Memory MCP Server

A hybrid long-term memory server for AI assistants built using the Model Context Protocol (MCP).

The server combines:

• PostgreSQL for structured storage

• ChromaDB for semantic retrieval

• OpenAI Embeddings for vector search

• MCP tools for interacting with memory

Features

✅ Long-term memory storage

✅ Semantic search

✅ Memory updates

✅ Structured memory models

✅ Separate memory types

• User Facts

• Memories

• Chat History

• Tasks

• Reminders

Architecture

                User
                  │
                  ▼
            MCP Client
                  │
                  ▼
          Memory MCP Server
                  │
     ┌────────────┴────────────┐
     │                         │
     ▼                         ▼
PostgreSQL                ChromaDB
Structured Data      Vector Embeddings

Memory Types

The server supports five different memory categories.

User Facts

Persistent user information.

Examples:

• Preferred IDE

• Name

• Job

• Location

Related MCP server: MCP Memory Server

Memories

General knowledge.

Examples:

• Project documentation

• Architecture decisions

• Workflows

Chat History

Important conversations worth preserving.

Tasks

Long-term tasks.

Reminders

Time-based reminders.

Available MCP Tools

Create

• remember_memory

• remember_user_fact

• remember_chat_history

• remember_task

• remember_reminder

Search

search(query, top_k)

Performs semantic search over stored memories.

Update

• update_memory

• update_user_fact

• update_chat_history

• update_task

• update_reminder

How it Works

Storing Memory

User
  │
  ▼
remember_*
  │
  ▼
Insert into PostgreSQL
  │
  ▼
Generate Embedding
  │
  ▼
Store in ChromaDB

Searching

Query
  │
  ▼
Embedding
  │
  ▼
Chroma Similarity Search
  │
  ▼
Retrieve PostgreSQL Records

Updating

Search Existing Memory
          │
          ▼
Update PostgreSQL
          │
          ▼
Update Chroma Embedding

Installation

Clone the repository.

git clone https://github.com/<username>/memory-mcp-server.git

Install dependencies.

pip install -r requirements.txt

Create a .env file.

OPENAI_API_KEY=YOUR_KEY

Start PostgreSQL.

Create a Chroma database directory.

Run the server.

python server.py

Example Workflow

Store memory

remember_user_fact()

↓

PostgreSQL
↓

ChromaDB

Search

search("What IDE do I use?")

Update

search()

↓

update_user_fact()

Tech Stack

• Python

• MCP

• PostgreSQL

• ChromaDB

• OpenAI Embeddings

• Pydantic

Future Improvements

• Delete memories

• Memory scoring

• Multi-user support

• Memory expiration

• Redis caching

• Hybrid keyword + semantic search

• Memory graph relationships

• Multiple embedding providers

• Local embedding support

License

MIT