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

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Architecture

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

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Memory Types

The server supports five different memory categories.

User Facts

Persistent user information.

Examples:

• Preferred IDE
• Name
• Job
• Location

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Memories

General knowledge.

Examples:

• Project documentation
• Architecture decisions
• Workflows

---

Chat History

Important conversations worth preserving.

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Tasks

Long-term tasks.

---

Reminders

Time-based reminders.

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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

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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

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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

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License

MIT