for the GraphFusionAI-Lite multi-agent system. This involves choosing a suitable persistence mechanism (e.g., database, file system), designing a serialization/deserialization strategy for agent objects, and integrating this functionality into the agent lifecycle.

AI Summary: Generate comprehensive API documentation for the GraphFusionAI-Lite project, including documentation for all public classes and methods, extension points, and configuration options. The documentation should be clear, complete, and include code examples and usage guidelines. It should be easily accessible and kept up-to-date with the codebase.

AI Summary: Implement an error recovery system for the GraphFusionAI-Lite framework, including state checkpointing, automatic restart mechanisms, and comprehensive failure diagnostics. The system should minimize downtime, be configurable, and provide actionable diagnostic information.

AI Summary: Develop a comprehensive test suite for the GraphFusionAI-Lite framework, covering asynchronous task handling, knowledge graph integrity, failure recovery, and persistence. The suite should be automated, integrated into CI/CD, and well-documented.