ai-agents-interoperability
このプロジェクトは、エージェントAIソリューションの相互運用性を実現するためのフレームワークに依存しないアプローチを提供します。TavilyやGoogle SERP APIを利用した検索ツールの実装を通じて、さまざまなAIエージェントが連携できる方法を学ぶことができます。これにより、異なるプラットフォーム間でのデータ交換や機能の統合が容易になります。
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AI Agents Interoperability Series
Learn how to architect Agentic AI solutions which are framework agnostic, LLM Agnostic. Refer to the Blog series below to learn more.
Reference Architecture
Medium articles
Read more about AI Agents Interoperability here: Medium.com
Pre-requirements
- I have used Tavily search for the web search tool implementation, create a Tavily API Key here: https://www.tavily.com
- I have used Google SERP APIs for the flight search tool implementation, create a SERP API key here: https://serpapi.com/manage-api-key
Setup codebase
Clone the repo.
git clone https://github.com/manojjahgirdar/ai-agents-interoperability.git
Note: UV Package manager is recommended.
Install the uv package manager.
pip install pipx pipx install uv
Once the uv package manager is installed, create a virtual environment and activate it.
uv venv source .venv/bin/activate
Install the python dependencies.
uv sync
Export env variables
cp env.example .env
Fill the env values
Launch the mcp/acp servers.
- To launch the mcp server run:
cd src/mcp/mcp-server uv run mcp_server.py
- To launch the acp server, in another terminal run:
cd src/acp/acp-server export REMOTE_MCP_URL=http://127.0.0.1:8000/sse uv run acp_server.py
- To launch the mcp server run:
To run the notebooks, goto
src/notebooks
directory and run the following command:jupyter notebook
I am a Software Engineer, Inventor, Mentor and an Open Source Contributor. I have over 6 years of professional experience in IT Industry.
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This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.