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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