MindClaw

> Persistent memory and knowledge graph for AI agents. Remember everything, forget nothing.

MindClaw is a structured long-term knowledge layer for OpenClaw agents. Where OpenClaw stores raw conversational memory in Markdown files, MindClaw stores curated facts, decisions, and relationships with full metadata — conflict detection, confirmation reinforcement, importance scoring, and a knowledge graph.

Memories sync back to OpenClaw's MEMORY.md so they are also searchable via OpenClaw's native memory_search tool.

Install

pip install mindclaw[mcp] && mindclaw setup

The setup wizard configures your workspace path, agent name, and registers MindClaw with Claude Desktop and/or OpenClaw in one step.

What agents can do

| MCP Tool | Purpose |

|---|---|

| setup_mindclaw | One-call setup: configure, register with OpenClaw, initial sync |

| remember | Store a fact, decision, preference, or error with metadata |

| recall | BM25 + semantic hybrid search with temporal decay and MMR diversity |

| context_block | Token-limited memory block ready to inject into any LLM prompt |

| capture | Auto-extract structured memories from conversation text |

| confirm | Reinforce a memory that proved correct (boosts importance) |

| forget | Archive or hard-delete a memory |

| pin_memory | Mark a memory as permanent — immune to decay |

| timeline | Reconstruct what happened in the last N hours |

| consolidate | Merge near-duplicate memories automatically |

| link | Connect two memories in the knowledge graph |

| stats | Check store health and memory breakdown |

| sync_openclaw | Export all memories to OpenClaw's MEMORY.md |

| import_markdown | Import from any OpenClaw MEMORY.md or daily log |

| unpin_memory | Remove a pin from a memory |

OpenClaw integration

MindClaw mirrors OpenClaw's search pipeline exactly:

| Feature | OpenClaw | MindClaw |

|---|---|---|

| BM25 keyword search | ✓ | ✓ |

| Semantic embeddings | local GGUF / OpenAI / Gemini | Ollama (auto-detect, zero deps) |

| Temporal decay | --temporalDecay | --decay + --halflife |

| MMR diversity | mmr.enabled | --mmr + --mmr-lambda |

| Per-agent isolation | per-agentId SQLite | --agent |

After mindclaw sync, all structured memories appear in MEMORY.md and are found by OpenClaw's native memory_search — no agent code changes needed.

Recommended agent loop

1. context_block(query)   → inject relevant context before answering
2. remember(content)      → store key facts and decisions after acting
3. capture(conversation)  → extract structured memories from session logs
4. confirm(id)            → reinforce memories that proved correct
5. sync_openclaw()        → push to OpenClaw's MEMORY.md (cross-tool visibility)
6. consolidate()          → periodic dedup maintenance

Configuration

Run once, never repeat flags:

mindclaw setup

Saves ~/.mindclaw/config.json with your workspace path, agent name, and DB path.

Priority chain: CLI flag > MINDCLAW_* env var > config file > built-in default

Requirements

• Python 3.10+
• Zero mandatory dependencies (core uses only stdlib)
• Optional: pip install mindclaw[mcp] for MCP server
• Optional: Ollama running locally for semantic search (auto-detected)

Source

GitHub: https://github.com/Blue8x/MindClaw