Examples

Small, complete programs, not fragments. Each one is a pattern the library was designed around. The shape of every example is exercised in CI against the in-process fake providers (tests/test_docs_examples.py); whether a program runs offline as written, and against what, is stated on each page.

Example	What it shows
Structured summaries with a fallback chain	Schema-validated output, bounded repair, multi-provider fallback, and the attempt trail
A local tool-calling assistant	The @ai.tool decorator, the tool loop, and running fully local
Distill a corpus	Map/reduce over material that will never fit, with cost preflight and a deterministic reducer
Regression-test fallback and repair	A golden run manifest that asserts inference behavior instead of model prose
Semantic search over a small corpus	Embedding a corpus, index/query space safety, and reranking, with your own in-memory similarity math

Comparing targets without spending anything is a guide rather than an example; see Will my request survive a target change?

If you are new to the library, read the Quickstart first; these examples assume you know what a target is.