Home/Compare/agentops vs kitaru

Comparison

agentops vs kitaru

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.

Markdown twin · agentops alternatives · kitaru alternatives

GraphCanon updated 1w

agentops logo

agentops

AgentOps-AI/agentops

5.8kpushed Jun 25, 2026
vs
kitaru logo

kitaru

zenml-io/kitaru

226pushed Aug 3, 2026

Trust & integrity

Signalagentopskitaru
Maintenance
Steady (49d since push)
As of 1w · github_public_v1
Very active (0d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 1w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

agentops
Python SDK for AI agent monitoring and LLM cost tracking
kitaru
Record, replay, and improve AI agents in production, built on ZenML

Stars

agentops
5.8k
kitaru
226

Forks

agentops
612
kitaru
15

Open issues

agentops
176
kitaru
49

Language

agentops
Python
kitaru
Python

Adopt for

agentops
AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage.
kitaru
Kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML.

Persona

agentops
-
kitaru
-

Runtime

agentops
-
kitaru
-

License

agentops
MIT
kitaru
Apache-2.0

Last pushed

agentops
Jun 25, 2026
kitaru
Aug 3, 2026

Categories

agentops
AI Agents, Evaluation & Observability
kitaru
AI Agents, Evaluation & Observability

Trust and health

Maintenance

agentops
Steady (60%)
kitaru
Very active (96%)

Days since push

agentops
49d
kitaru
0d

Open issues (now)

agentops
176
kitaru
49

Full report

agentops
Trust report

Shared compatibility

  • Python · agentops: Python runtime · kitaru: Python runtime

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

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

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 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.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: agentops 5.8k · kitaru 226 (synced Aug 14, 2026).

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 and kitaru alternatives (agentops markdown twin, kitaru markdown twin), 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 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; kitaru trust report.

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