--- title: "agentops vs kitaru" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentops-ai-agentops-vs-zenml-io-kitaru" tools: ["agentops-ai-agentops", "zenml-io-kitaru"] --- # agentops vs kitaru *GraphCanon updated Aug 14, 2026* ## Verdict Pick agentops if agentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage; pick kitaru if kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML. [agentops](https://agentops.ai) reports 5.8k GitHub stars, 612 forks, and 176 open issues, last pushed Jun 25, 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 [agentops's repository](https://github.com/AgentOps-AI/agentops) and [kitaru's repository](https://github.com/zenml-io/kitaru). | | [agentops](/tools/agentops-ai-agentops.md) | [kitaru](/tools/zenml-io-kitaru.md) | | --- | --- | --- | | Tagline | Python SDK for AI agent monitoring and LLM cost tracking | Record, replay, and improve AI agents in production, built on ZenML | | Stars | 5,771 | 226 | | Forks | 612 | 15 | | Open issues | 176 | 49 | | Language | Python | Python | | Adopt for | AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. | Kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentops](/tools/agentops-ai-agentops.md) | [kitaru](/tools/zenml-io-kitaru.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 49d | 0d | | Open issues (now) | 176 | 49 | | Full report | [trust report](/tools/agentops-ai-agentops/trust.md) | [trust report](/tools/zenml-io-kitaru/trust.md) | ## Shared compatibility - **Python**: [agentops](/tools/agentops-ai-agentops.md) - Python runtime; [kitaru](/tools/zenml-io-kitaru.md) - Python runtime ## Decision facts: agentops - **Adopt for:** AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage. ## 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 agentops if… - License: agentops is MIT, kitaru is Apache-2.0. - Tags unique to agentops: benchmarking, cost-tracking. - Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK ### Choose kitaru if… - License: kitaru is Apache-2.0, agentops is MIT. - 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. ## When NOT to use agentops - If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI - In case self-hosting of components is impractical due to resource constraints ## 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 agentops and kitaru? agentops: Python SDK for AI agent monitoring and LLM cost tracking. 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 agentops over kitaru? Choose agentops over kitaru when License: agentops is MIT, kitaru is Apache-2.0; Tags unique to agentops: benchmarking, cost-tracking; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK. ### When should I choose kitaru over agentops? Choose kitaru over agentops when License: kitaru is Apache-2.0, agentops is MIT; 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. ### When should I avoid agentops? If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI In case self-hosting of components is impractical due to resource constraints ### 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 agentops or kitaru more popular on GitHub? agentops has more GitHub stars (5,771 vs 226). Stars measure visibility, not whether either tool fits your constraints. ### Are agentops and kitaru open source? Yes - both are open-source projects on GitHub (agentops: MIT, kitaru: Apache-2.0). ### Where can I find alternatives to agentops or kitaru? GraphCanon lists graph-backed alternatives at [agentops alternatives](/tools/agentops-ai-agentops/alternatives) and [kitaru alternatives](/tools/zenml-io-kitaru/alternatives) ([agentops markdown twin](/tools/agentops-ai-agentops/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/agentops-ai-agentops-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, agentops or kitaru? agentops: 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 agentops and kitaru? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentops trust report](/tools/agentops-ai-agentops/trust); [kitaru trust report](/tools/zenml-io-kitaru/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentops-ai-agentops`](/api/graphcanon/graph?tool=agentops-ai-agentops) - 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/_