Comparison
agentdojo vs DevEval
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 DevEval if devEval suits organizations requiring Python-centric software development benchmarks and practices evaluation.
Markdown twin · agentdojo alternatives · DevEval alternatives
GraphCanon updated Oct 8, 2026
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Trust & integrity
| Signal | agentdojo | DevEval |
|---|---|---|
| Maintenance | Slowing (125d since push) As of Oct 6, 2026 · github_public_v1 | Dormant (860d since push) As of Oct 8, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Oct 6, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 8, 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
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- DevEval
- A Comprehensive Benchmark for Software Development
Stars
- agentdojo
- 893
- DevEval
- 138
Forks
- agentdojo
- 235
- DevEval
- 13
Open issues
- agentdojo
- 72
- DevEval
- 0
Language
- agentdojo
- Python
- DevEval
- 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.
- DevEval
- DevEval suits organizations requiring Python-centric software development benchmarks and practices evaluation.
Persona
- agentdojo
- -
- DevEval
- -
Runtime
- agentdojo
- -
- DevEval
- -
License
- agentdojo
- MIT
- DevEval
- Apache-2.0
Last pushed
- agentdojo
- Jun 2, 2026
- DevEval
- May 30, 2024
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- DevEval
- Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Slowing (36%)
- DevEval
- Dormant (18%)
Days since push
- agentdojo
- 125d
- DevEval
- 860d
Open issues (now)
- agentdojo
- 72
- DevEval
- 0
Stars delta
- agentdojo
- +91 (30d)
- DevEval
- 0 (30d)
Open issues delta
- agentdojo
- +21 (30d)
- DevEval
- 0 (30d)
Full report
- agentdojo
- Trust report
- DevEval
- Trust report
Choose agentdojo if…
- License: agentdojo is MIT, DevEval 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: 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
- 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.
Choose DevEval if…
- License: DevEval is Apache-2.0, agentdojo is MIT.
- Tags unique to DevEval: docker-supported, python, software-development.
- Choose DevEval when you require comprehensive benchmarking specifically for software development using Python.
When NOT to use DevEval
- Avoid DevEval if your software projects heavily rely on languages other than Python.
- Do not use it when a non-Dockerized evaluation tool is preferred due to organizational constraints or preferences.
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 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 (open-compass/DevEval) · observed Oct 8, 2026
- GitHub forks (open-compass/DevEval) · observed Oct 8, 2026
- Last push (open-compass/DevEval) · observed May 30, 2024
- License file (Apache-2.0) · observed Oct 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 893 · DevEval 138 (synced Oct 6, 2026).
Common questions
- What is the difference between agentdojo and DevEval?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. DevEval: A Comprehensive Benchmark for Software Development. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over DevEval?
- Choose agentdojo over DevEval when License: agentdojo is MIT, DevEval 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: 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 DevEval over agentdojo?
- Choose DevEval over agentdojo when License: DevEval is Apache-2.0, agentdojo is MIT; Tags unique to DevEval: docker-supported, python, software-development; Choose DevEval when you require comprehensive benchmarking specifically for software development using Python.
- 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.
- When should I avoid DevEval?
- Avoid DevEval if your software projects heavily rely on languages other than Python. Do not use it when a non-Dockerized evaluation tool is preferred due to organizational constraints or preferences.
- Is agentdojo or DevEval more popular on GitHub?
- agentdojo has more GitHub stars (893 vs 138). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and DevEval open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, DevEval: Apache-2.0).
- Where can I find alternatives to agentdojo or DevEval?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and DevEval alternatives (agentdojo markdown twin, DevEval 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 DevEval?
- agentdojo: Slowing. DevEval: Dormant. 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 DevEval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; DevEval trust report.