Comparison
agentops vs agentdojo
Verdict
Pick agentops if agentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
Markdown twin · agentops alternatives · agentdojo alternatives
GraphCanon updated Oct 6, 2026
9views this month
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Trust & integrity
| Signal | agentops | agentdojo |
|---|---|---|
| Maintenance | Steady (86d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (125d since push) As of Oct 6, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 6, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 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
- agentops
- Python SDK for AI agent monitoring and LLM cost tracking
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
Stars
- agentops
- 5.8k
- agentdojo
- 893
Forks
- agentops
- 625
- agentdojo
- 235
Open issues
- agentops
- 184
- agentdojo
- 72
Language
- agentops
- Python
- agentdojo
- Python
Adopt for
- agentops
- AgentOps is an open-source Python toolkit for monitoring AI agents and tracking costs associated with Large Language Model usage.
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
Persona
- agentops
- -
- agentdojo
- -
Runtime
- agentops
- -
- agentdojo
- -
License
- agentops
- MIT
- agentdojo
- MIT
Last pushed
- agentops
- Jun 25, 2026
- agentdojo
- Jun 2, 2026
Categories
- agentops
- AI Agents, Evaluation & Observability
- agentdojo
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- agentops
- Steady (60%)
- agentdojo
- Slowing (36%)
Days since push
- agentops
- 86d
- agentdojo
- 125d
Open issues (now)
- agentops
- 184
- agentdojo
- 72
Stars delta
- agentops
- +59 (30d)
- agentdojo
- +91 (30d)
Open issues delta
- agentops
- +8 (30d)
- agentdojo
- +21 (30d)
Full report
- agentops
- Trust report
- agentdojo
- Trust report
Shared compatibility
- Python · agentops: Python runtime · agentdojo: Python runtime
Choose agentops if…
- Tags unique to agentops: ai-agents, benchmarking, cost-tracking.
- Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK
- More GitHub stars (5.8k vs 893) - visibility, not fit.
When NOT to use agentops
- If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI
- In case self-hosting of components is impractical due to resource constraints
Choose agentdojo if…
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AgentOps-AI/agentops) · observed Sep 20, 2026
- GitHub forks (AgentOps-AI/agentops) · observed Sep 20, 2026
- Last push (AgentOps-AI/agentops) · observed Jun 25, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- 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 on cards: agentops 5.8k · agentdojo 893 (synced Sep 20, 2026).
Common questions
- What is the difference between agentops and agentdojo?
- agentops: Python SDK for AI agent monitoring and LLM cost tracking. agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentops over agentdojo?
- Choose agentops over agentdojo when Tags unique to agentops: ai-agents, benchmarking, cost-tracking; Integrations are needed specifically with Langchain, CrewAI, or OpenAI Agents SDK; More GitHub stars (5.8k vs 893) - visibility, not fit.
- When should I choose agentdojo over agentops?
- Choose agentdojo over agentops when 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 avoid agentops?
- If specific integration support is needed for frameworks not listed including Autogen AG2 CamelAI In case self-hosting of components is impractical due to resource constraints
- 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.
- Is agentops or agentdojo more popular on GitHub?
- agentops has more GitHub stars (5,830 vs 893). Stars measure visibility, not whether either tool fits your constraints.
- Are agentops and agentdojo open source?
- Yes - both are open-source projects on GitHub (agentops: MIT, agentdojo: MIT).
- Where can I find alternatives to agentops or agentdojo?
- GraphCanon lists graph-backed alternatives at agentops alternatives and agentdojo alternatives (agentops markdown twin, agentdojo 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, agentops or agentdojo?
- agentops: Steady. agentdojo: 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 agentops and agentdojo?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentops trust report; agentdojo trust report.