Tableau Next LangChain
Tableau LangChain: Build highly flexible AI-applications that extend your Tableau Server or Cloud Environment
What is Tableau LangChain?
Tableau_langchain implements a collection of Python classes and methods designed to work with the LangGraph framework. These classes and methods wrap Tableau’s APIs with Python functions, which become tools that can be leveraged within LangChain and LangGraph.
You can think of it like a toolkit that helps AI agents, or an LLM, talk directly to your Tableau data. For example, the simple_query_datasource method is implemented using the LangGraph syntax and makes it simple to create a LangGraph agent that can query your Tableau published data sources using the VizQL Data Service tool. Additional tools include search_datasource and get_pulse_insight.
Tools are an essential component of an AI agent because they enable agents to accomplish tasks that go beyond the scope of a standard response from an LLM. For example, if you’ve tried ChatGPT’s Deep Research feature, you have seen how ChatGPT will use web search as a tool to get more information and then come up with a higher quality and more accurate answer to your question.
Similarly, if you build a LangGraph agent that has access to the search_datasource and simple_datasource_qa tools, this would give the LLM the ability to search for data sources on your Tableau Server and then query them. At Tableau Conference 2025, I demoed this as a way a dashboard user could bring in new data. For the agent, the query "Do we have data on the Olympics?" would invoke a tool to search for data sources, and a follow-up query "Can I have a breakdown of medal counts by country?" would invoke the query data tool, and return the data via a dashboard extension.
The Tableau Next LangChain is a community partnership, so developers at Tableau are working with community members to build and develop agentic tools and AI applications.
Examples highlighted at Tableau Conference 2025 include:
- Dashboard Extensions: Query underlying data dynamically to answer ad hoc questions on your dashboard's data.
- Vector Search: Enhanced search capabilities using semantic understanding, for example, searching for "healthcare" retrieves data on "NHS Prescriptions".
- Report Writer: An agent that will analyze a data source and write a report on the data to a local file.
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