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.

Related MCP server: Memora

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"]
  )

Close with /rtclose. It exports the conversation, extracts what is worth keeping into the graph, and leaves a handoff for next time — which the next session loads on its own.

The schema, and why it looks like this

This is the part worth understanding, because it is what keeps the graph queryable over years rather than months.

Role and subject are separate fields

entityType answers what role does this node play. Closed list of ten:

domain answers what subject is it about: software, music, history, philosophy, health, writing, neuro, finance, personal, galaxy.

Keeping these apart is not tidiness. When one field carries both, the type list grows by one entry per subject — a real vault reached 46 types this way, with saglik-bulgusu, teknik-karar and bug-pattern sitting next to karar. At that point nothing can be filtered, because no two entries agree on what a type means.

Unknown relation types are rejected, not warned about

Canonical vocabulary: supports, contradicts, evolved_into, depends_on, exemplifies, part_of, caused_by, enables, supersedes, related_to, investigates, informs, uses.

Close synonyms fold automatically — connected_to → related_to, aspect_of → part_of. Anything else raises.

An earlier version accepted unknown types with a warning. Nothing acted on the warning and 56 one-off types accumulated. A warning that lets the write through is a decision to allow it, written in the voice of disapproval.

Epistemic status defaults to unknown

assertion, hypothesis, speculation — or unknown when unstated.

unknown is deliberate. If a session did not establish something, recording it as an assertion promotes a claim nobody made. The migration follows the same rule: 144 entities with no epistemic field became unknown, not assertion.

Revise, do not delete

> revise(entity_name="api/cache: read-through",
         observations=["read-through cache in front of the read model"],
         superseded_by="moved to write-through after the read model split",
         superseding_entity="api/cache: write-through")

The old observation is marked invalidated, dated, and linked to what replaced it. Delete tools exist but sit outside the default agent-facing profiles: an archive that forgets its own reversals cannot answer the question it was kept for.

Decisions record what was rejected

record_decision stores decided, because, and rejected as separately queryable observations. The rejected alternatives are the part worth keeping — what was chosen stays readable in the code forever, what was considered and dropped exists nowhere else. That is the question that gets asked six months later.

MCP tools

Tool definitions sit in the context window from the first token of a session, so the surface is a standing cost rather than a per-call one. Profiles load only what a given step needs.

RAWTHINK_TOOL_PROFILE=recall   #  4 tools,  ~900 tokens — read-only
RAWTHINK_TOOL_PROFILE=record   #  5 tools, ~1750 tokens — the write path
RAWTHINK_TOOL_PROFILE=full     # 17 tools, ~4200 tokens — everything (default)

A tool outside the active profile stays an ordinary function — reachable from the CLI and from tests. It simply is not in front of an agent that will not call it.

Search

Graph — reading

Graph — writing

record() validates the whole batch before writing any of it. A half-valid batch writes nothing — a graph left in a state nobody asked for is worse than a rejected write. Relations may only point at entities that already exist or are created in the same call.

Every tool carries MCP annotations (readOnlyHint, destructiveHint, idempotentHint), so a host can tell deletion apart from search.

Architecture

Claude Code
    │  MCP (stdio)
    ▼
rawthink-mcp
    ├── search  ──►  Qdrant        dense (BGE-M3) + sparse (BM25), RRF fusion
    ├── graph   ──►  memory.jsonl  entities, relations, temporal observations
    └── export  ──►  vault/        sessions, qnotes, handoffs as markdown
                         │
                    Ollama (bge-m3)

Everything runs locally. The vault is plain markdown with YAML frontmatter — open it in Obsidian to browse visually, no plugins needed.

Why JSONL for the graph

Human-readable, git-diffable, no dependency. You can open it, read it, and see a meaningful diff when it changes — which matters for something meant to hold your reasoning.

The tradeoff is load time: the whole file is parsed per read. Fine at a few hundred entities, slower as it grows. Past tens of thousands, SQLite is the obvious next step.

Session lifecycle

session start          handoff loads automatically (SessionStart hook)
      ↓
  think together
      ↓
   /rtclose            export → extract entities → write handoff → update MEMORY.md

/rtclose exports the conversation to clean markdown, extracts entities and relations through record(), writes a project-scoped handoff, and updates MEMORY.md.

The lifecycle commands currently require Claude Code. The search and graph tools work with any MCP client.

Configuration

Vocabularies — ENTITY_TYPES, DOMAINS, RELATION_TYPES, RELATION_ALIASES — live in rawthink_mcp/config.py. Adding a domain is a one-line change.

Customization

CLAUDE.md — the thinking companion's role, tone and modes.

THINKING_DIRECTIVES.md — discipline for the partnership. Every rule was written after failing at it. Add your own; the only bad version of that file is one followed without understanding why each rule exists.

Both are copied into your vault by rawthink-install. If you edit the repo copies, run python scripts/check_templates.py — the installer embeds them, and two copies of one document drift silently.

Known limitations

Stated plainly, because a README that lists only strengths is not much use.

Ollama being unavailable degrades to sparse-only. The embedding cache helps repeated queries; it is not a fallback. Retrieval quality drops noticeably.

Graceful shutdown is POSIX-only. Signal handlers release the Qdrant directory lock and the graph file lock on SIGINT/SIGTERM. Windows has no real SIGTERM — a terminating client calls TerminateProcess and no handler runs — so a hard stop there can leave a lock behind. Ctrl-C still unwinds, and rawthink-doctor reports the stale lock.

Load time grows with the graph. The whole JSONL is parsed on the first read after a change. Subsequent reads reuse a cache keyed on (mtime, size).

Search quality has not been benchmarked at scale. The retrieval numbers that used to be here were never re-measured, so they were removed rather than carried forward. tests/search_quality.py runs against a synthetic vault and reports MRR/nDCG; point RAWTHINK_EVAL_GT at your own evaluation set for a number that means something for your data.

Roadmap

Next — graph visualisation, MCP-native session lifecycle so the close command is not Claude Code specific, support for more MCP clients, and a retrieval benchmark that runs on data anyone can regenerate.

Contributing

Issues and pull requests welcome.

If you change the schema, change config.py, the migration in migrate.py, and the session-close instructions together. They are three views of one contract, and they drift apart quietly when they are not edited as a set.

docs/postmortem-bm25-term-drift.md is the clearest example — a defect that looked fine from every angle until someone evaluated the two retrievers separately.

License

MIT