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

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