Build a Deterministic Multi-Agent Pipeline with A2A in Python

Multi-agent examples often jump straight to models, tools, and production claims. That makes it difficult to see what the protocol is doing. Before adding an LLM, it is useful to watch a small system discover specialists, delegate a task, and return a result that you can inspect. This tutorial uses A2A Orchestration Lab , an open-source Python project by Fernando Paladini. It starts three local agents: an orchestrator, a researcher, and a writer. The researcher and writer are deterministic stubs, so the example isolates the Agent2Agent (A2A) communication flow from model behavior. The result is a runnable research-to-write pipeline that helps explain where A2A fits next to the Model Context Protocol (MCP). TL;DR Install the lab with uv , run its demo command, and inspect the three local Agent Cards and the delegated result. The project is a learning lab, not a production runtime. That is a feature for this tutorial because every moving part remains visible. Prerequisites You need: Python 3.12 or newer. uv for environment and dependency management. A terminal with network access for the initial dependency download. The repository declares version 0.1.0 , requires Python >=3.12 , and depends on the A2A Python SDK, httpx , and uvicorn . It is licensed under MIT. Create and run the lab Clone the public repository and let uv create the environment from the locked dependencies: git clone https://github.com/paladini/a2a-orchestration-lab.git cd a2a-orchestration-lab uv sync Run the bundled end-to-end demo: uv run a2a-lab demo "Explain A2A and how it relates to MCP" The CLI starts the three agents as subprocesses, waits for their Agent Cards, sends a message to the orchestrator, prints the response, and terminates the child processes. The default prompt is the same explanation used by the repository README, but using your own prompt makes the delegation easier to recognize. On a successful run, the output contains sections similar to these: [demo] asking orchestrator: 'Expl