LibreAI / Dev Tools / evalscope
// devtools · modelscope/evalscope

evalscope

A streamlined framework for evaluating and benchmarking LLMs, VLMs and AIGC models.

★ 3.5k stars⑂ 509 forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

evalscope is an open-source dev tools project by modelscope (modelscope/evalscope on GitHub). It has 3,509 GitHub stars and 509 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 8 October 2026.

GitHub stars
3,509
Forks
509
License
Apache-2.0
Last updated on GitHub
8 October 2026
Category
Dev Tools
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Clarify your goal

    Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.

  2. Get the project

    Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.

  3. Install and configure

    Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.

  4. Run your first task

    Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.

// features

Why people love it

Comprehensive benchmarks

Industry-recognised benchmarks including MMLU, C-Eval and GSM8K built in, with custom dataset support.

Multi-modal, multi-domain

Evaluate LLMs, VLMs, embedding, reranker and AIGC models in one framework.

Multiple backends

Native backend plus OpenCompass, VLMEvalKit and RAGEval integrations.

Agent evaluation mode

Drive benchmarks such as GSM8K, AIME and SWE-bench Agentic inside a controlled multi-turn AgentLoop with Docker sandbox and full traces.

Performance testing

Stress-test model services with TTFT, TPOT and throughput metrics, recordable to wandb/swanlab.

Reports and arena

Interactive web dashboard for comparison, plus pairwise Arena mode to rank models head-to-head.

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// run it

Run it yourself

  1. Clone the repository

    Get the source so you can inspect, configure and run it locally.

    git clone https://github.com/modelscope/evalscope.git
    cd evalscope
  2. Install EvalScope

    Install the package from PyPI.

    pip install evalscope
  3. Evaluate a model API

    Run a first benchmark against an OpenAI-compatible endpoint.

    evalscope eval --model your-model-name --api-url $OPENAI_API_BASE_URL --api-key $OPENAI_API_KEY --eval-type openai_api --datasets gsm8k --limit 5
// use cases

What you can build

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// faq

Questions, answered

Who maintains evalscope?

It is maintained by modelscope on GitHub (modelscope/evalscope), where the source, issues and releases live.

Is it free to use?

Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.

Does it need a paid API key?

For API models you supply that provider's key; local models downloaded from ModelScope need no paid key, just compute.

How does it compare to OpenCompass or lm-eval used standalone?

Compared with OpenCompass or lm-eval used standalone, evalscope's distinguishing focus is: a streamlined framework for evaluating and benchmarking LLMs, VLMs and AIGC models. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.

How do I get started?

Clone https://github.com/modelscope/evalscope (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.

// keep exploring

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// alternatives

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Get evalscope

Free and open — grab the source, star the repo, and start creating.