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teknium1 d87a1615ce docs: add ACP and internal systems implementation guides

- add ACP user and developer docs covering setup, lifecycle, callbacks,
  permissions, tool rendering, and runtime behavior
- add developer guides for agent loop, provider runtime resolution,
  prompt assembly, context caching/compression, gateway internals,
  session storage, tools runtime, trajectories, and cron internals
- refresh architecture, quickstart, installation, CLI reference, and
  environments docs to link the new implementation pages and ACP support

2026-03-14 00:29:48 -07:00

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10	Trajectories & Training Format	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

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