Comparison
awesome-evals vs ragtune
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick ragtune if ragtune is a Go-based benchmarking tool for RAG retrieval systems that allows users to inspect, debug, benchmark, and tune the retrieval layer.
Markdown twin · awesome-evals alternatives · ragtune alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | awesome-evals | ragtune |
|---|---|---|
| Maintenance | Active (26d since push) As of 3w · github_public_v1 | Slowing (129d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- awesome-evals
- A curated library of resources for building and evaluating AI agents
- ragtune
- Benchmarking tool for RAG retrieval, aiding in tuning and evaluating retrieval layers
Stars
- awesome-evals
- 761
- ragtune
- 13
Forks
- awesome-evals
- 71
- ragtune
- 1
Open issues
- awesome-evals
- 21
- ragtune
- 0
Language
- awesome-evals
- -
- ragtune
- Go
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- ragtune
- Ragtune is a Go-based benchmarking tool for RAG retrieval systems that allows users to inspect, debug, benchmark, and tune the retrieval layer.
Persona
- awesome-evals
- -
- ragtune
- -
Runtime
- awesome-evals
- -
- ragtune
- -
License
- awesome-evals
- Other
- ragtune
- MIT
Last pushed
- awesome-evals
- Jul 1, 2026
- ragtune
- Mar 25, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- ragtune
- Data & Retrieval, Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Active (82%)
- ragtune
- Slowing (36%)
Days since push
- awesome-evals
- 26d
- ragtune
- 129d
Open issues (now)
- awesome-evals
- 21
- ragtune
- 0
Owner type
- awesome-evals
- Organization
- ragtune
- User
OSV dependency advisories
- awesome-evals
- No lockfile (source not queried)
- ragtune
- Published findings
Full report
- awesome-evals
- Trust report
- ragtune
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, ragtune is MIT.
- 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 ragtune if…
- License: ragtune is MIT, awesome-evals is Other.
- Tags unique to ragtune: benchmarking, embeddings, metrics, retrieval-augmented-generation.
- Also covers Data & Retrieval.
- For organizations using multiple vector search engines like Chroma or Pinecone because Ragtune supports them directly.
When NOT to use ragtune
- If your project relies on languages other than Go, as Ragtune might not integrate smoothly without additional effort.
- When the primary focus of retrieval layer tuning lies outside supported vector search engines like Chroma or Qdrant and no customization can be applied via the tool.
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 Jul 28, 2026
- GitHub forks (benchflow-ai/awesome-evals) · observed Jul 28, 2026
- Last push (benchflow-ai/awesome-evals) · observed Jul 1, 2026
- License file (Other) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (metawake/ragtune) · observed Aug 2, 2026
- GitHub forks (metawake/ragtune) · observed Aug 2, 2026
- Last push (metawake/ragtune) · observed Mar 25, 2026
- License file (MIT) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 761 · ragtune 13 (synced Jul 28, 2026).
Common questions
- What is the difference between awesome-evals and ragtune?
- awesome-evals: A curated library of resources for building and evaluating AI agents. ragtune: Benchmarking tool for RAG retrieval, aiding in tuning and evaluating retrieval layers. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over ragtune?
- Choose awesome-evals over ragtune when License: awesome-evals is Other, ragtune is MIT; 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 ragtune over awesome-evals?
- Choose ragtune over awesome-evals when License: ragtune is MIT, awesome-evals is Other; Tags unique to ragtune: benchmarking, embeddings, metrics, retrieval-augmented-generation; Also covers Data & Retrieval; For organizations using multiple vector search engines like Chroma or Pinecone because Ragtune supports them directly.
- 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 ragtune?
- If your project relies on languages other than Go, as Ragtune might not integrate smoothly without additional effort. When the primary focus of retrieval layer tuning lies outside supported vector search engines like Chroma or Qdrant and no customization can be applied via the tool.
- Is awesome-evals or ragtune more popular on GitHub?
- awesome-evals has more GitHub stars (761 vs 13). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and ragtune open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, ragtune: MIT).
- Where can I find alternatives to awesome-evals or ragtune?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and ragtune alternatives (awesome-evals markdown twin, ragtune 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 ragtune?
- awesome-evals: Active. ragtune: Slowing. 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 ragtune?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; ragtune trust report.