Feat/refine memory

[suluyana]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @suluyana, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on a comprehensive refinement of the agent's memory management system, aiming to enhance its flexibility, efficiency, and robustness. Key changes include a significant overhaul of how memories are stored, retrieved, and configured, alongside improvements in LLM service integration and core utility functions. The goal is to provide a more scalable and maintainable foundation for the agent's long-term memory capabilities and external service interactions.

Highlights

• Refined Memory Management: The memory system has been significantly refactored, centralizing memory handling within DefaultMemory and introducing a SharedMemoryManager for efficient instance reuse. The dedicated mem0ai.py file has been removed, with its core functionalities integrated and enhanced within default_memory.py.
• Enhanced DefaultMemory Capabilities: The DefaultMemory class now supports asynchronous operations for adding and searching memories, includes improved metadata handling (user_id, agent_id, run_id), and offers more flexible search capabilities through meta_infos. It also features dynamic configuration of embedders, LLMs, and vector stores, along with robust error handling for memory operations.
• Standardized LLM Service Configuration: A new ServiceConfig system has been introduced to standardize the retrieval of default base URLs for various LLM services (Modelscope, Dashscope, Deepseek, Anthropic, OpenAI), improving consistency and ease of configuration across different LLM providers.
• Improved Tooling and Configuration: The read_file tool in the filesystem now supports reading multiple file paths simultaneously. SerpApi search clients are now lazily loaded to optimize resource usage. Additionally, the agent.yaml generation configuration has been adjusted, and the coding.yaml memory configuration has been updated to align with the new, more granular memory structure.

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[gemini-code-assist]

Code Review

This pull request introduces a significant and well-executed refactoring of the memory management system. The changes greatly improve modularity, configurability, and robustness. Key improvements include a complete overhaul of DefaultMemory, the introduction of a generic SharedMemoryManager, and a more powerful configuration system for memory triggers and sub-components. The code is now cleaner with better separation of concerns. Overall, this is a high-quality contribution that enhances the agent's memory capabilities.