Comparison
awesome-evals vs eval-view
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick eval-view if eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and.
Markdown twin · awesome-evals alternatives · eval-view alternatives
GraphCanon updated Sep 20, 2026
12views this month
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Trust & integrity
| Signal | awesome-evals | eval-view |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Active (13d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Sep 18, 2026 · 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
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- eval-view
- Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI
Stars
- awesome-evals
- 901
- eval-view
- 134
Forks
- awesome-evals
- 104
- eval-view
- 24
Open issues
- awesome-evals
- 34
- eval-view
- 2
Language
- awesome-evals
- -
- eval-view
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- eval-view
- Eval-view is a Python-based tool for regression testing of AI agents, supporting multiple platforms like LangGraph, CrewAI, OpenAI, and Anthropic. It snapshots AI behavior and detects regressions through diffing tool and
Persona
- awesome-evals
- -
- eval-view
- -
Runtime
- awesome-evals
- -
- eval-view
- -
License
- awesome-evals
- Other
- eval-view
- Apache-2.0
Last pushed
- awesome-evals
- Sep 15, 2026
- eval-view
- Sep 5, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- eval-view
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- eval-view
- Active (82%)
Days since push
- awesome-evals
- 4d
- eval-view
- 13d
Open issues (now)
- awesome-evals
- 34
- eval-view
- 2
Stars delta
- awesome-evals
- +140 (30d)
- eval-view
- +8 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- eval-view
- -1 (30d)
Owner type
- awesome-evals
- Organization
- eval-view
- User
Full report
- awesome-evals
- Trust report
- eval-view
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, eval-view is Apache-2.0.
- Tags unique to awesome-evals: awesome-list, benchmarks, llm-evaluation, rl-environments.
- Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation
When NOT to use awesome-evals
- Require real-time interactive support or direct tool integrations not covered by a static resource list
- Seeking proprietary tools from specific vendors rather than open resources and community content
Choose eval-view if…
- License: eval-view is Apache-2.0, awesome-evals is Other.
- Tags unique to eval-view: agent-benchmark, agentic-ai, anthropic, autogen.
- eval-view ships Docker support for self-hosted deployment.
- When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic.
When NOT to use eval-view
- If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic.
- When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes.
- If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag.
- If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (benchflow-ai/awesome-evals) · observed Sep 20, 2026
- GitHub forks (benchflow-ai/awesome-evals) · observed Sep 20, 2026
- Last push (benchflow-ai/awesome-evals) · observed Sep 15, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (hidai25/eval-view) · observed Sep 20, 2026
- GitHub forks (hidai25/eval-view) · observed Sep 20, 2026
- Last push (hidai25/eval-view) · observed Sep 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: awesome-evals 901 · eval-view 134 (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and eval-view?
- awesome-evals: A curated library of resources for building and evaluating AI agents. eval-view: Regression testing for AI agents, snapshots behavior, diffs tool calls, catches regressions in CI. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over eval-view?
- Choose awesome-evals over eval-view when License: awesome-evals is Other, eval-view is Apache-2.0; Tags unique to awesome-evals: awesome-list, benchmarks, llm-evaluation, rl-environments; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation.
- When should I choose eval-view over awesome-evals?
- Choose eval-view over awesome-evals when License: eval-view is Apache-2.0, awesome-evals is Other; Tags unique to eval-view: agent-benchmark, agentic-ai, anthropic, autogen; eval-view ships Docker support for self-hosted deployment; When you need to snapshot and diff the behavior of AI agents across multiple platforms, including LangGraph, CrewAI, OpenAI, and Anthropic.
- When should I avoid awesome-evals?
- Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content
- When should I avoid eval-view?
- If you are working exclusively with AI platforms not supported by eval-view, such as those not listed among LangGraph, CrewAI, OpenAI, and Anthropic. When you do not require regression testing or behavior snapshotting for your AI agents, as eval-view is specifically designed for these purposes. If you are looking for a tool that does not involve backend API charges for executing your agent, as eval-view does not skip these charges even with the --no-judge flag. If you need a tool that automatically handles the migration from the OpenAI Assistants API to the Responses API without manual intervention, as eval-view requires following a migration guide for this.
- Is awesome-evals or eval-view more popular on GitHub?
- awesome-evals has more GitHub stars (901 vs 134). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and eval-view open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, eval-view: Apache-2.0).
- Where can I find alternatives to awesome-evals or eval-view?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and eval-view alternatives (awesome-evals markdown twin, eval-view 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, awesome-evals or eval-view?
- awesome-evals: Very active. eval-view: 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 awesome-evals and eval-view?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; eval-view trust report.