Agno Agent Framework on Agentuity

Agno is a python framework for building Multi-Agent Systems. It's pretty cool if you ask us.

What if you could deploy your existing Agno agents, with just one command? That's where Agentuity comes in.

And..that's also where Dhilan comes in. He's an agent builder that has taken on the task of moving some of Agno's examples over to demonstrate just how easy it is.

Dhilan Fye is a rising senior studying Software Engineering at the University of Miami. He conducts research in federated learning and privacy-preserving machine learning, and is actively involved in Miami’s growing startup ecosystem. Thanks Dhilan for the contribution!

The Agno Agents

On top of the great community Agno has, they have some pretty solid examples. Dhilan has taken the time to show you how to easily deploy them to Agentuity.

Check out the repo

In Agentuity, there's only a few deployment conventions you need to follow to deploy any agent. A yaml file, and an agents folder with a handler that all the requests to your agent go through.

from agentuity import AgentRequest, AgentResponse, AgentContext
import asyncio
from agents.MovieAgent.movie_agent import movie_recommendation_agent  # Agno agent


async def run(request: AgentRequest, response: AgentResponse, context: AgentContext):
	prompt = await request.data.text()

	loop = asyncio.get_running_loop()
	raw = await loop.run_in_executor(None, lambda: movie_recommendation_agent.run(prompt))

	if isinstance(raw, str):
			output = raw
	elif hasattr(raw, "content"):
			output = raw.content
	elif hasattr(raw, "reply"):
			output = raw.reply
	else:
			output = str(raw)

	if not output.strip():
			context.logger.error("[MovieAgent] empty output")
			return response.text("⚠️ No recommendations generated. Try refining your request.")

	return response.text(output)

Integrating with Agentuity

Notice how little code is needed to make Agno agents work within Agentuity? The core logic remains the same. We just need to:

1. Wrap the agent invocation within an AgentHandler function.
2. Read the user's input from req.data.text().
3. Return the output using methods like resp.text().

Deploying to the Cloud

Once your agent is set up, deploying is trivial. Assuming you have the Agentuity CLI installed and configured, navigate to your project directory and run agentuity deploy.

That's it! Your Agno agent is now live and accessible via an API endpoint provided by Agentuity.

Conclusion

Agentuity makes it incredibly simple to take your existing Agno projects, like this agent focused on structured output, and deploy them to the cloud without fuss. Focus on building your agent logic, and let Agentuity handle the deployment. If you want to do some cooler things: You can even build out a Vercel AI SDK agent, or a CrewAI agent and have them all talk to each other! Cool eh?