MindClaw
Structured long-term memory for AI agents with fact curation, conflict detection, importance scoring, timeline reconstruction, and OpenClaw integration.
Description
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 <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 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
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