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https://github.com/crewAIInc/crewAI.git
synced 2026-05-01 15:22:37 +00:00
properly return tool call result
This commit is contained in:
Brandon Hancock
@@ -5,12 +5,13 @@ import sys
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import threading
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import warnings
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from contextlib import contextmanager
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from typing import Any, Dict, List, Optional, Union
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from typing import Any, Dict, List, Optional, Union, cast
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# Load environment variables from .env file
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import litellm
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from dotenv import load_dotenv
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from litellm import get_supported_openai_params
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from litellm import Choices, get_supported_openai_params
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from litellm.types.utils import ModelResponse
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from crewai.utilities.exceptions.context_window_exceeding_exception import (
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LLMContextLengthExceededException,
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@@ -209,24 +210,23 @@ class LLM:
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High-level call method that:
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1) Calls litellm.completion
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2) Checks for function/tool calls
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3) If tool calls found:
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a) executes each function
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b) appends their output as tool messages
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c) calls litellm.completion again with the updated messages
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4) Returns the final text response
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3) If a tool call is found:
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a) executes the function
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b) returns the result
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4) If no tool call, returns the text response
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:param messages: The conversation messages
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:param tools: Optional list of function schemas for function calling
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:param callbacks: Optional list of callbacks
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:param available_functions: A dictionary mapping function_name -> actual Python function
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:return: Final text response from the LLM
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:return: Final text response from the LLM or the tool result
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"""
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with suppress_warnings():
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if callbacks:
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self.set_callbacks(callbacks)
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try:
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# --- 1) Make first completion call
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# --- 1) Make the completion call
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params = {
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"model": self.model,
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"messages": messages,
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@@ -250,68 +250,61 @@ class LLM:
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"tools": tools, # pass the tool schema
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}
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# remove None values
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# Remove None values
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params = {k: v for k, v in params.items() if v is not None}
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print(f"Params: {params}")
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response = litellm.completion(**params)
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response_message = response.choices[0].message
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response_message = cast(Choices, cast(ModelResponse, response).choices)[
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0
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].message
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text_response = response_message.content or ""
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tool_calls = getattr(response_message, "tool_calls", [])
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# --- 2) If no tool calls, we can just return
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# --- 2) If no tool calls, return the text response
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if not tool_calls or not available_functions:
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return text_response
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# --- 3) We have tool calls and a dictionary of available functions
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# run them, append output to messages
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for tool_call in tool_calls:
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function_name = tool_call.function.name
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if function_name in available_functions:
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# parse arguments
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function_args = {}
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try:
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function_args = json.loads(tool_call.function.arguments)
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except Exception as e:
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logging.warning(f"Failed to parse function arguments: {e}")
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# --- 3) Handle the tool call
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tool_call = tool_calls[0]
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function_name = tool_call.function.name
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fn = available_functions[function_name]
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# call the actual tool function
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if function_name in available_functions:
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# Parse arguments
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try:
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function_args = json.loads(tool_call.function.arguments)
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except json.JSONDecodeError as e:
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logging.warning(f"Failed to parse function arguments: {e}")
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return text_response # Fallback to text response
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fn = available_functions[function_name]
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try:
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# Call the actual tool function
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result = fn(**function_args)
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# append the "tool" response to messages
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messages.append(
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{
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"tool_call_id": tool_call.id,
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"role": "function",
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"name": function_name,
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"content": str(result),
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}
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)
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else:
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logging.warning(
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f"Tool call requested unknown function '{function_name}'"
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)
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print(f"Result from function '{function_name}': {result}")
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# --- 4) Make a second call so the LLM can incorporate the tool results
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second_params = dict(params) # copy the same params
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second_params["messages"] = messages
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# We'll remove "tools" from second call, or keep it if you want more calls
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# but typically you keep it in case it wants additional calls
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second_response = litellm.completion(**second_params)
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second_msg = second_response.choices[0].message
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final_response = second_msg.content or ""
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# Return the result directly
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return result
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return final_response
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except Exception as e:
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logging.error(
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f"Error executing function '{function_name}': {e}"
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)
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return text_response # Fallback to text response
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else:
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logging.warning(
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f"Tool call requested unknown function '{function_name}'"
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)
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return text_response # Fallback to text response
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except Exception as e:
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# check if context length was exceeded, otherwise log
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# Check if context length was exceeded, otherwise log
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if not LLMContextLengthExceededException(
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str(e)
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)._is_context_limit_error(str(e)):
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logging.error(f"LiteLLM call failed: {str(e)}")
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# re-raise
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# Re-raise the exception
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raise
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def supports_function_calling(self) -> bool:
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