pydantic--pydantic-ai/pydantic_ai_slim/pydantic_ai/AGENTS.md

pydantic_ai_slim/pydantic_ai/ Guidelines

For non-trivial changes to this package, also read Pydantic AI Slim Architecture.

API Design

• Add capability flags to model profile classes instead of inline isinstance() checks — prevents scattered feature detection logic — Centralizing feature support as boolean flags (e.g., bedrock_supports_prompt_caching) in profile classes makes capability detection maintainable and prevents duplicating provider/model type checks across usage sites.

• Configure provider-specific API features in Provider.model_profile(), not in model profile functions — model profiles should contain only intrinsic model characteristics — Keeps provider-agnostic model traits separate from provider-specific API behaviors, enabling models to work consistently across different providers

• Store provider-specific metadata in structured provider_details or provider_metadata fields, not in id, content, or args — Prevents semantic field overloading and enables consistent provider behavior interpretation while keeping main fields normalized across providers

• In _otel_*.py modules, implement only spec-defined features — no custom additions from internal concepts or other standards — Prevents spec drift and ensures compatibility with external tooling that expects standard-compliant telemetry data

• Store provider metadata (provider_details, provider_name, id, signature) in dedicated provider_metadata fields, not encoded in string fields — Prevents data loss during JSON serialization/deserialization cycles across storage, UI exchange, and provider round-trips — structured metadata survives where encoded strings lose type information

• Extend WrapperToolset for cross-cutting toolset behavior — don't modify base classes or individual toolset implementations — Composable wrappers (like ApprovalRequiredToolset, DeferredLoadingToolset) apply features to any toolset without coupling or duplication

Type System

• End exhaustive if/elif chains with else: assert_never(value) for typed unions — Catches unhandled union variants at type-check time when unions are extended, preventing runtime errors from missing cases

• Avoid Any type annotations — use Union, Protocol, TypeVar, or schema-derived types for precision — Precise types catch bugs at type-check time and improve IDE autocomplete; when Any is unavoidable due to external constraints, document expected structure in docstrings

• Use TypedDict or dataclass instead of dict[str, Any] when structure is known — Enables static type checking, eliminates cast() calls, provides runtime validation, and self-documents expected structure

Code Style

• Consolidate methods with duplicated logic using helpers, delegation, or type overloads — Reduces maintenance burden and prevents bugs from divergent implementations of the same control flow

• Order required fields before optional fields in dataclasses — Python requires non-default args before defaults — This prevents syntax errors since Python's dataclass implementation enforces that fields without defaults must precede fields with defaults

General

• Use keyword-only params (place * after first 1-2 positional args) for optional/config parameters — Prevents breakage when adding parameters and eliminates positional argument confusion in functions with multiple optional parameters

• Prefix provider-specific fields with {provider}_ in ModelSettings subclasses and profiles — prevents confusion about which provider supports which parameters — Clear provider namespacing prevents users from assuming a field works across providers when it's actually provider-specific, reducing API misuse

• Consolidate try/except blocks catching the same exception into one block — reduces duplication and simplifies control flow — When multiple operations throw the same exception type with similar handling logic, merging the try blocks prevents code duplication and makes error handling easier to maintain and update consistently.