Generate config class dynamically from JSON configuration

Create streaming response, checking if the first chunk is an error. If the first chunk is an error, return a standard JSON error response.

Get the cache key for the given arguments. Args: **kwargs: kwargs to litellm.completion() or embedding() Return

Get the registered MCP Servers from the registry and union with the config MCP Servers

Get the base URL for Vertex AI API calls. - ``global`` uses the global control plane host. - Multi-region geographies (e.g. ``us``, ``eu

Calls async_post_call_response_headers_hook on all CustomLogger callbacks. Merges all returned header dicts (later callbacks override

Common utility function to process audio files for audio transcription APIs. Handles various input types: - File paths (str, os.PathLike

Release the lock if the current pod holds it. Uses an atomic Lua compare-and-delete to prevent TOCTOU races where a stale own

Common helper to route the request

Call PANW Prisma AIRS API to scan content or a tool_event.

Update the litellm params for health check. - gets a short `messages` param for health check - adds a bounded `max_tokens` when the depl

Auto-apply ``pytest.mark.vcr`` to collected items. Skip semantics (in priority order): 1. ``vcr_disabled()`` — global env-var off-switch (``

Main authentication and authorization builder

- handles scenario where content is list and not string - content list is just text, and no images Motivation: mistral api + azure ai do

Returns the deployment based on routing strategy

Get the `custom_llm_provider` from the request header `custom-llm-provider`

Retrieves a value from a nested dictionary using dot notation. Args: data: The dictionary to search in key_path: The path to

Raises Exception if Virtual Key is not allowed to call the route

Sets a span attribute safely with OTEL-compliant primitive typing for Arize/Phoenix.

Factory mirroring user_api_key_auth: SERVER span + url.path + http.route.

Validate that MCP servers requested on a key are within the allowed scope. Rules: - If key is in a team: key's mcp_servers must be a sub

for cloudflare ai gateway - https://github.com/BerriAI/litellm/issues/4317 Handles custom api_base for: 1. Gemini (Google AI

Get the AWS region name from the environment variables. Parameters: optional_params (dict): Optional parameters for the

Check if the given model supports function calling and return a boolean value. Parameters: model (str): The model name to be checked.

Add a success callback to `litellm.success_callback`. Auto-routes async callbacks to litellm._async_success_callback. Special

Get the `custom_llm_provider` from the request query parameters Safely reads the request query parameters

Get the MCP Server from the server id

Get the base URL for the request, considering X-Forwarded-* headers. Resolution order: ``PROXY_BASE_URL`` env var, then X-Forwarded-* when

Invoke an agent using the A2A protocol (JSON-RPC 2.0). Supported methods: - message/send: Send a message and get a response - messag

Pass through endpoint handler, makes the httpx request for pass-through endpoints and ensures logging hooks are called Args: request

Map OpenAI-style tools/functions to Vertex AI format. Args: value: List of tool definitions optional_params:

Encode provider and model_id into video_id using base64.

Guarantees response from 'get_secret' is either boolean or none. Used for fixing linting errors. Args: secret_name: The name of the

Setup guardrail with all Canadian PII patterns (mimics the policy template)

Check if the given model supports function calling and return a boolean value. Parameters: model (str): The model name to be checked.

Keep this state less - leave the state management (e.g. tracking current_output_item_id, current_response_id, current_conversation_id, curren

Get the OpenAI compatible provider info for Baseten

Ensure mock_prisma.db.litellm_config exists with async methods (the conftest only stubs ``litellm_configtable`` — this is a different table).

Build a mock httpx response with the given JSON body.

Transforms OpenAI JSONL content to Bedrock batch format Bedrock batch format: { "recordId": "alphanumeric string", "modelInput": {JS