Welcome to Kitaru

The runtime layer underneath your agent stack.

Kitaru is the runtime layer underneath your agent stack. It gives you durable execution for Python agents — checkpoints, replay, resume, wait(), versioned deployments — while the harness you already picked (Pydantic AI, Deep Agents, LangGraph, Claude Agent SDK, raw Python) keeps owning how the agent thinks, and your existing platform keeps owning auth, observability, and policy.

Kitaru is self-host-first: a single-service server on your own Kubernetes, artifacts in your own S3/GCS/Azure Blob. No mandatory SaaS control plane in the path of your agent's data. See Harness, Runtime, Platform for the full picture of where Kitaru fits.

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() logs model calls with prompt, response, tokens, and latency per call.

See the Quickstart to install and run this yourself.

What your agent can do with Kitaru

These are the runtime primitives Kitaru adds on top of your existing Python agent code. You keep your harness and your control flow; Kitaru makes the run durable.

• 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; after the polling timeout, compute is released and the run resumes when input lands

• Artifact lineage: Every checkpoint output is written to your object store as a typed, versioned artifact — step through runs, diff outputs across runs, and trace a bad final output back to the exact step that produced it

• 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 token/latency logging

• 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 — Kubernetes, Vertex AI, SageMaker, AzureML

Next steps

Installation

Install Kitaru with uv or pip.

Quickstart

Run a tiny flow end to end.

Examples

Browse runnable workflows grouped by goal.

Harness, Runtime, Platform

Where Kitaru fits in an agent stack, and where it doesn't.

How It Works

Server, runner, execution targets, and what lives where in local dev vs production.

Core Concepts

Flows, checkpoints, and the execution model.

Execution Management

Inspect runs, replay, retry, resume, and fetch logs.

Wait, Input, and Resume

Pause flows for external input and continue the same execution.

Tracked LLM Calls

Use kitaru.llm() with aliases, secrets, and captured artifacts.

Secrets + Model Registration

Store provider credentials, register a model alias, and use kitaru.llm().

Configuration

Set runtime defaults and understand override precedence.

Stacks

Create, inspect, switch, and clean up local and remote stacks across Kubernetes, AWS, GCP, and Azure.

MCP Server

Query and manage executions via MCP tools.

Agent Skills

Install quickstart, scoping, authoring, and adapter migration skills.

CLI Reference

Browse the generated command reference.

Blog

Read essays on durable execution, long-running agents, and Kitaru's design.