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MCP server · RAG

Fastmcp Chroma Ollama

Local memory system for LLM assistants using ChromaDB, Ollama, and FastMCP

Unclaimed rag
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Scored 6 months ago · breakdown

About Fastmcp Chroma Ollama

Fastmcp Chroma Ollama is an MCP server in the RAG category: local memory system for LLM assistants using ChromaDB, Ollama, and FastMCP.

Install

From source
git clone https://github.com/bjkemp/fastmcp-chroma-ollama

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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README

FastMCP ChromaDB Memory Server

Overview

A sophisticated, local memory system for AI assistants using ChromaDB, Ollama, and FastMCP. This project provides a persistent, semantically-aware memory storage and retrieval system with advanced features like memory importance scoring, automatic merging, and intelligent pruning.

🌟 Features

  • 🧠 Semantic Memory Storage
  • 🔒 Local-first Architecture
  • 🚀 High-Performance Vector Search
  • 🤖 Ollama Embedding Support
  • 📊 Memory Importance Scoring
  • 🔍 Advanced Retrieval Mechanisms

🛠 Requirements

📦 Installation

1. Install UV Package Manager

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Install Ollama

curl https://ollama.ai/install.sh | sh
ollama pull nomic-embed-text

3. Install Project

# Clone the repository
git clone https://github.com/bjkemp/fastmcp-chroma-ollama.git
cd fastmcp-chroma-ollama

# Create virtual environment
uv venv

# Activate virtual environment
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install project
uv pip install -e .

🚀 Usage

Starting the Server

# With stdio transport (for Claude Desktop)
uv run -m fastmcp_memory.server --transport stdio

# With SSE transport
uv run -m fastmcp_memory.server --transport sse --port 8080

Basic Python Example

from fastmcp_memory import MemoryManager

async def example():
    manager = MemoryManager()
    
    # Store a memory
    memory_id = await manager.add_memory("The capital of France is Paris")
    
    # Retrieve memories
    memories = await manager.retrieve_memories("France's capital")
    print(memories)

🧪 Development

Setup Development Environment

# Install development dependencies
uv pip install .[dev]

# Run tests
uv run pytest

# Run code formatters
uv run black src tests
uv run isort src tests

📋 Project Structure

fastmcp-chroma-ollama/
├── src/
│   └── fastmcp_memory/
│       ├── __init__.py
│       ├── server.py
│       ├── server_core.py
│       └── ...
├── tests/
├── .github/
│   └── workflows/
├── pyproject.toml
├── README.md
├── TODO.md
└── LICENSE

🤝 Contributing

Contributions are welcome! Please check our TODO.md for current development priorities and see our contribution guidelines.

📄 License

MIT License - See LICENSE for details.

🗺 Roadmap

Check out TODO.md for detailed development plans and future directions.

🙏 Acknowledgements

README copy captured when this server was indexed; it may lag the repository. The original is authoritative.

Questions

About Fastmcp Chroma Ollama

How do I install Fastmcp Chroma Ollama?

No package has been published for this server, so install it from source: clone the repository and follow its README.

Is Fastmcp Chroma Ollama safe to use with an AI agent?

Its trust score is 34 out of 100 (low). It passes 1 of the 1 static security checks run so far. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Fastmcp Chroma Ollama still maintained?

Conduid hasn't recorded a commit date for this repository yet. Check the repository directly for recent activity.