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sidebar_position: 10
title: "Trajectories & Training Format"
description: "How Hermes saves trajectories, normalizes tool calls, and produces training-friendly outputs"
---
# Trajectories & Training Format
Hermes can save conversation trajectories for training, evaluation, and batch data generation workflows.
Primary files:
- `agent/trajectory.py`
- `run_agent.py`
- `batch_runner.py`
- `trajectory_compressor.py`
## What trajectories are for
Trajectory outputs are used for:
- SFT data generation
- debugging agent behavior
- benchmark/evaluation artifact capture
- post-processing and compression pipelines
## Normalization strategy
Hermes converts live conversation structure into a training-friendly format.
Important behaviors include:
- representing reasoning in explicit markup
- converting tool calls into structured XML-like regions for dataset compatibility
- grouping tool outputs appropriately
- separating successful and failed trajectories
## Persistence boundaries
Trajectory files do **not** blindly mirror all runtime prompt state.
Some prompt-time-only layers are intentionally excluded from persisted trajectory content so datasets are cleaner and less environment-specific.
## Batch runner
`batch_runner.py` emits richer metadata than single-session trajectory saving, including:
- model/provider metadata
- toolset info
- partial/failure markers
- tool statistics
## Related docs
- [Environments, Benchmarks & Data Generation](./environments.md)
- [Agent Loop Internals](./agent-loop.md)
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