Home/Compare/agentdojo vs RagaAI-Catalyst

Comparison

agentdojo vs RagaAI-Catalyst

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 RagaAI-Catalyst if ragaAI-Catalyst emerges as a specialized Python framework designed for monitoring and evaluating AI agents, with unique features around self-hosted dashboards, advanced analytics, and support for tracing and debugging LL.

Markdown twin · agentdojo alternatives · RagaAI-Catalyst alternatives

GraphCanon updated today

agentdojo logo

agentdojo

ethz-spylab/agentdojo

659pushed Jun 2, 2026
vs
RagaAI-Catalyst logo

RagaAI-Catalyst

raga-ai-hub/RagaAI-Catalyst

16kpushed Feb 11, 2026

Trust & integrity

SignalagentdojoRagaAI-Catalyst
Maintenance
Steady (39d since push)
As of today · github_public_v1
Slowing (149d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
159 low (159 low)
As of today · osv@v1

Tagline

agentdojo
A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
RagaAI-Catalyst
Python SDK for AI agent observability and evaluation

Stars

agentdojo
659
RagaAI-Catalyst
16k

Forks

agentdojo
168
RagaAI-Catalyst
3.6k

Open issues

agentdojo
33
RagaAI-Catalyst
34

Language

agentdojo
Python
RagaAI-Catalyst
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.
RagaAI-Catalyst
RagaAI-Catalyst emerges as a specialized Python framework designed for monitoring and evaluating AI agents, with unique features around self-hosted dashboards, advanced analytics, and support for tracing and debugging LL

Persona

agentdojo
-
RagaAI-Catalyst
-

Runtime

agentdojo
-
RagaAI-Catalyst
-

License

agentdojo
MIT
RagaAI-Catalyst
Apache-2.0

Last pushed

agentdojo
Jun 2, 2026
RagaAI-Catalyst
Feb 11, 2026

Categories

agentdojo
AI Agents, Evaluation & Observability
RagaAI-Catalyst
AI Agents, Evaluation & Observability

Trust and health

Maintenance

agentdojo
Steady (60%)
RagaAI-Catalyst
Slowing (36%)

Days since push

agentdojo
39d
RagaAI-Catalyst
149d

Open issues (now)

agentdojo
33
RagaAI-Catalyst
34

Security scan

agentdojo
No lockfile
RagaAI-Catalyst
159 low (159 low)

Full report

agentdojo
Trust report
RagaAI-Catalyst
Trust report

Shared compatibility

  • Python · agentdojo: Python runtime · RagaAI-Catalyst: Python runtime

Choose agentdojo if…

  • License: agentdojo is MIT, RagaAI-Catalyst 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: prompt-injection, benchmark, large-language-models, 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

  • 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 RagaAI-Catalyst if…

  • License: RagaAI-Catalyst is Apache-2.0, agentdojo is MIT.
  • Tags unique to RagaAI-Catalyst: ai-performance-optimization, ai-application-debugging, ai-agent-monitoring, agents.
  • When you need comprehensive tools for the observability of complex multi-agentic systems.

When NOT to use RagaAI-Catalyst

  • When you prefer a language-agnostic solution or require support outside of the Python ecosystem.
  • If your primary need is focused solely on basic monitoring without advanced debugging and evaluation features.
  • For projects that do not utilize multi-agentic systems or do not benefit from timeline and execution graph visualizations.
  • In scenarios where a fully managed service with no self-hosting requirements is preferred.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: agentdojo 659 · RagaAI-Catalyst 16k (synced Jul 11, 2026).

Common questions

What is the difference between agentdojo and RagaAI-Catalyst?
agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. RagaAI-Catalyst: Python SDK for AI agent observability and evaluation. See the comparison table for live GitHub stats and shared categories.
When should I choose agentdojo over RagaAI-Catalyst?
Choose agentdojo over RagaAI-Catalyst when License: agentdojo is MIT, RagaAI-Catalyst 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: prompt-injection, benchmark, large-language-models, 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 choose RagaAI-Catalyst over agentdojo?
Choose RagaAI-Catalyst over agentdojo when License: RagaAI-Catalyst is Apache-2.0, agentdojo is MIT; Tags unique to RagaAI-Catalyst: ai-performance-optimization, ai-application-debugging, ai-agent-monitoring, agents; When you need comprehensive tools for the observability of complex multi-agentic systems.
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 RagaAI-Catalyst?
When you prefer a language-agnostic solution or require support outside of the Python ecosystem. If your primary need is focused solely on basic monitoring without advanced debugging and evaluation features. For projects that do not utilize multi-agentic systems or do not benefit from timeline and execution graph visualizations. In scenarios where a fully managed service with no self-hosting requirements is preferred.
Is agentdojo or RagaAI-Catalyst more popular on GitHub?
RagaAI-Catalyst has more GitHub stars (16,145 vs 659). Stars measure visibility, not whether either tool fits your constraints.
Are agentdojo and RagaAI-Catalyst open source?
Yes - both are open-source projects on GitHub (agentdojo: MIT, RagaAI-Catalyst: Apache-2.0).
Where can I find alternatives to agentdojo or RagaAI-Catalyst?
GraphCanon lists graph-backed alternatives at agentdojo alternatives and RagaAI-Catalyst alternatives (agentdojo markdown twin, RagaAI-Catalyst 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 RagaAI-Catalyst?
agentdojo: Steady. RagaAI-Catalyst: 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 RagaAI-Catalyst?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; RagaAI-Catalyst trust report.