rawthink
Persistent memory for Claude Code — hybrid search, knowledge graph, session lifecycle.
README
RAWThink
Your AI assistant forgets everything between sessions. RAWThink fixes that.
Persistent memory for Claude Code — hybrid search, knowledge graph, session lifecycle. Think of it as a second brain that grows with every conversation.
The Problem
Claude Code is stateless. Every session starts from zero. You explain the same context, re-establish the same decisions, re-discover the same insights. Yesterday's breakthrough is today's cold start.
RAWThink gives your AI a memory that persists, decays naturally, and revises itself when you change your mind. It's a 15-tool MCP server that turns Claude Code into a long-term thinking partner.
Article
Read the full story: Your AI Doesn't Remember You
Quick Start
Prerequisites
- Python 3.10+
- Docker (for Qdrant vector database)
- Ollama (for local embeddings)
- Node.js (for the SessionStart hook)
Install & set up
pip install rawthink-mcp
rawthink-install # creates project dir with vault, configs, and starter files
cd ~/rawthink-vault # go to your project directory
docker compose up -d # start Qdrant (docker-compose.yml is here)
ollama pull bge-m3 # download embedding model
Restart Claude Code. Done.
rawthink-install creates a self-contained project directory at ~/rawthink-vault with vault structure, CLAUDE.md, THINKING_DIRECTIVES.md, SETUP.md, docker-compose.yml, and /rtclose command. Run rawthink-install --vault ~/my-vault to customize the location.
<details> <summary>Manual setup (without rawthink-install)</summary>
Add to your ~/.claude.json:
{
"mcpServers": {
"rawthink": {
"command": "rawthink-mcp",
"env": {
"RAWTHINK_VAULT": "/path/to/your/vault"
}
}
}
}
Create vault structure:
mkdir -p vault/{sessions,qnotes,archive/raw,outputs}
Install from source instead of PyPI:
git clone https://github.com/ygtalp/rawthink-mcp.git
cd rawthink-mcp
pip install -e .
</details>
Your First Session
Your vault starts empty — that's the point. Open Claude Code and start talking:
You: "We're building a REST API. Let's use PostgreSQL with connection pooling."
Claude: [responds with technical discussion]
You: "/qnote we decided on PostgreSQL + pgBouncer for the API layer"
→ saved to your vault as a searchable quick note
You: "/rtclose"
→ Session exported, entities extracted, handoff written
Next session, Claude loads the handoff automatically and picks up where you left off. After a few sessions, your vault looks like this:
> search_thoughts("how did we handle the database?")
1. [2025-03-15_002] API Architecture Decisions score: 0.74
Decided on PostgreSQL + pgBouncer for connection pooling.
Key insight: pool_mode=transaction for serverless...
2. [2025-03-22_001] Performance Review score: 0.61
Revisited DB strategy — added read replicas for
reporting queries, write primary stays single-node...
> read_graph(summary=True)
Knowledge Graph: 42 entities, 38 relations
decision (5)
- postgres-connection-pooling (3 obs) act=0.97
- api-versioning-strategy (4 obs) act=0.85
- auth-jwt-vs-session (2 obs) act=0.72
concept (8)
- cqrs-pattern (3 obs) act=0.91
- event-sourcing (5 obs) act=0.88
...
What It Does
Three-layer memory
- Hot cache (MEMORY.md) — loaded every session, essential context
- Knowledge graph (JSONL) — entities, relations, temporal observations with belief revision
- Semantic search (Qdrant) — hybrid dense + BM25 across all sessions and qnotes
Session lifecycle
Session start (handoff loads) → Think together → /rtclose →
JSONL export + entity extraction + handoff for next session
Key capabilities
- Hybrid search: BGE-M3 dense embeddings + BM25 sparse vectors, fused with RRF
- Belief revision: observations can be invalidated, superseded, and tracked over time
- Activation decay: unused knowledge fades (~23-day half-life), accessed knowledge stays hot
- Epistemic typing: mark entities as
assertion,hypothesis, orspeculation - Multi-terminal safe: parallel sessions don't overwrite each other's handoffs
- Thinking directives: structured discipline for human-AI thinking partnerships
- Ollama fallback: embedding cache for when Ollama is temporarily unavailable
Architecture
┌─────────────────────────────────────────────────┐
│ Claude Code │
│ (MCP Client) │
└──────────────────┬──────────────────────────────┘
│ MCP (stdio)
┌──────────────────▼──────────────────────────────┐
│ rawthink │
│ (FastMCP Server) │
│ │
│ ┌─────────────┐ ┌──────────┐ ┌────────────┐ │
│ │ Indexer │ │ Graph │ │ Chunker │ │
│ │ (Qdrant + │ │ (JSONL │ │ (Markdown │ │
│ │ Ollama) │ │ KG) │ │ splitter) │ │
│ └──────┬──────┘ └────┬─────┘ └────────────┘ │
│ │ │ │
│ ┌──────▼──────┐ ┌────▼─────┐ │
│ │ Qdrant │ │ memory │ │
│ │ (Docker) │ │ .jsonl │ │
│ └─────────────┘ └──────────┘ │
└─────────────────────────────────────────────────┘
│
┌──────────▼──────────┐
│ Vault │
│ sessions/ qnotes/ │
│ archive/ outputs/ │
└─────────────────────┘
MCP Tools
Search (5 tools)
| Tool | Description |
|---|---|
search_thoughts |
Hybrid semantic + keyword search across vault |
get_session |
Retrieve full session content by ID |
get_related |
Quick related thoughts lookup (session-level) |
store_thought |
Store a new thought as a qnote |
reindex |
Re-index vault content into Qdrant |
Knowledge Graph (10 tools)
| Tool | Description |
|---|---|
search_nodes |
Search entities by name, type, and observations |
create_entities |
Create/merge entities with epistemic typing |
create_relations |
Create relations (controlled vocabulary) |
add_observations |
Add temporal observations to entities |
invalidate_observations |
Mark observations as invalidated (belief revision) |
delete_entities |
Delete entities and their relations |
delete_observations |
Remove specific observations |
delete_relations |
Remove specific relations |
read_graph |
Read full graph with pagination + summary mode |
open_nodes |
Open specific entities with their relations |
How It's Different
RAWThink is not a codebase analyzer or a simple key-value memory. It's a living knowledge system designed for thinking partnerships.
| RAWThink | Basic MCP Memory | Codebase Analyzers | |
|---|---|---|---|
| Search | Hybrid (dense + BM25 + RRF) | Key lookup | Graph query |
| Knowledge | Temporal KG with belief revision | Flat key-value | Static AST graph |
| Memory decay | Activation decay (~23 days) | None | None |
| Session lifecycle | Full (export, extract, handoff) | None | None |
| Epistemic tracking | assertion/hypothesis/speculation | None | extracted/inferred/ambiguous |
| Designed for | Thinking & decision memory | Simple facts | Code structure |
Why JSONL?
Human-readable, git-diffable, zero dependency. At 120 entities the graph loads in <50ms. At 10K entities ~1.4s. The optimization path is clear: mtime cache for repeated reads, lazy writes for activation updates, SQLite migration at 50K+ entities.
Configuration
All settings via environment variables or rawthink_mcp/config.py:
| Setting | Env Var | Default | Description |
|---|---|---|---|
| Vault path | RAWTHINK_VAULT |
../vault |
Path to vault directory |
| Qdrant URL | QDRANT_URL |
http://localhost:6333 |
Qdrant server URL |
| Qdrant path | QDRANT_PATH |
— | Set for embedded Qdrant (no Docker) |
| Ollama URL | OLLAMA_URL |
http://localhost:11434 |
Ollama server URL |
| Embedding model | OLLAMA_MODEL |
bge-m3 |
Ollama embedding model |
| KG file | MEMORY_FILE_PATH |
../vault/memory.jsonl |
Knowledge graph file path |
| Turkish normalization | RAWTHINK_TURKISH_NORMALIZATION |
false |
Set 1 or true to enable |
| Decay rate | — | 0.03 |
Activation decay (~23-day half-life) |
Session Lifecycle
RAWThink includes a session close command (/rtclose) that:
- Exports the current conversation to clean markdown + full transcript + HTML
- Extracts key entities and relations into the knowledge graph
- Writes a project-specific handoff file for the next session
- Updates MEMORY.md with essential context
A SessionStart hook (rawthink-handoff.js) runs when Claude Code starts and loads the most recent handoff + recent qnotes into context automatically.
Session lifecycle currently requires Claude Code. MCP-native session tools are planned for v0.2.
Customization
CLAUDE.md
Edit CLAUDE.md to customize the thinking companion's behavior:
- Role and tone
- Active modes (free-flow, socratic, debate, synthesis, deep-dive, technical, galaxy-brain)
- Epistemic transparency format
- Meta-commands
THINKING_DIRECTIVES.md
Structured discipline for the thinking partnership. Every rule was born from a violation — don't follow mechanically, understand why each exists. Customize to match your workflow.
Obsidian (optional)
Open your vault directory in Obsidian to browse sessions and qnotes visually. The vault uses standard markdown with YAML frontmatter — no plugins needed.
Entity Types
Default entity type is "concept". Common types: concept, decision, rule, insight, question, project.
Relation Types
Canonical types (non-standard accepted with warning):
supports, contradicts, evolved_into, depends_on, exemplifies, part_of, caused_by, enables, supersedes, related_to
Roadmap
- Interactive knowledge graph visualization
- Wiki export (agent-browsable markdown per entity)
.rawthinkignorefor vault indexing control- Graph traversal queries (unexpected connections, multi-hop)
- SQLite backend for 50K+ entities
- MCP-native session tools (no Claude Code dependency)
- Configurable language normalization
License
MIT — see LICENSE.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.