RAWThink MCP server

<!-- mcp-name: io.github.ygtalp/rawthink-mcp -->

RAWThink

Persistent memory for AI thinking partnerships. A knowledge graph you can argue with, search that spans every session you have ever had, and a record of not just what you decided but what you rejected.

MCP server for Claude Code. Python, local, no cloud.

The problem

Every conversation with an AI starts from nothing. You explain the same context, re-derive the same conclusions, and rediscover decisions you already made — and the reasoning that produced them is gone the moment the window scrolls.

Chat history does not fix this. History is a transcript; what you need is structure: which ideas connect, which beliefs you have since abandoned, which alternatives you considered and dropped, and why.

RAWThink keeps that structure in three layers, in files you own.

What it does

Hybrid semantic search across every session and note — BGE-M3 dense embeddings and BM25 sparse vectors, fused with RRF. Ask "what did I think about free will?" and get the passages, not a keyword match.

A temporal knowledge graph where observations carry dates and status. Beliefs can be marked invalidated and linked to what replaced them, so the archive remembers not only what you think but what you used to think.

Session lifecycle — a close command that exports the conversation, extracts entities into the graph, and writes a handoff the next session loads automatically.

Activation decay — unused knowledge fades on a ~23-day half-life, accessed knowledge stays warm. Old material is still there; it just stops crowding out what you are working on now.

Quick start

Prerequisites

• Python 3.10+
• Docker for Qdrant — or set QDRANT_PATH for embedded mode
• Ollama with bge-m3: ollama pull bge-m3

Install

pip install rawthink-mcp
rawthink-install                 # creates ~/rawthink-vault with everything inside
cd ~/rawthink-vault
docker compose up -d             # starts Qdrant

rawthink-install writes the vault structure, CLAUDE.md, THINKING_DIRECTIVES.md, SETUP.md, docker-compose.yml and the /rtclose command. Use rawthink-install --vault ~/my-vault for a different location.

Register with Claude Code

claude mcp add --scope user rawthink -- rawthink-mcp

Check the install

rawthink-doctor

Nine checks with a fix line for each failure: vault, graph schema, BM25 state format, Qdrant, index coverage, vector dimension, Ollama, MCP registration.

The index coverage check is the one worth knowing about. Indexing can stop partway and leave a collection that looks healthy — it exists, it has points, queries return results. They are results from part of the vault, and nothing else tells you that.

Upgrading from 0.x

1.5.0 changed the graph schema and 2.0.0 changes the sparse index. Migrate the graph before writing anything:

python -m rawthink_mcp.migrate --path vault/memory.jsonl --guess-domains --heal-dangling

That is a dry run — it prints what would change and writes nothing. Read the report, then re-run with --apply. A timestamped backup is taken first.

Then re-encode the search index, because BM25 term IDs changed:

reindex(full=True)

A plain reindex skips unchanged chunks and will leave the old encoding in place. rawthink-doctor tells you if this is still pending.

See CHANGELOG.md for what changed and why.

Your first session

> search_thoughts("what have I decided about caching?")

> record_decision(
    name="api/cache: read-through",
    domain="software",
    decided="read-through cache in front of the read model",
    because="the write path is already the bottleneck; adding invalidation there costs more",
    rejected=["write-through — couples the write path to cache health",
              "no cache — p99 was 400ms against a 200ms SLO"]
  )

Shortened. The full README is on GitHub.