generate-openenv-env

/generate-openenv-env

Build a production-ready OpenEnv environment from a use-case prompt.

Execute Workflow

When invoked, execute this workflow end-to-end.

1. Parse the use case and name the environment

Derive a repo path in the form envs/<name>_env/.

• Normalize to snake_case.
• Keep names short and domain-specific.
• Example: "generate an env for the library textarena" -> envs/textarena_env/.

2. Research the target library/API before coding

Gather the minimum interface facts needed to implement reset, step, and state serialization.

• Search local docs/examples first.
• Search upstream docs/repo for the target library when local context is insufficient.
• Extract only implementation-critical details:
  • installation/dependency requirements
  • environment creation API
  • action format
  • observation format
  • reward and done semantics
  • special setup (model files, downloads, auth, etc.)

3. Mine matching OpenEnv examples

Select 2-3 existing environments as implementation templates.

• Always read references/openenv-tutorial-01-environments.md (Part 10) and references/openenv-docs-environment-builder.md.
• Prefer envs/textarena_env for external-library wrappers with richer state.
• Add one simpler baseline (for example envs/snake_env or envs/echo_env) to keep the implementation minimal.
• Follow patterns, do not copy blindly.
• Exclude generated or vendored files when mining examples (.venv/, build/, site-packages/, __pycache__/).

For a compact checklist and mapping, read references/env-generation-checklist.md.

4. Ask focused implementation questions

Ask only the questions that materially affect architecture. Use the question bank in references/env-generation-checklist.md.

Cover at least:

• action space contract
• observation fields needed by agents
• reward design and terminal conditions
• episode/session configuration knobs
• deployment target and dependency constraints

If answers are unavailable, proceed with explicit assumptions and document them.

5. Choose the environment archetype

Choose one archetype before scaffolding:

• Typed step/reset environment (default): use EnvClient + typed Action/Observation[/State] models.
• MCP tool environment: use MCPEnvironment + MCPToolClient and MCP action/observation types.
• Specialized client flow (rare): only when the standard clients cannot express required behavior (for example local+remote hybrid clients).

6. Scaffold the environment

Use the CLI to scaffold:

PYTHONPATH=src uv run openenv init <name>_env --output-dir envs

This generates all files with correct placeholders replaced, including pyproject.toml, Dockerfile, and uv.lock.

If the CLI is unavailable (import errors, missing dependencies), create the structure manually matching:

envs/<name>_env/
├── __init__.py
├── client.py
├── models.py
├── openenv.yaml
├── pyproject.toml
└── server/
    ├── __init__.py
    ├── app.py
    ├── <name>_environment.py
    └── Dockerfile

Use assets/openenv_env_template/ as a reference for file contents when scaffolding manually.

7. Implement with OpenEnv contracts

Implement these files in order:

1. models.py
2. server/<name>_environment.py
3. server/app.py
4. client.py
5. openenv.yaml
6. README.md

Use these standards:

• Use typed models (Action/Observation/State).
• Use create_app(<factory_or_class>, ActionType, ObservationType, env_name=...) in server/app.py. Pass a class or factory callable, not an instantiated environment.
• Dual-import pattern (required in server/app.py and server/<name>_environment.py): Use try: from ..models import X / except ImportError: from models import X. Relative imports work in-repo (PYTHONPATH=src:envs); bare imports work in Docker (PYTHONPATH=/app/env). The same pattern applies to intra-server imports (e.g., from .foo import Bar vs from server.foo import Bar).
• client.py uses EnvClient[ActionType, ObservationType, State] (three type parameters).
• Keep server logic in server/, keep client parsing in client.py.
• Expose config through environment variables when behavior is likely to vary.
• Keep reward logic inside the environment.
• Prefer reset/step signatures compatible with Environment:
  • reset(seed=None, episode_id=None, **kwargs)
  • step(action, timeout_s=None, **kwargs)
• Set SUPPORTS_CONCURRENT_SESSIONS=True only when isolation is real. Set max_concurrent_envs in create_app accordingly (1 when False, >1 when True).
• For MCP/tool-call UIs that send stringified JSON arguments, add action validators/parsers in server/app.py.
• Export public client/models symbols in __init__.py.
• Keep openenv.yaml aligned with current scaffold format (spec_version: 1, name, type, runtime, app, port).
• Avoid training/evaluation code paths in this skill.

8. Validate before handoff

Run the narrowest useful checks:

# Verify in-repo imports work (catches missing dual-import pattern)
PYTHONPATH=src:envs uv run python -c "from envs.<name>_env.server.<name>_environment import <ClassName>Environment"

# Build and validate
cd envs/<name>_env
openenv build
openenv validate --verbose
PYTHONPATH=src:envs uv run pytest envs/<name>_env -q

If tests do not exist, run a smoke check:

PYTHONPATH=src:envs uv run uvicorn envs.<name>_env.server.app:app --port 8000
curl http://localhost:8000/health
openenv validate --url http://localhost:8000

9. Deliver with assumptions and gaps

Report:

• files created/updated
• chosen archetype (typed vs MCP vs specialized)
• assumptions made due to missing answers
• validation commands executed and outcomes
• remaining risks or follow-up questions

Guardrails

• Do not route into model training/evaluation workflows.
• Do not invent library APIs; confirm against source docs.
• Do not skip reading at least one existing OpenEnv env before implementation.
• Do not copy outdated manifest patterns from older envs (name/version/action/observation-only manifests).
• Do not copy build artifacts or virtualenv files from example envs.
• Do not set max_concurrent_envs > 1 unless the environment explicitly supports concurrent sessions.