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# pydantic_ai_slim/pydantic_ai/models/ Guidelines

## API Design

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- Silently ignore unsupported generic tuning settings (`temperature`, sampling params, penalties, …) at runtime and document them in docstrings — a model that simply no-ops an unsupported knob keeps client code portable across models; failing noisily would break that portability (provider-namespaced settings like `google_*`/`openai_*` are governed by a separate rule below, not this one)
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- Apply identical response processing to both `request()` and `request_stream()` — if `request()` calls `_process_response()`, `request_stream()` must apply it to each chunk — Ensures streaming and non-streaming code paths support the same message types (`ToolCallPart`, `NativeToolCallPart`, `TextPart`, etc.) with consistent behavior, preventing bugs where features work in one mode but fail in the other
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- Expose provider-specific data via `ModelResponse.provider_details` or `TextPart.provider_details` — prevents API bloat and maintains consistent provider integration patterns — Keeps the core response interface clean while allowing providers to expose logprobs, safety filters, content filtering, and usage metrics without breaking consistency across integrations
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- Don't add preemptive client-side guards that reject provider-namespaced settings (`google_*`, `openai_*`, …) based on assumed capability limits; forward the setting the user opted into and let the provider API surface the actual incompatibility — the API is the authority on what it currently supports, so a client-side guard degrades functionality on outdated assumptions
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- Token counting must mirror actual request parameters (`tools`, `system_prompt`, configs) and use identical message formatting — Ensures token count estimates match actual API usage, preventing billing surprises and quota errors
- Per-request injections or mutations of request content (message blocks, tool defs, instructions, cache breakpoints) must anchor to a position that doesn't move with history length (e.g. the first user message or a fixed index), never the last message or a length-based index — anchoring to a moving position shifts the cacheable prefix every turn, so the provider silently re-processes the tail instead of reading from cache, a cost/latency regression that surfaces no error

## Error Handling

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- Raise explicit errors for unsupported model features (e.g. function tools, JSON/native output modes) that can't be formed for a given model — never silently skip or degrade — makes capability limits discoverable at runtime; unsupported settings are governed by the settings rules above, and unrepresentable content/message-part types by the rule below
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- Use exhaustive pattern matching for message part/content types in model adapters; raise explicit errors for unsupported types instead of filtering or assertions — Prevents silent data loss during message mapping and provides clear feedback when model APIs don't support certain content types (e.g., `FileContent`), making integration failures debuggable rather than mysterious
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- Return `ModelResponse` with empty `parts=[]` but populated metadata (`finish_reason`, `timestamp`, `provider_response_id`) for recoverable API failures (content filters, empty content) — enables graceful degradation instead of cascading errors — Allows the system to handle provider-level failures gracefully by preserving response metadata for observability while signaling no usable content, preventing unnecessary exception propagation in model adapters

## Type System

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- Use typed settings classes (e.g., `OpenAISettings`, `AnthropicSettings`) with provider-prefixed fields instead of `extra_body` or dict literals — Enables type checking and autocomplete for provider-specific config, preventing runtime errors from typos or invalid values
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- Define Pydantic models to validate API responses — avoids `.get()` fragility and catches schema changes early — Prevents runtime errors from missing/malformed fields and provides type safety when parsing external API data

## General

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- Place provider-specific code in `models/{provider}.py`, not shared modules — add functions consistently across all providers even if some are simple — Maintains clear architectural boundaries and prevents shared compatibility layers from accumulating provider-specific logic that becomes hard to maintain

A model should read reveal modes through `self.tool_deferral_mode` and
`self.tool_addition_mode`, never directly from the corresponding profile keys. An adapter that
implements a reveal renderer must declare its supported values in
`supported_tool_deferral_modes` and `supported_tool_addition_modes`; the inherited empty sets are
the safe default for adapters with no renderer.

An adapter that honors `CompactionPart`s on the wire declares
`compaction_requires_encrypted_content` and `compaction_retains_standing_prompt` and calls
`self._trim_before_compaction()` from its own message-prep step — never `_trim_messages_before_compaction`
directly, and never restating what those declarations imply. Each states only what the API does
(does it need the encrypted blob to honor an item; does the item keep serving the window's system
items); turning that into trim behavior belongs to the one helper. The two are independent — today's
two adapters happen to answer both the same way, so don't infer one from the other when adding a
third. They belong on the adapter, not the profile: eight providers route a profile of their own
through `OpenAIResponsesModel`, so a profile key would be absent exactly where the wire format is
most certain. Where in a request build the trim belongs stays adapter mechanics (OpenAI Responses
resolves server-side state from the *untrimmed* history, so it keeps a separate trimmed view).

### Third-party model fallback

Custom `Model` subclasses that continue reading `tool_defs` degrade gracefully: all tools are fully
declared and availability deltas fall back to a system-text announcement. Deferral is not withheld
on that model. Reading `declared_tool_defs` and `visibility_of()` opts the adapter into withholding.

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- When a model forwards a generic `ModelSettings` field, add it to that field's `Supported by:` list in `pydantic_ai/settings.py`, and give a new `Model` class a case in `tests/models/test_model_settings_support.py` — that test probes each class's outgoing request and fails when a list and the wire disagree.
