Learning Path

Start with Installation, then complete the Quickstart. Everything below assumes one provider and one tool-capable model already complete a normal awaited turn.

By goal

Goal	Recommended reading
Embed the agent in Python	Python Library → Configuration → Sessions
Expose it from an async service	Programmatic Integration → Security → Session Storage
Generate fine-tuning trajectories	Batch Processing → Trajectory Format → Agent Loop
Add model-facing capabilities	Tools → Skills → MCP
Understand or extend the harness	Architecture → Agent Loop → Toolsets Reference

Recommended order for training work

1. Verify one provider can return reasoning and valid tool calls.
2. Inspect the exact tool schemas and enabled toolsets.
3. Run a single trajectory with save_trajectories=True.
4. Confirm reasoning → tool call → observation → final answer ordering.
5. Run a small resumable BatchRunner job.
6. Add dataset quality gates and model training outside this package.

The harness generates trajectories; it does not decide whether a sample is correct, safe, diverse, or suitable for a particular fine-tuning objective.

Recommended order for service work

1. Use one AIAgent per ordered conversation.
2. Map application identity to a deliberate session-isolation policy.
3. Register callbacks for interactive tools or disable those tools.
4. Bound concurrent agents and external tool work.
5. Propagate cancellation and always await agent.close().
6. Add authentication, quotas, and HTTP schemas in the host application.

You do not need to read every page. Follow the path matching the boundary your application owns, then return to the feature and reference sections as needed.