Comparison
awesome-evals vs agent-opt
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; 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.
Markdown twin · awesome-evals alternatives · agent-opt alternatives
GraphCanon updated Oct 3, 2026
7views this month
vs
Trust & integrity
| Signal | awesome-evals | agent-opt |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Archived (95d since push) As of Oct 3, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 3, 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 Jul 11, 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
- agent-opt
- Open Source Library for Automated Optimization of AI Agent Workflows
Stars
- awesome-evals
- 901
- agent-opt
- 75
Forks
- awesome-evals
- 104
- agent-opt
- 9
Open issues
- awesome-evals
- 34
- agent-opt
- 0
Language
- awesome-evals
- -
- agent-opt
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- agent-opt
- 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.
Persona
- awesome-evals
- -
- agent-opt
- -
Runtime
- awesome-evals
- -
- agent-opt
- -
License
- awesome-evals
- Other
- agent-opt
- Apache-2.0
Last pushed
- awesome-evals
- Sep 15, 2026
- agent-opt
- Jun 30, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- agent-opt
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- agent-opt
- Archived (8%)
Days since push
- awesome-evals
- 4d
- agent-opt
- 95d
Archived on GitHub
- awesome-evals
- No
- agent-opt
- Yes
Open issues (now)
- awesome-evals
- 34
- agent-opt
- 0
Stars delta
- awesome-evals
- +140 (30d)
- agent-opt
- +4 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- agent-opt
- 0 (30d)
Full report
- awesome-evals
- Trust report
- agent-opt
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, agent-opt is Apache-2.0.
- Tags unique to awesome-evals: agent-evaluation, awesome-list, benchmarks, llm-evaluation.
- 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 agent-opt if…
- License: agent-opt is Apache-2.0, awesome-evals is Other.
- Tags unique to agent-opt: agent, aioptimization, automation, cicd.
- - When your project needs seamless CI/CD integration alongside automated optimization
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
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 (future-agi/agent-opt) · observed Oct 3, 2026
- GitHub forks (future-agi/agent-opt) · observed Oct 3, 2026
- Last push (future-agi/agent-opt) · observed Jun 30, 2026
- License file (Apache-2.0) · observed Oct 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 901 · agent-opt 75 (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and agent-opt?
- awesome-evals: A curated library of resources for building and evaluating AI agents. agent-opt: Open Source Library for Automated Optimization of AI Agent Workflows. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over agent-opt?
- Choose awesome-evals over agent-opt when License: awesome-evals is Other, agent-opt is Apache-2.0; Tags unique to awesome-evals: agent-evaluation, awesome-list, benchmarks, llm-evaluation; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation.
- When should I choose agent-opt over awesome-evals?
- Choose agent-opt over awesome-evals when License: agent-opt is Apache-2.0, awesome-evals is Other; Tags unique to agent-opt: agent, aioptimization, automation, cicd; - When your project needs seamless CI/CD integration alongside automated optimization.
- 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 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
- Is awesome-evals or agent-opt more popular on GitHub?
- awesome-evals has more GitHub stars (901 vs 75). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and agent-opt open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, agent-opt: Apache-2.0).
- Where can I find alternatives to awesome-evals or agent-opt?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and agent-opt alternatives (awesome-evals markdown twin, agent-opt 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 agent-opt?
- awesome-evals: Very active. agent-opt: Archived. 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 agent-opt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; agent-opt trust report.