Copilot Memory MCP
Persistent semantic memory for AI agents using local ChromaDB vector search. No cloud required.
Open source Open in the app JSON README (API)
About
Persistent semantic memory for AI agents using local ChromaDB vector search. No cloud required.
Details
- Kind
- MCP servers
- Topic
- AI, RAG & memory
- Publisher
- labforgedev
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 1.0.0
- Stars
- 3
- Last push
- 2026-03-15T18:18:23Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 03:02:01
- Updated
- 2026-08-29 03:02:01
- Origin id
io.github.LabForgeDev/copilot-memory-mcp
README
# copilot-memory-mcp
[](LICENSE)
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
```bash
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`](examples/vscode/mcp.json). Copy it to your project's `.vscode/mcp.json`.
---
## 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) |
---
## Copilot instructions & agents (examples)
Ready-to-use examples are in [`examples/`](examples/). Copy the relevant file(s) into the root or `.github/` of your own project.
| File | Consumed by |
|---|---|
| [`AGENTS.md`](examples/AGENTS.md) | OpenAI Codex, Claude Code, and most agent runtimes |
| [`CLAUDE.md`](examples/CLAUDE.md) | Claude Code (`claude` CLI) |
| [`.cursorrules`](examples/.cursorrules) | Cursor (legacy format, project-root) |
| [`.cursor/rules/memory-tools.mdc`](examples/.cursor/rules/memory-tools.mdc) | Cursor (modern per-rule format) |
| [`.github/copilot-instructions.md`](examples/.github/copilot-instructions.md) | GitHub Copilot in VS Code |
| [`.github/prompts/save-memory.prompt.md`](examples/.github/prompts/save-memory.prompt.md) | VS Code `/save-memory` prompt |
| [`.github/prompts/recall-memory.prompt.md`](examples/.github/prompts/recall-memory.prompt.md) | VS Code `/recall-memory` prompt |
| [`.github/agents/memory.md`](examples/.github/agents/memory.md) | VS Code `@memory` custom agent mode |
---
## 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
```bash
pip install -e ".[dev]"
```
### Run tests
```bash
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)
```bash
pip install -e .
PYTHONPATH=. python app/main.py
```
---
## 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.
---
## License
MIT — see [LICENSE](LICENSE).