copilot-memory-mcp
Persistent semantic memory for GitHub Copilot in VS Code.
A local MCP server that gives Copilot durable, project-scoped memory across sessions. Memories are stored in an embedded ChromaDB vector database with all-MiniLM-L6-v2 embeddings, enabling semantic retrieval (RAG). Everything runs in a single Docker container — no cloud services required.
Why this exists
Copilot starts every session with a blank slate. This MCP server gives it a persistent, searchable knowledge base so decisions, conventions, and context carry over between sessions — all running locally in Docker.
Quick start
docker compose up -d
The server starts on http://localhost:8000/sse.
The .vscode/mcp.json already points Copilot at the server — no further VS Code configuration needed.
A sample config is provided in examples/vscode/mcp.json. Copy it to your project's .vscode/mcp.json.
MCP tools
Copilot instructions & agents (examples)
Ready-to-use examples are in examples/. Copy the relevant file(s) into the root or .github/ of your own project.
Architecture
VS Code / Copilot
│ MCP HTTP/SSE (port 8000)
▼
┌─────────────────────────────────┐
│ Docker Container │
│ │
│ FastMCP Server (port 8000) │
│ └── 5 MCP tools │
│ │
│ sentence-transformers │
│ └── all-MiniLM-L6-v2 │
│ (384-dim embeddings) │
│ │
│ ChromaDB (embedded) │
│ └── collection "memories" │
└──────────┬──────────────────────┘
│ Docker named volume
▼
/data/chroma (persisted DB)
Project layout
copilot-memory-mcp/
├── app/
│ ├── main.py # FastMCP server, tool registration
│ ├── memory_store.py # ChromaDB wrapper (CRUD + search)
│ ├── embeddings.py # sentence-transformers loader + encode()
│ └── tools/
│ ├── create_memory.py
│ ├── search_memories.py
│ ├── update_memory.py
│ ├── delete_memory.py
│ └── list_memories.py
├── tests/
│ ├── test_memory_store.py
│ └── test_tools.py
├── Dockerfile
├── docker-compose.yml
└── pyproject.toml
Development
Install dependencies
pip install -e ".[dev]"
Run tests
pytest
Tests use an ephemeral in-memory ChromaDB and a mocked embedding function — no Docker, no model download required.
Run the server locally (no Docker)
pip install -e .
PYTHONPATH=. python app/main.py
Environment variables
Copy .env.example to .env and adjust if needed.
License
MIT — see LICENSE.