Skip to main content

LangChain

Build autonomous data agents with LangChain that are grounded in your enterprise data context from DataHub — ownership, lineage, documentation, quality signals, and more.

Prerequisites​

  • Python 3.10+
  • LangChain (pip install langchain langchain-openai)
  • A DataHub instance and access token
  • An OpenAI API key (or another LLM provider with tool-calling support)

Installation​

pip install datahub-agent-context[langchain]

Quick Start​

from datahub.sdk.main_client import DataHubClient
from datahub_agent_context.langchain_tools import build_langchain_tools

client = DataHubClient.from_env()

# Read-only tools by default; set include_mutations=True for write operations
tools = build_langchain_tools(client, include_mutations=False)

Wire the tools into a LangChain agent:

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4", temperature=0)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a data catalog assistant. Use the available tools to search for datasets, get entity details, and trace lineage. Always include URNs in your answers."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({"input": "Find all datasets about customers"})

Full working examples: basic_agent.py · simple_search.py

Troubleshooting​

  • Tool execution errors? Verify your DataHub connection (client.config) and token permissions. Set verbose=True on the AgentExecutor to see tool calls.
  • Agent not using tools? Strengthen the system prompt, or try temperature=0 for more deterministic tool use.
  • Import errors? Run pip install datahub-agent-context[langchain] langchain langchain-openai.

Links: LangChain Agent Docs · Agent Context Kit · MCP Server Guide