MindClaw

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

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

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