OpenJudge
OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards
No outside reports on file
OpenJudge itself doesn't access accounts or files directly, but it can be tricked through malicious inputs and may expose the data it's judging, so restrict what gets evaluated and who sees the results.
Based on public adoption and transparency signals as of Oct 29, 2026. Our opinion, not a code audit or security guarantee.
What it can access
- Local filesNo local folder access notedLOW
- Command executionNo shell command runner notedLOW
- Network reachConnects to outside service APINOTED
- CredentialsNo API token requiredLOW
- Package sourceGitHub (agentscope-ai/OpenJudge)PUBLIC
Inferred from this listing’s public package details and description. Check the project’s source before installing.
Install
No npm or PyPI package is listed. Install from source: github.com/agentscope-ai/OpenJudge ↗
Everything known
We found nothing about this project in the sources we read (last checked 2026-10-30). That does not mean it has no problems.
0 incidents · 0 advisories · 0 news articles · 0 community threads · checked 2026-10-30
Popularity grade: C (from public signals such as stars and downloads, not a code review). Graded 2026-10-29.
Sources we read: OSV, GitHub Advisory Database, CISA Known Exploited Vulnerabilities, 44 security news feeds and our incident library. Last checked 2026-10-30.
About OpenJudge
OpenJudge is a framework that helps AI systems evaluate and judge the quality of their own work or other AI outputs. It provides tools to score, rate, and assess different types of content or task completion in a structured way.
Who it’s for
AI developers and researchers who build agents that need to evaluate their own performance or compare different AI outputs. Teams that want a standardized way to measure AI quality across their projects.
Alternatives
- Ragas – An evaluation framework focused on measuring AI retrieval and question-answering quality.
- DeepEval – A lightweight testing library that runs quality checks on LLM outputs.
- LangSmith – LangChain's monitoring and evaluation platform for AI agents.
- Custom scoring systems – Building your own evaluation logic tailored to your specific needs.
Setup
Check these steps against the project’s own instructions before running anything.
Follow the project's README on GitHub at https://github.com/agentscope-ai/OpenJudge (opens external site) for installation and configuration instructions specific to your environment.
Why this grade
Signal-based grade (3/7 points): well starred on GitHub, public source code. This reflects adoption and transparency, not a code review.
Popularity and upkeep
- GitHub stars
- 986
- Listing last refreshed
- 2026-10-30
These are public popularity signals, not a maintenance check. Look at the repository’s commit history and open issues.
Risks to watch for
- Prompt injection attacks: Malicious user inputs could trick the judge into giving false high or low scores by embedding special instructions in the content being evaluated.
- Biased scoring: The scoring rules might reflect the creator's biases, leading the AI to unfairly favor or penalize certain types of outputs.
- Data exposure in logs: Evaluation data and AI outputs being judged may be stored in logs or temporary files where they could be read by unauthorized users.
- Denial of service: Attackers could feed it huge amounts of content to evaluate, consuming all computing resources and slowing your system.
- Accidental information leakage: The framework might include sensitive details from evaluated content in its reports or summaries.
How to lower the risk
Least-privilege permissions
- Run OpenJudge with minimal API keys and access rights.
- Don't give it permission to modify files or systems it only needs to read.
Network isolation
- Keep evaluation processes on internal networks when possible.
- Limit outbound connections unless the framework needs to call external services.
Monitoring
- Log all evaluation requests and results for audit trails.
- Watch for unusual scoring patterns that might indicate prompt injection.
Red flags to watch for
- Sudden changes in how content is being scored.
- Very large batches of content submitted for evaluation at once.
- Scores that don't match expected ranges or patterns.
Questions
How do I install OpenJudge?
OpenJudge does not publish a standalone npm or PyPI package. Clone or follow the setup instructions in its source repository at github.com/agentscope-ai/OpenJudge.
Are there security reports about OpenJudge?
No reports found for this package in our research (last checked 2026-10-30). OpenJudge currently holds Grade C (Moderate risk signals) based on public adoption and transparency signals. Key risk areas to watch include prompt injection attacks, biased scoring, data exposure in logs.
Is OpenJudge actively maintained?
OpenJudge has 986 GitHub stars. Our directory data was last refreshed on 2026-10-30. Check recent commit activity, release tags, and open issues at github.com/agentscope-ai/OpenJudge before pinning it in production.
What are alternatives to OpenJudge?
Notable alternatives to OpenJudge include Ragas (An evaluation framework focused on measuring AI retrieval and question-answering quality), DeepEval (A lightweight testing library that runs quality checks on LLM outputs), LangSmith (LangChain's monitoring and evaluation platform for AI agents), Custom scoring systems (Building your own evaluation logic tailored to your specific needs). You can also compare OpenJudge side by side with other skills in the Related tools section on this page.
README badge and team notes
Maintain or use OpenJudge? Show its live grade in your GitHub README, docs site, or run a check from the terminal before you install.
GitHub README Markdown:
[](https://agentarmorhq.com/skills/agentscope-ai-openjudge)HTML embed:
<a href="https://agentarmorhq.com/skills/agentscope-ai-openjudge"><img src="https://agentarmorhq.com/api/badge/agentscope-ai-openjudge" alt="AgentArmorHQ Risk Grade for OpenJudge" height="20" /></a>Terminal check:
curl -fsSL https://agentarmorhq.com/api/cli | node --input-type=module - check "agentscope-ai-openjudge"Share with your security lead
Need sign-off to use OpenJudge? Copy this note for Slack, a ticket or a pull request.
### AgentArmorHQ Security Review Memo: OpenJudge
- **Listing URL:** https://agentarmorhq.com/skills/agentscope-ai-openjudge
- **Type:** AI agent skill
- **Risk Grade:** Grade C (Moderate risk signals)
- **Outside Security Advisories on File:** 0
- **Public Adoption:** 986 GitHub stars
#### Permission & Blast-Radius Signals
- **Local files:** No local folder access noted
- **Command execution:** No shell command runner noted
- **Network reach:** Connects to outside service API
- **Credentials:** No API token required
- **Package source:** GitHub (agentscope-ai/OpenJudge)
#### Recommended Hardened Setup
- **Source Repo:** https://github.com/agentscope-ai/OpenJudge
- **Controls:** Pin an exact version in `.mcp.json`, scope access to a single workspace folder, and use a least-privilege read-only token where supported.
_Based on public adoption and transparency signals as of Oct 29, 2026. Our opinion, not a code audit or security guarantee._Key facts
- Type
- AI agent skill
- Publisher
- agentscope-ai
- Source code
- github.com/agentscope-ai/OpenJudge
- Est. context load
- ~50 tokens idle · ~450 tokens when active (on-demand skill)
- Popularity
- 986 GitHub stars
- Last updated
- 2026-10-30
- Last graded
- 2026-10-29
Community
Discussions for OpenJudge
Ask questions or share configuration tips. Governed by our Community Rules & Terms.
No posts match this filter yet.
Be the first to ask a question or share a working tip.
Check before you install, in Claude
Your assistant answers Block, Review or No known issues for any package before it installs it.
Get this check inside Claude