d87a1615ce
- 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
57 lines
1.5 KiB
Markdown
57 lines
1.5 KiB
Markdown
---
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sidebar_position: 10
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title: "Trajectories & Training Format"
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description: "How Hermes saves trajectories, normalizes tool calls, and produces training-friendly outputs"
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---
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# Trajectories & Training Format
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Hermes can save conversation trajectories for training, evaluation, and batch data generation workflows.
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Primary files:
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- `agent/trajectory.py`
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- `run_agent.py`
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- `batch_runner.py`
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- `trajectory_compressor.py`
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## What trajectories are for
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Trajectory outputs are used for:
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- SFT data generation
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- debugging agent behavior
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- benchmark/evaluation artifact capture
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- post-processing and compression pipelines
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## Normalization strategy
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Hermes converts live conversation structure into a training-friendly format.
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Important behaviors include:
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- representing reasoning in explicit markup
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- converting tool calls into structured XML-like regions for dataset compatibility
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- grouping tool outputs appropriately
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- separating successful and failed trajectories
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## Persistence boundaries
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Trajectory files do **not** blindly mirror all runtime prompt state.
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Some prompt-time-only layers are intentionally excluded from persisted trajectory content so datasets are cleaner and less environment-specific.
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## Batch runner
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`batch_runner.py` emits richer metadata than single-session trajectory saving, including:
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- model/provider metadata
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- toolset info
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- partial/failure markers
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- tool statistics
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## Related docs
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- [Environments, Benchmarks & Data Generation](./environments.md)
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- [Agent Loop Internals](./agent-loop.md)
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