Home/Compare/agentdojo vs agent-learning-kit

Comparison

agentdojo vs agent-learning-kit

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 agent-learning-kit if agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language.

Markdown twin · agentdojo alternatives · agent-learning-kit alternatives

GraphCanon updated Oct 6, 2026

5views this month

agentdojo logo

agentdojo

ethz-spylab/agentdojo

★ 893pushed Jun 2, 2026
vs
agent-learning-kit logo

agent-learning-kit

future-agi/agent-learning-kit

★ 119pushed Sep 18, 2026

Trust & integrity

Signalagentdojoagent-learning-kit
Maintenance
Slowing (125d since push)
As of Oct 6, 2026 · github_public_v1
Very active (0d since push)
As of Sep 18, 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 Sep 18, 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 Sep 18, 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
agent-learning-kit
General Purpose Evaluation and Simulation Environment for all your AI related Workflows

Stars

agentdojo
893
agent-learning-kit
119

Forks

agentdojo
235
agent-learning-kit
44

Open issues

agentdojo
72
agent-learning-kit
19

Language

agentdojo
Python
agent-learning-kit
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.
agent-learning-kit
agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language

Persona

agentdojo
-
agent-learning-kit
-

Runtime

agentdojo
-
agent-learning-kit
-

License

agentdojo
MIT
agent-learning-kit
Other

Last pushed

agentdojo
Jun 2, 2026
agent-learning-kit
Sep 18, 2026

Categories

agentdojo
AI Agents, Evaluation & Observability
agent-learning-kit
AI Agents, Evaluation & Observability

Trust and health

Maintenance

agentdojo
Slowing (36%)
agent-learning-kit
Very active (96%)

Days since push

agentdojo
125d
agent-learning-kit
0d

Open issues (now)

agentdojo
72
agent-learning-kit
19

Stars delta

agentdojo
+91 (30d)
agent-learning-kit
+1 (30d)

Open issues delta

agentdojo
+21 (30d)
agent-learning-kit
+13 (30d)

Full report

agentdojo
Trust report
agent-learning-kit
Trust report

Shared compatibility

  • Python · agentdojo: Python runtime · agent-learning-kit: Python runtime

Choose agentdojo if…

  • License: agentdojo is MIT, agent-learning-kit 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.

Choose agent-learning-kit if…

  • License: agent-learning-kit is Other, agentdojo is MIT.
  • Tags unique to agent-learning-kit: agentic-ai, ai-agents, cicd, evaluation.
  • When you need a comprehensive environment for evaluating and simulating AI workflows, especially for AI agents

When NOT to use agent-learning-kit

  • If your project strictly requires a different programming language other than Python or TypeScript
  • When you need a tool that is already at a mature v1 release, as agent-learning-kit is still developing its TypeScript SDK and extras

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 893 · agent-learning-kit 119 (synced Oct 6, 2026).

Common questions

What is the difference between agentdojo and agent-learning-kit?
agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. agent-learning-kit: General Purpose Evaluation and Simulation Environment for all your AI related Workflows. See the comparison table for live GitHub stats and shared categories.
When should I choose agentdojo over agent-learning-kit?
Choose agentdojo over agent-learning-kit when License: agentdojo is MIT, agent-learning-kit 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 choose agent-learning-kit over agentdojo?
Choose agent-learning-kit over agentdojo when License: agent-learning-kit is Other, agentdojo is MIT; Tags unique to agent-learning-kit: agentic-ai, ai-agents, cicd, evaluation; When you need a comprehensive environment for evaluating and simulating AI workflows, especially for AI agents.
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 agent-learning-kit?
If your project strictly requires a different programming language other than Python or TypeScript When you need a tool that is already at a mature v1 release, as agent-learning-kit is still developing its TypeScript SDK and extras
Is agentdojo or agent-learning-kit more popular on GitHub?
agentdojo has more GitHub stars (893 vs 119). Stars measure visibility, not whether either tool fits your constraints.
Are agentdojo and agent-learning-kit open source?
Yes - both are open-source projects on GitHub (agentdojo: MIT, agent-learning-kit: Other).
Where can I find alternatives to agentdojo or agent-learning-kit?
GraphCanon lists graph-backed alternatives at agentdojo alternatives and agent-learning-kit alternatives (agentdojo markdown twin, agent-learning-kit 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 agent-learning-kit?
agentdojo: Slowing. agent-learning-kit: Very active. 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 agent-learning-kit?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; agent-learning-kit trust report.

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