IntraIntel.ai - Multi-LLM Agent Coding Challenge

Name: IntraIntel.ai - Multi-LLM Agent Coding Challenge
Author: Kush614

@Kush614

リポジトリ

IntraIntel.ai - Multi-LLM Agent Coding Challenge について

概要はまだありません

#multi-llm #medical-agent #mcp-server

基本情報

カテゴリ

AI とエージェント

ランタイム

python

トランスポート

stdio

公開者

Kush614

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "Multi-LLM-Medical-Agent-using-MCP-Server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is IntraIntel.ai - Multi-LLM Agent Coding Challenge?

It is a Python-based multi-agent system that answers medical questions by orchestrating multiple free LLMs from the Hugging Face Inference API. It uses a custom HTTP-based Model Context Protocol (MCP) to interact with separate tool servers for web search (DuckDuckGo) and PubMed search, and implements a Retrieval-Augmented Generation (RAG) pipeline with optional query refinement and snippet summarization.

How to use IntraIntel.ai - Multi-LLM Agent Coding Challenge?

Clone the project, create a Python virtual environment, install dependencies (FastAPI, uvicorn, httpx, duckduckgo-search, biopython, python-dotenv), and set HF_API_TOKEN and NCBI_EMAIL in a .env file. Accept the license terms for mistralai/Mistral-7B-Instruct-v0.3 on Hugging Face. Start the Web Search MCP server on port 8001 and the PubMed MCP server on port 8002 in separate terminals. Then run python agent/main_agent.py to process a predefined set of medical questions.

Key features of IntraIntel.ai - Multi-LLM Agent Coding Challenge

Multi-step RAG pipeline using up to three distinct LLMs.
Optional query refinement and snippet summarization steps.
Separate synthesized answers from web search and PubMed with source links.
Asynchronous HTTP communication via FastAPI and Uvicorn.
API-key-free web search via DuckDuckGo and free Hugging Face models.

Use cases of IntraIntel.ai - Multi-LLM Agent Coding Challenge

Answering medical questions by combining general web and PubMed literature.
Comparing synthesized answers from two independent information sources.
Automating evidence-based research for clinical or educational queries.
Demonstrating an agentic RAG workflow with multiple free LLMs.

FAQ from IntraIntel.ai - Multi-LLM Agent Coding Challenge

Which LLMs are used?

The system uses mistralai/Mistral-7B-Instruct-v0.3 for query refinement and answer synthesis, and sshleifer/distilbart-cnn-6-6 for snippet summarization, both accessed via the free Hugging Face Inference API.

What are the main dependencies?

FastAPI, uvicorn, httpx, duckduckgo-search, biopython, and python-dotenv.

How do the MCP servers communicate?

They use a simple HTTP-based JSON protocol: a POST request to /execute with {"query": "..."} returns a JSON response containing source, status, and results (title, snippet, URL or PubMed ID).

What authentication is required?

A Hugging Face API token (HF_API_TOKEN) with read permissions, and an email address (NCBI_EMAIL) for polite access to NCBI’s Entrez system.

Are there performance considerations?

The first call to a Hugging Face model may take 20–90 seconds as the model loads; subsequent calls are faster. The script includes pauses to respect API rate limits.