To help you build and evaluate data agents with the Conversational Analytics API, this page provides links to guided tutorials, interactive demos, sample applications, and development tools.
Interactive tutorials
This section links to codelabs, interactive Colaboratory notebooks, and blog posts that provide step-by-step guidance to help you learn core API concepts.
| Resource | Description | Format |
|---|---|---|
| Introduction to the Conversational Analytics API | Follow a step-by-step tutorial to learn how to use the Python SDK with a BigQuery data source, how to create a new agent by using the Conversational Analytics API, how to create and manage conversations, and how to send and stream responses from the API. | Codelab |
| Build a chat app with the Conversational Analytics API for Looker and BigQuery | Learn how to use the Conversational Analytics API with Looker and BigQuery to build a chat application, including how to set up the Streamlit Quickstart app, and learn about the benefits of Looker for semantic modeling. | Codelab |
| HTTP Colaboratory notebook | Explore an interactive, step-by-step guide to setting up your environment, building a data agent, and making API calls by using HTTP requests. | Notebook |
| Python SDK Colaboratory notebook | Explore an interactive, step-by-step guide to setting up your environment, building a data agent, and making API calls by using the Python SDK. | Notebook |
| Blog: Building a conversational agent in BigQuery using the Conversational Analytics API | Learn to build a BigQuery conversational agent by using the Python SDK, with guidance on setup, stateful conversations, and streaming responses. | Blog |
Sample applications and demos
This section links to sample applications and video demonstrations that showcase the API's capabilities.
Sample apps and videos
The following resources demonstrate how to integrate the Conversational Analytics API in various environments and provide overviews of API features:
- Streamlit Quickstart app: Learn how to integrate with the Conversational Analytics API in a local test environment.
- Conversational Analytics demos and tools: Review demos and tools that showcase Conversational Analytics API capabilities and provide practical integration patterns.
- YouTube: Conversational Analytics API: Learn about the Conversational Analytics API, including how the API processes natural language questions, connects to data sources such as BigQuery and Looker, and returns results as text or visualizations.
- YouTube: Embed analytics experiences in your applications using the Conversational Analytics API: Learn about the Conversational Analytics API and how to build a Conversational Analytics API agent by using the Agent Development Kit (ADK).
SDKs and development tools
This section provides links to client libraries, the Agent Development Kit (ADK), the MCP Toolbox, and other tools for building, managing, and evaluating data agents.
Client libraries
Installation instructions and reference documentation are available for the following Conversational Analytics API client libraries:
Agent Development Kit (ADK)
The Agent Development Kit (ADK) includes the following tools for building and managing Conversational Analytics API data agents:
DataAgentToolset: Manage and interact with data agents by using natural language.ask_data_insights: Generate natural language data insights from sources like BigQuery.
MCP Toolbox for Databases
The MCP Toolbox for Databases provides tools for querying your database data sources in natural language by using the Conversational Analytics API:
- BigQuery Conversational Analytics Tool (
bigquery-conversational-analytics) - Looker Conversational Analytics Tool (
looker-conversational-analytics)
Other development tools
The following tool can assist with other development tasks, such as agent evaluation:
- Prism: Use this open-source application to monitor and evaluate AI agent performance, run test suites, and capture traces.