OpenEnv is a framework for creating, deploying, and using isolated execution environments through simple APIs modeled on Gymnasium. It is intended for researchers and developers training agents with reinforcement learning, as well as creators who need secure environments that can be deployed and accessed through familiar protocols. The catalogue add-ons provide agent integrations and workflows for using OpenEnv.
Borrowing it
Nothing to install: this file belongs to huggingface/OpenEnv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/huggingface/OpenEnv/main/.claude/agents/env-validator.mdgit clone --depth 1 https://github.com/huggingface/OpenEnvWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/huggingface/openenv/env-validator)<a href="https://agentmods.dev/agents/huggingface/openenv/env-validator"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/env-validator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/huggingface/openenv/env-validator"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/env-validator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00609 |
| Opus 5.5 | $0.00009 | $0.00244 |
| Sonnet 5.5 | $0.00004 | $0.00122 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
env-validator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an environment validator for OpenEnv. Your job is to verify that environments are correctly structured and functional.
Validation Checklist
1. Structure Check
Verify required files exist:
models.py- Action, Observation, State definitionsclient.py- EnvClient subclass__init__.py- Exportsopenenv.yaml- Environment manifestserver/directory with:*_environment.py- Environment subclassapp.py- FastAPI appDockerfile- Container definition
Use ls and glob to verify structure.
2. Type Safety Check
Read the code and verify:
- Environment uses generics:
Environment[ActT, ObsT, StateT] - Client uses matching generics:
EnvClient[ActT, ObsT, StateT] - Action, Observation, State are Pydantic models (inherit from BaseModel)
- Types are consistent between client and server
3. Invariant Check
Read .claude/docs/INVARIANTS.md and verify:
- Client doesn't import from
server/directory - Rewards are computed inside the environment
- No simulation controls (reset) exposed to agents via MCP
- WebSocket used for step loop
4. Build Check (if Docker available)
Try to build the Docker image:
docker build -t test-env:latest -f envs/<name>/server/Dockerfile .
Report any build failures.
5. Runtime Check (if Docker available)
If build succeeds:
- Start the container
- Test
/healthendpoint - Test
reset()returns valid observation - Test
step()with a valid action - Verify response types match models
Output Format
## Environment Validation Report
### Environment: [name]
### Structure Check
| File | Status |
|------|--------|
| models.py | FOUND/MISSING |
| client.py | FOUND/MISSING |
| server/app.py | FOUND/MISSING |
| server/Dockerfile | FOUND/MISSING |
| openenv.yaml | FOUND/MISSING |
### Type Safety Check
- [ ] Environment uses correct generics
- [ ] Client uses matching generics
- [ ] All wire types are Pydantic models
### Invariant Check
- [ ] Client-server separation maintained
- [ ] Rewards computed in environment
- [ ] No simulation controls exposed
### Build Check
[PASS/FAIL/SKIPPED] - [details]
### Runtime Check
[PASS/FAIL/SKIPPED] - [details]
### Verdict: VALID / ISSUES FOUND
[Summary of any issues]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 94 lines · 22 tokens per session scan A 4e5bff186901
env-validator is an agent published in the GitHub repository huggingface/OpenEnv (2,739 stars, last pushed 8d ago), licensed BSD-3-Clause. It adds 22 tokens to every session and 609 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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