Comparison
awesome-evals vs BIG-bench
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick BIG-bench if decision-critical facts for BIG-bench.
Markdown twin · awesome-evals alternatives · BIG-bench alternatives
GraphCanon updated Oct 10, 2026
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Trust & integrity
| Signal | awesome-evals | BIG-bench |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Archived (812d since push) As of Oct 10, 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 10, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings 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
- BIG-bench
- Collaborative benchmark for language model capabilities
Stars
- awesome-evals
- 901
- BIG-bench
- 3.2k
Forks
- awesome-evals
- 104
- BIG-bench
- 618
Open issues
- awesome-evals
- 34
- BIG-bench
- 106
Language
- awesome-evals
- -
- BIG-bench
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- BIG-bench
- Decision-critical facts for BIG-bench
Persona
- awesome-evals
- -
- BIG-bench
- -
Runtime
- awesome-evals
- -
- BIG-bench
- -
License
- awesome-evals
- Other
- BIG-bench
- Apache-2.0
Last pushed
- awesome-evals
- Sep 15, 2026
- BIG-bench
- Jul 19, 2024
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- BIG-bench
- Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- BIG-bench
- Archived (8%)
Days since push
- awesome-evals
- 4d
- BIG-bench
- 812d
Archived on GitHub
- awesome-evals
- No
- BIG-bench
- Yes
Open issues (now)
- awesome-evals
- 34
- BIG-bench
- 106
Stars delta
- awesome-evals
- +140 (30d)
- BIG-bench
- -2 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- BIG-bench
- 0 (30d)
OSV dependency advisories
- awesome-evals
- No lockfile (source not queried)
- BIG-bench
- Published findings
Full report
- awesome-evals
- Trust report
- BIG-bench
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, BIG-bench is Apache-2.0.
- Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks.
- Also covers AI Agents.
- 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 BIG-bench if…
- License: BIG-bench is Apache-2.0, awesome-evals is Other.
- Requirements: Python 3.5-3.8 required.; `pytest` is necessary for running automated tests..
- Tags unique to BIG-bench: benchmarking, evaluation, language models, seqio.
- When you need a comprehensive benchmark that evaluates language models across various tasks and includes methods for extrapolating model capabilities.
When NOT to use BIG-bench
- If you are looking for a tool that simplifies benchmarking with minimal configuration, BIG-bench requires setting up an environment and can be more complex compared to streamlined benchmark tools.
- As BIG-bench relies on collaboration across various tasks and contributions from the community, it might not be ideal if you need benchmark tasks or evaluations immediately available without potential
- If your project does not require advanced extrapolation techniques for measuring model capabilities over a wide range of benchmarks, simpler evaluation tools may suffice.
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 (google/BIG-bench) · observed Oct 10, 2026
- GitHub forks (google/BIG-bench) · observed Oct 10, 2026
- Last push (google/BIG-bench) · observed Jul 19, 2024
- License file (Apache-2.0) · observed Oct 10, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 901 · BIG-bench 3.2k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and BIG-bench?
- awesome-evals: A curated library of resources for building and evaluating AI agents. BIG-bench: Collaborative benchmark for language model capabilities. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over BIG-bench?
- Choose awesome-evals over BIG-bench when License: awesome-evals is Other, BIG-bench is Apache-2.0; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation.
- When should I choose BIG-bench over awesome-evals?
- Choose BIG-bench over awesome-evals when License: BIG-bench is Apache-2.0, awesome-evals is Other; Requirements: Python 3.5-3.8 required.;
pytestis necessary for running automated tests.; Tags unique to BIG-bench: benchmarking, evaluation, language models, seqio; When you need a comprehensive benchmark that evaluates language models across various tasks and includes methods for extrapolating model capabilities. - 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 BIG-bench?
- If you are looking for a tool that simplifies benchmarking with minimal configuration, BIG-bench requires setting up an environment and can be more complex compared to streamlined benchmark tools. As BIG-bench relies on collaboration across various tasks and contributions from the community, it might not be ideal if you need benchmark tasks or evaluations immediately available without potential If your project does not require advanced extrapolation techniques for measuring model capabilities over a wide range of benchmarks, simpler evaluation tools may suffice.
- Is awesome-evals or BIG-bench more popular on GitHub?
- BIG-bench has more GitHub stars (3,244 vs 901). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and BIG-bench open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, BIG-bench: Apache-2.0).
- Where can I find alternatives to awesome-evals or BIG-bench?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and BIG-bench alternatives (awesome-evals markdown twin, BIG-bench 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 BIG-bench?
- awesome-evals: Very active. BIG-bench: 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 BIG-bench?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; BIG-bench trust report.