openclaw-mem0-python

openclaw-mem0-python

OpenClaw plugin that exposes the mem0 HTTP API as tools for conversational memory and a tool-driven knowledge base (RAG). Conversational memory can be injected by the hook; knowledge retrieval stays explicit via tool calls.

Features

• Conversational memory: write/search/read recent.
• Knowledge base tools: list books, describe a book, search within one book or across multiple books.

Install (local dev)

# link plugin into OpenClaw extensions
openclaw plugins install -l /home/lucky/.openclaw/extensions/openclaw-mem0-python

# enable if not already
openclaw plugins enable openclaw-mem0-python

# reload gateway
openclaw gateway restart

Configure

You can set defaults via environment or config:

• MEM0_BASE_URL (default: http://192.168.0.200:8420)
• MEM0_KNOWLEDGE_USER_ID (default: knowledge_base)

Tool surface

• write({ text, userId? })
• search({ query, userId?, limit? })
• read({ userId?, limit? })

Knowledge tools:

• listKnowledgeSources({ userId? })
• describeKnowledgeBook({ sourceFile, userId? })
• searchKnowledgeBook({ query, sourceFile, userId?, limit? })
• searchKnowledgeBooks({ query, sourceFiles, userId?, limit? })
• searchKnowledge({ query, userId?, limit? })
• writeKnowledge({ text, userId? })

Test (local)

node /home/lucky/.openclaw/extensions/openclaw-mem0-python/test_memories.mjs

This test writes/reads conversational memory, then lists knowledge sources and (if present) describes & searches the first book.

Notes

• describeKnowledgeBook uses /knowledge/search plus metadata filtering (metadata.source_file) to safely scope results to one book.
• If your knowledge source names are PDFs, the tool also exposes a hintQuery derived from source_file (same approach as the dashboard).