Mosaic AI Agent Framework: Author a custom schema tool-calling LangGraph agent

This notebook demonstrates how to author a LangGraph agent that's compatible with Mosaic AI Agent Framework features. In this notebook you learn to:

• Author a tool-calling LangGraph agent wrapped with ChatAgent with custom inputs and outputs
• Manually test the agent's output
• Log and deploy the agent

To learn more about authoring an agent using Mosaic AI Agent Framework, see Databricks documentation (AWS | Azure).

Prerequisites

• Address all TODOs in this notebook.

Define the agent in code

Define the agent code in a single cell below. This lets you easily write the agent code to a local Python file, using the %%writefile magic command, for subsequent logging and deployment.

Agent tools

This agent code adds the built-in Unity Catalog function system.ai.python_exec to the agent. The agent code also includes commented-out sample code for adding a vector search index to perform unstructured data retrieval.

For more examples of tools to add to your agent, see Databricks documentation (AWS | Azure)

Wrap the LangGraph agent using the ChatAgent interface

For compatibility with Databricks AI features, the LangGraphChatAgent class implements the ChatAgent interface to wrap the LangGraph agent. This example uses the provided convenience APIs ChatAgentState and ChatAgentToolNode for ease of use.

Databricks recommends using ChatAgent as it simplifies authoring multi-turn conversational agents using an open source standard. See MLflow's ChatAgent documentation.

Custom inputs and outputs

The agent is designed to handle custom inputs and outputs using the predict methods and the add_custom_outputs function.

• In the predict and predict_stream methods of the LangGraphChatAgent class:

  • Custom inputs are passed as an optional parameter and included in the request dictionary.
  • Custom outputs are captured from the agent's response and added to the ChatAgentResponse object.

• In the add_custom_outputs function:

  • This function is added as a node in the agent's workflow to append custom outputs to the state before returning the final response.

Test the agent

Interact with the agent to test its output. Since this notebook called mlflow.langchain.autolog() you can view the trace for each step the agent takes.

Replace this placeholder input with an appropriate domain-specific example for your agent.

Log the agent as an MLflow model

Log the agent as code from the agent.py file. See MLflow - Models from Code.

Enable automatic authentication for Databricks resources

For the most common Databricks resource types, Databricks supports and recommends declaring resource dependencies for the agent upfront during logging. This enables automatic authentication passthrough when you deploy the agent. With automatic authentication passthrough, Databricks automatically provisions, rotates, and manages short-lived credentials to securely access these resource dependencies from within the agent endpoint.

To enable automatic authentication, specify the dependent Databricks resources when calling mlflow.pyfunc.log_model().

• TODO: If your Unity Catalog tool queries a vector search index or leverages external functions, you need to include the dependent vector search index and UC connection objects, respectively, as resources. See docs (AWS | Azure).

Pre-deployment agent validation

Before registering and deploying the agent, perform pre-deployment checks using the mlflow.models.predict() API. See Databricks documentation (AWS | Azure).

Register the model to Unity Catalog

Before you deploy the agent, you must register the agent to Unity Catalog.

• TODO Update the catalog, schema, and model_name below to register the MLflow model to Unity Catalog.

Deploy the agent

Next steps

After your agent is deployed, you can chat with it in AI playground to perform additional checks, share it with SMEs in your organization for feedback, or embed it in a production application. See Databricks documentation (AWS | Azure).