Documentation Index
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Use Danube tools from LangChain agents and LangGraph graphs
pip install "danube[langchain]"
export DANUBE_API_KEY=...
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from danube import DanubeClient
from danube.integrations.langchain import danube_tools
tools = danube_tools(DanubeClient())
agent = create_react_agent(ChatOpenAI(model="gpt-4.1"), tools)
state = agent.invoke({"messages": [("user", "Post 'deploy finished' to #ops on Slack")]})
print(state["messages"][-1].content)
from langchain.agents import AgentExecutor, create_tool_calling_agent
agent = create_tool_calling_agent(llm, tools, prompt)
AgentExecutor(agent=agent, tools=tools).invoke({"input": "Top Hacker News stories, please"})
danube_tools(client) returns three StructuredTools with pydantic argument schemas:
| tool | arguments |
|---|---|
danube_search_tools | query: str, limit: int |
danube_describe_tool | tool_id: str |
danube_execute_tool | tool_id: str, parameters: dict |
service_id, ready_only, max_results, as in the other adapters. Results come back as JSON text, capped at 20,000 characters.