Add OrcaRouter as a named LLM provider

Registers ChatOrcaRouter (provider='orcarouter'), an OpenAI-compatible
BaseChatModel mirroring the existing ChatOpenRouter wiring, so the model
gateway is usable as a first-class provider:

- browser_use/llm/orcarouter/chat.py + serializer.py
- Registered in browser_use/llm/__init__.py and browser_use/__init__.py
- Token-cost guard: never attribute upstream prices to the gateway
- .env.example ORCAROUTER_API_KEY entry
- tests/ci/test_orcarouter.py and examples/models/orcarouter.py

Co-Authored-By: Claude <noreply@anthropic.com>

.env.example

@@ -39,6 +39,7 @@ BROWSER_USE_API_KEY=your_bu_api_key_here
 # DEEPSEEK_API_KEY=
 # GROK_API_KEY=
 # NOVITA_API_KEY=
+# ORCAROUTER_API_KEY=
 
 # AWS Bedrock Configuration (for AWS Bedrock models)
 # Requires: pip install browser-use[aws]

browser_use/__init__.py

@@ -67,6 +67,7 @@ if TYPE_CHECKING:
 	from browser_use.llm.ollama.chat import ChatOllama
 	from browser_use.llm.openai.chat import ChatOpenAI
 	from browser_use.llm.openrouter.chat import ChatOpenRouter
+	from browser_use.llm.orcarouter.chat import ChatOrcaRouter
 	from browser_use.llm.vercel.chat import ChatVercel
 	from browser_use.sandbox import sandbox
 	from browser_use.tools.service import Controller, Tools
@@ -105,6 +106,7 @@ _LAZY_IMPORTS = {
 	'ChatOCIRaw': ('browser_use.llm.oci_raw.chat', 'ChatOCIRaw'),
 	'ChatOllama': ('browser_use.llm.ollama.chat', 'ChatOllama'),
 	'ChatOpenRouter': ('browser_use.llm.openrouter.chat', 'ChatOpenRouter'),
+	'ChatOrcaRouter': ('browser_use.llm.orcarouter.chat', 'ChatOrcaRouter'),
 	'ChatVercel': ('browser_use.llm.vercel.chat', 'ChatVercel'),
 	# LLM models module
 	'models': ('browser_use.llm.models', None),
@@ -162,6 +164,7 @@ __all__ = [
 	'ChatOCIRaw',
 	'ChatOllama',
 	'ChatOpenRouter',
+	'ChatOrcaRouter',
 	'ChatVercel',
 	'Tools',
 	'Controller',

browser_use/llm/__init__.py

@@ -40,6 +40,7 @@ if TYPE_CHECKING:
 	from browser_use.llm.ollama.chat import ChatOllama
 	from browser_use.llm.openai.chat import ChatOpenAI
 	from browser_use.llm.openrouter.chat import ChatOpenRouter
+	from browser_use.llm.orcarouter.chat import ChatOrcaRouter
 	from browser_use.llm.vercel.chat import ChatVercel
 
 	# Type stubs for model instances - enables IDE autocomplete
@@ -93,6 +94,7 @@ _LAZY_IMPORTS = {
 	'ChatOllama': ('browser_use.llm.ollama.chat', 'ChatOllama'),
 	'ChatOpenAI': ('browser_use.llm.openai.chat', 'ChatOpenAI'),
 	'ChatOpenRouter': ('browser_use.llm.openrouter.chat', 'ChatOpenRouter'),
+	'ChatOrcaRouter': ('browser_use.llm.orcarouter.chat', 'ChatOrcaRouter'),
 	'ChatVercel': ('browser_use.llm.vercel.chat', 'ChatVercel'),
 }
 
@@ -156,6 +158,7 @@ __all__ = [
 	'ChatOCIRaw',
 	'ChatOllama',
 	'ChatOpenRouter',
+	'ChatOrcaRouter',
 	'ChatVercel',
 	'ChatCerebras',
 ]

browser_use/llm/orcarouter/chat.py

@@ -0,0 +1,205 @@
+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import Any, TypeVar, overload
+
+import httpx
+from openai import APIConnectionError, APIStatusError, AsyncOpenAI, RateLimitError
+from openai.types.chat.chat_completion import ChatCompletion
+from openai.types.shared_params.response_format_json_schema import (
+	JSONSchema,
+	ResponseFormatJSONSchema,
+)
+from pydantic import BaseModel
+
+from browser_use.llm.base import BaseChatModel
+from browser_use.llm.exceptions import ModelProviderError, ModelRateLimitError
+from browser_use.llm.messages import BaseMessage
+from browser_use.llm.orcarouter.serializer import OrcaRouterMessageSerializer
+from browser_use.llm.schema import SchemaOptimizer
+from browser_use.llm.views import ChatInvokeCompletion, ChatInvokeUsage
+
+T = TypeVar('T', bound=BaseModel)
+
+
+@dataclass
+class ChatOrcaRouter(BaseChatModel):
+	"""
+	A wrapper around OrcaRouter's OpenAI-compatible chat API, which routes to 190+ LLM models
+	through a single unified gateway.
+
+	This class implements the BaseChatModel protocol for OrcaRouter's API.
+	"""
+
+	# Model configuration
+	model: str
+
+	# Model params
+	temperature: float | None = None
+	top_p: float | None = None
+	seed: int | None = None
+
+	# Client initialization parameters
+	api_key: str | None = None
+	base_url: str | httpx.URL = 'https://api.orcarouter.ai/v1'
+	timeout: float | httpx.Timeout | None = None
+	max_retries: int = 10
+	default_headers: Mapping[str, str] | None = None
+	default_query: Mapping[str, object] | None = None
+	http_client: httpx.AsyncClient | None = None
+	_strict_response_validation: bool = False
+	extra_body: dict[str, Any] | None = None
+
+	# Static
+	@property
+	def provider(self) -> str:
+		return 'orcarouter'
+
+	def _get_client_params(self) -> dict[str, Any]:
+		"""Prepare client parameters dictionary."""
+		# Define base client params
+		base_params = {
+			'api_key': self.api_key,
+			'base_url': self.base_url,
+			'timeout': self.timeout,
+			'max_retries': self.max_retries,
+			'default_headers': self.default_headers,
+			'default_query': self.default_query,
+			'_strict_response_validation': self._strict_response_validation,
+			'top_p': self.top_p,
+			'seed': self.seed,
+		}
+
+		# Create client_params dict with non-None values
+		client_params = {k: v for k, v in base_params.items() if v is not None}
+
+		# Add http_client if provided
+		if self.http_client is not None:
+			client_params['http_client'] = self.http_client
+
+		return client_params
+
+	def get_client(self) -> AsyncOpenAI:
+		"""
+		Returns an AsyncOpenAI client configured for OrcaRouter.
+
+		Returns:
+		    AsyncOpenAI: An instance of the AsyncOpenAI client with OrcaRouter base URL.
+		"""
+		if not hasattr(self, '_client'):
+			client_params = self._get_client_params()
+			self._client = AsyncOpenAI(**client_params)
+		return self._client
+
+	@property
+	def name(self) -> str:
+		return str(self.model)
+
+	def _get_usage(self, response: ChatCompletion) -> ChatInvokeUsage | None:
+		"""Extract usage information from the OrcaRouter response."""
+		if response.usage is None:
+			return None
+
+		prompt_details = getattr(response.usage, 'prompt_tokens_details', None)
+		cached_tokens = prompt_details.cached_tokens if prompt_details else None
+
+		return ChatInvokeUsage(
+			prompt_tokens=response.usage.prompt_tokens,
+			prompt_cached_tokens=cached_tokens,
+			prompt_cache_creation_tokens=None,
+			prompt_image_tokens=None,
+			# Completion
+			completion_tokens=response.usage.completion_tokens,
+			total_tokens=response.usage.total_tokens,
+		)
+
+	@overload
+	async def ainvoke(
+		self, messages: list[BaseMessage], output_format: None = None, **kwargs: Any
+	) -> ChatInvokeCompletion[str]: ...
+
+	@overload
+	async def ainvoke(self, messages: list[BaseMessage], output_format: type[T], **kwargs: Any) -> ChatInvokeCompletion[T]: ...
+
+	async def ainvoke(
+		self, messages: list[BaseMessage], output_format: type[T] | None = None, **kwargs: Any
+	) -> ChatInvokeCompletion[T] | ChatInvokeCompletion[str]:
+		"""
+		Invoke the model with the given messages through OrcaRouter.
+
+		Args:
+		    messages: List of chat messages
+		    output_format: Optional Pydantic model class for structured output
+
+		Returns:
+		    Either a string response or an instance of output_format
+		"""
+		orcarouter_messages = OrcaRouterMessageSerializer.serialize_messages(messages)
+
+		try:
+			if output_format is None:
+				# Return string response
+				response = await self.get_client().chat.completions.create(
+					model=self.model,
+					messages=orcarouter_messages,
+					temperature=self.temperature,
+					top_p=self.top_p,
+					seed=self.seed,
+					**(self.extra_body or {}),
+				)
+
+				usage = self._get_usage(response)
+				return ChatInvokeCompletion(
+					completion=response.choices[0].message.content or '',
+					usage=usage,
+				)
+
+			else:
+				# Create a JSON schema for structured output
+				schema = SchemaOptimizer.create_optimized_json_schema(output_format)
+
+				response_format_schema: JSONSchema = {
+					'name': 'agent_output',
+					'strict': True,
+					'schema': schema,
+				}
+
+				# Return structured response
+				response = await self.get_client().chat.completions.create(
+					model=self.model,
+					messages=orcarouter_messages,
+					temperature=self.temperature,
+					top_p=self.top_p,
+					seed=self.seed,
+					response_format=ResponseFormatJSONSchema(
+						json_schema=response_format_schema,
+						type='json_schema',
+					),
+					**(self.extra_body or {}),
+				)
+
+				if response.choices[0].message.content is None:
+					raise ModelProviderError(
+						message='Failed to parse structured output from model response',
+						status_code=500,
+						model=self.name,
+					)
+				usage = self._get_usage(response)
+
+				parsed = output_format.model_validate_json(response.choices[0].message.content)
+
+				return ChatInvokeCompletion(
+					completion=parsed,
+					usage=usage,
+				)
+
+		except RateLimitError as e:
+			raise ModelRateLimitError(message=e.message, model=self.name) from e
+
+		except APIConnectionError as e:
+			raise ModelProviderError(message=str(e), model=self.name) from e
+
+		except APIStatusError as e:
+			raise ModelProviderError(message=e.message, status_code=e.status_code, model=self.name) from e
+
+		except Exception as e:
+			raise ModelProviderError(message=str(e), model=self.name) from e

browser_use/llm/orcarouter/serializer.py

@@ -0,0 +1,26 @@
+from openai.types.chat import ChatCompletionMessageParam
+
+from browser_use.llm.messages import BaseMessage
+from browser_use.llm.openai.serializer import OpenAIMessageSerializer
+
+
+class OrcaRouterMessageSerializer:
+	"""
+	Serializer for converting between custom message types and OrcaRouter message formats.
+
+	OrcaRouter exposes an OpenAI-compatible API, so we can reuse the OpenAI serializer.
+	"""
+
+	@staticmethod
+	def serialize_messages(messages: list[BaseMessage]) -> list[ChatCompletionMessageParam]:
+		"""
+		Serialize a list of browser_use messages to OrcaRouter-compatible messages.
+
+		Args:
+		    messages: List of browser_use messages
+
+		Returns:
+		    List of OrcaRouter-compatible messages (identical to OpenAI format)
+		"""
+		# OrcaRouter uses the same message format as OpenAI
+		return OpenAIMessageSerializer.serialize_messages(messages)

browser_use/tokens/service.py

@@ -405,6 +405,9 @@ class TokenCost:
 		if llm.provider == 'openrouter' or base_url == 'https://openrouter.ai/api/v1':
 			if not is_openrouter_pricing_model(model):
 				return f'openrouter/{model}'
+		# OrcaRouter is a gateway with its own pricing; never attribute upstream prices to it.
+		if llm.provider == 'orcarouter' or base_url == 'https://api.orcarouter.ai/v1':
+			return f'orcarouter/{model}'
 
 		return model
 

examples/models/orcarouter.py

@@ -0,0 +1,33 @@
+"""
+Simple try of the agent with OrcaRouter.
+
+@dev You need to add ORCAROUTER_API_KEY to your environment variables.
+"""
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+
+from browser_use import Agent, ChatOrcaRouter
+
+load_dotenv()
+
+# OrcaRouter is an OpenAI-compatible model gateway routing to 190+ models via one endpoint.
+llm = ChatOrcaRouter(
+	model='orcarouter/auto',
+	base_url='https://api.orcarouter.ai/v1',
+	api_key=os.getenv('ORCAROUTER_API_KEY'),
+)
+agent = Agent(
+	task='Find the number of stars of the browser-use repo',
+	llm=llm,
+	use_vision=False,
+)
+
+
+async def main():
+	await agent.run(max_steps=10)
+
+
+asyncio.run(main())

tests/ci/test_orcarouter.py

@@ -0,0 +1,64 @@
+import pytest
+
+from browser_use.llm.messages import ContentPartTextParam, SystemMessage, UserMessage
+from browser_use.llm.orcarouter.chat import ChatOrcaRouter
+from browser_use.llm.orcarouter.serializer import OrcaRouterMessageSerializer
+from browser_use.llm.views import ChatInvokeUsage
+from browser_use.tokens.service import TokenCost
+
+
+def test_orcarouter_serializer_uses_openai_format() -> None:
+	"""OrcaRouter speaks the OpenAI wire format, so the serializer must match OpenAI's."""
+	messages = [
+		SystemMessage(content=[ContentPartTextParam(text='You are a helpful assistant.', type='text')]),
+		UserMessage(content='What is the capital of France? Answer in one word.'),
+	]
+
+	serialized = OrcaRouterMessageSerializer.serialize_messages(messages)
+
+	assert serialized == [
+		{'role': 'system', 'content': [{'type': 'text', 'text': 'You are a helpful assistant.'}]},
+		{'role': 'user', 'content': 'What is the capital of France? Answer in one word.'},
+	]
+
+
+def test_orcarouter_chat_defaults() -> None:
+	"""ChatOrcaRouter must expose the OrcaRouter provider and default gateway base URL."""
+	chat = ChatOrcaRouter(model='orcarouter/auto', api_key='test-key')
+
+	assert chat.provider == 'orcarouter'
+	assert str(chat.base_url) == 'https://api.orcarouter.ai/v1'
+	assert chat.name == 'orcarouter/auto'
+
+
+async def test_registered_orcarouter_llm_never_matches_upstream_pricing(
+	monkeypatch: pytest.MonkeyPatch,
+) -> None:
+	"""OrcaRouter is a gateway; upstream model pricing must not be attributed to it."""
+	seen_model_names = []
+
+	async def fake_openrouter_pricing(model_name: str):
+		seen_model_names.append(model_name)
+		return None
+
+	monkeypatch.setattr('browser_use.tokens.service.get_openrouter_model_pricing', fake_openrouter_pricing)
+
+	token_cost = TokenCost(include_cost=True)
+	token_cost._initialized = True
+	token_cost._pricing_data = {}
+	token_cost.register_llm(ChatOrcaRouter(model='openai/gpt-4o-mini', api_key='test-key'))
+
+	cost = await token_cost.calculate_cost(
+		'openai/gpt-4o-mini',
+		ChatInvokeUsage(
+			prompt_tokens=10,
+			prompt_cached_tokens=None,
+			prompt_cache_creation_tokens=None,
+			prompt_image_tokens=None,
+			completion_tokens=5,
+			total_tokens=15,
+		),
+	)
+
+	assert seen_model_names == ['orcarouter/openai/gpt-4o-mini']
+	assert cost is None