Comparison
agentdojo vs rebuff
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 rebuff if rebuff is a detector for prompt injection attacks on large language models and operates in TypeScript under an Apache-2.0 license.
Markdown twin · agentdojo alternatives · rebuff alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | agentdojo | rebuff |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Archived (727d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · 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
- rebuff
- LLM Prompt Injection Detector
Stars
- agentdojo
- 716
- rebuff
- 1.5k
Forks
- agentdojo
- 188
- rebuff
- 141
Open issues
- agentdojo
- 41
- rebuff
- 33
Language
- agentdojo
- Python
- rebuff
- TypeScript
Adopt for
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- rebuff
- Rebuff is a detector for prompt injection attacks on large language models and operates in TypeScript under an Apache-2.0 license.
Persona
- agentdojo
- -
- rebuff
- -
Runtime
- agentdojo
- -
- rebuff
- -
License
- agentdojo
- MIT
- rebuff
- Apache-2.0
Last pushed
- agentdojo
- Jun 2, 2026
- rebuff
- Aug 7, 2024
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- rebuff
- Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- rebuff
- Archived (8%)
Days since push
- agentdojo
- 63d
- rebuff
- 727d
Archived on GitHub
- agentdojo
- No
- rebuff
- Yes
Open issues (now)
- agentdojo
- 41
- rebuff
- 33
Full report
- agentdojo
- Trust report
- rebuff
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · rebuff: Python runtime
Choose agentdojo if…
- agentdojo is primarily Python; rebuff is TypeScript.
- License: agentdojo is MIT, rebuff 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.
- 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 rebuff if…
- rebuff is primarily TypeScript; agentdojo is Python.
- License: rebuff is Apache-2.0, agentdojo is MIT.
- Tags unique to rebuff: llm, llmops, prompt-engineering, prompts.
- Use Rebuff when you need precise detection of prompt injection vulnerabilities specific to your deployment, especially if it relies heavily on interactions with large language models.
When NOT to use rebuff
- Do not use Rebuff if setting up and managing multiple provider services like Supabase, OpenAI, Pinecone, or Chroma is inconvenient or infeasible for your project requirements.
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 (protectai/rebuff) · observed Aug 5, 2026
- GitHub forks (protectai/rebuff) · observed Aug 5, 2026
- Last push (protectai/rebuff) · observed Aug 7, 2024
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · rebuff 1.5k (synced Aug 5, 2026).
Common questions
- What is the difference between agentdojo and rebuff?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. rebuff: LLM Prompt Injection Detector. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over rebuff?
- Choose agentdojo over rebuff when agentdojo is primarily Python; rebuff is TypeScript; License: agentdojo is MIT, rebuff 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; 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 rebuff over agentdojo?
- Choose rebuff over agentdojo when rebuff is primarily TypeScript; agentdojo is Python; License: rebuff is Apache-2.0, agentdojo is MIT; Tags unique to rebuff: llm, llmops, prompt-engineering, prompts; Use Rebuff when you need precise detection of prompt injection vulnerabilities specific to your deployment, especially if it relies heavily on interactions with large language models.
- 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 rebuff?
- Do not use Rebuff if setting up and managing multiple provider services like Supabase, OpenAI, Pinecone, or Chroma is inconvenient or infeasible for your project requirements.
- Is agentdojo or rebuff more popular on GitHub?
- rebuff has more GitHub stars (1,516 vs 716). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and rebuff open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, rebuff: Apache-2.0).
- Where can I find alternatives to agentdojo or rebuff?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and rebuff alternatives (agentdojo markdown twin, rebuff 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 rebuff?
- agentdojo: Steady. rebuff: Archived. 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 rebuff?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; rebuff trust report.