litellm/litellm-rust/crates/llms/AGENTS.md

litellm-llms mirrors litellm/llms/: base config traits, provider transformations, and the OCR request handler in base_llm/ocr/handler.rs. Transport code (clients, media fetching, header helpers, transport errors) lives in litellm-http. See ../core/AGENTS.md for how the crates layer.

Python/Rust transformation pairs

Use the base OCR and Mistral OCR pairs as the reference when aligning transformations. Derive src/<relative_path>.rs from litellm/llms/<relative_path>.py, preserving meaningful basenames such as messages_transformation

Keep corresponding operation names and parameter names when their responsibilities match. Rust types retain the Python semantic name with Rust acronym casing (BaseOCRConfig / BaseOcrConfig, MistralOCRConfig / MistralOcrConfig). Private Python helpers can drop their leading underscore. Give Rust adapter helpers distinct responsibility names rather than duplicating trait method names

Order OCR config methods as supported parameters, credential metadata and connection resolution, health-check input, parameter mapping, environment validation, URL construction, request transformation, async request transformation, response transformation, async response transformation, and error conversion. Put constants and data types before the config, private helpers after it in operation order, and tests last. Rust-only trait hooks follow the corresponding Python methods

Use trait defaults for unchanged inherited behavior and explicit delegation for shared provider behavior. Keep typed inputs, ownership, Result, and async I/O idiomatic. A matching path or symbol identifies the counterpart, not a claim of full behavioral parity

Use named #[rstest] cases for independent input/output scenarios instead of loops or repeated calls in one test. Inject reusable setup with #[fixture] arguments and use #[with(...)] for fixture overrides. Keep assertions about the same result together

For base OCR, Python response models live next to BaseOcrConfig in src/base_llm/ocr/transformation.rs, as they do in Python; Rust context/environment types support the runtime. BaseOcrConfig::prepare_request corresponds to Python's HTTP-handler preparation rather than a BaseOCRConfig method, and validate_request_body is a Rust-only hook. src/base_llm/ocr/error.rs and src/base_llm/ocr/document.rs are Rust-only: the OCR error taxonomy shared with the route, and inline-document helpers shared by several providers

For Mistral, async_transform_ocr_request uses the base default in both languages. resolve_headers and build_ocr_url implement the respective environment and URL operations, and normalize_response implements the typed part of response transformation. Existing auth key/header handling and top-level response-extra preservation differ between languages; layout refactors must preserve those behaviors and verify them with the existing tests

For non-OCR pairs, order corresponding methods as parameter support/mapping, environment validation, URL construction, request transformation, and response transformation, followed by Rust-only runtime hooks. Auth resolution remains split between configs and route preparation in litellm-core. Chat supported_openai_param_mappings describes accepted OpenAI/provider name pairs, unlike Python's get_supported_openai_params name list. Audio map_transcription_params remains a Rust filtering helper

Azure Messages maps to llms/azure_ai/anthropic/messages_transformation.py; Bedrock Converse maps to llms/bedrock/chat/converse_transformation.py. AnthropicConfig, AmazonConverseConfig, and the non-OCR base traits are partial ports. OpenAiResponsesApiConfig currently implements only the WebSocket surface. Preserve their acceptance gates, passthrough behavior, and host fallback contracts when aligning layout