> ## Documentation Index
> Fetch the complete documentation index at: https://docs.transluce.org/llms.txt
> Use this file to discover all available pages before exploring further.

# NeMo Gym

> Ingest NeMo Gym rollouts into Docent

NeMo Gym ([docs](https://docs.nvidia.com/nemo/gym/latest/index.html), [GitHub](https://github.com/NVIDIA-NeMo/Gym)) is a library for building reinforcement learning environments for large language models. We offer helpers that import NeMo Gym rollout exports into Docent.

## Use this when

Use NeMo Gym ingestion when your source data is either:

* one parsed rollout dictionary already loaded in Python
* a JSONL export where each line is one NeMo Gym rollout object

## Main helpers

* `convert_nemogym_rollout_to_agent_run(rollout)` converts one parsed rollout object into one `AgentRun`.
* `convert_nemogym_jsonl_file_to_agent_runs(file_path)` reads a JSONL file and converts each line into one `AgentRun`.

```python theme={null}
from docent.sdk.integrations import (
    convert_nemogym_jsonl_file_to_agent_runs,
    convert_nemogym_rollout_to_agent_run,
)
```

## Example

To convert a JSONL export:

```python theme={null}
from docent.sdk.integrations import convert_nemogym_jsonl_file_to_agent_runs

agent_runs = convert_nemogym_jsonl_file_to_agent_runs("rollouts.jsonl")
```

To convert one rollout dictionary already loaded in memory:

```python theme={null}
from docent.sdk.integrations import convert_nemogym_rollout_to_agent_run

agent_run = convert_nemogym_rollout_to_agent_run(rollout)
```

After conversion, upload normally:

```python theme={null}
from docent import Docent
from docent.sdk.integrations import convert_nemogym_jsonl_file_to_agent_runs

client = Docent()
agent_runs = convert_nemogym_jsonl_file_to_agent_runs("rollouts.jsonl")
client.add_agent_runs(collection_id, agent_runs)
```

## More on the conversion process

Each NeMo Gym rollout becomes one Docent `AgentRun`.

At a high level, the converter:

* turns the rollout input and output into a single Docent transcript
* maps `developer` messages to Docent `system` messages
* converts NeMo Gym function calls into Docent tool calls and tool messages
* stores the rollout reward in `agent_run.metadata["scores"]["reward"]`
* preserves extra request and response data under `agent_run.metadata["source"]`

The converter is strict. It expects the rollout to include `responses_create_params.input`, `response.output`, `agent_ref.name`, `_ng_task_index`, and `_ng_rollout_index`.

`responses_create_params.input` can be either:

* a string, which Docent converts into a single user message
* an array of structured input items

If the rollout uses unsupported message shapes, unsupported content-part types, invalid tool-call wiring, or missing required fields, the converter raises `ConversionError` instead of trying to approximate the data.