Copilot Memory MCP

io.github.LabForgeDevPublished by GitHub account LabForgeDevv1.0.0Updated Oct 24, 2026

Persistent semantic memory for AI agents using local ChromaDB vector search. No cloud required.

The publisher's own server: Memory.fit

VerifiedSTDIODesktop onlyDatabasesKnowledge & Memory

Overview

AI-generated overview

Gives an AI assistant durable, project-scoped semantic memory stored locally in an embedded ChromaDB vector database.

What you can do
Provides five tools for persistent memory: create_memory stores a memory with title, content, optional project scope and tags; search_memories runs semantic vector search with project and tag filters; update_memory edits a memory by ID and re-embeds it; delete_memory permanently removes a memory; list_memories browses memories with pagination. Memories are embedded with the all-MiniLM-L6-v2 model and kept in an embedded ChromaDB collection, so retrieval is semantic rather than keyword-based.
When to use it
Useful when an assistant should carry decisions, conventions, and project context across sessions instead of starting from a blank slate. Suited to per-project knowledge that should stay on the local machine, with no cloud service involved.
Requirements
Runs as a local Docker container (image ghcr.io/labforgedev/copilot-memory-mcp:1.0.0) started with docker compose, exposing an HTTP/SSE endpoint on port 8000. The embedding model is downloaded on first use and cached; optional environment variables SENTENCE_TRANSFORMERS_HOME, CHROMA_PATH and PORT adjust the model cache, database path and port. A Docker named volume persists the database. Desktop only; no authentication is declared.
Before you install
The server exposes an unauthenticated HTTP/SSE endpoint on localhost port 8000, so anything that can reach that port can read or change stored memories. delete_memory permanently removes a memory, and create_memory, update_memory and delete_memory write to the persisted database. Stored content is embedded locally, so no memory text is sent to a cloud service.

Installation

In SourceWeft

  1. Open Copilot Memory MCP in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

copilot-memory-mcp

[License: MIT]

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. Copy it to your project's .vscode/mcp.json.

MCP tools

ToolDescription
create_memoryStore a new memory with title, content, optional project scope and tags
search_memoriesSemantic vector search; filter by project and/or tags
update_memoryUpdate an existing memory by ID; re-embeds on change
delete_memoryPermanently delete a memory by ID
list_memoriesBrowse memories with pagination (lightweight, no content)

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.

FileConsumed by
AGENTS.mdOpenAI Codex, Claude Code, and most agent runtimes
CLAUDE.mdClaude Code (claude CLI)
.cursorrulesCursor (legacy format, project-root)
.cursor/rules/memory-tools.mdcCursor (modern per-rule format)
.github/copilot-instructions.mdGitHub Copilot in VS Code
.github/prompts/save-memory.prompt.mdVS Code /save-memory prompt
.github/prompts/recall-memory.prompt.mdVS Code /recall-memory prompt
.github/agents/memory.mdVS 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

VariableDefaultDescription
SENTENCE_TRANSFORMERS_HOME/app/modelsModel cache directory
CHROMA_PATH/data/chromaChromaDB persistence path
PORT8000HTTP server port

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

License

MIT — see LICENSE.

Source: README.md at commit 8a1835c

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v1.0.0LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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