Comparison
awesome-evals vs agentdojo
Verdict
Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
Markdown twin · awesome-evals alternatives · agentdojo alternatives
GraphCanon updated Oct 6, 2026
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Trust & integrity
| Signal | awesome-evals | agentdojo |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (125d since push) As of Oct 6, 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 6, 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
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
Stars
- awesome-evals
- 901
- agentdojo
- 893
Forks
- awesome-evals
- 104
- agentdojo
- 235
Open issues
- awesome-evals
- 34
- agentdojo
- 72
Language
- awesome-evals
- -
- agentdojo
- Python
Adopt for
- awesome-evals
- Curated resources for AI agent evaluation with BenchFlow backing its maintenance
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
Persona
- awesome-evals
- -
- agentdojo
- -
Runtime
- awesome-evals
- -
- agentdojo
- -
License
- awesome-evals
- Other
- agentdojo
- MIT
Last pushed
- awesome-evals
- Sep 15, 2026
- agentdojo
- Jun 2, 2026
Categories
- awesome-evals
- AI Agents, Evaluation & Observability
- agentdojo
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- awesome-evals
- Very active (96%)
- agentdojo
- Slowing (36%)
Days since push
- awesome-evals
- 4d
- agentdojo
- 125d
Open issues (now)
- awesome-evals
- 34
- agentdojo
- 72
Stars delta
- awesome-evals
- +140 (30d)
- agentdojo
- +91 (30d)
Open issues delta
- awesome-evals
- +13 (30d)
- agentdojo
- +21 (30d)
Full report
- awesome-evals
- Trust report
- agentdojo
- Trust report
Choose awesome-evals if…
- License: awesome-evals is Other, agentdojo is MIT.
- Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks.
- 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 agentdojo if…
- License: agentdojo is MIT, awesome-evals is Other.
- Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs..
- Requirements: Min 8 GB RAM.
- Tags unique to agentdojo: benchmark, large-language-models, prompt-injection, security.
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
When NOT to use agentdojo
- Last GitHub push was Jun 2, 2026 (slowing maintenance). Validate activity before betting a new project on agentdojo.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
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 (ethz-spylab/agentdojo) · observed Oct 6, 2026
- GitHub forks (ethz-spylab/agentdojo) · observed Oct 6, 2026
- Last push (ethz-spylab/agentdojo) · observed Jun 2, 2026
- License file (MIT) · observed Oct 6, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-evals 901 · agentdojo 893 (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-evals and agentdojo?
- awesome-evals: A curated library of resources for building and evaluating AI agents. agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-evals over agentdojo?
- Choose awesome-evals over agentdojo when License: awesome-evals is Other, agentdojo is MIT; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation.
- When should I choose agentdojo over awesome-evals?
- Choose agentdojo over awesome-evals when License: agentdojo is MIT, awesome-evals is Other; Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.; Requirements: Min 8 GB RAM; Tags unique to agentdojo: benchmark, large-language-models, prompt-injection, security; AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- 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 agentdojo?
- Last GitHub push was Jun 2, 2026 (slowing maintenance). Validate activity before betting a new project on agentdojo. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- Is awesome-evals or agentdojo more popular on GitHub?
- awesome-evals has more GitHub stars (901 vs 893). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-evals and agentdojo open source?
- Yes - both are open-source projects on GitHub (awesome-evals: Other, agentdojo: MIT).
- Where can I find alternatives to awesome-evals or agentdojo?
- GraphCanon lists graph-backed alternatives at awesome-evals alternatives and agentdojo alternatives (awesome-evals markdown twin, agentdojo 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 agentdojo?
- awesome-evals: Very active. agentdojo: 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 agentdojo?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-evals trust report; agentdojo trust report.