Add LiteLLM API integration

src/agentlab/llm/litellm_api.py

@@ -0,0 +1,322 @@
+import json
+import logging
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Dict, List, Optional, Type
+
+import litellm
+from litellm import completion
+from openai.types.chat import ChatCompletion as OpenAIChatCompletion
+
+from agentlab.llm.base_api import BaseModelArgs
+from agentlab.llm.response_api import (
+    AgentlabAction,
+    APIPayload,
+    BaseModelWithPricing,
+    LLMOutput,
+    Message,
+    MessageBuilder,
+    OpenAIChatCompletionAPIMessageBuilder,
+    ToolCall,
+    ToolCalls,
+)
+
+litellm.modify_params = True
+
+
+class LiteLLMModel(BaseModelWithPricing):
+    def __init__(
+        self,
+        model_name: str,
+        base_url: Optional[str] = None,
+        api_key: Optional[str] = None,
+        temperature: float | None = None,
+        max_tokens: int | None = 100,
+        use_only_first_toolcall: bool = False,
+    ):
+        super().__init__(
+            model_name=model_name,
+            temperature=temperature,
+            max_tokens=max_tokens,
+        )
+        self.action_space_as_tools = True  # this should be a config
+        client_args = {}
+        if base_url is not None:
+            client_args["base_url"] = base_url
+        if api_key is not None:
+            client_args["api_key"] = api_key
+        self.client = partial(completion, **client_args)
+        self.init_pricing_tracker(pricing_api="litellm")
+        self.use_only_first_toolcall = use_only_first_toolcall
+        try:
+            self.litellm_info = litellm.get_model_info(model_name)
+            # maybe log this in xray
+
+        except Exception as e:
+            logging.error(f"Failed to get litellm model info: {e}")
+
+    def _call_api(self, payload: APIPayload) -> "OpenAIChatCompletion":
+        """Calls the LiteLLM API with the given payload. LiteLLM API is a wrapper around OpenAI's API.
+
+        Args:
+            payload (APIPayload)
+
+        Returns:
+            OpenAIChatCompletion: A OpenAIChatCompletion Like object with the same keys
+        """
+        input = []
+        for msg in payload.messages:  # type: ignore
+            input.extend(msg.prepare_message())
+        api_params: Dict[str, Any] = {
+            "model": self.model_name,
+            "messages": input,
+        }
+        if self.temperature is not None:
+            api_params["temperature"] = self.temperature
+
+        if self.max_tokens is not None:
+            api_params["max_completion_tokens"] = self.max_tokens
+
+        if payload.tools is not None:
+            api_params["tools"] = (
+                self.format_tools_for_chat_completion(payload.tools)
+                if "function" not in payload.tools[0]  # convert if responses_api_tools
+                else payload.tools
+            )
+
+        if payload.tool_choice is not None and payload.force_call_tool is None:
+            api_params["tool_choice"] = (
+                "required" if payload.tool_choice in ("required", "any") else payload.tool_choice
+            )
+
+        if payload.force_call_tool is not None:
+            api_params["tool_choice"] = {
+                "type": "function",
+                "function": {"name": payload.force_call_tool},
+            }
+
+        if payload.reasoning_effort is not None:
+            api_params["reasoning_effort"] = payload.reasoning_effort
+
+        if "tools" in api_params and payload.cache_tool_definition:
+            api_params["tools"][-1]["cache_control"] = {"type": "ephemeral"}  # type: ignore
+
+        if payload.cache_complete_prompt:
+            # Indicating cache control for the last message enables caching of the complete prompt.
+            api_params["messages"][-1]["content"][-1]["cache_control"] = {"type": "ephemeral"}
+
+        response = self.client(**api_params, num_retries=5)
+
+        return response  # type: ignore
+
+    def _parse_response(self, response: "OpenAIChatCompletion") -> LLMOutput:
+        think_output = self._extract_thinking_content_from_response(response)
+        tool_calls = self._extract_tool_calls_from_response(response)
+
+        if self.action_space_as_tools:
+            env_action = self._extract_env_actions_from_toolcalls(tool_calls)  # type: ignore
+        else:
+            env_action = self._extract_env_actions_from_text_response(response)
+        return LLMOutput(
+            raw_response=response,
+            think=think_output,
+            action=env_action if env_action is not None else None,
+            tool_calls=tool_calls if tool_calls is not None else None,
+        )
+
+    def _extract_thinking_content_from_response(
+        self, response: OpenAIChatCompletion, wrap_tag="think"
+    ):
+        """Extracts the content from the message, including reasoning if available.
+        It wraps the reasoning around <think>...</think> for easy identification of reasoning content,
+        When LLM produces 'text' and 'reasoning' in the same message.
+        Note: The wrapping of 'thinking' content may not be nedeed and may be reconsidered.
+
+        Args:
+            response: The message object or dict containing content and reasoning.
+            wrap_tag: The tag name to wrap reasoning content (default: "think").
+
+        Returns:
+            str: The extracted content with reasoning wrapped in specified tags.
+        """
+        message = response.choices[0].message
+        if not isinstance(message, dict):
+            message = message.to_dict()
+
+        reasoning_content = message.get("reasoning", None)
+        msg_content = message.get("text", "")  # works for Open-router
+        if reasoning_content:
+            # Wrap reasoning in <think> tags with newlines for clarity
+            reasoning_content = f"<{wrap_tag}>{reasoning_content}</{wrap_tag}>\n"
+            logging.debug("Extracting content from response.choices[i].message.reasoning")
+        else:
+            reasoning_content = ""
+        return f"{reasoning_content}{msg_content}{message.get('content', '')}"
+
+    def _extract_tool_calls_from_response(self, response: OpenAIChatCompletion) -> ToolCalls | None:
+        """Extracts tool calls from the response."""
+        message = response.choices[0].message.to_dict()
+        tool_calls = message.get("tool_calls", None)
+        if tool_calls is None:
+            return None
+        tool_call_list = []
+        for tc in tool_calls:  # type: ignore
+            tool_call_list.append(
+                ToolCall(
+                    name=tc["function"]["name"],
+                    arguments=json.loads(tc["function"]["arguments"]),
+                    raw_call=tc,
+                )
+            )
+            if self.use_only_first_toolcall:
+                break
+        return ToolCalls(tool_calls=tool_call_list, raw_calls=response)  # type: ignore
+
+    def _extract_env_actions_from_toolcalls(self, toolcalls: ToolCalls) -> Any | None:
+        """Extracts actions from the response."""
+        if not toolcalls:
+            return None
+
+        actions = [
+            AgentlabAction.convert_toolcall_to_agentlab_action_format(call) for call in toolcalls
+        ]
+        actions = (
+            AgentlabAction.convert_multiactions_to_agentlab_action_format(actions)
+            if len(actions) > 1
+            else actions[0]
+        )
+        return actions
+
+    def _extract_env_actions_from_text_response(
+        self, response: "OpenAIChatCompletion"
+    ) -> str | None:
+        """Extracts environment actions from the text response."""
+        # Use when action space is not given as tools.
+        # TODO: Add support to pass action space as prompt in LiteLLM.
+        # Check: https://docs.litellm.ai/docs/completion/function_call#function-calling-for-models-wout-function-calling-support
+        pass
+
+    @staticmethod
+    def format_tools_for_chat_completion(tools):
+        """Formats response tools format for OpenAI Chat Completion API.
+        Why we need this?
+        Ans: actionset.to_tool_description() in bgym only returns description
+        format valid for OpenAI Response API.
+
+        Args:
+            tools: List of tool descriptions to format for Chat Completion API.
+
+        Returns:
+            Formatted tools list compatible with OpenAI Chat Completion API, or None if tools is None.
+        """
+        formatted_tools = None
+        if tools is not None:
+            formatted_tools = [
+                {
+                    "type": tool["type"],
+                    "function": {k: tool[k] for k in ("name", "description", "parameters")},
+                }
+                for tool in tools
+            ]
+        return formatted_tools
+
+
+class LiteLLMAPIMessageBuilder(OpenAIChatCompletionAPIMessageBuilder):
+    """Message builder for LiteLLM API, extending OpenAIChatCompletionAPIMessageBuilder."""
+
+    def prepare_message(self, use_only_first_toolcall: bool = False) -> List[Message]:
+        """Prepare the message for the OpenAI API."""
+        content = []
+        for item in self.content:
+            content.append(self.convert_content_to_expected_format(item))
+        output = [{"role": self.role, "content": content}]
+        return output if self.role != "tool" else self.handle_tool_call(use_only_first_toolcall)
+
+    def handle_tool_call(self, use_only_first_toolcall: bool = False) -> List[Message]:
+        """Handle the tool call response from the last raw response."""
+        if self.responded_tool_calls is None:
+            raise ValueError("No tool calls found in responded_tool_calls")
+        output = []
+        raw_call = self.responded_tool_calls.raw_calls.choices[0].message  # type: ignore
+        if use_only_first_toolcall:
+            raw_call.tool_calls = raw_call.tool_calls[:1]
+        output.append(raw_call)  # add raw calls to output
+        for fn_call in self.responded_tool_calls:
+            raw_call = fn_call.raw_call
+            assert (
+                "image" not in fn_call.tool_response
+            ), "Image output is not supported in function calls response."
+            # a function_call_output dict has keys "role", "tool_call_id" and "content"
+            tool_call_reponse = {
+                "name": raw_call["function"]["name"],  # required with OpenRouter
+                "role": "tool",
+                "tool_call_id": raw_call["id"],
+                "content": self.convert_content_to_expected_format(fn_call.tool_response)["text"],
+            }
+            output.append(tool_call_reponse)
+
+        return output
+
+
+@dataclass
+class LiteLLMModelArgs(BaseModelArgs):
+    """Serializable arguments for LiteLMMModel."""
+
+    api = "openai"  # tool description format used by actionset.to_tool_description() in bgym
+    base_url: Optional[str] = None
+    api_key: Optional[str] = None
+    use_only_first_toolcall: bool = False
+
+    def make_model(self):
+        return LiteLLMModel(
+            model_name=self.model_name,
+            base_url=self.base_url,
+            api_key=self.api_key,
+            max_tokens=self.max_new_tokens,
+            temperature=self.temperature,
+            use_only_first_toolcall=self.use_only_first_toolcall,
+        )
+
+    def get_message_builder(self) -> Type[MessageBuilder]:
+        """Returns a message builder for the LiteLMMModel."""
+        return LiteLLMAPIMessageBuilder
+
+
+if __name__ == "__main__":
+    """
+    Some simple tests to run the LiteLLMModel with different models.
+    """
+
+    from agentlab.agents.tool_use_agent import DEFAULT_PROMPT_CONFIG, ToolUseAgentArgs
+    from agentlab.experiments.study import Study
+    from agentlab.llm.litellm_api import LiteLLMModelArgs
+
+    def get_agent(model_name: str) -> ToolUseAgentArgs:
+        return ToolUseAgentArgs(
+            model_args=LiteLLMModelArgs(
+                model_name=model_name,
+                max_new_tokens=2000,
+                temperature=None,
+            ),
+            config=DEFAULT_PROMPT_CONFIG,
+        )
+
+    models = [
+        "openai/gpt-4.1",
+        "openai/gpt-4.1-mini",
+        "openai/gpt-4.1-nano",
+        "openai/o3-2025-04-16",
+        "anthropic/claude-3-7-sonnet-20250219",
+        "anthropic/claude-sonnet-4-20250514",
+        ## Add more models to test.
+    ]
+    agent_args = [get_agent(model) for model in models]
+
+
+    study = Study(agent_args, "miniwob_tiny_test", logging_level_stdout=logging.WARNING)
+    study.run(
+        n_jobs=5,
+        parallel_backend="ray",
+        strict_reproducibility=False,
+        n_relaunch=3,
+    )

src/agentlab/llm/response_api.py

@@ -27,6 +27,7 @@
     3. Factory classes (inherits from BaseModelArgs) for creating instances of LLM Response models.
 """
 
+logger = logging.getLogger(__name__)
 
 ContentItem = Dict[str, Any]
 Message = Dict[str, Union[str, List[ContentItem]]]
@@ -388,10 +389,13 @@ class APIPayload:
     cache_complete_prompt: bool = (
         False  # If True, will cache the complete prompt in the last message.
     )
+    reasoning_effort: Literal["low", "medium", "high"] | None = None
 
     def __post_init__(self):
         if self.tool_choice and self.force_call_tool:
             raise ValueError("tool_choice and force_call_tool are mutually exclusive")
+        if self.reasoning_effort is not None:
+            logger.info('In agentlab reasoning_effort is used by LiteLLM API only. We will eventually shift to LiteLLM API for all LLMs.')
 
 
 # # Base class for all API Endpoints