Agent Bricks quickstart

Beta

This feature is in Beta. No workspace setting is required to enable it. Install the Agent Bricks CLI to get started.

This quickstart takes you from an empty directory to a deployed custom agent using the Agent Bricks CLI (databricks-agentbricks), the Databricks command-line tool for building and deploying agents. The Agent Bricks CLI manages model access, memory, sessions, tools, and tracing for you, so you can focus on your agent's logic.

How the Agent Bricks CLI works

The Agent Bricks CLI scaffolds a local project from a framework template, with the runtime, tests, and an optional chat UI already wired up. You write the application logic (model, tools, and prompts), and the CLI runs it locally and deploys it. The declarative agent.toml file is the source of truth for the Databricks-managed resources that your agent depends on, such as tools, memory, sessions, and tracing. agentbricks deploy reads it to provision and wire everything up.

Three commands take an agent from a blank directory to production:

• agentbricks init scaffolds the project and writes agent.toml.
• agentbricks dev runs the agent locally, using the same runtime and environment that Databricks uses.
• agentbricks deploy provisions the declared resources and rolls out the deployment.

Prerequisites

• The Databricks CLI, installed and on your path.

• Python 3.10 or above, with pip.

• Install the Agent Bricks CLI:

  Bash

  pip install databricks-agentbricks

Step 1: Authenticate

Authenticate to your workspace with OAuth, save a named profile, and set it as the Agent Bricks CLI's default:

Bash

databricks auth login --host https://<your-workspace-url> --profile <profile>
agentbricks login --profile <profile>

Step 2: Scaffold the project

Scaffold a new agent project, choosing a framework template with --framework:

Bash

agentbricks init --framework langgraph my-agent
cd my-agent

Step 3: Run the agent locally

Run the agent on your machine to test it before you deploy:

Bash

agentbricks dev

This starts a local server on port 8000 using the same command and environment as the Databricks agent runtime, connected to Databricks model serving. Send requests to http://localhost:8000 to interact with the agent.

Step 4: Deploy the agent

Deploy the agent to the Databricks agent runtime:

Bash

agentbricks deploy my-agent

The Agent Bricks CLI provisions any bound stores, grants the agent's service principal access to them, and rolls out the deployment. When it finishes, the CLI returns the deployment's URL. Open the URL to interact with your live agent.

Next steps

• Full command walkthrough and Agent Bricks CLI capabilities: Agent Bricks CLI.
• Bind managed memory and sessions: Managed agent memory.
• Add tools and MCP services: MCPs and agent tools.
• View agent traces with MLflow: Tracing overview.