Overview

Kitaru is an open-source runtime and orchestration layer for long-running Python agents. It keeps agent workflows persistent, replayable, observable, and stateful without requiring you to learn a graph DSL or change your Python control flow.

Create a durable agent

import kitaru
from kitaru import checkpoint, flow

@checkpoint
def research(topic: str) -> str:
    return kitaru.llm(f"Summarize {topic} in two sentences.")

@checkpoint
def draft_report(summary: str) -> str:
    return kitaru.llm(f"Write a short report based on: {summary}")

@flow
def research_agent(topic: str) -> str:
    summary = research(topic)
    return draft_report(summary)

if __name__ == "__main__":
    research_agent.run(topic="Why do AI agents need durable execution?")

Each @checkpoint is a durable unit of work — its output is persisted automatically. If the flow fails at draft_report, replaying it skips research and reuses its recorded result. kitaru.llm() tracks model calls with prompt, response, usage, and cost capture built in.

See the Quickstart to install and run this yourself.

What your agent can do with Kitaru

These are the shipped primitives Kitaru adds to ordinary Python agent code — no rewrites required.

• Durable execution: Wrap steps in @checkpoint and your agent picks up where it left off without re-running expensive work
• Replay from failure: Re-run only the failed part of a flow by replaying from a checkpoint instead of starting from scratch
• Wait and resume: Add kitaru.wait() and let agents pause for a human, another system, or later input while compute is released
• Durable memory: kitaru.memory stores scoped, versioned key-value state you can seed, inspect, compact, and reuse across executions
• Execution management: KitaruClient lets you inspect, replay, retry, resume, and cancel executions from code or CLI
• Tracked LLM calls: Use kitaru.llm() and every call gets automatic secret resolution, prompt/response capture, and cost tracking
• Persistent data: kitaru.save() / kitaru.load() let agents store and retrieve files, objects, and results across executions
• Structured observability: kitaru.log() attaches key-value metadata to any checkpoint or flow for debugging and the UI
• Runtime configuration: kitaru.configure() sets your model, log store, and stack defaults in one call
• Framework and infrastructure portability: Keep your Python control flow, use your preferred framework, and run locally or on remote stacks across Kubernetes, Vertex, SageMaker, and AzureML

Next Steps