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+---
+name: Environment Integration
+about: Propose integrating an existing environment or benchmark into NeMo Gym
+title: '[Environment] '
+labels: 'env-integration'
+assignees: ''
+
+---
+
+### Environment Overview
+
+- Name:
+- Source repo:
+- Paper/reference (if applicable):
+- License:
+- Brief description: What does this environment evaluate? (e.g. web navigation, code generation, tool use)
+
+### How does the agent interact with the environment?
+
+Describe what a typical task looks like from the agent's perspective. For example:
+- Does the agent receive a natural language prompt and return an answer?
+- Does the model use tools (function calling, code execution, web browsing)?
+- Is it single-turn or multi-turn (does the model get feedback and retry)?
+
+### Verifier Shape
+
+Describe the reward signal — what constitutes a successful completion? Is it binary pass/fail, a score, or multiple metrics? How is correctness determined (exact match, test cases, judge model, human eval)?
+
+### External Dependencies
+
+Does this environment require external tools, specific runtimes, or sandboxes (e.g. compilers, browsers, Docker, VMs)?
+If so, list them and note whether they can be auto-installed on server startup.
+
+### Data
+
+- Dataset source (e.g. HuggingFace, custom):
+- Approximate size (number of tasks):
+- Splits available (train/validation/test):
+
+### Known Results
+
+Are there published or known results to use as a reference? Link to leaderboards, papers, or repos with reported numbers.
+
+### Constraints & Requirements
+
+Note anything an engineer should know about running this environment:
+- Does it need specific hardware (GPUs, large memory)?
+- Does it require network access, Docker, or a VM?
+- Are there known limitations on parallelism or throughput?
+- Any OS or platform restrictions?
+
+### Implementation Request
+- [ ] I plan to implement this myself
+- [ ] I'm requesting help to implement this
+
+### Definition of Done
+
+- [ ] Environment can be launched with `ng_run`
+- [ ] Rollouts can be collected end-to-end with `ng_collect_rollouts`
+- [ ] Reward scores reproduce known/expected results
+- [ ] Example data committed for smoke testing
+- [ ] Train/validation datasets uploaded to dataset registry
+- [ ] Tests passing
+- [ ] Documentation in environment README
+- [ ] Benchmark config defined if applicable (e.g. pinned agent harness, dataset subset, num_repeats)
+
+### Additional Context
+
+Add any other context, links, or screenshots here.