LycheeMem — Lightweight Long-Term Agent Memory

Main

• Use it when you need recall across long projects: README describes structured consolidation + adaptive retrieval over SQLite + LanceDB.

• Start with the CLI server (lycheemem-cli) and a minimal .env, then add reranking once you’ve confirmed end-to-end recall works.

• Prefer runtime-native plugins (README links Claude Code/OpenClaw/Hermes install guides) to wire memory into your agent loop automatically.

Source-backed notes

• README documents installation via pip install lycheemem and a recommended extra pip install "lycheemem[rerank]" for transformer reranking.
• README says semantic memory uses SQLite + LanceDB and explicitly notes “no Neo4j required”.
• README includes multiple runtime integrations (Claude Code plugin, OpenClaw plugin, Hermes plugin) and an MCP-compatible mode.

FAQ

• Do I need a vector database server?: No — README uses embedded storage (SQLite + LanceDB) instead of running a separate DB service.
• Is reranking required?: No — README says the core works without the rerank extra; it improves evidence selection when installed.
• How do I integrate with Claude Code?: README links a Claude Code plugin install guide under claude-plugin/.