Comparison
agentdojo vs ragas
Verdict
Pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents; pick ragas if ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights.
Markdown twin · agentdojo alternatives · ragas alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | agentdojo | ragas |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Slowing (176d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- ragas
- Supercharge Your LLM Application Evaluations 🚀
Stars
- agentdojo
- 716
- ragas
- 15k
Forks
- agentdojo
- 188
- ragas
- 1.6k
Open issues
- agentdojo
- 41
- ragas
- 562
Language
- agentdojo
- Python
- ragas
- Python
Adopt for
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- ragas
- Ragas is a Python-based tool designed to enhance the evaluation process of Large Language Model (LLM) applications through specialized workflows and performance insights.
Persona
- agentdojo
- -
- ragas
- developer harness
Runtime
- agentdojo
- -
- ragas
- -
License
- agentdojo
- MIT
- ragas
- Apache-2.0
Last pushed
- agentdojo
- Jun 2, 2026
- ragas
- Feb 24, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- ragas
- Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- ragas
- Slowing (36%)
Days since push
- agentdojo
- 63d
- ragas
- 176d
Open issues (now)
- agentdojo
- 41
- ragas
- 562
Stars delta
- agentdojo
- Unknown
- ragas
- +470 (30d)
Open issues delta
- agentdojo
- Unknown
- ragas
- +45 (30d)
Full report
- agentdojo
- Trust report
- ragas
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · ragas: Python runtime
Choose agentdojo if…
- License: agentdojo is MIT, ragas is Apache-2.0.
- 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.
- Also covers AI Agents.
- 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
- 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.
Choose ragas if…
- License: ragas is Apache-2.0, agentdojo is MIT.
- Requirements: Min 4 GB RAM.
- Tags unique to ragas: evaluation, llm, llmops.
- When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks.
When NOT to use ragas
- If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems.
- For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ethz-spylab/agentdojo) · observed Aug 5, 2026
- GitHub forks (ethz-spylab/agentdojo) · observed Aug 5, 2026
- Last push (ethz-spylab/agentdojo) · observed Jun 2, 2026
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (vibrantlabsai/ragas) · observed Aug 20, 2026
- GitHub forks (vibrantlabsai/ragas) · observed Aug 20, 2026
- Last push (vibrantlabsai/ragas) · observed Feb 24, 2026
- License file (Apache-2.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · ragas 15k (synced Aug 5, 2026).
Common questions
- What is the difference between agentdojo and ragas?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. ragas: Supercharge Your LLM Application Evaluations 🚀. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over ragas?
- Choose agentdojo over ragas when License: agentdojo is MIT, ragas is Apache-2.0; 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; Also covers AI Agents; AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- When should I choose ragas over agentdojo?
- Choose ragas over agentdojo when License: ragas is Apache-2.0, agentdojo is MIT; Requirements: Min 4 GB RAM; Tags unique to ragas: evaluation, llm, llmops; When you need advanced tools tailored for evaluating LLM applications, as RAGAS offers specific optimizations not found in generic testing frameworks.
- When should I avoid 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.
- When should I avoid ragas?
- If your application does not involve Large Language Models or if the evaluation needs are basic; RAGAS is optimized for LLM-specific evaluations which may be overkill for simpler systems. For projects that require real-time monitoring or continuous testing of live models where more dynamic observability tools might offer better support.
- Is agentdojo or ragas more popular on GitHub?
- ragas has more GitHub stars (15,388 vs 716). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and ragas open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, ragas: Apache-2.0).
- Where can I find alternatives to agentdojo or ragas?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and ragas alternatives (agentdojo markdown twin, ragas 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, agentdojo or ragas?
- agentdojo: Steady. ragas: 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 agentdojo and ragas?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; ragas trust report.