--- title: "Kiln vs kitaru" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kiln-ai-kiln-vs-zenml-io-kitaru" tools: ["kiln-ai-kiln", "zenml-io-kitaru"] --- # Kiln vs kitaru *GraphCanon updated Aug 23, 2026* ## Verdict Pick Kiln if kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes; pick kitaru if kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML. [Kiln](https://kiln.tech) reports 5.0k GitHub stars, 375 forks, and 69 open issues, last pushed Aug 23, 2026. [kitaru](https://kitaru.ai) has 226 stars, 15 forks, and 49 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [Kiln's repository](https://github.com/Kiln-AI/Kiln) and [kitaru's repository](https://github.com/zenml-io/kitaru). | | [Kiln](/tools/kiln-ai-kiln.md) | [kitaru](/tools/zenml-io-kitaru.md) | | --- | --- | --- | | Tagline | Build, Evaluate, and Optimize AI Systems | Record, replay, and improve AI agents in production, built on ZenML | | Stars | 5,034 | 226 | | Forks | 375 | 15 | | Open issues | 69 | 49 | | Language | Python | Python | | Adopt for | Kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. | Kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | AI Agents, Data & Retrieval, Evaluation & Observability, Model Training | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Kiln](/tools/kiln-ai-kiln.md) | [kitaru](/tools/zenml-io-kitaru.md) | | --- | --- | --- | | Open issues (now) | 69 | 49 | | Stars delta | +63 (30d) | Unknown | | Open issues delta | +3 (30d) | Unknown | | Full report | [trust report](/tools/kiln-ai-kiln/trust.md) | [trust report](/tools/zenml-io-kitaru/trust.md) | ## Shared compatibility - **Python**: [Kiln](/tools/kiln-ai-kiln.md) - Python runtime; [kitaru](/tools/zenml-io-kitaru.md) - Python runtime ## Decision facts: Kiln - **Adopt for:** Kiln is a versatile AI systems development toolkit that excels in comprehensive evaluation frameworks for agents, RAG components, and fine-tuning processes. ## Decision facts: kitaru - **Adopt for:** Kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML. ## Choose when ### Choose Kiln if… - License: Kiln is Other, kitaru is Apache-2.0. - Tags unique to Kiln: ai, chain-of-thought, collaboration, dataset-generation. - Also covers Data & Retrieval, Model Training. - When you need extensive tools for evaluating custom AI agents ### Choose kitaru if… - License: kitaru is Apache-2.0, Kiln is Other. - Tags unique to kitaru: agent-framework, ai-agents, checkpoints, durable-execution. - - You need to ensure the continuous improvement of AI agents that are already deployed; Kitaru allows you to replay scenarios with different approaches to identify improvements. ## When NOT to use Kiln - If your project strictly requires a lightweight tool without comprehensive dataset management options - Avoid if you do not require advanced synthetic data generation capabilities ## When NOT to use kitaru - - If your project is in the early stages of development without a clear need for replaying historical data or improving upon past behaviors; - - When working outside Python, as Kitaru does not currently offer support for other programming languages. ## Common questions ### What is the difference between Kiln and kitaru? Kiln: Build, Evaluate, and Optimize AI Systems. kitaru: Record, replay, and improve AI agents in production, built on ZenML. See the comparison table for live GitHub stats and shared categories. ### When should I choose Kiln over kitaru? Choose Kiln over kitaru when License: Kiln is Other, kitaru is Apache-2.0; Tags unique to Kiln: ai, chain-of-thought, collaboration, dataset-generation; Also covers Data & Retrieval, Model Training; When you need extensive tools for evaluating custom AI agents. ### When should I choose kitaru over Kiln? Choose kitaru over Kiln when License: kitaru is Apache-2.0, Kiln is Other; Tags unique to kitaru: agent-framework, ai-agents, checkpoints, durable-execution; - You need to ensure the continuous improvement of AI agents that are already deployed; Kitaru allows you to replay scenarios with different approaches to identify improvements. ### When should I avoid Kiln? If your project strictly requires a lightweight tool without comprehensive dataset management options Avoid if you do not require advanced synthetic data generation capabilities ### When should I avoid kitaru? - If your project is in the early stages of development without a clear need for replaying historical data or improving upon past behaviors; - When working outside Python, as Kitaru does not currently offer support for other programming languages. ### Is Kiln or kitaru more popular on GitHub? Kiln has more GitHub stars (5,034 vs 226). Stars measure visibility, not whether either tool fits your constraints. ### Are Kiln and kitaru open source? Yes - both are open-source projects on GitHub (Kiln: Other, kitaru: Apache-2.0). ### Where can I find alternatives to Kiln or kitaru? GraphCanon lists graph-backed alternatives at [Kiln alternatives](/tools/kiln-ai-kiln/alternatives) and [kitaru alternatives](/tools/zenml-io-kitaru/alternatives) ([Kiln markdown twin](/tools/kiln-ai-kiln/alternatives.md), [kitaru markdown twin](/tools/zenml-io-kitaru/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/kiln-ai-kiln-vs-zenml-io-kitaru.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Kiln or kitaru? Kiln: Very active. kitaru: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Kiln and kitaru? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Kiln trust report](/tools/kiln-ai-kiln/trust); [kitaru trust report](/tools/zenml-io-kitaru/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kiln-ai-kiln`](/api/graphcanon/graph?tool=kiln-ai-kiln) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_