ai-agents-interoperability
This project provides a framework-agnostic approach to achieve interoperability of agentic AI solutions. By implementing search tools using Tavily and Google SERP APIs, users can learn how different AI agents can collaborate. This facilitates easy data exchange and integration of functionalities across different platforms.
GitHub Stars
17
User Rating
Not Rated
Favorites
0
Views
30
Forks
2
Issues
0
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.gitNote: UV Package manager is recommended.
Install the uv package manager.
pip install pipx pipx install uvOnce the uv package manager is installed, create a virtual environment and activate it.
uv venv source .venv/bin/activateInstall the python dependencies.
uv syncExport env variables
cp env.example .envFill 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/notebooksdirectory 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.
14
Followers
58
Repositories
0
Gists
0
Total Contributions
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.