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 command-line tool for evaluating and optimizing retrieval-augmented generation systems, supporting various vector databases and embedding models.
Markdown twin · awesome-evals alternatives · ragtune alternatives
GraphCanon updated Sep 20, 2026
10views this month
vs
Trust & integrity
| Signal | awesome-evals | ragtune |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (176d 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 | Published findings 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
- ragtune
- Tools for inspecting, debugging, benchmarking, and tuning RAG retrieval
Stars
- awesome-evals
- 901
- ragtune
- 13
Forks
- awesome-evals
- 104
- ragtune
- 1
Open issues
- awesome-evals
- 34
- 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 command-line tool for evaluating and optimizing retrieval-augmented generation systems, supporting various vector databases and embedding models.
Persona
- awesome-evals
- -
- ragtune
- -
Runtime
- awesome-evals
- -
- ragtune
- -
License
- awesome-evals
- Other
- ragtune
- MIT License
Last pushed
- awesome-evals
- Sep 15, 2026
- ragtune
- Mar 25, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- ragtune
- Developer Tools, Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- ragtune
- Slowing (36%)
Days since push
- awesome-evals
- 4d
- ragtune
- 176d
Open issues (now)
- awesome-evals
- 34
- ragtune
- 0
Stars delta
- awesome-evals
- +140 (30d)
- ragtune
- 0 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- ragtune
- 0 (30d)
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.
- Pricing: Free and open-source under the MIT License..
- Requirements: Min 2 GB RAM; Go installation is required to use Ragtune..
- Tags unique to ragtune: benchmarking, chroma, cli, embeddings.
- Also covers Developer Tools.
- When you need to inspect, debug, benchmark, and tune the retrieval layer of your RAG systems specifically.
When NOT to use ragtune
- If your project does not involve retrieval-augmented generation systems or if you are not using supported vector databases.
- When you prefer a tool that is not written in Go or does not offer command-line interface capabilities.
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 (metawake/ragtune) · observed Sep 20, 2026
- GitHub forks (metawake/ragtune) · observed Sep 20, 2026
- Last push (metawake/ragtune) · observed Mar 25, 2026
- License file (MIT) · 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 · ragtune 13 (synced Sep 20, 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: Tools for inspecting, debugging, benchmarking, and tuning RAG retrieval. 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; Pricing: Free and open-source under the MIT License.; Requirements: Min 2 GB RAM; Go installation is required to use Ragtune.; Tags unique to ragtune: benchmarking, chroma, cli, embeddings; Also covers Developer Tools; When you need to inspect, debug, benchmark, and tune the retrieval layer of your RAG systems specifically.
- 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 does not involve retrieval-augmented generation systems or if you are not using supported vector databases. When you prefer a tool that is not written in Go or does not offer command-line interface capabilities.
- Is awesome-evals or ragtune more popular on GitHub?
- awesome-evals has more GitHub stars (901 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: Very 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.