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 setupThe 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 <name> |
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 maintenanceConfiguration
Run once, never repeat flags:
mindclaw setupSaves ~/.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