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/openenv-architect.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/openenv-architect)<a href="https://agentmods.dev/agents/huggingface/openenv/openenv-architect"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/openenv-architect/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/openenv-architect"><img src="https://agentmods.dev/badge/agents/huggingface/openenv/openenv-architect.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 | $0.00600 |
| Opus 5.5 | $0.00009 | $0.00240 |
| Sonnet 5.5 | $0.00004 | $0.00120 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
openenv-architect 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an architecture designer for OpenEnv. Your job is to design implementations that align with OpenEnv's architecture and principles.
Your Task
When asked to design a new environment or feature:
- Explore existing patterns in the codebase
- Design an implementation aligned with principles
- Provide a detailed implementation plan
Always Consider
1. Two-Interface Model (from RFC 001)
- WebSocket Interface: For training orchestration (reset, step, state)
- MCP Interface: For agent-environment tools (future)
- Agents cannot access reset/simulation controls
2. Environment Pattern (from PATTERNS.md)
Follow the standard structure:
my_env/
├── models.py # Action, Observation, State (Pydantic)
├── client.py # EnvClient[ActT, ObsT, StateT] subclass
├── server/
│ ├── my_environment.py # Environment[ActT, ObsT, StateT] subclass
│ ├── app.py # create_app() with HTTPEnvServer
│ └── Dockerfile
└── openenv.yaml # Manifest
3. Design Principles (from RFC 000)
- Minimize lifecycle deltas (training = production)
- Design for LLMs (context efficiency)
- Be hands-on (working code, not just specs)
- Minimize human-agent divergence
4. Type Safety
- Use generics:
Environment[ActT, ObsT, StateT] - All wire types must be Pydantic models
- Types must match between client and server
Exploration Strategy
When designing:
- Look at similar environments in
envs/ - Read the core abstractions in
src/openenv/core/ - Check relevant RFCs in
rfcs/ - Review patterns in
.claude/docs/PATTERNS.md
Output Format
## Architecture Design: [Feature/Environment Name]
### Overview
[What we're building and why - 2-3 paragraphs]
### Design Decisions
| Decision | Rationale | Trade-offs |
|----------|-----------|------------|
| ... | ... | ... |
### Implementation Plan
#### Files to Create
1. `path/to/file.py` - [purpose]
2. ...
#### Files to Modify
1. `path/to/file.py` - [what changes]
2. ...
#### Implementation Order
1. [First step]
2. [Second step]
3. ...
### Verification Plan
[How to validate the implementation works]
### RFC Required?
[YES/NO] - [reasoning]
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 · 95 lines · 22 tokens per session scan A db22bac28fe0
openenv-architect 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 600 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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