Fremem (formerly MCP Memory Server)

License Release

A persistent vector memory server for Windsurf, VS Code, and other MCP-compliant editors.

🌟 Philosophy

Privacy-first, local-first AI memory: Your data stays on your machine.
No vendor lock-in: Uses open standards and local files.
Built for MCP: Designed specifically to enhance Windsurf, Cursor, and other MCP-compatible IDEs.

ℹ️ Status (v0.2.0)

Stable:

✅ Local MCP memory with Windsurf/Cursor
✅ Multi-project isolation
✅ Ingestion of Markdown docs

Not stable yet:

🚧 Auto-ingest (file watching)
🚧 Memory pruning
🚧 Remote sync

Note: There are two ways to run this server:

Local IDE (stdio): Used by Windsurf/Cursor (default).

Docker/Server (HTTP): Used for remote deployments or Docker (exposes port 8000).

🏥 Health Check

To verify the server binary runs correctly:

# From within the virtual environment
python -m fremem.server --help

✅ Quickstart (5-Minute Setup)

There are two ways to set this up: Global Install (recommended for ease of use) or Local Dev.

Option A: Global Install (Like `npm -g`)

This method allows you to run fremem from anywhere without managing virtual environments manually.

1. Install pipx (if not already installed):

MacOS (via Homebrew):

brew install pipx
pipx ensurepath
# Restart your terminal after this!

Linux/Windows: See pipx installation instructions.

2. Install fremem:

# Install from PyPI
pipx install fremem

# Verify installation
fremem --help

Configure Windsurf / VS Code:

Since pipx puts the executable in your PATH, the config is simpler:

{
  "mcpServers": {
    "memory": {
      "command": "fremem",
      "args": [],
      "env": {
        "MCP_MEMORY_PATH": "/Users/YOUR_USERNAME/mcp-memory-data"
      }
    }
  }
}

Note on MCP_MEMORY_PATH: This is where fremem will store its persistent database. You can point this to any directory you like (checks locally or creating it if it doesn't exist). We recommend something like ~/mcp-memory-data or ~/.fremem-data. It must be an absolute path.

Option B: Local Dev Setup

1. Clone and Setup

git clone https://github.com/iamjpsharma/fremem.git
cd fremem

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies AND the package in editable mode
pip install -e .

2. Configure Windsurf / VS Code (Local Dev)

Add this to your mcpServers configuration (e.g., ~/.codeium/windsurf/mcp_config.json):

Note: Replace /ABSOLUTE/PATH/TO/fremem with the actual full path to the cloned directory.

{
  "mcpServers": {
    "memory": {
      "command": "/ABSOLUTE/PATH/TO/fremem/.venv/bin/python",
      "args": ["-m", "fremem.server"],
      "env": {
        "MCP_MEMORY_PATH": "/ABSOLUTE/PATH/TO/fremem/mcp_memory_data"
      }
    }
  }
}

In local dev mode, it's common to store the data inside the repo (ignored by git), but you can use any absolute path.

🚀 Usage

0. HTTP Server (New)

You can run the server via HTTP (SSE) if you prefer:

# Run on port 8000
python -m fremem.server_http

Access the SSE endpoint at http://localhost:8000/sse and send messages to http://localhost:8000/messages.

🐳 Run with Docker

To run the server in a container:

# Build the image
docker build -t fremem .

# Run the container
# Mount your local data directory to /data inside the container
docker run -p 8000:8000 -v $(pwd)/mcp_memory_data:/data fremem

The server will be available at http://localhost:8000/sse.

1. Ingestion (Adding Context)

Use the included helper script ingest.sh to add files to a specific project.

# ingest.sh <project_name> <file1> <file2> ...

# Example: Project "Thaama"
./ingest.sh project-thaama \
  docs/architecture.md \
  src/main.py

# Example: Project "OpenClaw"
./ingest.sh project-openclaw \
  README.md \
  CONTRIBUTING.md

💡 Project ID Naming Convention

It is recommended to use a consistent prefix for your project IDs to avoid collisions:

project-thaama
project-openclaw
project-myapp

2. Connect in Editor

Once configured, the following tools will be available to the AI Assistant:

memory_search(project_id, q, filter=None): Semantic search. Supports metadata filtering (e.g., filter={"type": "code"}). Returns distance scores.
memory_add(project_id, id, text): Manual addition.
memory_list_sources(project_id): specific files ingested.
memory_delete_source(project_id, source): Remove a specific file.
memory_stats(project_id): Get chunk count.
memory_reset(project_id): Clear all memories for a project.

The AI will effectively have "long-term memory" of the files you ingested.

🛠 Troubleshooting

"fremem: command not found" after installing:
- This means pipx installed the binary to a location not in your system's PATH (e.g., ~/.local/bin).
- Fix: Run pipx ensurepath and restart your terminal.
- Manual Fix: Add export PATH="$PATH:$HOME/.local/bin" to your shell config (e.g., ~/.zshrc).
"No MCP server found" or Connection errors:
- Check the output of pwd to ensure your absolute paths in mcp_config.json are 100% correct.
- Ensure the virtual environment (.venv) is created and dependencies are installed.
"Wrong project_id used":
- The AI sometimes guesses the project ID. You can explicitly tell it: "Use project_id 'project-thaama'".
Embedding Model Downloads:
- On the first run, the server downloads the all-MiniLM-L6-v2 model (approx 100MB). This may cause a slight delay on the first request.

🗑️ Uninstalling

To remove fremem from your system:

If installed via pipx (Global):

pipx uninstall fremem

If installed locally (Dev): Just delete the directory.

📁 Repo Structure

/
├── src/fremem/
│   ├── server.py       # Main MCP server entry point
│   ├── ingest.py       # Ingestion logic
│   └── db.py           # LanceDB wrapper
├── ingest.sh           # Helper script
├── requirements.txt    # Top-level dependencies
├── pyproject.toml      # Package config
├── mcp_memory_data/    # Persistent vector storage (gitignored)
└── README.md

🗺️ Roadmap

✅ Completed (v0.1.x)

🚀 Near-Term (v0.2.x – Production Readiness)

🧠 Memory Governance

List memory sources per project
Delete memory by source (file-level deletion)
Reset memory per project
Replace / reindex mode (prevent stale chunks)
Memory stats (chunk count, last updated, size)

🎯 Retrieval Quality

Metadata filtering (e.g., type=decision | rules | context)
Similarity scoring in results
Hybrid search (semantic + keyword)
Return evidence + similarity scores with search results
Configurable top_k defaults per project

⚙️ Dev Workflow

Auto-ingest on git commit / file change
mcp-memory init <project-id> bootstrap command
Project templates (PROJECT_CONTEXT.md, DECISIONS.md, AI_RULES.md)

🧠 Advanced RAG (v0.3.x – Differentiators)

Hierarchical retrieval (summary-first, detail fallback)
Memory compression (old chunks → summaries)
Temporal ranking (prefer newer decisions)
Scoped retrieval (planner vs coder vs reviewer agents)
Query rewrite / expansion for better recall

🏢 Team / SaaS Mode (Optional)

Philosophy: Local-first remains the default. SaaS is an optional deployment mode.

🔐 Auth & Multi-Tenancy

Project-level auth (API keys or JWT)
Org / team separation
Audit logs for memory changes

☁️ Remote Storage Backends (Pluggable)

S3-compatible vector store backend
Postgres / pgvector backend
Sync & Federation (Local ↔ Remote)

🚫 Non-Goals

❌ No mandatory cloud dependency
❌ No vendor lock-in
❌ No chat history as “memory” by default (signal > noise)
❌ No model fine-tuning

MCP Memory Server