OpenDCAI / One-Eval
Automated system for LLM evaluation via agents. Doc as below:
Why this job: topics: evaluation, benchmark, llm evaluation
What it does
One-Eval is an automated Agent-based evaluation framework for Large Language Models, designed to achieve NL2Eval: automatically orchestrating evaluation workflows and generating reports from natural language requirements.\ Built on DataFlow and LangGraph, it emphasizes a traceable, interruptible, and scalable evaluation loop.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 2 months ago
- Stars
- 165
- +1 this week
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v0.1.0
- Released 7 months ago
- Built with
- Python
- 69% of the code
Is it looked after?
- Contributors
- 2
- Last 90 days
- Issues
- 89% closed
- 1 still open
- Pull requests
- 38 merged
- 1 waiting
- Releases
- 1
- 1 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 69%
- TypeScript 31%
- JavaScript <1%
- Dockerfile <1%
Topics
- agent
- agents
- benchmark
- data
- data-analysis
- data-science
- evaluation
- llm
- llms
- sglang
- vllm
Project record
- JobEvals and observability
- StatusListed
- Commits96
- Forks29
- Watching2
- Stars in 28 days+2
- CreatedNov 10, 2025
- First seen by usNov 7, 2026
- CheckedNov 7, 2026