This release is a pre-release and may not be stable for production use.
Kitaru LangGraph adapter
Record and replay LangGraph agent runs with Kitaru.
Install
uv add kitaru-langgraph
Install kitaru-langgraph[deepagents] to also record agents built with deepagents.create_deep_agent through KitaruGraphRunner.from_agent_factory. The langchain.agents.create_agent factory and direct graph wrapping work without the extra.
Model providers
This distribution does not install any model-provider packages. Model strings passed to init_chat_model, such as "openai:gpt-5-nano", require the matching LangChain provider package (for example langchain-openai) in the agent environment. Install the provider package for each model your agent uses.
Use
import uuid
from langchain.agents import create_agent
from kitaru_langgraph import KitaruGraphRunner
runner = KitaruGraphRunner.from_agent_factory(
create_agent,
factory_kwargs={"model": "openai:gpt-5-nano", "tools": []},
agent_id=uuid.UUID("018f0000-0000-7000-8000-000000000100"),
)
result = runner.invoke({"messages": [{"role": "user", "content": "Hello"}]})
print(result)
Wrap an existing compiled graph directly with KitaruGraphRunner(graph) when you build the graph yourself. The runner accepts the same invocation arguments as the wrapped runnable. Kitaru workers provide task, replay, and authentication context through the standard task environment.
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