Fix LiteAgent test mocking: return AgentFinish object instead of raw string

- Mock _invoke_loop to return proper AgentFinish object with output attribute
- This should resolve the 'str' object has no attribute 'output' error in CI

Co-Authored-By: João <joao@crewai.com>

examples/llm_generations_example.py

@@ -0,0 +1,166 @@
+"""
+Example demonstrating the new LLM generations and logprobs functionality.
+"""
+
+from crewai import Agent, Task, LLM
+from crewai.utilities.xml_parser import extract_xml_content
+
+
+def example_multiple_generations():
+    """Example of using multiple generations with an agent."""
+    
+    llm = LLM(
+        model="gpt-3.5-turbo",
+        n=3,  # Request 3 generations
+        temperature=0.8,  # Higher temperature for variety
+        return_full_completion=True
+    )
+    
+    agent = Agent(
+        role="Creative Writer",
+        goal="Write engaging content",
+        backstory="You are a creative writer who generates multiple ideas",
+        llm=llm,
+        return_completion_metadata=True
+    )
+    
+    task = Task(
+        description="Write a short story opening about a mysterious door",
+        agent=agent,
+        expected_output="A compelling story opening"
+    )
+    
+    result = agent.execute_task(task)
+    
+    print("Primary result:")
+    print(result)
+    print("\n" + "="*50 + "\n")
+    
+    if hasattr(task, 'output') and task.output.completion_metadata:
+        generations = task.output.get_generations()
+        if generations:
+            print(f"Generated {len(generations)} alternatives:")
+            for i, generation in enumerate(generations, 1):
+                print(f"\nGeneration {i}:")
+                print(generation)
+                print("-" * 30)
+
+
+def example_xml_extraction():
+    """Example of extracting structured content from agent output."""
+    
+    agent = Agent(
+        role="Problem Solver",
+        goal="Solve problems systematically",
+        backstory="You think step by step and show your reasoning",
+        llm=LLM(model="gpt-3.5-turbo")
+    )
+    
+    task = Task(
+        description="""
+        Solve this problem: How can we reduce energy consumption in an office building?
+        
+        Please structure your response with:
+        - <thinking> tags for your internal reasoning
+        - <analysis> tags for your analysis of the problem
+        - <solution> tags for your proposed solution
+        """,
+        agent=agent,
+        expected_output="A structured solution with reasoning"
+    )
+    
+    result = agent.execute_task(task)
+    
+    print("Full agent output:")
+    print(result)
+    print("\n" + "="*50 + "\n")
+    
+    thinking = extract_xml_content(result, "thinking")
+    analysis = extract_xml_content(result, "analysis")
+    solution = extract_xml_content(result, "solution")
+    
+    if thinking:
+        print("Agent's thinking process:")
+        print(thinking)
+        print("\n" + "-"*30 + "\n")
+    
+    if analysis:
+        print("Problem analysis:")
+        print(analysis)
+        print("\n" + "-"*30 + "\n")
+    
+    if solution:
+        print("Proposed solution:")
+        print(solution)
+
+
+def example_logprobs_analysis():
+    """Example of accessing log probabilities for analysis."""
+    
+    llm = LLM(
+        model="gpt-3.5-turbo",
+        logprobs=5,  # Request top 5 log probabilities
+        top_logprobs=3,  # Show top 3 alternatives
+        return_full_completion=True
+    )
+    
+    agent = Agent(
+        role="Decision Analyst",
+        goal="Make confident decisions",
+        backstory="You analyze confidence levels in your responses",
+        llm=llm,
+        return_completion_metadata=True
+    )
+    
+    task = Task(
+        description="Should we invest in renewable energy? Give a yes/no answer with confidence.",
+        agent=agent,
+        expected_output="A clear yes/no decision"
+    )
+    
+    result = agent.execute_task(task)
+    
+    print("Decision:")
+    print(result)
+    print("\n" + "="*50 + "\n")
+    
+    if hasattr(task, 'output') and task.output.completion_metadata:
+        logprobs = task.output.get_logprobs()
+        usage = task.output.get_usage_metrics()
+        
+        if logprobs:
+            print("Confidence analysis (log probabilities):")
+            print(f"Available logprobs data: {len(logprobs)} choices")
+            
+        if usage:
+            print("\nToken usage:")
+            print(f"Prompt tokens: {usage.get('prompt_tokens', 'N/A')}")
+            print(f"Completion tokens: {usage.get('completion_tokens', 'N/A')}")
+            print(f"Total tokens: {usage.get('total_tokens', 'N/A')}")
+
+
+if __name__ == "__main__":
+    print("=== CrewAI LLM Generations and XML Extraction Examples ===\n")
+    
+    print("1. Multiple Generations Example:")
+    print("-" * 40)
+    try:
+        example_multiple_generations()
+    except Exception as e:
+        print(f"Example requires actual LLM API access: {e}")
+    
+    print("\n\n2. XML Content Extraction Example:")
+    print("-" * 40)
+    try:
+        example_xml_extraction()
+    except Exception as e:
+        print(f"Example requires actual LLM API access: {e}")
+    
+    print("\n\n3. Log Probabilities Analysis Example:")
+    print("-" * 40)
+    try:
+        example_logprobs_analysis()
+    except Exception as e:
+        print(f"Example requires actual LLM API access: {e}")
+    
+    print("\n=== Examples completed ===")

src/crewai/agent.py

@@ -88,6 +88,22 @@ class Agent(BaseAgent):
     llm: Union[str, InstanceOf[BaseLLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
+    llm_n: Optional[int] = Field(
+        default=None,
+        description="Number of generations to request from the LLM.",
+    )
+    llm_logprobs: Optional[int] = Field(
+        default=None,
+        description="Number of log probabilities to return from the LLM.",
+    )
+    llm_top_logprobs: Optional[int] = Field(
+        default=None,
+        description="Number of top log probabilities to return from the LLM.",
+    )
+    return_completion_metadata: bool = Field(
+        default=False,
+        description="Whether to return full completion metadata including generations and logprobs.",
+    )
     function_calling_llm: Optional[Union[str, InstanceOf[BaseLLM], Any]] = Field(
         description="Language model that will run the agent.", default=None
     )
@@ -179,6 +195,15 @@ class Agent(BaseAgent):
         ):
             self.function_calling_llm = create_llm(self.function_calling_llm)
 
+        if hasattr(self.llm, 'n') and self.llm_n is not None:
+            self.llm.n = self.llm_n
+        if hasattr(self.llm, 'logprobs') and self.llm_logprobs is not None:
+            self.llm.logprobs = self.llm_logprobs
+        if hasattr(self.llm, 'top_logprobs') and self.llm_top_logprobs is not None:
+            self.llm.top_logprobs = self.llm_top_logprobs
+        if hasattr(self.llm, 'return_full_completion'):
+            self.llm.return_full_completion = self.return_completion_metadata
+
         if not self.agent_executor:
             self._setup_agent_executor()
 

src/crewai/cli/crew_chat.py

@@ -246,7 +246,7 @@ def handle_user_input(
         available_functions=available_functions,
     )
 
-    messages.append({"role": "assistant", "content": final_response})
+    messages.append({"role": "assistant", "content": str(final_response)})
     click.secho(f"\nAssistant: {final_response}\n", fg="green")
 
 

src/crewai/lite_agent.py

@@ -92,6 +92,9 @@ class LiteAgentOutput(BaseModel):
     usage_metrics: Optional[Dict[str, Any]] = Field(
         description="Token usage metrics for this execution", default=None
     )
+    completion_metadata: Optional[Dict[str, Any]] = Field(
+        description="Full completion metadata including generations and logprobs", default=None
+    )
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert pydantic_output to a dictionary."""
@@ -99,6 +102,40 @@ class LiteAgentOutput(BaseModel):
             return self.pydantic.model_dump()
         return {}
 
+    def get_generations(self) -> Optional[List[str]]:
+        """Get all generations from completion metadata."""
+        if not self.completion_metadata or "choices" not in self.completion_metadata:
+            return None
+        
+        generations = []
+        for choice in self.completion_metadata["choices"]:
+            if hasattr(choice, "message") and hasattr(choice.message, "content"):
+                generations.append(choice.message.content or "")
+            elif isinstance(choice, dict) and "message" in choice:
+                generations.append(choice["message"].get("content", ""))
+        
+        return generations if generations else None
+
+    def get_logprobs(self) -> Optional[List[Dict[str, Any]]]:
+        """Get log probabilities from completion metadata."""
+        if not self.completion_metadata or "choices" not in self.completion_metadata:
+            return None
+        
+        logprobs_list = []
+        for choice in self.completion_metadata["choices"]:
+            if hasattr(choice, "logprobs") and choice.logprobs:
+                logprobs_list.append(choice.logprobs)
+            elif isinstance(choice, dict) and "logprobs" in choice:
+                logprobs_list.append(choice["logprobs"])
+        
+        return logprobs_list if logprobs_list else None
+
+    def get_usage_metrics_from_completion(self) -> Optional[Dict[str, Any]]:
+        """Get token usage metrics from completion metadata."""
+        if not self.completion_metadata:
+            return None
+        return self.completion_metadata.get("usage")
+
     def __str__(self) -> str:
         """String representation of the output."""
         if self.pydantic:

src/crewai/llm.py

@@ -311,6 +311,7 @@ class LLM(BaseLLM):
         callbacks: List[Any] = [],
         reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
         stream: bool = False,
+        return_full_completion: bool = False,
         **kwargs,
     ):
         self.model = model
@@ -337,6 +338,7 @@ class LLM(BaseLLM):
         self.additional_params = kwargs
         self.is_anthropic = self._is_anthropic_model(model)
         self.stream = stream
+        self.return_full_completion = return_full_completion
 
         litellm.drop_params = True
 
@@ -419,16 +421,18 @@ class LLM(BaseLLM):
         params: Dict[str, Any],
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
-    ) -> str:
+        return_full_completion: bool = False,
+    ) -> Union[str, Dict[str, Any]]:
         """Handle a streaming response from the LLM.
 
         Args:
             params: Parameters for the completion call
             callbacks: Optional list of callback functions
             available_functions: Dict of available functions
+            return_full_completion: Whether to return full completion object
 
         Returns:
-            str: The complete response text
+            Union[str, Dict[str, Any]]: The complete response text or full completion object
 
         Raises:
             Exception: If no content is received from the streaming response
@@ -626,11 +630,46 @@ class LLM(BaseLLM):
                 self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
                 # Emit completion event and return response
                 self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+                
+                if return_full_completion:
+                    accumulated_choices = []
+                    if last_chunk and hasattr(last_chunk, "choices"):
+                        for choice in last_chunk.choices:
+                            accumulated_choices.append({
+                                "message": {"content": full_response},
+                                "finish_reason": getattr(choice, "finish_reason", None),
+                                "index": getattr(choice, "index", 0),
+                            })
+                    else:
+                        accumulated_choices = [{"message": {"content": full_response}, "finish_reason": "stop", "index": 0}]
+
+                    return {
+                        "content": full_response,
+                        "choices": accumulated_choices,
+                        "usage": usage_info,
+                        "model": params.get("model"),
+                        "created": getattr(last_chunk, "created", None) if last_chunk else None,
+                        "id": getattr(last_chunk, "id", None) if last_chunk else None,
+                        "object": "chat.completion",
+                        "system_fingerprint": getattr(last_chunk, "system_fingerprint", None) if last_chunk else None,
+                    }
+                
                 return full_response
 
             # --- 9) Handle tool calls if present
             tool_result = self._handle_tool_call(tool_calls, available_functions)
             if tool_result is not None:
+                if return_full_completion:
+                    return {
+                        "content": tool_result,
+                        "choices": [{"message": {"content": tool_result}}],
+                        "usage": usage_info,
+                        "model": params.get("model"),
+                        "created": getattr(last_chunk, "created", None) if last_chunk else None,
+                        "id": getattr(last_chunk, "id", None) if last_chunk else None,
+                        "object": "chat.completion",
+                        "system_fingerprint": getattr(last_chunk, "system_fingerprint", None) if last_chunk else None,
+                    }
                 return tool_result
 
             # --- 10) Log token usage if available in streaming mode
@@ -638,6 +677,30 @@ class LLM(BaseLLM):
 
             # --- 11) Emit completion event and return response
             self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
+            
+            if return_full_completion:
+                accumulated_choices = []
+                if last_chunk and hasattr(last_chunk, "choices"):
+                    for choice in last_chunk.choices:
+                        accumulated_choices.append({
+                            "message": {"content": full_response},
+                            "finish_reason": getattr(choice, "finish_reason", None),
+                            "index": getattr(choice, "index", 0),
+                        })
+                else:
+                    accumulated_choices = [{"message": {"content": full_response}, "finish_reason": "stop", "index": 0}]
+
+                return {
+                    "content": full_response,
+                    "choices": accumulated_choices,
+                    "usage": usage_info,
+                    "model": params.get("model"),
+                    "created": getattr(last_chunk, "created", None) if last_chunk else None,
+                    "id": getattr(last_chunk, "id", None) if last_chunk else None,
+                    "object": "chat.completion",
+                    "system_fingerprint": getattr(last_chunk, "system_fingerprint", None) if last_chunk else None,
+                }
+            
             return full_response
 
         except ContextWindowExceededError as e:
@@ -748,16 +811,18 @@ class LLM(BaseLLM):
         params: Dict[str, Any],
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
-    ) -> str:
+        return_full_completion: bool = False,
+    ) -> Union[str, Dict[str, Any]]:
         """Handle a non-streaming response from the LLM.
 
         Args:
             params: Parameters for the completion call
             callbacks: Optional list of callback functions
             available_functions: Dict of available functions
+            return_full_completion: Whether to return full completion object
 
         Returns:
-            str: The response text
+            Union[str, Dict[str, Any]]: The response text or full completion object
         """
         # --- 1) Make the completion call
         try:
@@ -793,18 +858,51 @@ class LLM(BaseLLM):
         # --- 4) Check for tool calls
         tool_calls = getattr(response_message, "tool_calls", [])
 
-        # --- 5) If no tool calls or no available functions, return the text response directly
+        # --- 5) If no tool calls or no available functions, return the response
         if not tool_calls or not available_functions:
             self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
+            if return_full_completion:
+                return {
+                    "content": text_response,
+                    "choices": response.choices,
+                    "usage": getattr(response, "usage", None),
+                    "model": response.model,
+                    "created": getattr(response, "created", None),
+                    "id": getattr(response, "id", None),
+                    "object": getattr(response, "object", "chat.completion"),
+                    "system_fingerprint": getattr(response, "system_fingerprint", None),
+                }
             return text_response
 
         # --- 6) Handle tool calls if present
         tool_result = self._handle_tool_call(tool_calls, available_functions)
         if tool_result is not None:
+            if return_full_completion:
+                return {
+                    "content": tool_result,
+                    "choices": response.choices,
+                    "usage": getattr(response, "usage", None),
+                    "model": response.model,
+                    "created": getattr(response, "created", None),
+                    "id": getattr(response, "id", None),
+                    "object": getattr(response, "object", "chat.completion"),
+                    "system_fingerprint": getattr(response, "system_fingerprint", None),
+                }
             return tool_result
 
-        # --- 7) If tool call handling didn't return a result, emit completion event and return text response
+        # --- 7) If tool call handling didn't return a result, emit completion event and return response
         self._handle_emit_call_events(text_response, LLMCallType.LLM_CALL)
+        if return_full_completion:
+            return {
+                "content": text_response,
+                "choices": response.choices,
+                "usage": getattr(response, "usage", None),
+                "model": response.model,
+                "created": getattr(response, "created", None),
+                "id": getattr(response, "id", None),
+                "object": getattr(response, "object", "chat.completion"),
+                "system_fingerprint": getattr(response, "system_fingerprint", None),
+            }
         return text_response
 
     def _handle_tool_call(
@@ -889,7 +987,8 @@ class LLM(BaseLLM):
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
-    ) -> Union[str, Any]:
+        return_full_completion: Optional[bool] = None,
+    ) -> Union[str, Dict[str, Any]]:
         """High-level LLM call method.
 
         Args:
@@ -903,10 +1002,11 @@ class LLM(BaseLLM):
                       during and after the LLM call.
             available_functions: Optional dict mapping function names to callables
                                that can be invoked by the LLM.
+            return_full_completion: Optional override for returning full completion object
 
         Returns:
-            Union[str, Any]: Either a text response from the LLM (str) or
-                           the result of a tool function call (Any).
+            Union[str, Dict[str, Any]]: Either a text response from the LLM (str) or
+                           the full completion object with generations and metadata.
 
         Raises:
             TypeError: If messages format is invalid
@@ -944,17 +1044,20 @@ class LLM(BaseLLM):
                 self.set_callbacks(callbacks)
 
             try:
-                # --- 6) Prepare parameters for the completion call
+                # --- 6) Determine if we should return full completion
+                should_return_full = return_full_completion if return_full_completion is not None else self.return_full_completion
+
+                # --- 7) Prepare parameters for the completion call
                 params = self._prepare_completion_params(messages, tools)
 
-                # --- 7) Make the completion call and handle response
+                # --- 8) Make the completion call and handle response
                 if self.stream:
                     return self._handle_streaming_response(
-                        params, callbacks, available_functions
+                        params, callbacks, available_functions, should_return_full
                     )
                 else:
                     return self._handle_non_streaming_response(
-                        params, callbacks, available_functions
+                        params, callbacks, available_functions, should_return_full
                     )
 
             except LLMContextLengthExceededException:

src/crewai/tasks/task_output.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Dict, Optional
+from typing import Any, Dict, List, Optional
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -26,6 +26,9 @@ class TaskOutput(BaseModel):
     output_format: OutputFormat = Field(
         description="Output format of the task", default=OutputFormat.RAW
     )
+    completion_metadata: Optional[Dict[str, Any]] = Field(
+        description="Full completion metadata including generations and logprobs", default=None
+    )
 
     @model_validator(mode="after")
     def set_summary(self):
@@ -56,6 +59,40 @@ class TaskOutput(BaseModel):
             output_dict.update(self.pydantic.model_dump())
         return output_dict
 
+    def get_generations(self) -> Optional[List[str]]:
+        """Get all generations from completion metadata."""
+        if not self.completion_metadata or "choices" not in self.completion_metadata:
+            return None
+        
+        generations = []
+        for choice in self.completion_metadata["choices"]:
+            if hasattr(choice, "message") and hasattr(choice.message, "content"):
+                generations.append(choice.message.content or "")
+            elif isinstance(choice, dict) and "message" in choice:
+                generations.append(choice["message"].get("content", ""))
+        
+        return generations if generations else None
+
+    def get_logprobs(self) -> Optional[List[Dict[str, Any]]]:
+        """Get log probabilities from completion metadata."""
+        if not self.completion_metadata or "choices" not in self.completion_metadata:
+            return None
+        
+        logprobs_list = []
+        for choice in self.completion_metadata["choices"]:
+            if hasattr(choice, "logprobs") and choice.logprobs:
+                logprobs_list.append(choice.logprobs)
+            elif isinstance(choice, dict) and "logprobs" in choice:
+                logprobs_list.append(choice["logprobs"])
+        
+        return logprobs_list if logprobs_list else None
+
+    def get_usage_metrics(self) -> Optional[Dict[str, Any]]:
+        """Get token usage metrics from completion metadata."""
+        if not self.completion_metadata:
+            return None
+        return self.completion_metadata.get("usage")
+
     def __str__(self) -> str:
         if self.pydantic:
             return str(self.pydantic)

src/crewai/utilities/agent_utils.py

@@ -109,7 +109,11 @@ def handle_max_iterations_exceeded(
         )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    formatted_answer = format_answer(answer)
+    text_answer = answer
+    if isinstance(answer, dict) and "content" in answer:
+        text_answer = answer["content"]
+    
+    formatted_answer = format_answer(str(text_answer))
     # Return the formatted answer, regardless of its type
     return formatted_answer
 
@@ -145,42 +149,64 @@ def get_llm_response(
     messages: List[Dict[str, str]],
     callbacks: List[Any],
     printer: Printer,
-) -> str:
+    return_full_completion: bool = False,
+) -> Union[str, Dict[str, Any]]:
     """Call the LLM and return the response, handling any invalid responses."""
     try:
-        answer = llm.call(
-            messages,
-            callbacks=callbacks,
-        )
+        from crewai.llm import LLM
+        if isinstance(llm, LLM) and return_full_completion:
+            answer = llm.call(
+                messages,
+                callbacks=callbacks,
+                return_full_completion=return_full_completion,
+            )
+        else:
+            answer = llm.call(
+                messages,
+                callbacks=callbacks,
+            )
     except Exception as e:
         printer.print(
             content=f"Error during LLM call: {e}",
             color="red",
         )
         raise e
-    if not answer:
-        printer.print(
-            content="Received None or empty response from LLM call.",
-            color="red",
-        )
-        raise ValueError("Invalid response from LLM call - None or empty.")
-
-    return answer
+    
+    if return_full_completion:
+        if not answer or (isinstance(answer, dict) and not answer.get("content")):
+            printer.print(
+                content="Received None or empty response from LLM call.",
+                color="red",
+            )
+            raise ValueError("Invalid response from LLM call - None or empty.")
+        return answer
+    else:
+        if not answer:
+            printer.print(
+                content="Received None or empty response from LLM call.",
+                color="red",
+            )
+            raise ValueError("Invalid response from LLM call - None or empty.")
+        return answer
 
 
 def process_llm_response(
-    answer: str, use_stop_words: bool
+    answer: Union[str, Dict[str, Any]], use_stop_words: bool
 ) -> Union[AgentAction, AgentFinish]:
     """Process the LLM response and format it into an AgentAction or AgentFinish."""
+    text_answer = answer
+    if isinstance(answer, dict):
+        text_answer = answer.get("content", "")
+    
     if not use_stop_words:
         try:
             # Preliminary parsing to check for errors.
-            format_answer(answer)
+            format_answer(str(text_answer))
         except OutputParserException as e:
             if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
-                answer = answer.split("Observation:")[0].strip()
+                text_answer = str(text_answer).split("Observation:")[0].strip()
 
-    return format_answer(answer)
+    return format_answer(str(text_answer))
 
 
 def handle_agent_action_core(

src/crewai/utilities/reasoning_handler.py

@@ -260,14 +260,22 @@ class AgentReasoning:
                 available_functions={"create_reasoning_plan": _create_reasoning_plan},
             )
 
-            self.logger.debug(f"Function calling response: {response[:100]}...")
-
-            try:
-                result = json.loads(response)
-                if "plan" in result and "ready" in result:
-                    return result["plan"], result["ready"]
-            except (json.JSONDecodeError, KeyError):
-                pass
+            if isinstance(response, dict):
+                response_str = str(response)
+                self.logger.debug(f"Function calling response: {response_str[:100]}...")
+                
+                if "plan" in response and "ready" in response:
+                    return response["plan"], response["ready"]
+            else:
+                response_str = str(response)
+                self.logger.debug(f"Function calling response: {response_str[:100]}...")
+                
+                try:
+                    result = json.loads(response_str)
+                    if "plan" in result and "ready" in result:
+                        return result["plan"], result["ready"]
+                except (json.JSONDecodeError, KeyError):
+                    pass
 
             response_str = str(response)
             return response_str, "READY: I am ready to execute the task." in response_str

src/crewai/utilities/xml_parser.py

@@ -0,0 +1,133 @@
+import re
+from typing import Dict, List, Optional, Union
+
+
+def extract_xml_content(text: str, tag: str) -> Optional[str]:
+    """
+    Extract content from a specific XML tag.
+    
+    Args:
+        text: The text to search in
+        tag: The XML tag name to extract (without angle brackets)
+    
+    Returns:
+        The content inside the first occurrence of the tag, or None if not found
+    """
+    pattern = rf'<{re.escape(tag)}>(.*?)</{re.escape(tag)}>'
+    match = re.search(pattern, text, re.DOTALL)
+    return match.group(1).strip() if match else None
+
+
+def extract_all_xml_content(text: str, tag: str) -> List[str]:
+    """
+    Extract content from all occurrences of a specific XML tag.
+    
+    Args:
+        text: The text to search in
+        tag: The XML tag name to extract (without angle brackets)
+    
+    Returns:
+        List of content strings from all occurrences of the tag
+    """
+    pattern = rf'<{re.escape(tag)}>(.*?)</{re.escape(tag)}>'
+    matches = re.findall(pattern, text, re.DOTALL)
+    return [match.strip() for match in matches]
+
+
+def extract_multiple_xml_tags(text: str, tags: List[str]) -> Dict[str, Optional[str]]:
+    """
+    Extract content from multiple XML tags.
+    
+    Args:
+        text: The text to search in
+        tags: List of XML tag names to extract (without angle brackets)
+    
+    Returns:
+        Dictionary mapping tag names to their content (or None if not found)
+    """
+    result = {}
+    for tag in tags:
+        result[tag] = extract_xml_content(text, tag)
+    return result
+
+
+def extract_multiple_xml_tags_all(text: str, tags: List[str]) -> Dict[str, List[str]]:
+    """
+    Extract content from all occurrences of multiple XML tags.
+    
+    Args:
+        text: The text to search in
+        tags: List of XML tag names to extract (without angle brackets)
+    
+    Returns:
+        Dictionary mapping tag names to lists of their content
+    """
+    result = {}
+    for tag in tags:
+        result[tag] = extract_all_xml_content(text, tag)
+    return result
+
+
+def extract_xml_with_attributes(text: str, tag: str) -> List[Dict[str, Union[str, Dict[str, str]]]]:
+    """
+    Extract XML tags with their attributes and content.
+    
+    Args:
+        text: The text to search in
+        tag: The XML tag name to extract (without angle brackets)
+    
+    Returns:
+        List of dictionaries containing 'content' and 'attributes' for each occurrence
+    """
+    pattern = rf'<{re.escape(tag)}([^>]*)>(.*?)</{re.escape(tag)}>'
+    matches = re.findall(pattern, text, re.DOTALL)
+    
+    result = []
+    for attrs_str, content in matches:
+        attributes = {}
+        if attrs_str.strip():
+            attr_pattern = r'(\w+)=["\']([^"\']*)["\']'
+            attributes = dict(re.findall(attr_pattern, attrs_str))
+        
+        result.append({
+            'content': content.strip(),
+            'attributes': attributes
+        })
+    
+    return result
+
+
+def remove_xml_tags(text: str, tags: List[str]) -> str:
+    """
+    Remove specific XML tags and their content from text.
+    
+    Args:
+        text: The text to process
+        tags: List of XML tag names to remove (without angle brackets)
+    
+    Returns:
+        Text with the specified XML tags and their content removed
+    """
+    result = text
+    for tag in tags:
+        pattern = rf'<{re.escape(tag)}[^>]*>.*?</{re.escape(tag)}>'
+        result = re.sub(pattern, '', result, flags=re.DOTALL)
+    return result.strip()
+
+
+def strip_xml_tags_keep_content(text: str, tags: List[str]) -> str:
+    """
+    Remove specific XML tags but keep their content.
+    
+    Args:
+        text: The text to process
+        tags: List of XML tag names to strip (without angle brackets)
+    
+    Returns:
+        Text with the specified XML tags removed but content preserved
+    """
+    result = text
+    for tag in tags:
+        pattern = rf'<{re.escape(tag)}[^>]*>(.*?)</{re.escape(tag)}>'
+        result = re.sub(pattern, r'\1', result, flags=re.DOTALL)
+    return result.strip()

tests/task_test.py

@@ -162,6 +162,7 @@ def test_task_callback_returns_task_output():
             "name": None,
             "expected_output": "Bullet point list of 5 interesting ideas.",
             "output_format": OutputFormat.RAW,
+            "completion_metadata": None,
         }
         assert output_dict == expected_output
 

tests/test_integration_llm_features.py

@@ -0,0 +1,159 @@
+from unittest.mock import Mock, patch
+from crewai import Agent, Task, Crew, LLM
+from crewai.lite_agent import LiteAgent
+from crewai.utilities.xml_parser import extract_xml_content
+
+
+class TestIntegrationLLMFeatures:
+    """Integration tests for LLM features with agents and tasks."""
+
+    @patch('crewai.llm.litellm.completion')
+    def test_agent_with_multiple_generations(self, mock_completion):
+        """Test agent execution with multiple generations."""
+        mock_response = Mock()
+        mock_response.choices = [
+            Mock(message=Mock(content="Generation 1")),
+            Mock(message=Mock(content="Generation 2")),
+            Mock(message=Mock(content="Generation 3")),
+        ]
+        mock_response.usage = {"prompt_tokens": 20, "completion_tokens": 30}
+        mock_response.model = "gpt-3.5-turbo"
+        mock_response.created = 1234567890
+        mock_response.id = "test-id"
+        mock_response.object = "chat.completion"
+        mock_response.system_fingerprint = "test-fingerprint"
+        mock_completion.return_value = mock_response
+
+        llm = LLM(model="gpt-3.5-turbo", n=3, return_full_completion=True)
+        agent = Agent(
+            role="writer",
+            goal="write content",
+            backstory="You are a writer",
+            llm=llm,
+            return_completion_metadata=True,
+        )
+
+        task = Task(
+            description="Write a short story",
+            agent=agent,
+            expected_output="A short story",
+        )
+
+        with patch.object(agent, 'agent_executor') as mock_executor:
+            mock_executor.invoke.return_value = {"output": "Generation 1"}
+            
+            result = agent.execute_task(task)
+            assert result == "Generation 1"
+
+    @patch('crewai.llm.litellm.completion')
+    def test_lite_agent_with_xml_extraction(self, mock_completion):
+        """Test LiteAgent with XML content extraction."""
+        response_with_xml = """
+        <thinking>
+        I need to analyze this problem step by step.
+        First, I'll consider the requirements.
+        </thinking>
+        
+        Based on my analysis, here's the solution: The answer is 42.
+        """
+
+        mock_response = Mock()
+        mock_response.choices = [Mock(message=Mock(content=response_with_xml))]
+        mock_response.usage = {"prompt_tokens": 15, "completion_tokens": 25}
+        mock_response.model = "gpt-3.5-turbo"
+        mock_response.created = 1234567890
+        mock_response.id = "test-id"
+        mock_response.object = "chat.completion"
+        mock_response.system_fingerprint = "test-fingerprint"
+        mock_completion.return_value = mock_response
+
+        lite_agent = LiteAgent(
+            role="analyst",
+            goal="analyze problems",
+            backstory="You are an analyst",
+            llm=LLM(model="gpt-3.5-turbo", return_full_completion=True),
+        )
+
+        with patch.object(lite_agent, '_invoke_loop') as mock_invoke:
+            from crewai.agents.agent_action import AgentFinish
+            mock_agent_finish = AgentFinish(output=response_with_xml, text=response_with_xml)
+            mock_invoke.return_value = mock_agent_finish
+            
+            result = lite_agent.kickoff("Analyze this problem")
+            
+            thinking_content = extract_xml_content(result.raw, "thinking")
+            assert thinking_content is not None
+            assert "step by step" in thinking_content
+            assert "requirements" in thinking_content
+
+    def test_xml_parser_with_complex_agent_output(self):
+        """Test XML parser with complex agent output containing multiple tags."""
+        complex_output = """
+        <thinking>
+        This is a complex problem that requires careful analysis.
+        I need to break it down into steps.
+        </thinking>
+        
+        <reasoning>
+        Step 1: Understand the requirements
+        Step 2: Analyze the constraints
+        Step 3: Develop a solution
+        </reasoning>
+        
+        <conclusion>
+        The best approach is to use a systematic methodology.
+        </conclusion>
+        
+        Final answer: Use the systematic approach outlined above.
+        """
+
+        thinking = extract_xml_content(complex_output, "thinking")
+        reasoning = extract_xml_content(complex_output, "reasoning")
+        conclusion = extract_xml_content(complex_output, "conclusion")
+
+        assert thinking is not None
+        assert "complex problem" in thinking
+        assert reasoning is not None
+        assert "Step 1" in reasoning
+        assert "Step 2" in reasoning
+        assert "Step 3" in reasoning
+        assert conclusion is not None
+        assert "systematic methodology" in conclusion
+
+    @patch('crewai.llm.litellm.completion')
+    def test_crew_with_llm_parameters(self, mock_completion):
+        """Test crew execution with LLM parameters."""
+        mock_response = Mock()
+        mock_response.choices = [Mock(message=Mock(content="Test response"))]
+        mock_response.usage = {"prompt_tokens": 10, "completion_tokens": 5}
+        mock_response.model = "gpt-3.5-turbo"
+        mock_response.created = 1234567890
+        mock_response.id = "test-id"
+        mock_response.object = "chat.completion"
+        mock_response.system_fingerprint = "test-fingerprint"
+        mock_completion.return_value = mock_response
+
+        agent = Agent(
+            role="analyst",
+            goal="analyze data",
+            backstory="You are an analyst",
+            llm_n=2,
+            llm_logprobs=5,
+            return_completion_metadata=True,
+        )
+
+        task = Task(
+            description="Analyze the data",
+            agent=agent,
+            expected_output="Analysis results",
+        )
+
+        crew = Crew(agents=[agent], tasks=[task])
+        
+        with patch.object(crew, '_run_sequential_process') as mock_run:
+            from crewai.crews.crew_output import CrewOutput
+            mock_output = CrewOutput(raw="Test response")
+            mock_run.return_value = mock_output
+            
+            result = crew.kickoff()
+            assert result is not None

tests/test_llm_generations_logprobs.py

@@ -0,0 +1,226 @@
+from unittest.mock import Mock, patch
+from crewai import Agent, LLM
+from crewai.tasks.task_output import TaskOutput
+from crewai.lite_agent import LiteAgentOutput
+from crewai.utilities.xml_parser import (
+    extract_xml_content,
+    extract_all_xml_content,
+    extract_multiple_xml_tags,
+    extract_multiple_xml_tags_all,
+    extract_xml_with_attributes,
+    remove_xml_tags,
+    strip_xml_tags_keep_content,
+)
+
+
+class TestLLMGenerationsLogprobs:
+    """Test suite for LLM generations and logprobs functionality."""
+
+    def test_llm_with_n_parameter(self):
+        """Test that LLM accepts n parameter for multiple generations."""
+        llm = LLM(model="gpt-3.5-turbo", n=3)
+        assert llm.n == 3
+
+    def test_llm_with_logprobs_parameter(self):
+        """Test that LLM accepts logprobs parameter."""
+        llm = LLM(model="gpt-3.5-turbo", logprobs=5)
+        assert llm.logprobs == 5
+
+    def test_llm_with_return_full_completion(self):
+        """Test that LLM accepts return_full_completion parameter."""
+        llm = LLM(model="gpt-3.5-turbo", return_full_completion=True)
+        assert llm.return_full_completion is True
+
+    def test_agent_with_llm_parameters(self):
+        """Test that Agent accepts LLM generation parameters."""
+        agent = Agent(
+            role="test",
+            goal="test",
+            backstory="test",
+            llm_n=3,
+            llm_logprobs=5,
+            llm_top_logprobs=3,
+            return_completion_metadata=True,
+        )
+        assert agent.llm_n == 3
+        assert agent.llm_logprobs == 5
+        assert agent.llm_top_logprobs == 3
+        assert agent.return_completion_metadata is True
+
+    @patch('crewai.llm.litellm.completion')
+    def test_llm_call_returns_full_completion(self, mock_completion):
+        """Test that LLM.call can return full completion object."""
+        mock_response = Mock()
+        mock_response.choices = [Mock()]
+        mock_response.choices[0].message.content = "Test response"
+        mock_response.usage = {"prompt_tokens": 10, "completion_tokens": 5}
+        mock_response.model = "gpt-3.5-turbo"
+        mock_response.created = 1234567890
+        mock_response.id = "test-id"
+        mock_response.object = "chat.completion"
+        mock_response.system_fingerprint = "test-fingerprint"
+        mock_completion.return_value = mock_response
+
+        llm = LLM(model="gpt-3.5-turbo", return_full_completion=True)
+        result = llm.call("Test message")
+
+        assert isinstance(result, dict)
+        assert result["content"] == "Test response"
+        assert "choices" in result
+        assert "usage" in result
+        assert result["model"] == "gpt-3.5-turbo"
+
+    def test_task_output_completion_metadata(self):
+        """Test TaskOutput with completion metadata."""
+        mock_choices = [
+            Mock(message=Mock(content="Generation 1")),
+            Mock(message=Mock(content="Generation 2")),
+        ]
+        mock_usage = {"prompt_tokens": 10, "completion_tokens": 15}
+        
+        completion_metadata = {
+            "choices": mock_choices,
+            "usage": mock_usage,
+            "model": "gpt-3.5-turbo",
+        }
+
+        task_output = TaskOutput(
+            description="Test task",
+            raw="Generation 1",
+            agent="test-agent",
+            completion_metadata=completion_metadata,
+        )
+
+        generations = task_output.get_generations()
+        assert generations == ["Generation 1", "Generation 2"]
+
+        usage = task_output.get_usage_metrics()
+        assert usage == mock_usage
+
+    def test_lite_agent_output_completion_metadata(self):
+        """Test LiteAgentOutput with completion metadata."""
+        mock_choices = [
+            Mock(message=Mock(content="Generation 1")),
+            Mock(message=Mock(content="Generation 2")),
+        ]
+        mock_usage = {"prompt_tokens": 10, "completion_tokens": 15}
+        
+        completion_metadata = {
+            "choices": mock_choices,
+            "usage": mock_usage,
+            "model": "gpt-3.5-turbo",
+        }
+
+        output = LiteAgentOutput(
+            raw="Generation 1",
+            agent_role="test-agent",
+            completion_metadata=completion_metadata,
+        )
+
+        generations = output.get_generations()
+        assert generations == ["Generation 1", "Generation 2"]
+
+        usage = output.get_usage_metrics_from_completion()
+        assert usage == mock_usage
+
+
+class TestXMLParser:
+    """Test suite for XML parsing functionality."""
+
+    def test_extract_xml_content_basic(self):
+        """Test basic XML content extraction."""
+        text = "Some text <thinking>This is my thought</thinking> more text"
+        result = extract_xml_content(text, "thinking")
+        assert result == "This is my thought"
+
+    def test_extract_xml_content_not_found(self):
+        """Test XML content extraction when tag not found."""
+        text = "Some text without the tag"
+        result = extract_xml_content(text, "thinking")
+        assert result is None
+
+    def test_extract_xml_content_multiline(self):
+        """Test XML content extraction with multiline content."""
+        text = """Some text
+        <thinking>
+        This is a multiline
+        thought process
+        </thinking>
+        more text"""
+        result = extract_xml_content(text, "thinking")
+        assert "multiline" in result
+        assert "thought process" in result
+
+    def test_extract_all_xml_content(self):
+        """Test extracting all occurrences of XML content."""
+        text = """
+        <thinking>First thought</thinking>
+        Some text
+        <thinking>Second thought</thinking>
+        """
+        result = extract_all_xml_content(text, "thinking")
+        assert len(result) == 2
+        assert result[0] == "First thought"
+        assert result[1] == "Second thought"
+
+    def test_extract_multiple_xml_tags(self):
+        """Test extracting multiple different XML tags."""
+        text = """
+        <thinking>My thoughts</thinking>
+        <reasoning>My reasoning</reasoning>
+        <conclusion>My conclusion</conclusion>
+        """
+        result = extract_multiple_xml_tags(text, ["thinking", "reasoning", "conclusion"])
+        assert result["thinking"] == "My thoughts"
+        assert result["reasoning"] == "My reasoning"
+        assert result["conclusion"] == "My conclusion"
+
+    def test_extract_multiple_xml_tags_all(self):
+        """Test extracting all occurrences of multiple XML tags."""
+        text = """
+        <thinking>First thought</thinking>
+        <reasoning>First reasoning</reasoning>
+        <thinking>Second thought</thinking>
+        """
+        result = extract_multiple_xml_tags_all(text, ["thinking", "reasoning"])
+        assert len(result["thinking"]) == 2
+        assert len(result["reasoning"]) == 1
+        assert result["thinking"][0] == "First thought"
+        assert result["thinking"][1] == "Second thought"
+
+    def test_extract_xml_with_attributes(self):
+        """Test extracting XML with attributes."""
+        text = '<thinking type="deep" level="2">Complex thought</thinking>'
+        result = extract_xml_with_attributes(text, "thinking")
+        assert len(result) == 1
+        assert result[0]["content"] == "Complex thought"
+        assert result[0]["attributes"]["type"] == "deep"
+        assert result[0]["attributes"]["level"] == "2"
+
+    def test_remove_xml_tags(self):
+        """Test removing XML tags and their content."""
+        text = "Keep this <thinking>Remove this</thinking> and this"
+        result = remove_xml_tags(text, ["thinking"])
+        assert result == "Keep this  and this"
+
+    def test_strip_xml_tags_keep_content(self):
+        """Test stripping XML tags but keeping content."""
+        text = "Keep this <thinking>Keep this too</thinking> and this"
+        result = strip_xml_tags_keep_content(text, ["thinking"])
+        assert result == "Keep this Keep this too and this"
+
+    def test_nested_xml_tags(self):
+        """Test handling of nested XML tags."""
+        text = "<outer>Before <inner>nested content</inner> after</outer>"
+        result = extract_xml_content(text, "outer")
+        assert "Before" in result
+        assert "nested content" in result
+        assert "after" in result
+
+    def test_xml_with_special_characters(self):
+        """Test XML parsing with special characters."""
+        text = "<thinking>Content with & < > \" ' characters</thinking>"
+        result = extract_xml_content(text, "thinking")
+        assert "&" in result
+        assert "<" in result
+        assert ">" in result

tests/test_xml_parser_examples.py

@@ -0,0 +1,161 @@
+from crewai.utilities.xml_parser import (
+    extract_xml_content,
+    extract_all_xml_content,
+    extract_multiple_xml_tags,
+    remove_xml_tags,
+    strip_xml_tags_keep_content,
+)
+
+
+class TestXMLParserExamples:
+    """Test XML parser with realistic agent output examples."""
+
+    def test_agent_thinking_extraction(self):
+        """Test extracting thinking content from agent output."""
+        agent_output = """
+        I need to solve this problem step by step.
+        
+        <thinking>
+        Let me break this down:
+        1. First, I need to understand the requirements
+        2. Then, I'll analyze the constraints
+        3. Finally, I'll propose a solution
+        
+        The key insight is that we need to balance efficiency with accuracy.
+        </thinking>
+        
+        Based on my analysis, here's my recommendation: Use approach A.
+        """
+        
+        thinking = extract_xml_content(agent_output, "thinking")
+        assert thinking is not None
+        assert "break this down" in thinking
+        assert "requirements" in thinking
+        assert "constraints" in thinking
+        assert "efficiency with accuracy" in thinking
+
+    def test_multiple_reasoning_tags(self):
+        """Test extracting multiple reasoning sections."""
+        agent_output = """
+        <reasoning>
+        Initial analysis shows three possible approaches.
+        </reasoning>
+        
+        Let me explore each option:
+        
+        <reasoning>
+        Option A: Fast but less accurate
+        Option B: Slow but very accurate  
+        Option C: Balanced approach
+        </reasoning>
+        
+        My final recommendation is Option C.
+        """
+        
+        reasoning_sections = extract_all_xml_content(agent_output, "reasoning")
+        assert len(reasoning_sections) == 2
+        assert "three possible approaches" in reasoning_sections[0]
+        assert "Option A" in reasoning_sections[1]
+        assert "Option B" in reasoning_sections[1]
+        assert "Option C" in reasoning_sections[1]
+
+    def test_complex_agent_workflow(self):
+        """Test complex agent output with multiple tag types."""
+        complex_output = """
+        <thinking>
+        This is a complex problem requiring systematic analysis.
+        I need to consider multiple factors.
+        </thinking>
+        
+        <analysis>
+        Factor 1: Performance requirements
+        Factor 2: Cost constraints
+        Factor 3: Time limitations
+        </analysis>
+        
+        <reasoning>
+        Given the analysis above, I believe we should prioritize performance
+        while keeping costs reasonable. Time is less critical in this case.
+        </reasoning>
+        
+        <conclusion>
+        Recommend Solution X with performance optimizations.
+        </conclusion>
+        
+        Final answer: Implement Solution X with the following optimizations...
+        """
+        
+        extracted = extract_multiple_xml_tags(
+            complex_output, 
+            ["thinking", "analysis", "reasoning", "conclusion"]
+        )
+        
+        assert extracted["thinking"] is not None
+        assert "systematic analysis" in extracted["thinking"]
+        
+        assert extracted["analysis"] is not None
+        assert "Factor 1" in extracted["analysis"]
+        assert "Factor 2" in extracted["analysis"]
+        assert "Factor 3" in extracted["analysis"]
+        
+        assert extracted["reasoning"] is not None
+        assert "prioritize performance" in extracted["reasoning"]
+        
+        assert extracted["conclusion"] is not None
+        assert "Solution X" in extracted["conclusion"]
+
+    def test_clean_output_for_user(self):
+        """Test cleaning agent output for user presentation."""
+        raw_output = """
+        <thinking>
+        Internal reasoning that user shouldn't see.
+        This contains implementation details.
+        </thinking>
+        
+        <debug>
+        Debug information: variable X = 42
+        </debug>
+        
+        Here's the answer to your question: The solution is to use method Y.
+        
+        <internal_notes>
+        Remember to update the documentation later.
+        </internal_notes>
+        
+        This approach will give you the best results.
+        """
+        
+        clean_output = remove_xml_tags(
+            raw_output, 
+            ["thinking", "debug", "internal_notes"]
+        )
+        
+        assert "Internal reasoning" not in clean_output
+        assert "Debug information" not in clean_output
+        assert "update the documentation" not in clean_output
+        assert "Here's the answer" in clean_output
+        assert "method Y" in clean_output
+        assert "best results" in clean_output
+
+    def test_preserve_structured_content(self):
+        """Test preserving structured content while removing tags."""
+        structured_output = """
+        <steps>
+        1. Initialize the system
+        2. Load the configuration
+        3. Process the data
+        4. Generate the report
+        </steps>
+        
+        Follow these steps to complete the task.
+        """
+        
+        clean_output = strip_xml_tags_keep_content(structured_output, ["steps"])
+        
+        assert "<steps>" not in clean_output
+        assert "</steps>" not in clean_output
+        assert "1. Initialize" in clean_output
+        assert "2. Load" in clean_output
+        assert "3. Process" in clean_output
+        assert "4. Generate" in clean_output
+        assert "Follow these steps" in clean_output