Comparison
eval-view vs kitaru
Verdict
Pick eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time; pick kitaru if kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML.
Markdown twin · eval-view alternatives · kitaru alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | eval-view | kitaru |
|---|---|---|
| Maintenance | Very active (6d since push) As of 3w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- eval-view
- Regression testing for AI agents
- kitaru
- Record, replay, and improve AI agents in production, built on ZenML
Stars
- eval-view
- 126
- kitaru
- 226
Forks
- eval-view
- 21
- kitaru
- 15
Open issues
- eval-view
- 3
- kitaru
- 49
Language
- eval-view
- Python
- kitaru
- Python
Adopt for
- eval-view
- Regression testing for AI agents to detect behavioral changes and output quality regressions over time.
- kitaru
- Kitaru focuses on recording, replaying, and enhancing the performance of AI agents in production environments using technology from ZenML.
Persona
- eval-view
- -
- kitaru
- -
Runtime
- eval-view
- -
- kitaru
- -
License
- eval-view
- The software uses the Apache-2.0 license, offering permissive terms for use and distribution.
- kitaru
- Apache-2.0
Last pushed
- eval-view
- Jul 26, 2026
- kitaru
- Aug 3, 2026
Categories
- eval-view
- AI Agents, Evaluation & Observability
- kitaru
- AI Agents, Evaluation & Observability
Trust and health
Days since push
- eval-view
- 6d
- kitaru
- 0d
Open issues (now)
- eval-view
- 3
- kitaru
- 49
Owner type
- eval-view
- User
- kitaru
- Organization
Full report
- eval-view
- Trust report
- kitaru
- Trust report
Shared compatibility
- Python · eval-view: Python runtime · kitaru: Python runtime
Choose eval-view if…
- Pricing: Free to use under the terms of the Apache License, Version 2.0..
- Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality..
- Tags unique to eval-view: agent-benchmark, agent-evaluation, regression-testing.
- eval-view ships Docker support for self-hosted deployment.
- When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls.
When NOT to use eval-view
- If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time.
- When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge.
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 126) - visibility, not fit.
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 (hidai25/eval-view) · observed Aug 2, 2026
- GitHub forks (hidai25/eval-view) · observed Aug 2, 2026
- Last push (hidai25/eval-view) · observed Jul 26, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zenml-io/kitaru) · observed Aug 3, 2026
- GitHub forks (zenml-io/kitaru) · observed Aug 3, 2026
- Last push (zenml-io/kitaru) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: eval-view 126 · kitaru 226 (synced Aug 2, 2026).
Common questions
- What is the difference between eval-view and kitaru?
- eval-view: Regression testing for AI agents. 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 eval-view over kitaru?
- Choose eval-view over kitaru when Pricing: Free to use under the terms of the Apache License, Version 2.0.; Requirements: Python environment is required for installation and usage.; Installation with pip:
pip install evalview; Offline support means no live API keys necessary for the basic diff functionality.; Tags unique to eval-view: agent-benchmark, agent-evaluation, regression-testing; eval-view ships Docker support for self-hosted deployment; When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. - When should I choose kitaru over eval-view?
- Choose kitaru over eval-view 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 126) - visibility, not fit.
- When should I avoid eval-view?
- If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge.
- 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 eval-view or kitaru more popular on GitHub?
- kitaru has more GitHub stars (226 vs 126). Stars measure visibility, not whether either tool fits your constraints.
- Are eval-view and kitaru open source?
- Yes - both are open-source projects on GitHub (eval-view: Apache-2.0, kitaru: Apache-2.0).
- Where can I find alternatives to eval-view or kitaru?
- GraphCanon lists graph-backed alternatives at eval-view alternatives and kitaru alternatives (eval-view 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, eval-view or kitaru?
- eval-view: 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 eval-view and kitaru?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: eval-view trust report; kitaru trust report.