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.

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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.

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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.

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MCP tools

Tool	Description
create_memory	Store a new memory with title, content, optional project scope and tags
search_memories	Semantic vector search; filter by project and/or tags
update_memory	Update an existing memory by ID; re-embeds on change
delete_memory	Permanently delete a memory by ID
list_memories	Browse memories with pagination (lightweight, no content)

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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.

File	Consumed by
AGENTS.md	OpenAI Codex, Claude Code, and most agent runtimes
CLAUDE.md	Claude Code (claude CLI)
.cursorrules	Cursor (legacy format, project-root)
.cursor/rules/memory-tools.mdc	Cursor (modern per-rule format)
.github/copilot-instructions.md	GitHub Copilot in VS Code
.github/prompts/save-memory.prompt.md	VS Code /save-memory prompt
.github/prompts/recall-memory.prompt.md	VS Code /recall-memory prompt
.github/agents/memory.md	VS Code @memory custom agent mode

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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)

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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

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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

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Environment variables

Variable	Default	Description
SENTENCE_TRANSFORMERS_HOME	/app/models	Model cache directory
CHROMA_PATH	/data/chroma	ChromaDB persistence path
PORT	8000	HTTP server port

Copy .env.example to .env and adjust if needed.

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License

MIT — see LICENSE.