v1/messages → /responses Parameter Mapping

When you send a request to /v1/messages targeting an OpenAI or Azure model, LiteLLM internally routes it through the OpenAI Responses API. This page documents exactly how every parameter gets translated in both directions.

The transformation lives in litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py.

Request: Anthropic → Responses API

Top-level parameters

Anthropic (/v1/messages)	Responses API	Notes
model	model	Passed through as-is
messages	input	Structurally transformed — see the messages section below
system (string)	instructions	Passed as a plain string
system (list of content blocks)	instructions	Text blocks are joined with \n; non-text blocks are ignored
max_tokens	max_output_tokens	Renamed
temperature	temperature	Passed through as-is
top_p	top_p	Passed through as-is
tools	tools	Format-translated — see the tools section below
tool_choice	tool_choice	Type-remapped — see the tool_choice section below
thinking	reasoning	Budget tokens mapped to effort level — see the thinking section below
output_format or output_config.format	text	Wrapped as {"format": {"type": "json_schema", "name": "structured_output", "schema": ..., "strict": true}}
context_management	context_management	Converted from Anthropic dict to OpenAI array format — see the context_management section below
metadata.user_id	user	Extracted from the metadata object and truncated to 64 characters
stop_sequences	❌ Not mapped	Dropped silently
top_k	❌ Not mapped	Dropped silently
speed	❌ Not mapped	Only used to set Anthropic beta headers on the native path

How messages get converted

Each Anthropic message is expanded into one or more Responses API input items. The key difference is that tool_result and tool_use blocks become top-level items in the input array rather than being nested inside a message.

Anthropic message	Responses API input item
user role, string content	{"type": "message", "role": "user", "content": [{"type": "input_text", "text": "..."}]}
user role, {"type": "text"} block	{"type": "input_text", "text": "..."} inside a user message
user role, {"type": "image", "source": {"type": "base64"}}	{"type": "input_image", "image_url": "data:<media_type>;base64,<data>"} inside a user message
user role, {"type": "image", "source": {"type": "url"}}	{"type": "input_image", "image_url": "<url>"} inside a user message
user role, {"type": "tool_result"} block	Top-level {"type": "function_call_output", "call_id": "...", "output": "..."} — pulled out of the message entirely
assistant role, string content	{"type": "message", "role": "assistant", "content": [{"type": "output_text", "text": "..."}]}
assistant role, {"type": "text"} block	{"type": "output_text", "text": "..."} inside an assistant message
assistant role, {"type": "tool_use"} block	Top-level {"type": "function_call", "call_id": "<id>", "name": "...", "arguments": "<JSON string>"} — pulled out of the message entirely
assistant role, {"type": "thinking"} block	{"type": "output_text", "text": "<thinking text>"} inside an assistant message

tools

Anthropic tool	Responses API tool
Any tool where type starts with "web_search" or name == "web_search"	{"type": "web_search_preview"}
All other tools	{"type": "function", "name": "...", "description": "...", "parameters": <input_schema>}

tool_choice

Anthropic tool_choice.type	Responses API tool_choice
"auto"	{"type": "auto"}
"any"	{"type": "required"}
"tool"	{"type": "function", "name": "<tool name>"}

thinking → reasoning

The budget_tokens value is mapped to a string effort level. summary is always set to "detailed".

thinking.budget_tokens	reasoning.effort
>= 10000	"high"
>= 5000	"medium"
>= 2000	"low"
< 2000	"minimal"

If thinking.type is anything other than "enabled", the reasoning field is not sent at all.

context_management

Anthropic uses a nested dict with an edits array. OpenAI uses a flat array of compaction objects.

Anthropic input:
{
  "edits": [
    {
      "type": "compact_20260112",
      "trigger": {"type": "input_tokens", "value": 150000}
    }
  ]
}

Responses API output:
[
  {"type": "compaction", "compact_threshold": 150000}
]

Response: Responses API → Anthropic

When the Responses API reply comes back, LiteLLM converts it into an Anthropic AnthropicMessagesResponse.

Responses API field	Anthropic response field	Notes
response.id	id
response.model	model	Falls back to "unknown-model" if missing
ResponseReasoningItem — summary[*].text	content block {"type": "thinking", "thinking": "..."}	Each non-empty summary text becomes a thinking block
ResponseOutputMessage — content[*] where type == "output_text"	content block {"type": "text", "text": "..."}
ResponseFunctionToolCall — {call_id, name, arguments}	content block {"type": "tool_use", "id": "...", "name": "...", "input": {...}}	arguments is JSON-parsed back into a dict
Any function_call present in output	stop_reason: "tool_use"
response.status == "incomplete"	stop_reason: "max_tokens"	Takes precedence over the default
Everything else	stop_reason: "end_turn"	Default
response.usage.input_tokens	usage.input_tokens
response.usage.output_tokens	usage.output_tokens
(hardcoded)	type: "message"	Always set
(hardcoded)	role: "assistant"	Always set
(hardcoded)	stop_sequence: null	Always null on this path