nemotron-agentic-v1

Cleaned version of nvidia/Nemotron-Agentic-v1 converted to a uniform OpenAI-compatible tool-calling format.

Source

• Original dataset: nvidia/Nemotron-Agentic-Tool-Use-v1 — multi-turn conversations where LLMs decompose goals, call tools, and reason over tool outputs.
• Splits: interactive_agent, tool_calling

Schema

Column	Type	Description
messages	JSON string	List of message dicts
tools_json	JSON string	List of tool definitions in OpenAI function-calling format

Message fields

Field	Present on roles	Notes
role	all	system / user / assistant / tool
content	all	null when assistant emits tool calls
reasoning_content	assistant	Thinking; set when both content and tool_calls exist
tool_calls	assistant	arguments is a JSON string
tool_call_id	tool	Matches triggering tool call id

Processing Rules

1. Arguments normalisation: Raw tool_calling split stores arguments as a Python dict; converted to JSON string.
2. Dict content normalisation: Raw tool_calling split sometimes has tool-result content as a Python dict; serialised to JSON string.
3. content / tool_calls mutual exclusivity: assistant content moved to reasoning_content when tool_calls is non-empty.
4. Schema workaround: interactive_agent loaded via streaming; tool_calling loaded directly from JSONL (both avoid PyArrow mixed-type inference errors in the source).

Statistics

Split	Rows
interactive_agent	19,028
tool_calling	316,094

Loading

from datasets import load_dataset
import json

ds = load_dataset("tuandunghcmut/nemotron-agentic-v1", split="interactive_agent")
sample = ds[0]
messages = json.loads(sample["messages"])
tools    = json.loads(sample["tools_json"])

Citation

@misc{nemotron_agentic_v1,
  title  = {Nemotron-Agentic-Tool-Use-v1},
  author = {NVIDIA},
  year   = {2025},
  url    = {https://huggingface.co/datasets/nvidia/Nemotron-Agentic-v1}
}