Local Memory MCP Server for Coding/AI Agents

This project is a local MCP (Model Context Protocol) server that exposes a small set of tools:

• memory.search – semantic search over stored memories

• memory.save – store a new memory

• memory.supersede – mark an old memory as superseded

• memory.delete – permanently remove a memory by id

• memory.ping – sanity check / version output

What you can do with this project

• Keep durable coding context across chat sessions (decisions, preferences, gotchas, API contracts).

• Retrieve relevant past context semantically (not only keyword matching).

• Scope memory per project using WORKSPACE_KEY while keeping one shared local database.

• Correct memory over time by superseding outdated entries or deleting irrelevant ones.

• Run everything locally (no external vector DB required).

Typical workflow

1. User asks a question in chat.

2. Agent calls memory.search to fetch relevant context.

3. Agent answers using retrieved memory + current codebase context.

4. New durable insight is stored via memory.save.

5. Old memory is updated via memory.supersede or removed via memory.delete.

It uses:

• Bun + TypeScript

• Zvec (@zvec/zvec) as embedded in-process vector database (docs: https://zvec.org/en/docs/)

• Ollama /api/embed with embeddinggemma for embeddings (docs: https://docs.ollama.com/capabilities/embeddings, model: https://ollama.com/library/embeddinggemma)

Prerequisites

• Bun installed

• Ollama installed and running locally

Pull the embedding model:

ollama pull embeddinggemma

Install

bun install

Run

bun run start

This runs an MCP server over stdio.

Test

bun run test

Current tests include:

• tests/embed.test.ts – validates Ollama embedding response parsing and error handling

• tests/memory-db.test.ts – validates save, search, supersede, and delete on the Zvec-backed store

Environment variables

• MEMORY_DB_PATH (default ./data/memory.zvec)

• OLLAMA_BASE_URL (default http://localhost:11434)

• OLLAMA_EMBED_MODEL (default embeddinggemma)

• EMBEDDING_DIM (default 768, must match your embedding model)

• WORKSPACE_KEY (default default)

VS Code

Workspace setup

This repo includes .vscode/mcp.json that registers this server:

• command: bun

• args: run start

You can adjust environment variables in that file.

Always-on across all projects

If you want this MCP server available in all workspaces, add it to your User MCP configuration instead of only .vscode/mcp.json:

1. Open Command Palette: MCP: Open User Configuration

2. Add a server entry that starts this repo from a fixed directory.

Example (Linux):

{
  "servers": {
    "local-memory-mcp": {
      "type": "stdio",
      "command": "bun",
      "args": ["--cwd", "/path/to/local-memory-mcp", "run", "start"],
      "env": {
        "MEMORY_DB_PATH": "/path/to/local-memory-mcp/data/memory.zvec",
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_EMBED_MODEL": "embeddinggemma",
        "EMBEDDING_DIM": "768",
        "WORKSPACE_KEY": "${workspaceFolderBasename}"
      }
    }
  }
}

Notes:

• Use an absolute MEMORY_DB_PATH so all projects use the same database.

• WORKSPACE_KEY=${workspaceFolderBasename} keeps memories separated per project automatically.

• Enable VS Code setting chat.mcp.autoStart (Experimental) to auto-start/restart MCP servers when needed.

Docs:

• https://code.visualstudio.com/docs/copilot/customization/mcp-servers

Claude Code

Add this server to Claude Code as a local stdio MCP server.

This repository already includes:

• .mcp.json for project-scoped Claude MCP configuration

• CLAUDE.md for memory-first agent behavior guidelines

User scope (all projects)

claude mcp add --transport stdio --scope user \
  --env MEMORY_DB_PATH=/absolute/path/to/local-memory-mcp/data/memory.zvec \
  --env OLLAMA_BASE_URL=http://localhost:11434 \
  --env OLLAMA_EMBED_MODEL=embeddinggemma \
  --env EMBEDDING_DIM=768 \
  --env WORKSPACE_KEY=default \
  local-memory-mcp -- bun --cwd /absolute/path/to/local-memory-mcp run start

Project scope (shared in repository)

claude mcp add --transport stdio --scope project \
  --env MEMORY_DB_PATH=./data/memory.zvec \
  --env OLLAMA_BASE_URL=http://localhost:11434 \
  --env OLLAMA_EMBED_MODEL=embeddinggemma \
  --env EMBEDDING_DIM=768 \
  --env WORKSPACE_KEY=${PWD##*/} \
  local-memory-mcp -- bun run start

Project .mcp.json example:

{
  "mcpServers": {
    "local-memory-mcp": {
      "type": "stdio",
      "command": "bun",
      "args": ["run", "start"],
      "env": {
        "MEMORY_DB_PATH": "./data/memory.zvec",
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_EMBED_MODEL": "embeddinggemma",
        "EMBEDDING_DIM": "768",
        "WORKSPACE_KEY": "${PWD##*/}"
      }
    }
  }
}

Notes:

• --scope project writes to .mcp.json in the project root.

• --scope user stores the server in your user Claude configuration.

• Keep all Claude flags before the server name, and put -- before the server command.

Useful commands:

claude mcp list
claude mcp get local-memory-mcp
claude mcp remove local-memory-mcp

Docs:

• https://code.claude.com/docs/en/mcp

Tool usage (examples)

Search

{
  "tool": "memory.search",
  "arguments": {
    "query": "What is our policy for multi-session memory?",
    "topK": 8,
    "workspaceKey": "my-repo"
  }
}

Save

{
  "tool": "memory.save",
  "arguments": {
    "workspaceKey": "my-repo",
    "type": "decision",
    "summary": "We use zvec with Ollama embeddinggemma for long-term memory.",
    "text": "Decision: The Copilot/agent memory sidecar stores vectors in zvec and generates embeddings via Ollama /api/embed using embeddinggemma.",
    "tags": ["memory", "zvec", "ollama", "embeddinggemma"],
    "importance": 0.8
  }
}

Delete

{
  "tool": "memory.delete",
  "arguments": {
    "workspaceKey": "my-repo",
    "id": 42
  }
}

Implementation notes

• The DB uses one Zvec collection with:

  • dense vector field embedding

  • scalar fields for metadata (workspaceKey, type, summary, etc.)

• KNN queries are executed through Zvec querySync with metadata filters.

Tests

Run all tests:

bun run test

Current test coverage:

• tests/embed.test.ts

  • parses successful Ollama /api/embed responses into Float32Array

  • verifies error handling when Ollama returns non-2xx responses

• tests/memory-db.test.ts

  • validates save + search behavior with workspace/type filtering

  • validates supersede behavior (superseded items are excluded from search)

  • validates delete behavior and returned payload semantics