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Multi Agent Research Poc
What is Multi Agent Research Poc
Multi-Agent-Research-POC is a proof of concept for a local-first multi-agent system that utilizes local LLMs via Ollama, tool-call detection, and web search capabilities through the Brave Search API or Brave MCP plugin server. It features two collaborating agents: Searcher and Synthesizer.
Use cases
Use cases include academic research where the Searcher agent retrieves relevant information and the Synthesizer agent summarizes findings, as well as automated customer support systems that require information gathering and response generation.
How to use
To use Multi-Agent-Research-POC, clone the repository, install the required dependencies, set up your Brave API key in the .env file, run Ollama locally, and execute the main program. You can switch between using the Brave Search API and the Brave MCP plugin server.
Key features
Key features include local LLMs via Ollama, tool-call detection using a specific syntax, web search integration through Brave Search API or MCP plugin, and two collaborative agents for searching and synthesizing information.
Where to use
Multi-Agent-Research-POC can be used in various fields such as research, data analysis, and any application requiring collaborative information retrieval and synthesis using AI agents.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Multi Agent Research Poc
Multi-Agent-Research-POC is a proof of concept for a local-first multi-agent system that utilizes local LLMs via Ollama, tool-call detection, and web search capabilities through the Brave Search API or Brave MCP plugin server. It features two collaborating agents: Searcher and Synthesizer.
Use cases
Use cases include academic research where the Searcher agent retrieves relevant information and the Synthesizer agent summarizes findings, as well as automated customer support systems that require information gathering and response generation.
How to use
To use Multi-Agent-Research-POC, clone the repository, install the required dependencies, set up your Brave API key in the .env file, run Ollama locally, and execute the main program. You can switch between using the Brave Search API and the Brave MCP plugin server.
Key features
Key features include local LLMs via Ollama, tool-call detection using a specific syntax, web search integration through Brave Search API or MCP plugin, and two collaborative agents for searching and synthesizing information.
Where to use
Multi-Agent-Research-POC can be used in various fields such as research, data analysis, and any application requiring collaborative information retrieval and synthesis using AI agents.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
๐ง AutoGen-Compatible Multi-Agent Research POC with Ollama + BraveSearch
This project is a proof of concept for a local-first multi-agent system using:
- ๐ค Local LLMs via Ollama
- ๐งฉ Tool-call detection using
<tool_call>...
syntax - ๐ Web search via Brave Search API or Brave MCP plugin server
- ๐ง Two collaborating agents:
Searcher
andSynthesizer
๐ Folder Structure
MultiResearchPOC/
โโโ main.py # Entry point
โโโ agents/
โ โโโ searcher.py # Ollama-powered research agent
โ โโโ synthesizer.py # Summarizer agent
โโโ tools/
โ โโโ tool_parser.py # Tool call detection logic
โ โโโ tool_registry.py # Tool dispatcher (API or MCP)
โโโ .env # Contains BRAVE_API_KEY
โโโ requirements.txt # Python dependencies
๐ Getting Started
1. Clone the project
git clone <your-repo-url>
cd MultiResearchPOC
2. Install dependencies
pip install -r requirements.txt
3. Set up your .env
echo "BRAVE_API_KEY=your_brave_api_key_here" > .env
Get your Brave API key at: https://developer.brave.com/api-search/
4. Run Ollama locally
ollama run llama3:8b
If using Docker: make sure to reference the host as
http://host.docker.internal:11434
5. Run the program
python main.py
You should see:
- A response from the
Searcher
agent - A tool call triggered
- Search results pulled from Brave
- A final summary from the
Synthesizer
agent
๐ Switching Between API and MCP Plugin
Option 1: Brave Search API (default)
Used by default via:
"BraveSearch": call_brave_api
Option 2: Brave MCP Plugin Server
- Start the plugin server:
npx @modelcontextprotocol/server-brave-search
- Update
tools/tool_registry.py
:
# "BraveSearch": call_brave_api,
"BraveSearch": call_brave_mcp_server
๐ฎ Next Steps & Improvements
Feature | Description |
---|---|
๐ง Add Planner Agent | Dynamically decide which agent/tool to call |
๐งฉ Add More Tools | CrunchbaseSearch, TwitterTrends, YouTubeSearch, etc. |
๐ Markdown Output | Save session logs for review or integration with Obsidian |
๐ผ๏ธ Add UI | Use Chainlit, FastAPI, or Discord bot for interaction |
๐ Wrap as API | Convert to a local API for web or CLI usage |
๐ Sample Output
๐ค Searcher Response: <tool_call>BraveSearch({"query": "African AI startups 2024 promising not mainstream"})</tool_call> ๐ Tool Output: โข AI 100: ... โข Five African AI startups to watch in 2023 ... ๐ง Final Summary: - CB Insights lists top private AI companies... - 5 African startups solving problems in healthcare, marketing...
๐ Credits
- Ollama
- Brave Search API
- Semantic Kernel
- Inspired by AutoGen
For questions or ideas, open an issue or start a discussion!
๐๏ธ Built for the Microsoft AI Agents Hackathon
This project was created as part of the Microsoft AI Agents Hackathon โ a challenge focused on building intelligent, tool-using, autonomous agents powered by open-source and Microsoft technologies.
The goal of this project is to showcase a local-first, multi-agent system that can:
Generate dynamic tool calls
Perform live web research using Brave Search
Collaborate between agents to synthesize useful insights
๐ Submission: [TBD]
local multi-agent AI research bot | Ollama + Brave + AutoGen | Built for Microsoft AI Agents Hackathon
Disclaimer
This project is a personal proof-of-concept developed entirely outside of my employment, using personal time and tools. It is unrelated to any current or anticipated business activities of my employer and contains no proprietary or confidential information.
DevTools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.