Hands-On Guide: Create Event-Driven Python AI Agents with UAgents and Google Gemini

In a hands-on session, developers learned to leverage the UAgents framework to craft a lean, event-driven AI agent system on Google’s Gemini API. The demo begins by applying nest_asyncio to unlock nested event loops, then walks through setting the Gemini API key and creating a GenAI client. After that, it defines Pydantic-based message schemas for Question and Answer, then launches two UAgents: the first, named gemini_agent, awaits Question messages, uses the Gemini flash model to produce answers, and emits corresponding Answer messages; the second, client_agent, dispatches a query at startup and processes any received answer. The example wraps up by running both agents side by side with Python’s multiprocessing features, then shutting down the event loop once the response cycle finishes, showcasing UAgents’ built-in support for agent-to-agent communication.

To get started, the tutorial installs the UAgents package alongside the Google GenAI client library. The pip install -q flag keeps setup output to a minimum, making notebooks tidier.

Then the code imports os, time, multiprocessing, and asyncio for core utilities, nest_asyncio to allow nested loops in notebook environments, the Google GenAI client, Pydantic for schema checks, and key classes from UAgents. A call to nest_asyncio.apply() patches the event loop, enabling seamless async operations.

The script then sets the GEMINI_API_KEY environment variable to your personal access token before instantiating a GenAI client instance. That client will authenticate and handle all future calls to Gemini’s language models.

Two Pydantic classes define the message formats for this example: Question, which holds a single question string, and Answer, which contains a single answer string. Pydantic enforces validation and serialization rules for these messages, guaranteeing that both incoming and outgoing content matches the schema.

Building on these schemas, the code creates a UAgents agent called gemini_agent. It assigns a seed phrase for a reproducible identity, a listening port, and an HTTP endpoint for incoming messages. A startup handler logs a “ready” message once the agent comes online. A helper function, ask_gemini, wraps the GenAI client’s call to the Gemini flash model. The gemini_agent listens for Question messages, forwards the prompt to ask_gemini, then sends back an Answer message to the originator.

An additional UAgents instance, client_agent, kicks off on startup by sending a Question asking, “What is the capital of France?” to gemini_agent. It then awaits an Answer message, prints the content on receipt, pauses briefly, and then stops the running loop.

Finally, a run_agent helper invokes agent.run() to start each service. Python’s multiprocessing spawns the gemini_agent in a separate process. After a short pause to allow it to initialize, the script runs client_agent in the main process. When the answer returns, the code joins the background process, guaranteeing a clean exit.

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