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gentel-lab/origenemcp

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Repository Install

OrigeneMCP is a core component of the OriGene ecosystem, a self-evolving multi-agent system designed to act as a virtual disease biologist.

Обзор

OrigeneMCP is a core component of the OriGene ecosystem, a self-evolving multi-agent system designed to act as a virtual disease biologist. OriGene provides a unified platform for biomedical AI research and was officially launched at the 2025 WAIC. Paper | OriGene Code | OrigeneMCP Code | Recent updates: - 🚀 Update new tools: DepMap, etc. - 🚀 OriGene officially launched at WAIC 2025! - 🔥 Integrated OrigeneMCP into the Bohrium AI for Science platform. - 🔥 Integrated OrigeneMCP into CAMEL. - 🚀 OrigeneMCP officially open-sourced!

README

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OrigeneMCP

OrigeneMCP is a core component of the OriGene ecosystem, a self-evolving multi-agent system designed to act as a virtual disease biologist. OriGene provides a unified platform for biomedical AI research and was officially launched at the 2025 WAIC.

| Product | Paper | OriGene Code | OrigeneMCP Code |

1. OrigeneMCP Overview

Recent updates:

  • 2026-02-24 🚀 Update new tools: DepMap, etc.
  • 2025-07-28 🚀 OriGene officially launched at WAIC 2025!
  • 2025-07-22 🔥 Integrated OrigeneMCP into the Bohrium AI for Science platform.
  • 2025-07-19 🔥 Integrated OrigeneMCP into CAMEL.
  • 2025-07-18 🚀 OrigeneMCP officially open-sourced!

OrigeneMCP is the MCP toolkit for the OriGene project that integrates over 600 tools and multiple databases (including ChEMBL, PubChem, FDA, OpenTargets, NCBI, UniProt, PDB, Ensembl, UCSC, KEGG, STRING, TCGA, Monarch, ClinicalTrials, and more) into an advanced integrated MCP server platform, combining disease biology and drug discovery tools to enable comprehensive multi-dimensional information retrieval across small molecules, proteins, genes, diseases, and other biological entities, serving as a unified interface for accessing and analyzing complex biomedical data to accelerate therapeutic research and development.

2. Deploy OrigeneMCP

Clone the repository:

git clone https://github.com/GENTEL-lab/OrigeneMCP.git

The dependencies of this repository are managed by uv. Before deployment, please make sure that uv is already installed. Use the following commands to install uv:

pip install uv

uv venv .venv --python=3.13

source .venv/bin/activate

Initialize the dependencies of this repository:

uv sync

Deploy the mcp service:

cp default.conf.toml local.conf.toml
# get your tavily_api_key in https://tavily.com/
# get your jina_api_key in https://jina.ai/

export PYTHONPATH=`fab pypath`
uv run -m deploy.web

NOTE:

  • Please ensure that port 8788 is not occupied. If it is occupied, please modify the port in file local.conf.toml to another port;
  • If you need to use Tavily search or Jina search, please configure the tavily_api_key or jina_api_key in file local.conf.toml;

3. Use OrigeneMCP

3.1. Use locally deployed OrigeneMCP

First, connect to OrigeneMCP and get all available tools.

from langchain_mcp_adapters.client import MultiServerMCPClient

# Choose the packages you want to use
tool_packages = [
    "chembl", "kegg", "string", "search", "pubchem", "ncbi",
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Установка

This server does not publish a one-line install command.

Open the repository installation guide Новичок в MCP? Начните с руководства на mcpskill.org →

Конфигурация

import asyncio from camel.agents import ChatAgent from camel.toolkits import OrigeneToolkit # Choose the packages you want to use tool_packages = [ "chembl", "kegg", "string", "search", "pubchem", "ncbi", "uniprot", "tcga", "ensembl", "ucsc", "fda_drug", "pdb", "monarch", "clinicaltrials", "dbsearch", "opentargets" ] tool_packages = [server+'_mcp' for server in tool_packages] config = { "mcpServers": { package: { "transport": "streamable_http", "url": f"http://127.0.0.1:8788/{package}/mcp/" } for package in tool_packages } } async def main(): # Use async context manager for automatic connection management async with OrigeneToolkit(config_dict=config) as origene_toolkit: user_msg = "what can you do?" agent = ChatAgent( "You are named origene assistant.", model="gpt-4o", tools=[*origene_toolkit.get_tools()], ) response = agent.step(user_msg) print(response.msgs[0].content) asyncio.run(main())