Evaligo
LLM agent benchmarks and RL environments.
At a glance
Price not confirmed · Source review 2026-10-31- Price & billing
Test on public tasks free with your own model API key; no other pricing is shown in the reviewed text.
- Free allowance
Test on public tasks free with your own model API key; no other pricing is shown in the reviewed text.
- Best suited to
Model teams training and benchmarking LLM agents on real work with held-out scoring.
Fit is editorial guidance, not a measured result.- Platforms
Access: not separately verified. Operating systems not separately verified.
Published-source facts and editorial guidance. No product test is implied. Confirm current conditions before purchase.
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What to know before choosing
Good fit
Model teams training and benchmarking LLM agents on real work with held-out scoring.
Limits and trade-offs
Guardrail and tool-use families in preparation; private split stays with Evaligo; public scores are page figures.
Plans and free allowances
Test on public tasks free with your own model API key; no other pricing is shown in the reviewed text.
Evidence
Evaligo homepage ↗ (reviewed 2026-10-31)
Official published page review; no authenticated product test
Dates: Last reviewed 2026-10-31. Last reviewed is the latest source or editorial review date; it is not a content-change date.
A closer look at Evaligo
Evaligo is benchmarks and RL environments for LLM agents: build an environment from real work, train your model on it, and measure the gain on held-out tasks, starting with SWE-Race coding (188 tasks, 106 projects) and guardrail and tool-use families in preparation.
Editorial guidance based on published documentation. The workflow below is a suggested starting point, not a measured product result.
When to choose Evaligo
- Model teams that want to train agents on real work and measure gains on held-out tasks.
- Researchers benchmarking coding agents against hidden tests.
- Teams that want guardrail benchmarks for prompt injection and data exfiltration (in preparation).
When to choose something else
- Users who want a general chatbot benchmark, since Evaligo is built for LLM agents and RL environments.
- Teams that need guardrail or tool-use benchmarks now, since those families are in preparation.
- Anyone who wants the private split locally, since it stays with Evaligo for official scoring.
A practical starting workflow
- Pick a benchmark such as SWE-Race public (95 tasks).
- Connect a model with any provider or your own endpoint.
- Change the scaffold (system prompt, tools, step budget, reasoning effort, router) and compare runs.
- Submit the scaffold for a held-out private-split score, published or kept private.
Check before committing
- The public model scores shown (GLM-5.3 Flash 81.9%, GPT-5.6 Luna 81.3%, DeepSeek V4 Flash 72.1%) are page figures at fetch time, not a live leaderboard.
- Guardrails and tool-use benchmark families are marked in preparation; only the coding family (SWE-Race) is detailed.
- Held-out scores require submitting the scaffold for Evaligo to run the private split.
Common questions
What is Evaligo?
Evaligo builds benchmarks from real work, each with an RL environment: build an environment, train on it, and measure the gain on held-out tasks.
What does SWE-Race cover?
SWE-Race covers race conditions, deadlocks and cancellation bugs in real repositories, with 188 tasks, 106 projects, 93 held out and 2,006 graded attempts.
How does training work?
The environment runs in Harbor format, so any RL trainer can use it, with rollouts on the training split only and Pass@1 measured on the private split nobody trains on.
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Sources and checks
Last source review is not a date of product testing. No rating or test result is implied by inclusion. Check the current plan and terms before buying.