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🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + Bra…

@chin3

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About 🧠 AutoGen-Compatible Multi-Agent Research POC with Ollama + Bra…

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

#multi-agent#ollama#brave-search

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "Multi-Agent-Research-POC": {
      "command": "python",
      "args": [
        "main.py"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Frequently asked questions

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.

Comments

Basic information

Category

AI & Agents

Transports

stdio

Author

chin3

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