Kitaru
A self-hosted, framework-agnostic runtime for autonomous agents that records and replays every step for debugging and improvement.
Kitaru
What is Kitaru?
Kitaru vs Similar AI Tools
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How to use Kitaru?
- 1Install Kitaru via pip. Import @flow and @checkpoint decorators from kitaru. Write your agent logic as normal Python functions decorated with @checkpoint, and compose them with @flow. Run the flow and automatically record executions. Use the CLI or UI to inspect, replay, and deploy versioned snapshots.
Kitaru Key Features
- Every step recorded as typed, versioned artifacts
- Replay runs with overrides (model, input) from any checkpoint
- Crash recovery and pause/resume with kitaru.wait()
- Versioned deployments with flow.deploy()
- Isolated execution for risky or heavy steps
- Built-in UI for inspecting runs and approving human-in-the-loop steps
- Python-first, no graph DSL
- Self-hosted on local, Kubernetes, GCP, AWS, or Azure
Kitaru Use Cases
- Debugging and diagnosing agent failures
- Comparing agent behavior across model versions or parameters
- Testing changes safely before production deployment
- Recovering from crashes without losing progress
- Pausing and resuming long-running agent workflows
Kitaru Pricing & Free Credits
Kitaru currently operates on a Free model.
This tool is completely free to use
Kitaru Pros & Cons
Pros
- Framework-agnostic, works with any agent SDK
- Self-hosted, no mandatory SaaS control plane
- Versioned deployments enable easy rollback
- Built-in UI for observability
- Supports crash recovery and pause/resume
Cons
- Still in early development (version not specified)
- Requires self-hosting infrastructure for full features
- Limited ecosystem compared to more established platforms
What is Kitaru best for?
- AI teams building and deploying autonomous agents
- Platform teams needing an execution layer for agent governance
- Developers wanting to record, replay, and improve agent behavior
Kitaru FAQ
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