Metadata-Version: 2.5
Name: kitaru-langgraph
Version: 0.1.1
Summary: LangGraph recording and replay adapter for Kitaru.
Project-URL: Homepage, https://kitaru.ai
Project-URL: Documentation, https://docs.zenml.io/kitaru/adapters/langgraph
Project-URL: Repository, https://github.com/zenml-io/kitaru
Project-URL: Issues, https://github.com/zenml-io/kitaru/issues
Project-URL: Changelog, https://github.com/zenml-io/kitaru/blob/develop/plugins/packages/langgraph/CHANGELOG.md
Author-email: ZenML GmbH <info@zenml.io>
License-Expression: Apache-2.0
Keywords: ai-agents,kitaru,langgraph,recording,replay
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.11
Requires-Dist: kitaru>=0.23.0
Requires-Dist: langchain-core<2,>=1.5.3
Requires-Dist: langchain<2,>=1.3.14
Requires-Dist: langgraph<2,>=1.2.10
Provides-Extra: deepagents
Requires-Dist: deepagents<0.8,>=0.7.4; extra == 'deepagents'
Description-Content-Type: text/markdown

# Kitaru LangGraph adapter

Record and replay LangGraph agent runs with Kitaru.

## Install

```bash
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

```python
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.

Replay support depends on how the graph was constructed. See the [LangGraph adapter guide and capability matrix](https://docs.zenml.io/kitaru/adapters/langgraph) for supported invocation methods, overrides, tool policies, interrupts, and failure behavior.

## Links

- [Kitaru documentation](https://docs.zenml.io/kitaru)
- [Source code](https://github.com/zenml-io/kitaru)
- [Issue tracker](https://github.com/zenml-io/kitaru/issues)

Licensed under Apache-2.0.
