Databricks Agents SDK

Warnings

• gotcha The `databricks-agents` library requires a Databricks workspace and a deployed LLM serving endpoint to function correctly, even for basic usage. It is not designed for standalone, local-only operation without a Databricks backend.
  Fix: Ensure you have access to a Databricks workspace, an appropriate Databricks Model Serving endpoint deployed (e.g., for Mixtral, Llama 2), and configure `DATABRICKS_HOST`, `DATABRICKS_TOKEN`, and `DATABRICKS_LLM_ENDPOINT` environment variables or pass them directly.
• gotcha The library heavily integrates with the LangChain ecosystem. Breaking changes or significant API shifts in underlying LangChain components (e.g., `langchain_core`, `langchain-community`) can indirectly impact usage patterns or require code adjustments in `databricks-agents` applications.
  Fix: Regularly consult the `databricks-agents` and LangChain documentation, and pin your `langchain` related dependencies to specific minor versions to avoid unexpected changes. Test your agent implementations thoroughly after any dependency updates.
• gotcha For optimal logging, tracing, and experiment tracking, `databricks-agents` is designed to work seamlessly with MLflow. While not strictly mandatory, users who skip MLflow integration might miss out on valuable debugging and performance monitoring capabilities.
  Fix: Familiarize yourself with MLflow's LLM logging capabilities. Ensure MLflow is configured in your environment or explicitly enabled in your agent's context to leverage automatic logging of prompts, responses, and tool calls.

Install

• pip install databricks-agents Install core library

Imports

• ChatAgent

  from databricks.agents.chat.agent import ChatAgent

• DatabricksLLM

  from databricks.agents.providers.databricks import DatabricksLLM

• DollyTool

  from databricks.agents.tools.dolly.tool import DollyTool

Quickstart

This quickstart demonstrates how to set up a `ChatAgent` using a Databricks-served LLM. It highlights the requirement for Databricks host, token, and an LLM serving endpoint, typically configured via environment variables. The agent is initialized with the LLM and can then process chat prompts. Ensure your Databricks workspace has a suitable model serving endpoint deployed for the `DatabricksLLM`.

import os
from databricks.agents.chat.agent import ChatAgent
from databricks.agents.providers.databricks import DatabricksLLM

# Ensure these environment variables are set for Databricks connectivity
# export DATABRICKS_HOST="https://<your-workspace-url>"
# export DATABRICKS_TOKEN="dapi<your-token>"
# export DATABRICKS_LLM_ENDPOINT="<your-model-serving-endpoint>" (e.g., 'databricks-mixtral-8x7b-instruct')

databricks_host = os.environ.get('DATABRICKS_HOST', 'https://your-workspace.cloud.databricks.com')
databricks_token = os.environ.get('DATABRICKS_TOKEN', 'YOUR_DATABRICKS_TOKEN')
llm_endpoint = os.environ.get('DATABRICKS_LLM_ENDPOINT', 'databricks-mixtral-8x7b-instruct')

# Initialize a Databricks-backed LLM provider
llm = DatabricksLLM(
    endpoint=llm_endpoint,
    host=databricks_host,
    api_token=databricks_token,
    temperature=0.1
)

# Create a ChatAgent
agent = ChatAgent(
    llm=llm,
    # Add tools as needed, e.g., tools=[DollyTool()]
    verbose=True
)

# Interact with the agent
response = agent.chat("What is the capital of France?")
print(f"Agent response: {response}")

# Example with a follow-up question
response_follow_up = agent.chat("And what is its most famous landmark?")
print(f"Agent follow-up response: {response_follow_up}")