rebrain 0.1.3

Transform chat history into structured, navigable memory graphs

🧠 Rebrain

Transform chat history into structured, personalized AI memory.

Rebrain processes your ChatGPT conversations through a 5-step pipeline, extracting observations, synthesizing learnings and cognitions, then building a user persona for hyper-personalized AI interactions.

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🚀 Quick Start (Recommended)

Using UV - Zero Setup Required

# 1. Install UV (one-time)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Set your API key
export GEMINI_API_KEY=your_key_here

# 3. Process your conversations (place conversations.json in current directory)
uvx rebrain pipeline run

# 4. Start MCP server (auto-loads processed data)
uvx rebrain mcp --port 9999

# Advanced: Custom paths and config
uvx rebrain pipeline run --input /path/to/conversations.json --data-path ./my-data
uvx rebrain pipeline run --config custom.yaml  # Custom clustering parameters
uvx rebrain mcp --data-path ./my-data --port 9999 --user-id myproject

That's it! No Python installation, no virtual environments, no dependencies to manage.

See INSTALL.md for detailed installation options.

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🎯 For Developers

# Clone and setup
git clone https://github.com/yasinsb/rebrain.git
cd rebrain
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp env.template .env  # Add your GEMINI_API_KEY

# Run pipeline (bash CLI)
scripts/pipeline/cli.sh all

# Or step by step
scripts/pipeline/cli.sh step1  # Transform & filter
scripts/pipeline/cli.sh step2  # Extract & cluster observations
scripts/pipeline/cli.sh step3  # Synthesize learnings
scripts/pipeline/cli.sh step4  # Synthesize cognitions
scripts/pipeline/cli.sh step5  # Build persona

# Load into memg-core
python scripts/load_memg.py

Output: data/persona/persona.md - ready for system prompts!

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🤖 MCP Integration (Claude Desktop / Cursor)

HTTP Mode (Recommended for Stability)

Start the server:

# Start with default user_id="rebrain"
uvx --from rebrain rebrain-mcp --data-path ./data --port 9999

# Or use custom user_id for multi-user setups
uvx --from rebrain rebrain-mcp --data-path ./data --port 9999 --user-id myproject

# Using rebrain CLI (equivalent)
uvx rebrain mcp --data-path ./data --port 9999

Add to ~/.cursor/mcp.json or Claude Desktop config:

{
  "mcpServers": {
    "rebrain": {
      "url": "http://localhost:9999/mcp"
    }
  }
}

Direct Mode (stdio)

⚠️ Known Issue: stdio mode has stability issues with Cursor/Claude Desktop. HTTP mode is recommended.

If you still want to try stdio mode:

{
  "mcpServers": {
    "rebrain": {
      "command": "uvx",
      "args": ["--from", "rebrain", "rebrain-mcp", "--data-path", "/absolute/path/to/your/data"],
      "env": {
        "GEMINI_API_KEY": "your_key_here"
      }
    }
  }
}

User ID Configuration

The MCP server now supports configurable user_id for memory isolation:

• Default: user_id="rebrain" - used if not specified
• Custom: Pass --user-id myproject when starting the server
• Multi-user: Each user_id maintains separate memory space
• Agent Usage: Agents don't need to provide user_id (uses server default)

Benefits:

• 💰 Process once (~$0.10-0.20), query forever for free (local memg-core)
• ⚡ Instant restarts - database persists, no reprocessing
• 🔒 100% local - no ongoing API costs, no cloud lock-in

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Quick Start (Legacy)

1. Setup

git clone <repo-url>
cd rebrain

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure
cp env.template .env
# Edit .env: add GEMINI_API_KEY

2. Prepare Data

Export your ChatGPT conversations and place the JSON file at:

data/raw/conversations.json

3. Run Pipeline

# Full pipeline (5 steps)
scripts/pipeline/cli.sh all

# Or run individual steps
scripts/pipeline/cli.sh step1
scripts/pipeline/cli.sh step2
# ... etc

4. Check Results

# View pipeline status
scripts/pipeline/cli.sh status

# Read your persona
cat data/persona/persona.md

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Pipeline Overview

Rebrain uses a 5-stage synthesis pipeline:

Raw Conversations (JSON export)
    ↓ Step 1: Transform & Filter
Clean Conversations (date filtered, code removed)
    ↓ Step 2: Extract & Cluster Observations (AI + K-Means)
Clustered Observations (~100 clusters)
    ↓ Step 3: Synthesize Learnings (AI + K-Means)
Clustered Learnings (~20 clusters)
    ↓ Step 4: Synthesize Cognitions (AI)
High-Level Cognitions (~20 patterns)
    ↓ Step 5: Build Persona (AI)
User Persona (3 plain text sections)

Key Features:

• Privacy-First: Category-specific filtering at observation extraction
• Adaptive Clustering: Finds local optima with tolerance-based K-Means
• Flexible Models: Override per-task via prompt template metadata
• Provenance Tracking: Full lineage from conversation → observation → learning → cognition
• Dual Output: JSON (structured) + Markdown (human-readable)

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Configuration

All pipeline parameters live in config/pipeline.yaml:

ingestion:
  date_cutoff_days: 180
  remove_code_blocks: true

insight_extraction:
  max_concurrent: 20
  batch_size: 40

learning_clustering:
  target_clusters: 20
  tolerance: 0.2

Model selection via prompt templates:

# rebrain/prompts/templates/persona_synthesis.yaml
metadata:
  model_recommendation: "gemini-2.5-flash"

See config/README.md for details.

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CLI Usage

# Check what's been generated
./cli.sh status

# Run individual steps
./cli.sh step1 -i data/raw/my_convos.json
./cli.sh step2 --cluster-only  # Re-cluster existing insights
./cli.sh step3

# Clean outputs
./cli.sh clean --all

# Full help
./cli.sh help

See scripts/pipeline/README.md for details.

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Project Structure

rebrain/
├── rebrain/              # Core library
│   ├── core/            # GenAI client
│   ├── ingestion/       # Data loading & chunking
│   ├── operations/      # Embedder, clusterer, synthesizer
│   ├── prompts/         # Prompt templates (YAML)
│   ├── retrieval/       # Query interface (future)
│   └── schemas/         # Pydantic models
├── config/              # Pipeline configuration
├── scripts/pipeline/    # 5-step pipeline + CLI
├── data/               # Raw → processed → persona
└── notebooks/          # Exploration & testing

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Output

Persona (Step 5)

JSON (data/persona/persona.json):

{
  "model": "gemini-2.5-flash",
  "persona": {
    "personal_profile": "...",
    "communication_preferences": "...",
    "professional_profile": "..."
  }
}

Markdown (data/persona/persona.md):

# User Persona Information for AI

## Personal Profile
...

## Communication Preferences
...

## Professional Profile
...

Copy-paste ready for system prompts!

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Development

# Install dev dependencies
pip install -r requirements_dev.txt

# Run with custom config
python scripts/pipeline/01_transform_filter.py --input data/raw/test.json

# Check specific step
python scripts/pipeline/02_extract_cluster_insights.py --skip-cluster

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Documentation

• Pipeline Details: scripts/pipeline/README.md
• Configuration: config/README.md
• Data Structure: data/README.md
• Model Override Pattern: MODEL_OVERRIDE_PATTERN.md
• Persona Builder: PERSONA_BUILDER_REFACTOR.md

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License

MIT License - see LICENSE file

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Built by Yasin Salimibeni