🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch
@chin3
🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch について
This project is a proof of concept for running a local-first multi-agent system using: 🤖 Local LLMs via Ollama 🧩 Simple function/tool-call detection using <tool_call>... 🔍 Brave Search API or optional Brave MCP plugin server 🧠 Two collaborating agents: Searcher and Synthesize
基本情報
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-Agent-Research-POC": {
"command": "python",
"args": [
"main.py"
]
}
}
}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 🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch?
A proof-of-concept for a local-first multi-agent system using Ollama (local LLMs) and Brave Search. It features two collaborating agents—Searcher and Synthesizer—that detect tool calls via <tool_call> syntax and can optionally integrate with the Brave MCP plugin server. Built for developers exploring autonomous, tool-using agents.
How to use 🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch?
Clone the repo, install Python dependencies (pip install -r requirements.txt), set a BRAVE_API_KEY in .env, run Ollama locally (ollama run llama3:8b), then execute python main.py. To switch from the default Brave Search API to the Brave MCP plugin, start the plugin server (npx @modelcontextprotocol/server-brave-search) and update tools/tool_registry.py.
Key features of 🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch
- Local-first multi-agent system with Searcher and Synthesizer agents
- Web search via Brave Search API or Brave MCP plugin server
- Tool-call detection using
<tool_call>syntax - Supports switching between API and MCP backends
- Designed for the Microsoft AI Agents Hackathon
Use cases of 🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch
- Conduct web research entirely with local AI agents
- Synthesize multiple search results into a coherent summary
- Prototype autonomous, tool-using agents without cloud dependencies
- Test multi-agent collaboration patterns with local LLMs
FAQ from 🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch
What does this project do?
It runs two agents (Searcher and Synthesizer) locally with Ollama, queries the web via Brave Search (API or MCP plugin), and produces a final summary from search results.
What runtime dependencies are required?
Ollama (with a model like llama3:8b), Python 3, and a Brave Search API key. Optionally, Node.js/npx for the MCP plugin.
How do I switch between the Brave API and the MCP plugin?
By default the tool uses call_brave_api. To use the MCP plugin, start the plugin server (npx @modelcontextprotocol/server-brave-search) and change tools/tool_registry.py to use call_brave_mcp_server instead.
Where does data from searches live?
Search results are fetched from Brave and processed entirely locally; no data is stored externally. The project saves no session logs by default.
Is this project ready for production?
No—it is a proof of concept built for the Microsoft AI Agents Hackathon. The README lists planned improvements like a Planner agent, more tools, a UI, and API wrapping.
「AI とエージェント」の他のコンテンツ
MCP-NixOS - Because Your AI Assistant Shouldn't Hallucinate About Packages
utensilsMCP-NixOS - Model Context Protocol Server for NixOS resources
1Panel
1Panel-dev🔥 1Panel is a modern, open-source VPS control panel — and the only one with native AI agent support. Run Ollama models, deploy OpenClaw agents, and manage your entire server stack from one clean web interface.
meGPT - upload an author's content into an LLM
adriancoCode to process many kinds of content by an author into an MCP server
MCP-LLM Bridge
patruffBridge between Ollama and MCP servers, enabling local LLMs to use Model Context Protocol tools
Hass-MCP
voskaControl and query Home Assistant from Claude and other LLMs — a Model Context Protocol (MCP) server.
コメント