LangChain and LangGraph

LangChain tools

from_langchain_tool wraps a LangChain BaseTool as a Pyrula @tool. Hand the result to an agent like any native tool.

from pyrula.agents.integrations import from_langchain_tool
from pyrula.agents.decorators.agent import agent

search = from_langchain_tool(my_langchain_tool, timeout=30.0, retry=0)

@agent
async def assistant(ctx, message: str) -> None:
    async for _ in ctx.llm.stream(messages=[{"role": "user", "content": message}], tools=[search]):
        ...

The tool’s input schema comes from its args_schema when it has one, so the model sees the same parameters. Tools without a schema fall back to a single input: str. Needs pip install pyrula-agents[langchain].

Durable Pyrula nodes in a LangGraph graph

as_langgraph_node drops a durable Pyrula @workflow or @agent into a graph you already run, as one node. That node’s body is a Pyrula run, so it is journaled, replayable, and crash-safe, and it can pause for a human.

from pyrula.agents.integrations import as_langgraph_node

node = as_langgraph_node("provision_infra", store=store)
graph.add_node("provision", node)

The run id is derived from the graph’s thread_id, so resuming the graph re-attaches the same run rather than starting a new one, and a crash mid-step won’t double-execute. When the workflow calls ctx.interrupt, the node bridges to LangGraph’s native interrupt() / Command(resume=...): the graph pauses and checkpoints, and resuming it feeds the answer back into the Pyrula run.

It needs a checkpointed graph (for the thread_id and resume) and a Pyrula worker consuming the store, since the node submits and awaits the run rather than running it inline. Install with pip install pyrula-agents[langgraph].

A complete, runnable version of this - a real StateGraph compiled with a checkpointer, a durable workflow node, and an in-process worker - is in examples/langgraph_durable_node.py. For the fuller picture of which integrations are durable, see What’s durable, and what isn’t.