LycheeMem/LycheeMem

Health v2 · maintenance only

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1155 stars · 16 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

LycheeMem is a lightweight long-term memory framework for LLM agents, combining working session context, a compact semantic memory store (SQLite FTS5 + LanceDB), and a procedural skill layer with HyDE retrieval. It integrates via a Python API, an HTTP MCP server, and native plugins for OpenClaw, Claude Code, and Hermes.

Use cases

• give my llm agent long-term memory
• remember conversations across chatbot sessions
• add persistent memory to claude code
• store and retrieve reusable skills for ai agents
• run an mcp memory server
• semantic memory store without neo4j
• compress conversation history for agents

When to choose

• you need lightweight long-term memory for LLM agents without heavy infrastructure like Neo4j
• you want MCP or plugin integration with agent runtimes like OpenClaw or Claude Code
• you want both semantic knowledge and procedural skill memory in one framework

When to avoid

• you need a fully managed or hosted memory service
• you require graph-based memory with complex entity relations
• your stack is not Python-based and you cannot use MCP

Facets

library · maturity active

rag vector-database mcp plugin-system search-engine artificial-intelligence large-language-models developer-tools python self-hosted cross-platform agent-memory llm-memory mcp-server openclaw-plugin lancedb sqlite-fts5 semantic-memory procedural-memory hyde-retrieval claude-code-plugin ai-agents retrieval-augmented-generation

Member repositories

For agents

markdown · JSON · MCP: product_card(name="LycheeMem/LycheeMem")