--- title: "agent-opt vs kitaru" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/future-agi-agent-opt-vs-zenml-io-kitaru" tools: ["future-agi-agent-opt", "zenml-io-kitaru"] --- # agent-opt vs kitaru *GraphCanon updated Aug 4, 2026* ## Verdict Pick agent-opt if agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies; pick kitaru if kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML. [agent-opt](https://app.futureagi.com) reports 71 GitHub stars, 7 forks, and 0 open issues, last pushed Jun 30, 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 [agent-opt's repository](https://github.com/future-agi/agent-opt) and [kitaru's repository](https://github.com/zenml-io/kitaru). | | [agent-opt](/tools/future-agi-agent-opt.md) | [kitaru](/tools/zenml-io-kitaru.md) | | --- | --- | --- | | Tagline | Open Source Library for Automated Optimization of AI Agent Workflows | Record, replay, and improve AI agents in production, built on ZenML | | Stars | 71 | 226 | | Forks | 7 | 15 | | Open issues | 0 | 49 | | Language | Python | Python | | Adopt for | Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. | Kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-opt](/tools/future-agi-agent-opt.md) | [kitaru](/tools/zenml-io-kitaru.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 35d | 0d | | Open issues (now) | 0 | 49 | | Full report | [trust report](/tools/future-agi-agent-opt/trust.md) | [trust report](/tools/zenml-io-kitaru/trust.md) | ## Shared compatibility - **Python**: [agent-opt](/tools/future-agi-agent-opt.md) - Python runtime; [kitaru](/tools/zenml-io-kitaru.md) - Python runtime ## Decision facts: agent-opt - **Adopt for:** Agent-opt is tailored for teams that require automated optimization of AI workflows and support for continuous integration/continuous delivery (CI/CD), relying on Python and specific library dependencies. ## 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 agent-opt if… - Tags unique to agent-opt: agent, aioptimization, automation, cicd. - - When your project needs seamless CI/CD integration alongside automated optimization - Leaner open-issue backlog (0). ### Choose kitaru if… - Tags unique to kitaru: agent-framework, checkpoints, durable-execution, llm. - - 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. - More GitHub stars (226 vs 71) - visibility, not fit. ## When NOT to use agent-opt - - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ## 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 agent-opt and kitaru? agent-opt: Open Source Library for Automated Optimization of AI Agent Workflows. 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 agent-opt over kitaru? Choose agent-opt over kitaru when Tags unique to agent-opt: agent, aioptimization, automation, cicd; - When your project needs seamless CI/CD integration alongside automated optimization; Leaner open-issue backlog (0). ### When should I choose kitaru over agent-opt? Choose kitaru over agent-opt when Tags unique to kitaru: agent-framework, checkpoints, durable-execution, llm; - 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; More GitHub stars (226 vs 71) - visibility, not fit. ### When should I avoid agent-opt? - If your project does not require Python or if it cannot meet the specific requirement of having Python ≥ 3.10 - In scenarios where CI/CD integration is not a priority for your AI workflow optimization ### 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 agent-opt or kitaru more popular on GitHub? kitaru has more GitHub stars (226 vs 71). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-opt and kitaru open source? Yes - both are open-source projects on GitHub (agent-opt: Apache-2.0, kitaru: Apache-2.0). ### Where can I find alternatives to agent-opt or kitaru? GraphCanon lists graph-backed alternatives at [agent-opt alternatives](/tools/future-agi-agent-opt/alternatives) and [kitaru alternatives](/tools/zenml-io-kitaru/alternatives) ([agent-opt markdown twin](/tools/future-agi-agent-opt/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/future-agi-agent-opt-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, agent-opt or kitaru? agent-opt: Steady. 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 agent-opt and kitaru? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-opt trust report](/tools/future-agi-agent-opt/trust); [kitaru trust report](/tools/zenml-io-kitaru/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=future-agi-agent-opt`](/api/graphcanon/graph?tool=future-agi-agent-opt) - 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/_