Content
<p align="center">
<a href="https://kitaru.ai">
<img src="https://kitaru.ai/kitaru-logo.svg" alt="Kitaru" width="240">
</a>
</p>
<h3 align="center">You build your agents. We make them durable.</h3>
<p align="center">
Kitaru (来る, "to arrive") — open-source agent infrastructure for Python. Any framework. Any cloud. Built on <a href="https://zenml.io">ZenML</a>.
</p>
<p align="center">
<a href="https://pypi.org/project/kitaru/"><img alt="PyPI" src="https://img.shields.io/pypi/v/kitaru?color=blue"></a>
<a href="https://pypi.org/project/kitaru/"><img alt="Python" src="https://img.shields.io/pypi/pyversions/kitaru"></a>
<a href="https://github.com/zenml-io/kitaru/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/zenml-io/kitaru"></a>
<a href="https://zenml.io/slack"><img alt="Slack" src="https://img.shields.io/badge/chat-on%20slack-blueviolet"></a>
</p>
<p align="center">
<a href="https://kitaru.ai/docs">Docs</a> ·
<a href="#quick-start">Quick Start</a> ·
<a href="https://kitaru.ai/docs/getting-started/examples">Examples</a> ·
<a href="GETTING_STARTED.md">Getting Started Guide</a>
</p>
---
<p align="center">
<img src="assets/dashboard.png" alt="Kitaru Dashboard" width="720">
</p>
Your agent crashed at step 7. Kitaru replays from step 7 — not from scratch.
Add two decorators to your existing Python agent and get crash recovery, human
approval gates, cost tracking, and a full dashboard. No rewrite. No framework
lock-in. No distributed systems overhead.
## Why Kitaru?
### Python-first, no graph DSL
Write normal Python. Use `if`, `for`, `try/except` — whatever your agent needs.
Kitaru gives you two decorators (`@flow` and `@checkpoint`) and a handful of
utility functions. That's it.
```python
from kitaru import checkpoint, flow
@checkpoint
def research(topic: str) -> str:
return do_research(topic)
@checkpoint
def write_draft(research: str) -> str:
return generate_draft(research)
@flow
def writing_agent(topic: str) -> str:
data = research(topic)
return write_draft(data)
result = writing_agent.run("quantum computing").wait()
```
### Deployment flexibility
No workers, no message queues, no distributed systems PhD required. Kitaru runs
locally with zero config, and scales to production with a single server backed by
a SQL database. Deploy your agents anywhere — Kubernetes, Vertex AI, SageMaker,
or AzureML — using Kitaru's **stack** abstraction.
### Built-in dashboard
Every execution is observable from day one. See your agent runs, inspect
checkpoint outputs, track LLM costs, and approve human-in-the-loop wait steps —
all from a visual dashboard that ships with the Kitaru server. The dashboard
ships free, with the server, from day one.
To start that server locally, run `kitaru login` after installing `kitaru[local]`.
To connect to an existing remote server, run `kitaru login <server>`.
## Quick Start
### Install
```bash
pip install kitaru
```
Or with [uv](https://docs.astral.sh/uv/) (recommended):
```bash
uv pip install kitaru
```
### Optional: start a local Kitaru server
Flows run locally by default with the base install. If you also want the local
dashboard and REST API, install the local extra and then run bare `kitaru login`:
```bash
uv pip install "kitaru[local]"
kitaru login
kitaru status
```
### Optional: connect to an existing remote Kitaru server
If you already have a deployed Kitaru server, connect to it explicitly:
```bash
kitaru login https://my-server.example.com
# add --project <PROJECT> or other remote-login flags if your setup requires them
kitaru status
```
### Initialize your project
```bash
kitaru init
```
### Write your first flow
```python
# agent.py
from kitaru import checkpoint, flow
@checkpoint
def fetch_data(url: str) -> str:
return "some data"
@checkpoint
def process_data(data: str) -> str:
return data.upper()
@flow
def my_agent(url: str) -> str:
data = fetch_data(url)
return process_data(data)
result = my_agent.run("https://example.com").wait()
print(result) # SOME DATA
```
### Run it
```bash
python agent.py
```
Every checkpoint's output is persisted automatically. You can inspect what
happened, replay from any checkpoint, or resume a waiting flow:
```bash
kitaru executions list
kitaru executions get <EXECUTION_ID>
kitaru executions logs <EXECUTION_ID>
kitaru executions replay <EXECUTION_ID> --from process_data
```
## Learn more
| Resource | Description |
|---|---|
| [Getting Started Guide](GETTING_STARTED.md) | Full setup walkthrough with all examples |
| [Documentation](https://kitaru.ai/docs) | Complete reference and guides |
| [Examples](https://kitaru.ai/docs/getting-started/examples) | Runnable workflows for every feature |
| [Stack Selection Guide](https://kitaru.ai/docs/getting-started/stack-selection) | Deploy to Kubernetes, Vertex AI, SageMaker, or AzureML |
## Contributing
We welcome contributions! See [CONTRIBUTING.md](CONTRIBUTING.md) for development
setup, code style, and how to submit changes. The default branch is `develop` —
all PRs should target it.
## Community and support
- [Slack](https://zenml.io/slack) — chat with the team and other users
- [GitHub Issues](https://github.com/zenml-io/kitaru/issues) — bug reports and feature requests
- [kitaru.ai](https://kitaru.ai) — landing page and docs
## License
[Apache 2.0](LICENSE)
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