memsearch is a markdown-first memory system designed to provide long-term memory capabilities for AI agents through structured storage and semantic retrieval. It enables agents to store, organize, and retrieve information using embeddings and hybrid search techniques, ensuring that relevant context is always available. The system supports advanced features such as reranking and progressive disclosure, which help prioritize the most useful information for a given query. It integrates with vector databases like Milvus, enabling scalable storage and retrieval of large datasets. Memsearch is designed to be agent-friendly, making it easy to plug into existing AI workflows and enhance reasoning capabilities. Its markdown-first approach ensures transparency and portability of stored knowledge. Overall, it provides a robust foundation for building AI systems with persistent and intelligent memory.

Features

  • Markdown-first storage for AI memory
  • Semantic search with embeddings
  • Integration with vector databases like Milvus
  • Reranking for improved retrieval accuracy
  • Progressive disclosure of relevant information
  • Designed for agent-based workflows

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License

MIT License

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Programming Language

Python

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Python Agent Skills

Registered

2026-04-23