Create an agent

Quickly create an AI agent with built-in agent templates.

An agent is an AI-powered application that uses a large language model (LLM) to reason and act. Agents can call tools, connect to MCP servers, and carry out multi-step tasks on behalf of a user.

Solo Enterprise for agentregistry comes with a built-in agent scaffold that generates a demo dice-rolling agent along with all the files you need to build, run, and publish it: an agent definition (agent.yaml), a Dockerfile, a Docker Compose file, and the agent source code. You can use this scaffold to create your own agent.

In this guide, you initialize an agent from that scaffold and explore the generated files.

Before you begin

1. Follow one of the Get started guides to install Solo Enterprise for agentregistry, set up the arctl CLI, and connect a provider.
2. Set up and start a Docker Engine on your local machine, such as Docker Desktop. Solo Enterprise for agentregistry uses Docker compose to build Docker images and spin up AI artifacts locally.

Create an agent

Solo Enterprise for agentregistry comes with built-in agent templates that you can use to quickly spin up agents or customize them to your needs.

1. Create an agent.

   The following command creates a myagent Python agent with the Google ADK agent framework that is configured to use the Gemini provider. When you run the command, a myagent directory is created on your local machine that contains the scaffold for your agent. You see the directory structure in your CLI output.

   arctl init agent myagent --framework adk --language python --model-provider gemini --model-name gemini-2.5-flash

   Example output:

   ✓ Created agent: myagent (framework: adk, language: python, model: gemini/gemini-2.5-flash)
   
   🚀 Next steps:
    1. Run locally (optional):
       arctl run myagent
       (export GOOGLE_API_KEY in your shell or set it in .env first)
    2. Publish to the registry:
       arctl apply -f myagent/agent.yaml
   

2. Explore the agent scaffold that was created for you. You can optionally make changes to the files to customize your agent further.

   ls myagent
   info

   The agent.yaml file sets the image location that is used by agentregistry when you build the image and push it to your container registry. By default, agentregistry uses localhost:5001 as the container registry and latest as the image tag, such as localhost:5001/myagent:latest. If you want to use agentregistry to push images to your container registry, make sure to update this file with the registry and image tag that you want to use. Note that this update is not required if you want to build images locally only without pushing them to your container registry.

   File	Description
   arctl.yaml	The agent details that you specified with the arctl CLI.
   agent.yaml	The agent definition, including the agent framework, LLM provider, model, and agent image location. If you add MCP servers or skills to an agent, they are added to the agent definition.
   docker-compose.yaml	A Docker compose file that is used to spin up and run your agent on your local machine when you use the arctl agent run command.
   Dockerfile	The Dockerfile to spin up and run your agent in a containerized environment.
   myagent	A directory that includes the agent.py script that defines the agent, including the provider and model that you want to use. The directory also includes the agent card for agent discovery and any MCP tools or skills that the agent has access to.
   pyproject.toml	The dependency definition of your agent.
   README.md	An introduction to the agent that you created with instructions for how to further customize it.

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