Comparison
auto-evaluator vs awesome
Verdict
Pick auto-evaluator when tags unique to auto-evaluator: python; pick awesome when tags unique to awesome: resources, awesome-list.
Markdown twin · auto-evaluator alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | auto-evaluator | awesome |
|---|---|---|
| Maintenance | Dormant (1158d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | 118 low (118 low) As of today · osv@v1 | No lockfile As of today · none |
Tagline
- auto-evaluator
- Evaluation tool for LLM QA chains
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- auto-evaluator
- 1.1k
- awesome
- 484k
Forks
- auto-evaluator
- 92
- awesome
- 36k
Open issues
- auto-evaluator
- 3
- awesome
- 92
Language
- auto-evaluator
- Python
- awesome
- -
Adopt for
- auto-evaluator
- -
- awesome
- -
Persona
- auto-evaluator
- -
- awesome
- -
Runtime
- auto-evaluator
- -
- awesome
- -
License
- auto-evaluator
- -
- awesome
- CC0-1.0
Last pushed
- auto-evaluator
- May 10, 2023
- awesome
- Jun 30, 2026
Categories
- auto-evaluator
- Vector Databases, LLM Frameworks, Data & Retrieval
- awesome
- LLM Frameworks
Trust and health
Maintenance
- auto-evaluator
- Dormant (18%)
- awesome
- Active (82%)
Days since push
- auto-evaluator
- 1158d
- awesome
- 11d
Open issues (now)
- auto-evaluator
- 3
- awesome
- 92
Security scan
- auto-evaluator
- 118 low (118 low)
- awesome
- No lockfile
Full report
- auto-evaluator
- Trust report
- awesome
- Trust report
Choose auto-evaluator if…
- Tags unique to auto-evaluator: python.
- Also covers Vector Databases, Data & Retrieval.
- Leaner open-issue backlog (3).
When NOT to use auto-evaluator
- Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (rlancemartin/auto-evaluator) · observed Jul 11, 2026
- GitHub forks (rlancemartin/auto-evaluator) · observed Jul 11, 2026
- Last push (rlancemartin/auto-evaluator) · observed May 10, 2023
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: auto-evaluator 1.1k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between auto-evaluator and awesome?
- auto-evaluator: Evaluation tool for LLM QA chains. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose auto-evaluator over awesome?
- Choose auto-evaluator over awesome when Tags unique to auto-evaluator: python; Also covers Vector Databases, Data & Retrieval; Leaner open-issue backlog (3).
- When should I choose awesome over auto-evaluator?
- Choose awesome over auto-evaluator when Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 1.1k) - visibility, not fit.
- When should I avoid auto-evaluator?
- Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is auto-evaluator or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 1,102). Stars measure visibility, not whether either tool fits your constraints.
- Are auto-evaluator and awesome open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to auto-evaluator or awesome?
- GraphCanon lists graph-backed alternatives at auto-evaluator alternatives and awesome alternatives (auto-evaluator markdown twin, awesome 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, auto-evaluator or awesome?
- auto-evaluator: Dormant. awesome: 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 auto-evaluator and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: auto-evaluator trust report; awesome trust report.