Merge branch 'main' into feat-emit-stream-tool

Since we have fixed streaming response issue we can remove this @skip_streaming_in_ci

docs/concepts/knowledge.mdx

@@ -42,16 +42,6 @@ CrewAI supports various types of knowledge sources out of the box:
 | `collection_name`          | **str**                              | No       | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided.     |
 | `storage`                  | **Optional[KnowledgeStorage]**       | No       | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.              |
 
-
-<Tip>
-Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
-Simply add the relevant knowledge sources your agent or crew needs to function.
-
-Knowledge sources can be added at the agent or crew level.
-Crew level knowledge sources will be used by **all agents** in the crew.
-Agent level knowledge sources will be used by the **specific agent** that is preloaded with the knowledge.
-</Tip>
-
 ## Quickstart Example
 
 <Tip>
@@ -156,26 +146,6 @@ result = crew.kickoff(
 )
 ```
 
-## Knowledge Configuration
-
-You can configure the knowledge configuration for the crew or agent.
-
-```python Code
-from crewai.knowledge.knowledge_config import KnowledgeConfig
-
-knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
-
-agent = Agent(
-    ...
-    knowledge_config=knowledge_config
-)
-```
-
-<Tip>
-  `results_limit`: is the number of relevant documents to return. Default is 3.
-  `score_threshold`: is the minimum score for a document to be considered relevant. Default is 0.35.
-</Tip>
-
 ## More Examples
 
 Here are examples of how to use different types of knowledge sources:

docs/examples/jinja_templating.md

@@ -1,133 +0,0 @@
-# Enhanced Templating with Jinja2
-
-CrewAI now supports enhanced templating using Jinja2, while maintaining compatibility with the existing templating system.
-
-## Basic Usage
-
-The basic templating syntax remains the same:
-
-```python
-from crewai import Agent, Task, Crew
-
-# Define inputs
-inputs = {
-    "topic": "Artificial Intelligence",
-    "year": 2024,
-    "count": 5
-}
-
-# Create an agent with template variables
-researcher = Agent(
-    role="{topic} Researcher",
-    goal="Research the latest developments in {topic} for {year}",
-    backstory="You're an expert in {topic} with years of experience"
-)
-
-# Create a task with template variables
-research_task = Task(
-    description="Research {topic} and provide {count} key insights",
-    expected_output="A list of {count} key insights about {topic} in {year}",
-    agent=researcher
-)
-
-# Create a crew and pass inputs
-crew = Crew(
-    agents=[researcher],
-    tasks=[research_task],
-    inputs=inputs
-)
-
-# Run the crew
-result = crew.kickoff()
-```
-
-## Advanced Features
-
-The new templating system adds support for container types, object attributes, conditional statements, loops, and filters:
-
-### Container Types
-
-```python
-inputs = {
-    "topics": ["AI", "Machine Learning", "Data Science"],
-    "details": {"main_theme": "Technology Trends", "subtopics": ["Ethics", "Applications"]}
-}
-
-# Access list items
-task = Task(
-    description="Research {{topics[0]}} and {{topics[1]}}",
-    expected_output="Analysis of the topics"
-)
-
-# Access dictionary items
-task = Task(
-    description="Research {{details.main_theme}} with focus on {{details.subtopics[0]}}",
-    expected_output="Detailed analysis"
-)
-```
-
-### Conditional Statements
-
-```python
-inputs = {
-    "topic": "AI",
-    "priority": "high",
-    "deadline": "2024-12-31"
-}
-
-task = Task(
-    description="{% if priority == 'high' %}URGENT: {% endif %}Research {topic}{% if deadline %} by {{deadline}}{% endif %}",
-    expected_output="A report on {topic}"
-)
-```
-
-### Loop Statements
-
-```python
-inputs = {
-    "topics": ["AI", "Machine Learning", "Data Science"]
-}
-
-task = Task(
-    description="Research the following topics: {% for topic in topics %}{{topic}}{% if not loop.last %}, {% endif %}{% endfor %}",
-    expected_output="A report covering multiple topics"
-)
-```
-
-### Filters
-
-```python
-from datetime import datetime
-
-inputs = {
-    "topic": "AI",
-    "date": datetime.now()
-}
-
-task = Task(
-    description="Research {topic} as of {{date|date('%Y-%m-%d')}}",
-    expected_output="A report on {topic}"
-)
-```
-
-### Custom Objects
-
-```python
-from pydantic import BaseModel
-
-class Person(BaseModel):
-    name: str
-    age: int
-    
-    def __str__(self):
-        return f"{self.name} ({self.age})"
-
-inputs = {
-    "author": Person(name="John Doe", age=35)
-}
-
-task = Task(
-    description="Write a report authored by {author}",
-    expected_output="A report by {{author.name}}"
-)
-```

docs/how-to/bring-your-own-agent.mdx

@@ -1,443 +0,0 @@
----
-title: Bring your own agent
-description: Learn how to bring your own agents that work within a Crew.
-icon: robots
----
-
-Interoperability is a core concept in CrewAI. This guide will show you how to bring your own agents that work within a Crew.
-
-
-## Adapter Guide for Bringing your own agents (Langgraph Agents, OpenAI Agents, etc...)
-We require 3 adapters to turn any agent from different frameworks to work within crew.
-
-1. BaseAgentAdapter
-2. BaseToolAdapter
-3. BaseConverter
-
-
-## BaseAgentAdapter
-This abstract class defines the common interface and functionality that all
-agent adapters must implement. It extends BaseAgent to maintain compatibility
-with the CrewAI framework while adding adapter-specific requirements.
-
-Required Methods:
-
-1. `def configure_tools`
-2. `def configure_structured_output`
-
-## Creating your own Adapter
-To integrate an agent from a different framework (e.g., LangGraph, Autogen, OpenAI Assistants) into CrewAI, you need to create a custom adapter by inheriting from `BaseAgentAdapter`. This adapter acts as a compatibility layer, translating between the CrewAI interfaces and the specific requirements of your external agent.
-
-Here's how you implement your custom adapter:
-
-1.  **Inherit from `BaseAgentAdapter`**:
-    ```python
-    from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter
-    from crewai.tools import BaseTool
-    from typing import List, Optional, Any, Dict
-
-    class MyCustomAgentAdapter(BaseAgentAdapter):
-        # ... implementation details ...
-    ```
-
-2.  **Implement `__init__`**:
-    The constructor should call the parent class constructor `super().__init__(**kwargs)` and perform any initialization specific to your external agent. You can use the optional `agent_config` dictionary passed during CrewAI's `Agent` initialization to configure your adapter and the underlying agent.
-
-    ```python
-    def __init__(self, agent_config: Optional[Dict[str, Any]] = None, **kwargs: Any):
-        super().__init__(agent_config=agent_config, **kwargs)
-        # Initialize your external agent here, possibly using agent_config
-        # Example: self.external_agent = initialize_my_agent(agent_config)
-        print(f"Initializing MyCustomAgentAdapter with config: {agent_config}")
-    ```
-
-3.  **Implement `configure_tools`**:
-    This abstract method is crucial. It receives a list of CrewAI `BaseTool` instances. Your implementation must convert or adapt these tools into the format expected by your external agent framework. This might involve wrapping them, extracting specific attributes, or registering them with the external agent instance.
-
-    ```python
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
-        if tools:
-            adapted_tools = []
-            for tool in tools:
-                # Adapt CrewAI BaseTool to the format your agent expects
-                # Example: adapted_tool = adapt_to_my_framework(tool)
-                # adapted_tools.append(adapted_tool)
-                pass # Replace with your actual adaptation logic
-
-            # Configure the external agent with the adapted tools
-            # Example: self.external_agent.set_tools(adapted_tools)
-            print(f"Configuring tools for MyCustomAgentAdapter: {adapted_tools}") # Placeholder
-        else:
-            # Handle the case where no tools are provided
-            # Example: self.external_agent.set_tools([])
-            print("No tools provided for MyCustomAgentAdapter.")
-    ```
-
-4.  **Implement `configure_structured_output`**:
-    This method is called when the CrewAI `Agent` is configured with structured output requirements (e.g., `output_json` or `output_pydantic`). Your adapter needs to ensure the external agent is set up to comply with these requirements. This might involve setting specific parameters on the external agent or ensuring its underlying model supports the requested format. If the external agent doesn't support structured output in a way compatible with CrewAI's expectations, you might need to handle the conversion or raise an appropriate error.
-
-    ```python
-    def configure_structured_output(self, structured_output: Any) -> None:
-        # Configure your external agent to produce output in the specified format
-        # Example: self.external_agent.set_output_format(structured_output)
-        self.adapted_structured_output = True # Signal that structured output is handled
-        print(f"Configuring structured output for MyCustomAgentAdapter: {structured_output}")
-    ```
-
-By implementing these methods, your `MyCustomAgentAdapter` will allow your custom agent implementation to function correctly within a CrewAI crew, interacting with tasks and tools seamlessly. Remember to replace the example comments and print statements with your actual adaptation logic specific to the external agent framework you are integrating.
-
-## BaseToolAdapter implementation
-The `BaseToolAdapter` class is responsible for converting CrewAI's native `BaseTool` objects into a format that your specific external agent framework can understand and utilize. Different agent frameworks (like LangGraph, OpenAI Assistants, etc.) have their own unique ways of defining and handling tools, and the `BaseToolAdapter` acts as the translator.
-
-Here's how you implement your custom tool adapter:
-
-1.  **Inherit from `BaseToolAdapter`**:
-    ```python
-    from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
-    from crewai.tools import BaseTool
-    from typing import List, Any
-
-    class MyCustomToolAdapter(BaseToolAdapter):
-        # ... implementation details ...
-    ```
-
-2.  **Implement `configure_tools`**:
-    This is the core abstract method you must implement. It receives a list of CrewAI `BaseTool` instances provided to the agent. Your task is to iterate through this list, adapt each `BaseTool` into the format expected by your external framework, and store the converted tools in the `self.converted_tools` list (which is initialized in the base class constructor).
-
-    ```python
-    def configure_tools(self, tools: List[BaseTool]) -> None:
-        """Configure and convert CrewAI tools for the specific implementation."""
-        self.converted_tools = [] # Reset in case it's called multiple times
-        for tool in tools:
-            # Sanitize the tool name if required by the target framework
-            sanitized_name = self.sanitize_tool_name(tool.name)
-
-            # --- Your Conversion Logic Goes Here ---
-            # Example: Convert BaseTool to a dictionary format for LangGraph
-            # converted_tool = {
-            #     "name": sanitized_name,
-            #     "description": tool.description,
-            #     "parameters": tool.args_schema.schema() if tool.args_schema else {},
-            #     # Add any other framework-specific fields
-            # }
-
-            # Example: Convert BaseTool to an OpenAI function definition
-            # converted_tool = {
-            #     "type": "function",
-            #     "function": {
-            #         "name": sanitized_name,
-            #         "description": tool.description,
-            #         "parameters": tool.args_schema.schema() if tool.args_schema else {"type": "object", "properties": {}},
-            #     }
-            # }
-
-            # --- Replace above examples with your actual adaptation ---
-            converted_tool = self.adapt_tool_to_my_framework(tool, sanitized_name) # Placeholder
-
-            self.converted_tools.append(converted_tool)
-            print(f"Adapted tool '{tool.name}' to '{sanitized_name}' for MyCustomToolAdapter") # Placeholder
-
-        print(f"MyCustomToolAdapter finished configuring tools: {len(self.converted_tools)} adapted.") # Placeholder
-
-    # --- Helper method for adaptation (Example) ---
-    def adapt_tool_to_my_framework(self, tool: BaseTool, sanitized_name: str) -> Any:
-        # Replace this with the actual logic to convert a CrewAI BaseTool
-        # to the format needed by your specific external agent framework.
-        # This will vary greatly depending on the target framework.
-        adapted_representation = {
-            "framework_specific_name": sanitized_name,
-            "framework_specific_description": tool.description,
-            "inputs": tool.args_schema.schema() if tool.args_schema else None,
-            "implementation_reference": tool.run # Or however the framework needs to call it
-        }
-        # Also ensure the tool works both sync and async
-        async def async_tool_wrapper(*args, **kwargs):
-            output = tool.run(*args, **kwargs)
-            if inspect.isawaitable(output):
-                return await output
-            else:
-                return output
-
-        adapted_tool = MyFrameworkTool(
-            name=sanitized_name,
-            description=tool.description,
-            inputs=tool.args_schema.schema() if tool.args_schema else None,
-            implementation_reference=async_tool_wrapper
-        )
-        
-        return adapted_representation
-
-    ```
-
-3.  **Using the Adapter**:
-    Typically, you would instantiate your `MyCustomToolAdapter` within your `MyCustomAgentAdapter`'s `configure_tools` method and use it to process the tools before configuring your external agent.
-
-    ```python
-    # Inside MyCustomAgentAdapter.configure_tools
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
-        if tools:
-            tool_adapter = MyCustomToolAdapter() # Instantiate your tool adapter
-            tool_adapter.configure_tools(tools)  # Convert the tools
-            adapted_tools = tool_adapter.tools() # Get the converted tools
-
-            # Now configure your external agent with the adapted_tools
-            # Example: self.external_agent.set_tools(adapted_tools)
-            print(f"Configuring external agent with adapted tools: {adapted_tools}") # Placeholder
-        else:
-            # Handle no tools case
-            print("No tools provided for MyCustomAgentAdapter.")
-    ```
-
-By creating a `BaseToolAdapter`, you decouple the tool conversion logic from the agent adaptation, making the integration cleaner and more modular. Remember to replace the placeholder examples with the actual conversion logic required by your specific external agent framework.
-
-## BaseConverter
-The `BaseConverterAdapter` plays a crucial role when a CrewAI `Task` requires an agent to return its final output in a specific structured format, such as JSON or a Pydantic model. It bridges the gap between CrewAI's structured output requirements and the capabilities of your external agent.
-
-Its primary responsibilities are:
-1.  **Configuring the Agent for Structured Output:** Based on the `Task`'s requirements (`output_json` or `output_pydantic`), it instructs the associated `BaseAgentAdapter` (and indirectly, the external agent) on what format is expected.
-2.  **Enhancing the System Prompt:** It modifies the agent's system prompt to include clear instructions on *how* to generate the output in the required structure.
-3.  **Post-processing the Result:** It takes the raw output from the agent and attempts to parse, validate, and format it according to the required structure, ultimately returning a string representation (e.g., a JSON string).
-
-Here's how you implement your custom converter adapter:
-
-1.  **Inherit from `BaseConverterAdapter`**:
-    ```python
-    from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
-    # Assuming you have your MyCustomAgentAdapter defined
-    # from .my_custom_agent_adapter import MyCustomAgentAdapter
-    from crewai.task import Task
-    from typing import Any
-
-    class MyCustomConverterAdapter(BaseConverterAdapter):
-        # Store the expected output type (e.g., 'json', 'pydantic', 'text')
-        _output_type: str = 'text' 
-        _output_schema: Any = None # Store JSON schema or Pydantic model
-
-        # ... implementation details ...
-    ```
-
-2.  **Implement `__init__`**:
-    The constructor must accept the corresponding `agent_adapter` instance it will work with.
-
-    ```python
-    def __init__(self, agent_adapter: Any): # Use your specific AgentAdapter type hint
-        self.agent_adapter = agent_adapter
-        print(f"Initializing MyCustomConverterAdapter for agent adapter: {type(agent_adapter).__name__}")
-    ```
-
-3.  **Implement `configure_structured_output`**:
-    This method receives the CrewAI `Task` object. You need to check the task's `output_json` and `output_pydantic` attributes to determine the required output structure. Store this information (e.g., in `_output_type` and `_output_schema`) and potentially call configuration methods on your `self.agent_adapter` if the external agent needs specific setup for structured output (which might have been partially handled in the agent adapter's `configure_structured_output` already).
-
-    ```python
-    def configure_structured_output(self, task: Task) -> None:
-        """Configure the expected structured output based on the task."""
-        if task.output_pydantic:
-            self._output_type = 'pydantic'
-            self._output_schema = task.output_pydantic
-            print(f"Converter: Configured for Pydantic output: {self._output_schema.__name__}")
-        elif task.output_json:
-            self._output_type = 'json'
-            self._output_schema = task.output_json
-            print(f"Converter: Configured for JSON output with schema: {self._output_schema}")
-        else:
-            self._output_type = 'text'
-            self._output_schema = None
-            print("Converter: Configured for standard text output.")
-
-        # Optionally, inform the agent adapter if needed
-        # self.agent_adapter.set_output_mode(self._output_type, self._output_schema)
-    ```
-
-4.  **Implement `enhance_system_prompt`**:
-    This method takes the agent's base system prompt string and should append instructions tailored to the currently configured `_output_type` and `_output_schema`. The goal is to guide the LLM powering the agent to produce output in the correct format.
-
-    ```python
-    def enhance_system_prompt(self, base_prompt: str) -> str:
-        """Enhance the system prompt with structured output instructions."""
-        if self._output_type == 'text':
-            return base_prompt # No enhancement needed for plain text
-
-        instructions = "\n\nYour final answer MUST be formatted as "
-        if self._output_type == 'json':
-            schema_str = json.dumps(self._output_schema, indent=2)
-            instructions += f"a JSON object conforming to the following schema:\n```json\n{schema_str}\n```"
-        elif self._output_type == 'pydantic':
-            schema_str = json.dumps(self._output_schema.model_json_schema(), indent=2)
-            instructions += f"a JSON object conforming to the Pydantic model '{self._output_schema.__name__}' with the following schema:\n```json\n{schema_str}\n```"
-
-        instructions += "\nEnsure your entire response is ONLY the valid JSON object, without any introductory text, explanations, or concluding remarks."
-        
-        print(f"Converter: Enhancing prompt for {self._output_type} output.")
-        return base_prompt + instructions
-    ```
-    *Note: The exact prompt engineering might need tuning based on the agent/LLM being used.* 
-
-5.  **Implement `post_process_result`**:
-    This method receives the raw string output from the agent. If structured output was requested (`json` or `pydantic`), you should attempt to parse the string into the expected format. Handle potential parsing errors (e.g., log them, attempt simple fixes, or raise an exception). Crucially, the method must **always return a string**, even if the intermediate format was a dictionary or Pydantic object (e.g., by serializing it back to a JSON string).
-
-    ```python
-    import json
-    from pydantic import ValidationError
-
-    def post_process_result(self, result: str) -> str:
-        """Post-process the agent's result to ensure it matches the expected format."""
-        print(f"Converter: Post-processing result for {self._output_type} output.")
-        if self._output_type == 'json':
-            try:
-                # Attempt to parse and re-serialize to ensure validity and consistent format
-                parsed_json = json.loads(result)
-                # Optional: Validate against self._output_schema if it's a JSON schema dictionary
-                # from jsonschema import validate
-                # validate(instance=parsed_json, schema=self._output_schema)
-                return json.dumps(parsed_json)
-            except json.JSONDecodeError as e:
-                print(f"Error: Failed to parse JSON output: {e}\nRaw output:\n{result}")
-                # Handle error: return raw, raise exception, or try to fix
-                return result # Example: return raw output on failure
-            # except Exception as e: # Catch validation errors if using jsonschema
-            #     print(f"Error: JSON output failed schema validation: {e}\nRaw output:\n{result}")
-            #     return result
-        elif self._output_type == 'pydantic':
-            try:
-                # Attempt to parse into the Pydantic model
-                model_instance = self._output_schema.model_validate_json(result)
-                # Return the model serialized back to JSON
-                return model_instance.model_dump_json()
-            except ValidationError as e:
-                print(f"Error: Failed to validate Pydantic output: {e}\nRaw output:\n{result}")
-                # Handle error
-                return result # Example: return raw output on failure
-            except json.JSONDecodeError as e:
-                 print(f"Error: Failed to parse JSON for Pydantic model: {e}\nRaw output:\n{result}")
-                 return result
-        else: # 'text'
-            return result # No processing needed for plain text
-    ```
-
-By implementing these methods, your `MyCustomConverterAdapter` ensures that structured output requests from CrewAI tasks are correctly handled by your integrated external agent, improving the reliability and usability of your custom agent within the CrewAI framework.
-
-## Out of the Box Adapters
-
-We provide out of the box adapters for the following frameworks:
-1. LangGraph
-2. OpenAI Agents
-
-## Kicking off a crew with adapted agents:
-
-```python
-import json
-import os
-from typing import List
-
-from crewai_tools import SerperDevTool
-from src.crewai import Agent, Crew, Task
-from langchain_openai import ChatOpenAI
-from pydantic import BaseModel
-
-from crewai.agents.agent_adapters.langgraph.langgraph_adapter import (
-    LangGraphAgentAdapter,
-)
-from crewai.agents.agent_adapters.openai_agents.openai_adapter import OpenAIAgentAdapter
-
-# CrewAI Agent
-code_helper_agent = Agent(
-    role="Code Helper",
-    goal="Help users solve coding problems effectively and provide clear explanations.",
-    backstory="You are an experienced programmer with deep knowledge across multiple programming languages and frameworks. You specialize in solving complex coding challenges and explaining solutions clearly.",
-    allow_delegation=False,
-    verbose=True,
-)
-# OpenAI Agent Adapter
-link_finder_agent = OpenAIAgentAdapter(
-    role="Link Finder",
-    goal="Find the most relevant and high-quality resources for coding tasks.",
-    backstory="You are a research specialist with a talent for finding the most helpful resources. You're skilled at using search tools to discover documentation, tutorials, and examples that directly address the user's coding needs.",
-    tools=[SerperDevTool()],
-    allow_delegation=False,
-    verbose=True,
-)
-
-# LangGraph Agent Adapter
-reporter_agent = LangGraphAgentAdapter(
-    role="Reporter",
-    goal="Report the results of the tasks.",
-    backstory="You are a reporter who reports the results of the other tasks",
-    llm=ChatOpenAI(model="gpt-4o"),
-    allow_delegation=True,
-    verbose=True,
-)
-
-
-class Code(BaseModel):
-    code: str
-
-
-task = Task(
-    description="Give an answer to the coding question: {task}",
-    expected_output="A thorough answer to the coding question: {task}",
-    agent=code_helper_agent,
-    output_json=Code,
-)
-task2 = Task(
-    description="Find links to resources that can help with coding tasks. Use the serper tool to find resources that can help.",
-    expected_output="A list of links to resources that can help with coding tasks",
-    agent=link_finder_agent,
-)
-
-
-class Report(BaseModel):
-    code: str
-    links: List[str]
-
-
-task3 = Task(
-    description="Report the results of the tasks.",
-    expected_output="A report of the results of the tasks. this is the code produced and then the links to the resources that can help with the coding task.",
-    agent=reporter_agent,
-    output_json=Report,
-)
-# Use in CrewAI
-crew = Crew(
-    agents=[code_helper_agent, link_finder_agent, reporter_agent],
-    tasks=[task, task2, task3],
-    verbose=True,
-)
-
-result = crew.kickoff(
-    inputs={"task": "How do you implement an abstract class in python?"}
-)
-
-# Print raw result first
-print("Raw result:", result)
-
-# Handle result based on its type
-if hasattr(result, "json_dict") and result.json_dict:
-    json_result = result.json_dict
-    print("\nStructured JSON result:")
-    print(f"{json.dumps(json_result, indent=2)}")
-
-    # Access fields safely
-    if isinstance(json_result, dict):
-        if "code" in json_result:
-            print("\nCode:")
-            print(
-                json_result["code"][:200] + "..."
-                if len(json_result["code"]) > 200
-                else json_result["code"]
-            )
-
-        if "links" in json_result:
-            print("\nLinks:")
-            for link in json_result["links"][:5]:  # Print first 5 links
-                print(f"- {link}")
-            if len(json_result["links"]) > 5:
-                print(f"...and {len(json_result['links']) - 5} more links")
-elif hasattr(result, "pydantic") and result.pydantic:
-    print("\nPydantic model result:")
-    print(result.pydantic.model_dump_json(indent=2))
-else:
-    # Fallback to raw output
-    print("\nNo structured result available, using raw output:")
-    print(result.raw[:500] + "..." if len(result.raw) > 500 else result.raw)
-
-```

src/crewai/agent.py

@@ -114,14 +114,6 @@ class Agent(BaseAgent):
         default=None,
         description="Embedder configuration for the agent.",
     )
-    agent_knowledge_context: Optional[str] = Field(
-        default=None,
-        description="Knowledge context for the agent.",
-    )
-    crew_knowledge_context: Optional[str] = Field(
-        default=None,
-        description="Knowledge context for the crew.",
-    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
@@ -185,7 +177,7 @@ class Agent(BaseAgent):
         self,
         task: Task,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -196,11 +188,6 @@ class Agent(BaseAgent):
 
         Returns:
             Output of the agent
-
-        Raises:
-            TimeoutError: If execution exceeds the maximum execution time.
-            ValueError: If the max execution time is not a positive integer.
-            RuntimeError: If the agent execution fails for other reasons.
         """
         if self.tools_handler:
             self.tools_handler.last_used_tool = {}  # type: ignore # Incompatible types in assignment (expression has type "dict[Never, Never]", variable has type "ToolCalling")
@@ -242,30 +229,22 @@ class Agent(BaseAgent):
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
-        knowledge_config = (
-            self.knowledge_config.model_dump() if self.knowledge_config else {}
-        )
+
         if self.knowledge:
-            agent_knowledge_snippets = self.knowledge.query(
-                [task.prompt()], **knowledge_config
-            )
+            agent_knowledge_snippets = self.knowledge.query([task.prompt()])
             if agent_knowledge_snippets:
-                self.agent_knowledge_context = extract_knowledge_context(
+                agent_knowledge_context = extract_knowledge_context(
                     agent_knowledge_snippets
                 )
-                if self.agent_knowledge_context:
-                    task_prompt += self.agent_knowledge_context
+                if agent_knowledge_context:
+                    task_prompt += agent_knowledge_context
 
         if self.crew:
-            knowledge_snippets = self.crew.query_knowledge(
-                [task.prompt()], **knowledge_config
-            )
+            knowledge_snippets = self.crew.query_knowledge([task.prompt()])
             if knowledge_snippets:
-                self.crew_knowledge_context = extract_knowledge_context(
-                    knowledge_snippets
-                )
-                if self.crew_knowledge_context:
-                    task_prompt += self.crew_knowledge_context
+                crew_knowledge_context = extract_knowledge_context(knowledge_snippets)
+                if crew_knowledge_context:
+                    task_prompt += crew_knowledge_context
 
         tools = tools or self.tools or []
         self.create_agent_executor(tools=tools, task=task)
@@ -285,26 +264,14 @@ class Agent(BaseAgent):
                     task=task,
                 ),
             )
-
-            # Determine execution method based on timeout setting
-            if self.max_execution_time is not None:
-                if not isinstance(self.max_execution_time, int) or self.max_execution_time <= 0:
-                    raise ValueError("Max Execution time must be a positive integer greater than zero")
-                result = self._execute_with_timeout(task_prompt, task, self.max_execution_time)
-            else:
-                result = self._execute_without_timeout(task_prompt, task)
-                
-        except TimeoutError as e:
-            # Propagate TimeoutError without retry
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionErrorEvent(
-                    agent=self,
-                    task=task,
-                    error=str(e),
-                ),
-            )
-            raise e
+            result = self.agent_executor.invoke(
+                {
+                    "input": task_prompt,
+                    "tool_names": self.agent_executor.tools_names,
+                    "tools": self.agent_executor.tools_description,
+                    "ask_for_human_input": task.human_input,
+                }
+            )["output"]
         except Exception as e:
             if e.__class__.__module__.startswith("litellm"):
                 # Do not retry on litellm errors
@@ -345,66 +312,6 @@ class Agent(BaseAgent):
         )
         return result
 
-    def _execute_with_timeout(
-        self,
-        task_prompt: str,
-        task: Task,
-        timeout: int
-    ) -> str:
-        """Execute a task with a timeout.
-        
-        Args:
-            task_prompt: The prompt to send to the agent.
-            task: The task being executed.
-            timeout: Maximum execution time in seconds.
-            
-        Returns:
-            The output of the agent.
-            
-        Raises:
-            TimeoutError: If execution exceeds the timeout.
-            RuntimeError: If execution fails for other reasons.
-        """
-        import concurrent.futures
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            future = executor.submit(
-                self._execute_without_timeout,
-                task_prompt=task_prompt,
-                task=task
-            )
-            
-            try:
-                return future.result(timeout=timeout)
-            except concurrent.futures.TimeoutError:
-                future.cancel()
-                raise TimeoutError(f"Task '{task.description}' execution timed out after {timeout} seconds. Consider increasing max_execution_time or optimizing the task.")
-            except Exception as e:
-                future.cancel()
-                raise RuntimeError(f"Task execution failed: {str(e)}")
-
-    def _execute_without_timeout(
-        self,
-        task_prompt: str,
-        task: Task
-    ) -> str:
-        """Execute a task without a timeout.
-        
-        Args:
-            task_prompt: The prompt to send to the agent.
-            task: The task being executed.
-            
-        Returns:
-            The output of the agent.
-        """
-        return self.agent_executor.invoke(
-            {
-                "input": task_prompt,
-                "tool_names": self.agent_executor.tools_names,
-                "tools": self.agent_executor.tools_description,
-                "ask_for_human_input": task.human_input,
-            }
-        )["output"]
-
     def create_agent_executor(
         self, tools: Optional[List[BaseTool]] = None, task=None
     ) -> None:

src/crewai/agents/agent_adapters/base_agent_adapter.py

@@ -1,42 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Optional
-
-from pydantic import PrivateAttr
-
-from crewai.agent import BaseAgent
-from crewai.tools import BaseTool
-
-
-class BaseAgentAdapter(BaseAgent, ABC):
-    """Base class for all agent adapters in CrewAI.
-
-    This abstract class defines the common interface and functionality that all
-    agent adapters must implement. It extends BaseAgent to maintain compatibility
-    with the CrewAI framework while adding adapter-specific requirements.
-    """
-
-    adapted_structured_output: bool = False
-    _agent_config: Optional[Dict[str, Any]] = PrivateAttr(default=None)
-
-    model_config = {"arbitrary_types_allowed": True}
-
-    def __init__(self, agent_config: Optional[Dict[str, Any]] = None, **kwargs: Any):
-        super().__init__(adapted_agent=True, **kwargs)
-        self._agent_config = agent_config
-
-    @abstractmethod
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
-        """Configure and adapt tools for the specific agent implementation.
-
-        Args:
-            tools: Optional list of BaseTool instances to be configured
-        """
-        pass
-
-    def configure_structured_output(self, structured_output: Any) -> None:
-        """Configure the structured output for the specific agent implementation.
-
-        Args:
-            structured_output: The structured output to be configured
-        """
-        pass

src/crewai/agents/agent_adapters/base_converter_adapter.py

@@ -1,29 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class BaseConverterAdapter(ABC):
-    """Base class for all converter adapters in CrewAI.
-
-    This abstract class defines the common interface and functionality that all
-    converter adapters must implement for converting structured output.
-    """
-
-    def __init__(self, agent_adapter):
-        self.agent_adapter = agent_adapter
-
-    @abstractmethod
-    def configure_structured_output(self, task) -> None:
-        """Configure agents to return structured output.
-        Must support json and pydantic output.
-        """
-        pass
-
-    @abstractmethod
-    def enhance_system_prompt(self, base_prompt: str) -> str:
-        """Enhance the system prompt with structured output instructions."""
-        pass
-
-    @abstractmethod
-    def post_process_result(self, result: str) -> str:
-        """Post-process the result to ensure it matches the expected format: string."""
-        pass

src/crewai/agents/agent_adapters/base_tool_adapter.py

@@ -1,37 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Any, List, Optional
-
-from crewai.tools.base_tool import BaseTool
-
-
-class BaseToolAdapter(ABC):
-    """Base class for all tool adapters in CrewAI.
-
-    This abstract class defines the common interface that all tool adapters
-    must implement. It provides the structure for adapting CrewAI tools to
-    different frameworks and platforms.
-    """
-
-    original_tools: List[BaseTool]
-    converted_tools: List[Any]
-
-    def __init__(self, tools: Optional[List[BaseTool]] = None):
-        self.original_tools = tools or []
-        self.converted_tools = []
-
-    @abstractmethod
-    def configure_tools(self, tools: List[BaseTool]) -> None:
-        """Configure and convert tools for the specific implementation.
-
-        Args:
-            tools: List of BaseTool instances to be configured and converted
-        """
-        pass
-
-    def tools(self) -> List[Any]:
-        """Return all converted tools."""
-        return self.converted_tools
-
-    def sanitize_tool_name(self, tool_name: str) -> str:
-        """Sanitize tool name for API compatibility."""
-        return tool_name.replace(" ", "_")

src/crewai/agents/agent_adapters/langgraph/langgraph_adapter.py

@@ -1,226 +0,0 @@
-from typing import Any, AsyncIterable, Dict, List, Optional
-
-from pydantic import Field, PrivateAttr
-
-from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter
-from crewai.agents.agent_adapters.langgraph.langgraph_tool_adapter import (
-    LangGraphToolAdapter,
-)
-from crewai.agents.agent_adapters.langgraph.structured_output_converter import (
-    LangGraphConverterAdapter,
-)
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.tools.base_tool import BaseTool
-from crewai.utilities import Logger
-from crewai.utilities.converter import Converter
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-
-try:
-    from langchain_core.messages import ToolMessage
-    from langgraph.checkpoint.memory import MemorySaver
-    from langgraph.prebuilt import create_react_agent
-
-    LANGGRAPH_AVAILABLE = True
-except ImportError:
-    LANGGRAPH_AVAILABLE = False
-
-
-class LangGraphAgentAdapter(BaseAgentAdapter):
-    """Adapter for LangGraph agents to work with CrewAI."""
-
-    model_config = {"arbitrary_types_allowed": True}
-
-    _logger: Logger = PrivateAttr(default_factory=lambda: Logger())
-    _tool_adapter: LangGraphToolAdapter = PrivateAttr()
-    _graph: Any = PrivateAttr(default=None)
-    _memory: Any = PrivateAttr(default=None)
-    _max_iterations: int = PrivateAttr(default=10)
-    function_calling_llm: Any = Field(default=None)
-    step_callback: Any = Field(default=None)
-
-    model: str = Field(default="gpt-4o")
-    verbose: bool = Field(default=False)
-
-    def __init__(
-        self,
-        role: str,
-        goal: str,
-        backstory: str,
-        tools: Optional[List[BaseTool]] = None,
-        llm: Any = None,
-        max_iterations: int = 10,
-        agent_config: Optional[Dict[str, Any]] = None,
-        **kwargs,
-    ):
-        """Initialize the LangGraph agent adapter."""
-        if not LANGGRAPH_AVAILABLE:
-            raise ImportError(
-                "LangGraph Agent Dependencies are not installed. Please install it using `uv add langchain-core langgraph`"
-            )
-        super().__init__(
-            role=role,
-            goal=goal,
-            backstory=backstory,
-            tools=tools,
-            llm=llm or self.model,
-            agent_config=agent_config,
-            **kwargs,
-        )
-        self._tool_adapter = LangGraphToolAdapter(tools=tools)
-        self._converter_adapter = LangGraphConverterAdapter(self)
-        self._max_iterations = max_iterations
-        self._setup_graph()
-
-    def _setup_graph(self) -> None:
-        """Set up the LangGraph workflow graph."""
-        try:
-            self._memory = MemorySaver()
-
-            converted_tools: List[Any] = self._tool_adapter.tools()
-            if self._agent_config:
-                self._graph = create_react_agent(
-                    model=self.llm,
-                    tools=converted_tools,
-                    checkpointer=self._memory,
-                    debug=self.verbose,
-                    **self._agent_config,
-                )
-            else:
-                self._graph = create_react_agent(
-                    model=self.llm,
-                    tools=converted_tools or [],
-                    checkpointer=self._memory,
-                    debug=self.verbose,
-                )
-
-        except ImportError as e:
-            self._logger.log(
-                "error", f"Failed to import LangGraph dependencies: {str(e)}"
-            )
-            raise
-        except Exception as e:
-            self._logger.log("error", f"Error setting up LangGraph agent: {str(e)}")
-            raise
-
-    def _build_system_prompt(self) -> str:
-        """Build a system prompt for the LangGraph agent."""
-        base_prompt = f"""
-            You are {self.role}.
-        
-            Your goal is: {self.goal}
-
-            Your backstory: {self.backstory}
-
-            When working on tasks, think step-by-step and use the available tools when necessary.
-        """
-        return self._converter_adapter.enhance_system_prompt(base_prompt)
-
-    def execute_task(
-        self,
-        task: Any,
-        context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
-    ) -> str:
-        """Execute a task using the LangGraph workflow."""
-        self.create_agent_executor(tools)
-
-        self.configure_structured_output(task)
-
-        try:
-            task_prompt = task.prompt() if hasattr(task, "prompt") else str(task)
-
-            if context:
-                task_prompt = self.i18n.slice("task_with_context").format(
-                    task=task_prompt, context=context
-                )
-
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionStartedEvent(
-                    agent=self,
-                    tools=self.tools,
-                    task_prompt=task_prompt,
-                    task=task,
-                ),
-            )
-
-            session_id = f"task_{id(task)}"
-
-            config = {"configurable": {"thread_id": session_id}}
-
-            result = self._graph.invoke(
-                {
-                    "messages": [
-                        ("system", self._build_system_prompt()),
-                        ("user", task_prompt),
-                    ]
-                },
-                config,
-            )
-
-            messages = result.get("messages", [])
-            last_message = messages[-1] if messages else None
-
-            final_answer = ""
-            if isinstance(last_message, dict):
-                final_answer = last_message.get("content", "")
-            elif hasattr(last_message, "content"):
-                final_answer = getattr(last_message, "content", "")
-
-            final_answer = (
-                self._converter_adapter.post_process_result(final_answer)
-                or "Task execution completed but no clear answer was provided."
-            )
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionCompletedEvent(
-                    agent=self, task=task, output=final_answer
-                ),
-            )
-
-            return final_answer
-
-        except Exception as e:
-            self._logger.log("error", f"Error executing LangGraph task: {str(e)}")
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionErrorEvent(
-                    agent=self,
-                    task=task,
-                    error=str(e),
-                ),
-            )
-            raise
-
-    def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> None:
-        """Configure the LangGraph agent for execution."""
-        self.configure_tools(tools)
-
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
-        """Configure tools for the LangGraph agent."""
-        if tools:
-            all_tools = list(self.tools or []) + list(tools or [])
-            self._tool_adapter.configure_tools(all_tools)
-            available_tools = self._tool_adapter.tools()
-            self._graph.tools = available_tools
-
-    def get_delegation_tools(self, agents: List[BaseAgent]) -> List[BaseTool]:
-        """Implement delegation tools support for LangGraph."""
-        agent_tools = AgentTools(agents=agents)
-        return agent_tools.tools()
-
-    def get_output_converter(
-        self, llm: Any, text: str, model: Any, instructions: str
-    ) -> Any:
-        """Convert output format if needed."""
-        return Converter(llm=llm, text=text, model=model, instructions=instructions)
-
-    def configure_structured_output(self, task) -> None:
-        """Configure the structured output for LangGraph."""
-        self._converter_adapter.configure_structured_output(task)

src/crewai/agents/agent_adapters/langgraph/langgraph_tool_adapter.py

@@ -1,61 +0,0 @@
-import inspect
-from typing import Any, List, Optional
-
-from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
-from crewai.tools.base_tool import BaseTool
-
-
-class LangGraphToolAdapter(BaseToolAdapter):
-    """Adapts CrewAI tools to LangGraph agent tool compatible format"""
-
-    def __init__(self, tools: Optional[List[BaseTool]] = None):
-        self.original_tools = tools or []
-        self.converted_tools = []
-
-    def configure_tools(self, tools: List[BaseTool]) -> None:
-        """
-        Configure and convert CrewAI tools to LangGraph-compatible format.
-        LangGraph expects tools in langchain_core.tools format.
-        """
-        from langchain_core.tools import BaseTool, StructuredTool
-
-        converted_tools = []
-        if self.original_tools:
-            all_tools = tools + self.original_tools
-        else:
-            all_tools = tools
-        for tool in all_tools:
-            if isinstance(tool, BaseTool):
-                converted_tools.append(tool)
-                continue
-
-            sanitized_name = self.sanitize_tool_name(tool.name)
-
-            async def tool_wrapper(*args, tool=tool, **kwargs):
-                output = None
-                if len(args) > 0 and isinstance(args[0], str):
-                    output = tool.run(args[0])
-                elif "input" in kwargs:
-                    output = tool.run(kwargs["input"])
-                else:
-                    output = tool.run(**kwargs)
-
-                if inspect.isawaitable(output):
-                    result = await output
-                else:
-                    result = output
-                return result
-
-            converted_tool = StructuredTool(
-                name=sanitized_name,
-                description=tool.description,
-                func=tool_wrapper,
-                args_schema=tool.args_schema,
-            )
-
-            converted_tools.append(converted_tool)
-
-        self.converted_tools = converted_tools
-
-    def tools(self) -> List[Any]:
-        return self.converted_tools or []

src/crewai/agents/agent_adapters/langgraph/structured_output_converter.py

@@ -1,80 +0,0 @@
-import json
-
-from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
-from crewai.utilities.converter import generate_model_description
-
-
-class LangGraphConverterAdapter(BaseConverterAdapter):
-    """Adapter for handling structured output conversion in LangGraph agents"""
-
-    def __init__(self, agent_adapter):
-        """Initialize the converter adapter with a reference to the agent adapter"""
-        self.agent_adapter = agent_adapter
-        self._output_format = None
-        self._schema = None
-        self._system_prompt_appendix = None
-
-    def configure_structured_output(self, task) -> None:
-        """Configure the structured output for LangGraph."""
-        if not (task.output_json or task.output_pydantic):
-            self._output_format = None
-            self._schema = None
-            self._system_prompt_appendix = None
-            return
-
-        if task.output_json:
-            self._output_format = "json"
-            self._schema = generate_model_description(task.output_json)
-        elif task.output_pydantic:
-            self._output_format = "pydantic"
-            self._schema = generate_model_description(task.output_pydantic)
-
-        self._system_prompt_appendix = self._generate_system_prompt_appendix()
-
-    def _generate_system_prompt_appendix(self) -> str:
-        """Generate an appendix for the system prompt to enforce structured output"""
-        if not self._output_format or not self._schema:
-            return ""
-
-        return f"""
-Important: Your final answer MUST be provided in the following structured format:
-
-{self._schema}
-
-DO NOT include any markdown code blocks, backticks, or other formatting around your response. 
-The output should be raw JSON that exactly matches the specified schema.
-"""
-
-    def enhance_system_prompt(self, original_prompt: str) -> str:
-        """Add structured output instructions to the system prompt if needed"""
-        if not self._system_prompt_appendix:
-            return original_prompt
-
-        return f"{original_prompt}\n{self._system_prompt_appendix}"
-
-    def post_process_result(self, result: str) -> str:
-        """Post-process the result to ensure it matches the expected format"""
-        if not self._output_format:
-            return result
-
-        # Try to extract valid JSON if it's wrapped in code blocks or other text
-        if self._output_format in ["json", "pydantic"]:
-            try:
-                # First, try to parse as is
-                json.loads(result)
-                return result
-            except json.JSONDecodeError:
-                # Try to extract JSON from the text
-                import re
-
-                json_match = re.search(r"(\{.*\})", result, re.DOTALL)
-                if json_match:
-                    try:
-                        extracted = json_match.group(1)
-                        # Validate it's proper JSON
-                        json.loads(extracted)
-                        return extracted
-                    except:
-                        pass
-
-        return result

src/crewai/agents/agent_adapters/openai_agents/openai_adapter.py

@@ -1,178 +0,0 @@
-from typing import Any, List, Optional
-
-from pydantic import Field, PrivateAttr
-
-from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter
-from crewai.agents.agent_adapters.openai_agents.structured_output_converter import (
-    OpenAIConverterAdapter,
-)
-from crewai.agents.agent_builder.base_agent import BaseAgent
-from crewai.tools import BaseTool
-from crewai.tools.agent_tools.agent_tools import AgentTools
-from crewai.utilities import Logger
-from crewai.utilities.events import crewai_event_bus
-from crewai.utilities.events.agent_events import (
-    AgentExecutionCompletedEvent,
-    AgentExecutionErrorEvent,
-    AgentExecutionStartedEvent,
-)
-
-try:
-    from agents import Agent as OpenAIAgent  # type: ignore
-    from agents import Runner, enable_verbose_stdout_logging  # type: ignore
-
-    from .openai_agent_tool_adapter import OpenAIAgentToolAdapter
-
-    OPENAI_AVAILABLE = True
-except ImportError:
-    OPENAI_AVAILABLE = False
-
-
-class OpenAIAgentAdapter(BaseAgentAdapter):
-    """Adapter for OpenAI Assistants"""
-
-    model_config = {"arbitrary_types_allowed": True}
-
-    _openai_agent: "OpenAIAgent" = PrivateAttr()
-    _logger: Logger = PrivateAttr(default_factory=lambda: Logger())
-    _active_thread: Optional[str] = PrivateAttr(default=None)
-    function_calling_llm: Any = Field(default=None)
-    step_callback: Any = Field(default=None)
-    _tool_adapter: "OpenAIAgentToolAdapter" = PrivateAttr()
-    _converter_adapter: OpenAIConverterAdapter = PrivateAttr()
-
-    def __init__(
-        self,
-        model: str = "gpt-4o-mini",
-        tools: Optional[List[BaseTool]] = None,
-        agent_config: Optional[dict] = None,
-        **kwargs,
-    ):
-        if not OPENAI_AVAILABLE:
-            raise ImportError(
-                "OpenAI Agent Dependencies are not installed. Please install it using `uv add openai-agents`"
-            )
-        else:
-            role = kwargs.pop("role", None)
-            goal = kwargs.pop("goal", None)
-            backstory = kwargs.pop("backstory", None)
-            super().__init__(
-                role=role,
-                goal=goal,
-                backstory=backstory,
-                tools=tools,
-                agent_config=agent_config,
-                **kwargs,
-            )
-            self._tool_adapter = OpenAIAgentToolAdapter(tools=tools)
-            self.llm = model
-            self._converter_adapter = OpenAIConverterAdapter(self)
-
-    def _build_system_prompt(self) -> str:
-        """Build a system prompt for the OpenAI agent."""
-        base_prompt = f"""
-            You are {self.role}.
-        
-            Your goal is: {self.goal}
-
-            Your backstory: {self.backstory}
-
-            When working on tasks, think step-by-step and use the available tools when necessary.
-        """
-        return self._converter_adapter.enhance_system_prompt(base_prompt)
-
-    def execute_task(
-        self,
-        task: Any,
-        context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
-    ) -> str:
-        """Execute a task using the OpenAI Assistant"""
-        self._converter_adapter.configure_structured_output(task)
-        self.create_agent_executor(tools)
-
-        if self.verbose:
-            enable_verbose_stdout_logging()
-
-        try:
-            task_prompt = task.prompt()
-            if context:
-                task_prompt = self.i18n.slice("task_with_context").format(
-                    task=task_prompt, context=context
-                )
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionStartedEvent(
-                    agent=self,
-                    tools=self.tools,
-                    task_prompt=task_prompt,
-                    task=task,
-                ),
-            )
-            result = self.agent_executor.run_sync(self._openai_agent, task_prompt)
-            final_answer = self.handle_execution_result(result)
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionCompletedEvent(
-                    agent=self, task=task, output=final_answer
-                ),
-            )
-            return final_answer
-
-        except Exception as e:
-            self._logger.log("error", f"Error executing OpenAI task: {str(e)}")
-            crewai_event_bus.emit(
-                self,
-                event=AgentExecutionErrorEvent(
-                    agent=self,
-                    task=task,
-                    error=str(e),
-                ),
-            )
-            raise
-
-    def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> None:
-        """
-        Configure the OpenAI agent for execution.
-        While OpenAI handles execution differently through Runner,
-        we can use this method to set up tools and configurations.
-        """
-        all_tools = list(self.tools or []) + list(tools or [])
-
-        instructions = self._build_system_prompt()
-        self._openai_agent = OpenAIAgent(
-            name=self.role,
-            instructions=instructions,
-            model=self.llm,
-            **self._agent_config or {},
-        )
-
-        if all_tools:
-            self.configure_tools(all_tools)
-
-        self.agent_executor = Runner
-
-    def configure_tools(self, tools: Optional[List[BaseTool]] = None) -> None:
-        """Configure tools for the OpenAI Assistant"""
-        if tools:
-            self._tool_adapter.configure_tools(tools)
-            if self._tool_adapter.converted_tools:
-                self._openai_agent.tools = self._tool_adapter.converted_tools
-
-    def handle_execution_result(self, result: Any) -> str:
-        """Process OpenAI Assistant execution result converting any structured output to a string"""
-        return self._converter_adapter.post_process_result(result.final_output)
-
-    def get_delegation_tools(self, agents: List[BaseAgent]) -> List[BaseTool]:
-        """Implement delegation tools support"""
-        agent_tools = AgentTools(agents=agents)
-        tools = agent_tools.tools()
-        return tools
-
-    def configure_structured_output(self, task) -> None:
-        """Configure the structured output for the specific agent implementation.
-
-        Args:
-            structured_output: The structured output to be configured
-        """
-        self._converter_adapter.configure_structured_output(task)

src/crewai/agents/agent_adapters/openai_agents/openai_agent_tool_adapter.py

@@ -1,91 +0,0 @@
-import inspect
-from typing import Any, List, Optional
-
-from agents import FunctionTool, Tool
-
-from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
-from crewai.tools import BaseTool
-
-
-class OpenAIAgentToolAdapter(BaseToolAdapter):
-    """Adapter for OpenAI Assistant tools"""
-
-    def __init__(self, tools: Optional[List[BaseTool]] = None):
-        self.original_tools = tools or []
-
-    def configure_tools(self, tools: List[BaseTool]) -> None:
-        """Configure tools for the OpenAI Assistant"""
-        if self.original_tools:
-            all_tools = tools + self.original_tools
-        else:
-            all_tools = tools
-        if all_tools:
-            self.converted_tools = self._convert_tools_to_openai_format(all_tools)
-
-    def _convert_tools_to_openai_format(
-        self, tools: Optional[List[BaseTool]]
-    ) -> List[Tool]:
-        """Convert CrewAI tools to OpenAI Assistant tool format"""
-        if not tools:
-            return []
-
-        def sanitize_tool_name(name: str) -> str:
-            """Convert tool name to match OpenAI's required pattern"""
-            import re
-
-            sanitized = re.sub(r"[^a-zA-Z0-9_-]", "_", name).lower()
-            return sanitized
-
-        def create_tool_wrapper(tool: BaseTool):
-            """Create a wrapper function that handles the OpenAI function tool interface"""
-
-            async def wrapper(context_wrapper: Any, arguments: Any) -> Any:
-                # Get the parameter name from the schema
-                param_name = list(
-                    tool.args_schema.model_json_schema()["properties"].keys()
-                )[0]
-
-                # Handle different argument types
-                if isinstance(arguments, dict):
-                    args_dict = arguments
-                elif isinstance(arguments, str):
-                    try:
-                        import json
-
-                        args_dict = json.loads(arguments)
-                    except json.JSONDecodeError:
-                        args_dict = {param_name: arguments}
-                else:
-                    args_dict = {param_name: str(arguments)}
-
-                # Run the tool with the processed arguments
-                output = tool._run(**args_dict)
-
-                # Await if the tool returned a coroutine
-                if inspect.isawaitable(output):
-                    result = await output
-                else:
-                    result = output
-
-                # Ensure the result is JSON serializable
-                if isinstance(result, (dict, list, str, int, float, bool, type(None))):
-                    return result
-                return str(result)
-
-            return wrapper
-
-        openai_tools = []
-        for tool in tools:
-            schema = tool.args_schema.model_json_schema()
-
-            schema.update({"additionalProperties": False, "type": "object"})
-
-            openai_tool = FunctionTool(
-                name=sanitize_tool_name(tool.name),
-                description=tool.description,
-                params_json_schema=schema,
-                on_invoke_tool=create_tool_wrapper(tool),
-            )
-            openai_tools.append(openai_tool)
-
-        return openai_tools

src/crewai/agents/agent_adapters/openai_agents/structured_output_converter.py

@@ -1,122 +0,0 @@
-import json
-import re
-
-from crewai.agents.agent_adapters.base_converter_adapter import BaseConverterAdapter
-from crewai.utilities.converter import generate_model_description
-from crewai.utilities.i18n import I18N
-
-
-class OpenAIConverterAdapter(BaseConverterAdapter):
-    """
-    Adapter for handling structured output conversion in OpenAI agents.
-
-    This adapter enhances the OpenAI agent to handle structured output formats
-    and post-processes the results when needed.
-
-    Attributes:
-        _output_format: The expected output format (json, pydantic, or None)
-        _schema: The schema description for the expected output
-        _output_model: The Pydantic model for the output
-    """
-
-    def __init__(self, agent_adapter):
-        """Initialize the converter adapter with a reference to the agent adapter"""
-        self.agent_adapter = agent_adapter
-        self._output_format = None
-        self._schema = None
-        self._output_model = None
-
-    def configure_structured_output(self, task) -> None:
-        """
-        Configure the structured output for OpenAI agent based on task requirements.
-
-        Args:
-            task: The task containing output format requirements
-        """
-        # Reset configuration
-        self._output_format = None
-        self._schema = None
-        self._output_model = None
-
-        # If no structured output is required, return early
-        if not (task.output_json or task.output_pydantic):
-            return
-
-        # Configure based on task output format
-        if task.output_json:
-            self._output_format = "json"
-            self._schema = generate_model_description(task.output_json)
-            self.agent_adapter._openai_agent.output_type = task.output_json
-            self._output_model = task.output_json
-        elif task.output_pydantic:
-            self._output_format = "pydantic"
-            self._schema = generate_model_description(task.output_pydantic)
-            self.agent_adapter._openai_agent.output_type = task.output_pydantic
-            self._output_model = task.output_pydantic
-
-    def enhance_system_prompt(self, base_prompt: str) -> str:
-        """
-        Enhance the base system prompt with structured output requirements if needed.
-
-        Args:
-            base_prompt: The original system prompt
-
-        Returns:
-            Enhanced system prompt with output format instructions if needed
-        """
-        if not self._output_format:
-            return base_prompt
-
-        output_schema = (
-            I18N()
-            .slice("formatted_task_instructions")
-            .format(output_format=self._schema)
-        )
-
-        return f"{base_prompt}\n\n{output_schema}"
-
-    def post_process_result(self, result: str) -> str:
-        """
-        Post-process the result to ensure it matches the expected format.
-
-        This method attempts to extract valid JSON from the result if necessary.
-
-        Args:
-            result: The raw result from the agent
-
-        Returns:
-            Processed result conforming to the expected output format
-        """
-        if not self._output_format:
-            return result
-        # Try to extract valid JSON if it's wrapped in code blocks or other text
-        if isinstance(result, str) and self._output_format in ["json", "pydantic"]:
-            # First, try to parse as is
-            try:
-                json.loads(result)
-                return result
-            except json.JSONDecodeError:
-                # Try to extract JSON from markdown code blocks
-                code_block_pattern = r"```(?:json)?\s*([\s\S]*?)```"
-                code_blocks = re.findall(code_block_pattern, result)
-
-                for block in code_blocks:
-                    try:
-                        json.loads(block.strip())
-                        return block.strip()
-                    except json.JSONDecodeError:
-                        continue
-
-                # Try to extract any JSON-like structure
-                json_pattern = r"(\{[\s\S]*\})"
-                json_matches = re.findall(json_pattern, result, re.DOTALL)
-
-                for match in json_matches:
-                    try:
-                        json.loads(match)
-                        return match
-                    except json.JSONDecodeError:
-                        continue
-
-        # If all extraction attempts fail, return the original
-        return str(result)

src/crewai/agents/agent_builder/base_agent.py

@@ -19,7 +19,6 @@ from crewai.agents.agent_builder.utilities.base_token_process import TokenProces
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.security.security_config import SecurityConfig
 from crewai.tools.base_tool import BaseTool, Tool
@@ -63,6 +62,8 @@ class BaseAgent(ABC, BaseModel):
             Abstract method to execute a task.
         create_agent_executor(tools=None) -> None:
             Abstract method to create an agent executor.
+        _parse_tools(tools: List[BaseTool]) -> List[Any]:
+            Abstract method to parse tools.
         get_delegation_tools(agents: List["BaseAgent"]):
             Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew.
         get_output_converter(llm, model, instructions):
@@ -153,13 +154,6 @@ class BaseAgent(ABC, BaseModel):
     callbacks: List[Callable] = Field(
         default=[], description="Callbacks to be used for the agent"
     )
-    adapted_agent: bool = Field(
-        default=False, description="Whether the agent is adapted"
-    )
-    knowledge_config: Optional[KnowledgeConfig] = Field(
-        default=None,
-        description="Knowledge configuration for the agent such as limits and threshold",
-    )
 
     @model_validator(mode="before")
     @classmethod
@@ -176,15 +170,15 @@ class BaseAgent(ABC, BaseModel):
         tool meets these criteria, it is processed and added to the list of
         tools. Otherwise, a ValueError is raised.
         """
-        if not tools:
-            return []
-
         processed_tools = []
-        required_attrs = ["name", "func", "description"]
         for tool in tools:
             if isinstance(tool, BaseTool):
                 processed_tools.append(tool)
-            elif all(hasattr(tool, attr) for attr in required_attrs):
+            elif (
+                hasattr(tool, "name")
+                and hasattr(tool, "func")
+                and hasattr(tool, "description")
+            ):
                 # Tool has the required attributes, create a Tool instance
                 processed_tools.append(Tool.from_langchain(tool))
             else:
@@ -266,6 +260,13 @@ class BaseAgent(ABC, BaseModel):
         """Set the task tools that init BaseAgenTools class."""
         pass
 
+    @abstractmethod
+    def get_output_converter(
+        self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
+    ) -> Converter:
+        """Get the converter class for the agent to create json/pydantic outputs."""
+        pass
+
     def copy(self: T) -> T:  # type: ignore # Signature of "copy" incompatible with supertype "BaseModel"
         """Create a deep copy of the Agent."""
         exclude = {

src/crewai/crew.py

@@ -304,7 +304,9 @@ class Crew(BaseModel):
         """Initialize private memory attributes."""
         self._external_memory = (
             # External memory doesn’t support a default value since it was designed to be managed entirely externally
-            self.external_memory.set_crew(self) if self.external_memory else None
+            self.external_memory.set_crew(self)
+            if self.external_memory
+            else None
         )
 
         self._long_term_memory = self.long_term_memory
@@ -1134,13 +1136,9 @@ class Crew(BaseModel):
         result = self._execute_tasks(self.tasks, start_index, True)
         return result
 
-    def query_knowledge(
-        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> Union[List[Dict[str, Any]], None]:
+    def query_knowledge(self, query: List[str]) -> Union[List[Dict[str, Any]], None]:
         if self.knowledge:
-            return self.knowledge.query(
-                query, results_limit=results_limit, score_threshold=score_threshold
-            )
+            return self.knowledge.query(query)
         return None
 
     def fetch_inputs(self) -> Set[str]:
@@ -1222,13 +1220,9 @@ class Crew(BaseModel):
         copied_data = self.model_dump(exclude=exclude)
         copied_data = {k: v for k, v in copied_data.items() if v is not None}
         if self.short_term_memory:
-            copied_data["short_term_memory"] = self.short_term_memory.model_copy(
-                deep=True
-            )
+            copied_data["short_term_memory"] = self.short_term_memory.model_copy(deep=True)
         if self.long_term_memory:
-            copied_data["long_term_memory"] = self.long_term_memory.model_copy(
-                deep=True
-            )
+            copied_data["long_term_memory"] = self.long_term_memory.model_copy(deep=True)
         if self.entity_memory:
             copied_data["entity_memory"] = self.entity_memory.model_copy(deep=True)
         if self.external_memory:
@@ -1236,6 +1230,7 @@ class Crew(BaseModel):
         if self.user_memory:
             copied_data["user_memory"] = self.user_memory.model_copy(deep=True)
 
+
         copied_data.pop("agents", None)
         copied_data.pop("tasks", None)
 
@@ -1408,10 +1403,7 @@ class Crew(BaseModel):
             "short": (getattr(self, "_short_term_memory", None), "short term"),
             "entity": (getattr(self, "_entity_memory", None), "entity"),
             "knowledge": (getattr(self, "knowledge", None), "knowledge"),
-            "kickoff_outputs": (
-                getattr(self, "_task_output_handler", None),
-                "task output",
-            ),
+            "kickoff_outputs": (getattr(self, "_task_output_handler", None), "task output"),
             "external": (getattr(self, "_external_memory", None), "external"),
         }
 

src/crewai/knowledge/knowledge.py

@@ -43,9 +43,7 @@ class Knowledge(BaseModel):
         self.storage.initialize_knowledge_storage()
         self._add_sources()
 
-    def query(
-        self, query: List[str], results_limit: int = 3, score_threshold: float = 0.35
-    ) -> List[Dict[str, Any]]:
+    def query(self, query: List[str], limit: int = 3) -> List[Dict[str, Any]]:
         """
         Query across all knowledge sources to find the most relevant information.
         Returns the top_k most relevant chunks.
@@ -58,8 +56,7 @@ class Knowledge(BaseModel):
 
         results = self.storage.search(
             query,
-            limit=results_limit,
-            score_threshold=score_threshold,
+            limit,
         )
         return results
 

src/crewai/knowledge/knowledge_config.py

@@ -1,16 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class KnowledgeConfig(BaseModel):
-    """Configuration for knowledge retrieval.
-
-    Args:
-        results_limit (int): The number of relevant documents to return.
-        score_threshold (float): The minimum score for a document to be considered relevant.
-    """
-
-    results_limit: int = Field(default=3, description="The number of results to return")
-    score_threshold: float = Field(
-        default=0.35,
-        description="The minimum score for a result to be considered relevant",
-    )

src/crewai/knowledge/storage/knowledge_storage.py

@@ -4,7 +4,7 @@ import io
 import logging
 import os
 import shutil
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union, cast
 
 import chromadb
 import chromadb.errors

src/crewai/task.py

@@ -485,19 +485,6 @@ class Task(BaseModel):
         tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
 
-    def interpolate_inputs(self, inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]]) -> None:
-        """Interpolate inputs into the task description, expected output, and output file path.
-
-        Args:
-            inputs: Dictionary mapping template variables to their values.
-               Supported value types are strings, integers, floats, dicts, lists,
-               and other objects with string representation.
-
-        Raises:
-            ValueError: If a required template variable is missing from inputs.
-        """
-        self.interpolate_inputs_and_add_conversation_history(inputs)
-        
     def interpolate_inputs_and_add_conversation_history(
         self, inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]]
     ) -> None:
@@ -506,8 +493,7 @@ class Task(BaseModel):
 
         Args:
             inputs: Dictionary mapping template variables to their values.
-                   Supported value types are strings, integers, floats, dicts, lists,
-                   and other objects with string representation.
+                   Supported value types are strings, integers, and floats.
 
         Raises:
             ValueError: If a required template variable is missing from inputs.
@@ -522,65 +508,23 @@ class Task(BaseModel):
         if not inputs:
             return
 
-        # Check for complex indexing patterns like {topics[0]} in the description
-        has_complex_indexing = re.search(r"\{([A-Za-z_][A-Za-z0-9_]*)\[[0-9]+\]\}", self._original_description)
-        
-        if has_complex_indexing:
-            complex_patterns = re.findall(r"\{([A-Za-z_][A-Za-z0-9_]*)\[([0-9]+)\]\}", self._original_description)
-            result = self._original_description
-            
-            for var_name, index in complex_patterns:
-                if var_name in inputs and isinstance(inputs[var_name], list):
-                    try:
-                        idx = int(index)
-                        list_value = inputs[var_name]
-                        if isinstance(list_value, list) and 0 <= idx < len(list_value):
-                            placeholder = f"{{{var_name}[{index}]}}"
-                            value = str(list_value[idx])
-                            result = result.replace(placeholder, value)
-                    except (ValueError, IndexError):
-                        pass
-            
-            self.description = result
-        else:
-            try:
-                self.description = interpolate_only(
-                    input_string=self._original_description, inputs=inputs
-                )
-            except KeyError as e:
-                raise ValueError(
-                    f"Missing required template variable '{e.args[0]}' in description"
-                ) from e
-            except ValueError as e:
-                raise ValueError(f"Error interpolating description: {str(e)}") from e
+        try:
+            self.description = interpolate_only(
+                input_string=self._original_description, inputs=inputs
+            )
+        except KeyError as e:
+            raise ValueError(
+                f"Missing required template variable '{e.args[0]}' in description"
+            ) from e
+        except ValueError as e:
+            raise ValueError(f"Error interpolating description: {str(e)}") from e
 
-        # Check for complex indexing patterns in the expected output
-        has_complex_indexing = re.search(r"\{([A-Za-z_][A-Za-z0-9_]*)\[[0-9]+\]\}", self._original_expected_output)
-        
-        if has_complex_indexing:
-            complex_patterns = re.findall(r"\{([A-Za-z_][A-Za-z0-9_]*)\[([0-9]+)\]\}", self._original_expected_output)
-            result = self._original_expected_output
-            
-            for var_name, index in complex_patterns:
-                if var_name in inputs and isinstance(inputs[var_name], list):
-                    try:
-                        idx = int(index)
-                        list_value = inputs[var_name]
-                        if isinstance(list_value, list) and 0 <= idx < len(list_value):
-                            placeholder = f"{{{var_name}[{index}]}}"
-                            value = str(list_value[idx])
-                            result = result.replace(placeholder, value)
-                    except (ValueError, IndexError):
-                        pass
-            
-            self.expected_output = result
-        else:
-            try:
-                self.expected_output = interpolate_only(
-                    input_string=self._original_expected_output, inputs=inputs
-                )
-            except (KeyError, ValueError) as e:
-                raise ValueError(f"Error interpolating expected_output: {str(e)}") from e
+        try:
+            self.expected_output = interpolate_only(
+                input_string=self._original_expected_output, inputs=inputs
+            )
+        except (KeyError, ValueError) as e:
+            raise ValueError(f"Error interpolating expected_output: {str(e)}") from e
 
         if self.output_file is not None:
             try:

src/crewai/utilities/converter.py

@@ -216,7 +216,7 @@ def convert_with_instructions(
 
 def get_conversion_instructions(model: Type[BaseModel], llm: Any) -> str:
     instructions = "Please convert the following text into valid JSON."
-    if llm and not isinstance(llm, str) and llm.supports_function_calling():
+    if llm.supports_function_calling():
         model_schema = PydanticSchemaParser(model=model).get_schema()
         instructions += (
             f"\n\nOutput ONLY the valid JSON and nothing else.\n\n"

src/crewai/utilities/jinja_templating.py

@@ -1,98 +0,0 @@
-import re
-from datetime import datetime
-from typing import Any, Dict, List, Optional, Union
-
-import jinja2
-
-
-def to_jinja_template(input_string: str) -> str:
-    """
-    Convert CrewAI-style {var} templates to Jinja2-style {{var}} templates.
-    
-    This function preserves existing Jinja2 syntax if present and only converts
-    CrewAI-style variables.
-    
-    Args:
-        input_string: String containing CrewAI-style templates.
-        
-    Returns:
-        String with CrewAI-style templates converted to Jinja2 syntax.
-    """
-    if not input_string or ("{" not in input_string and "}" not in input_string):
-        return input_string
-        
-    pattern = r'(?<!\{)\{([A-Za-z_][A-Za-z0-9_]*)\}(?!\})'
-    
-    return re.sub(pattern, r'{{\1}}', input_string)
-
-def render_template(
-    input_string: Optional[str],
-    inputs: Dict[str, Any],
-) -> str:
-    """
-    Render a template string using Jinja2 with the provided inputs.
-    
-    This function supports:
-    - Container types (List, Dict, Set)
-    - Standard objects (datetime, time)
-    - Custom objects
-    - Conditional and loop statements
-    - Filtering options
-    
-    Args:
-        input_string: The string containing template variables to interpolate.
-                     Can be None or empty, in which case an empty string is returned.
-        inputs: Dictionary mapping template variables to their values.
-               Supports all types of values.
-               
-    Returns:
-        The rendered template string.
-        
-    Raises:
-        ValueError: If inputs dictionary is empty when interpolating variables.
-        jinja2.exceptions.TemplateError: If there's an error in the template syntax.
-        KeyError: If a required template variable is missing from inputs.
-    """
-    if input_string is None or not input_string:
-        return ""
-        
-    if "{" not in input_string and "}" not in input_string:
-        return input_string
-        
-    if not inputs:
-        raise ValueError("Inputs dictionary cannot be empty when interpolating variables")
-        
-    jinja_template = to_jinja_template(input_string)
-    
-    # Create a custom undefined class that allows loop variables
-    class LoopUndefined(jinja2.StrictUndefined):
-        """Custom undefined class that allows loop variables."""
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            
-        def __str__(self):
-            if self._undefined_name in ('loop', 'item', 'topic'):
-                return ''
-            return super().__str__()
-            
-        def __getattr__(self, name):
-            if self._undefined_name in ('loop', 'item', 'topic'):
-                return self
-            return super().__getattr__(name)
-    
-    env = jinja2.Environment(
-        undefined=LoopUndefined,  # Use custom undefined class for loop variables
-        autoescape=True  # Enable autoescaping for security
-    )
-    
-    env.filters['date'] = lambda d, format='%Y-%m-%d': d.strftime(format) if isinstance(d, datetime) else str(d)
-    
-    template = env.from_string(jinja_template)
-    
-    try:
-        return template.render(**inputs)
-    except jinja2.exceptions.UndefinedError as e:
-        var_name = str(e).split("'")[1] if "'" in str(e) else None
-        if var_name:
-            raise KeyError(f"Template variable '{var_name}' not found in inputs dictionary")
-        raise KeyError(f"Missing required template variable: {str(e)}")

src/crewai/utilities/string_utils.py

@@ -1,39 +1,31 @@
 import re
-from datetime import datetime
 from typing import Any, Dict, List, Optional, Union
 
-from crewai.utilities.jinja_templating import render_template
-
 
 def interpolate_only(
     input_string: Optional[str],
-    inputs: Dict[str, Any],
+    inputs: Dict[str, Union[str, int, float, Dict[str, Any], List[Any]]],
 ) -> str:
     """Interpolate placeholders (e.g., {key}) in a string while leaving JSON untouched.
     Only interpolates placeholders that follow the pattern {variable_name} where
     variable_name starts with a letter/underscore and contains only letters, numbers, and underscores.
-    
-    This function now supports advanced Jinja2 templating features:
-    - Container types (List, Dict, Set)
-    - Standard objects (datetime, time)
-    - Custom objects
-    - Conditional and loop statements
-    - Filtering options
 
     Args:
         input_string: The string containing template variables to interpolate.
                      Can be None or empty, in which case an empty string is returned.
         inputs: Dictionary mapping template variables to their values.
-               Supports all types of values including complex objects.
+               Supported value types are strings, integers, floats, and dicts/lists
+               containing only these types and other nested dicts/lists.
 
     Returns:
         The interpolated string with all template variables replaced with their values.
         Empty string if input_string is None or empty.
 
     Raises:
-        ValueError: If inputs dictionary is empty when interpolating variables.
-        KeyError: If a required template variable is missing from inputs.
+        ValueError: If a value contains unsupported types or a template variable is missing
     """
+
+    # Validation function for recursive type checking
     def validate_type(value: Any) -> None:
         if value is None:
             return
@@ -43,21 +35,12 @@ def interpolate_only(
             for item in value.values() if isinstance(value, dict) else value:
                 validate_type(item)
             return
-        if isinstance(value, datetime):
-            return
-        # Check if it's a Pydantic model or other known custom type
-        try:
-            from pydantic import BaseModel
-            if isinstance(value, BaseModel):
-                return
-        except ImportError:
-            pass
-            
         raise ValueError(
             f"Unsupported type {type(value).__name__} in inputs. "
-            "Only str, int, float, bool, dict, list, datetime, and custom objects are allowed."
+            "Only str, int, float, bool, dict, and list are allowed."
         )
 
+    # Validate all input values
     for key, value in inputs.items():
         try:
             validate_type(value)
@@ -73,13 +56,6 @@ def interpolate_only(
             "Inputs dictionary cannot be empty when interpolating variables"
         )
 
-    # Check if the template contains Jinja2 syntax ({% ... %} or {{ ... }})
-    has_jinja_syntax = "{{" in input_string or "{%" in input_string
-    has_complex_indexing = re.search(r"\{([A-Za-z_][A-Za-z0-9_]*)\[[0-9]+\]\}", input_string)
-    
-    if has_jinja_syntax or has_complex_indexing:
-        return render_template(input_string, inputs)
-    
     # The regex pattern to find valid variable placeholders
     # Matches {variable_name} where variable_name starts with a letter/underscore
     # and contains only letters, numbers, and underscores
@@ -87,7 +63,8 @@ def interpolate_only(
 
     # Find all matching variables in the input string
     variables = re.findall(pattern, input_string)
-    
+    result = input_string
+
     # Check if all variables exist in inputs
     missing_vars = [var for var in variables if var not in inputs]
     if missing_vars:
@@ -95,10 +72,11 @@ def interpolate_only(
             f"Template variable '{missing_vars[0]}' not found in inputs dictionary"
         )
 
-    result = input_string
+    # Replace each variable with its value
     for var in variables:
         if var in inputs:
             placeholder = "{" + var + "}"
             value = str(inputs[var])
             result = result.replace(placeholder, value)
+
     return result

tests/agent_test.py

@@ -10,8 +10,6 @@ from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
 from crewai.agents.parser import CrewAgentParser, OutputParserException
-from crewai.knowledge.knowledge import Knowledge
-from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
@@ -261,9 +259,7 @@ def test_cache_hitting():
     def handle_tool_end(source, event):
         received_events.append(event)
 
-    with (
-        patch.object(CacheHandler, "read") as read,
-    ):
+    with (patch.object(CacheHandler, "read") as read,):
         read.return_value = "0"
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
@@ -1615,78 +1611,6 @@ def test_agent_with_knowledge_sources():
         assert "red" in result.raw.lower()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-    knowledge_config = KnowledgeConfig(results_limit=10, score_threshold=0.5)
-    with patch(
-        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
-        with patch.object(Knowledge, "query") as mock_knowledge_query:
-            agent = Agent(
-                role="Information Agent",
-                goal="Provide information based on knowledge sources",
-                backstory="You have access to specific knowledge sources.",
-                llm=LLM(model="gpt-4o-mini"),
-                knowledge_sources=[string_source],
-                knowledge_config=knowledge_config,
-            )
-            task = Task(
-                description="What is Brandon's favorite color?",
-                expected_output="Brandon's favorite color.",
-                agent=agent,
-            )
-            crew = Crew(agents=[agent], tasks=[task])
-            crew.kickoff()
-
-            assert agent.knowledge is not None
-            mock_knowledge_query.assert_called_once_with(
-                [task.prompt()],
-                **knowledge_config.model_dump(),
-            )
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default():
-    content = "Brandon's favorite color is red and he likes Mexican food."
-    string_source = StringKnowledgeSource(content=content)
-    knowledge_config = KnowledgeConfig()
-    with patch(
-        "crewai.knowledge.storage.knowledge_storage.KnowledgeStorage"
-    ) as MockKnowledge:
-        mock_knowledge_instance = MockKnowledge.return_value
-        mock_knowledge_instance.sources = [string_source]
-        mock_knowledge_instance.query.return_value = [{"content": content}]
-        with patch.object(Knowledge, "query") as mock_knowledge_query:
-            string_source = StringKnowledgeSource(content=content)
-            knowledge_config = KnowledgeConfig()
-            agent = Agent(
-                role="Information Agent",
-                goal="Provide information based on knowledge sources",
-                backstory="You have access to specific knowledge sources.",
-                llm=LLM(model="gpt-4o-mini"),
-                knowledge_sources=[string_source],
-                knowledge_config=knowledge_config,
-            )
-            task = Task(
-                description="What is Brandon's favorite color?",
-                expected_output="Brandon's favorite color.",
-                agent=agent,
-            )
-            crew = Crew(agents=[agent], tasks=[task])
-            crew.kickoff()
-
-            assert agent.knowledge is not None
-            mock_knowledge_query.assert_called_once_with(
-                [task.prompt()],
-                **knowledge_config.model_dump(),
-            )
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_with_knowledge_sources_extensive_role():
     content = "Brandon's favorite color is red and he likes Mexican food."

tests/agents/agent_adapters/test_base_agent_adapter.py

@@ -1,113 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.agent import BaseAgent
-from crewai.agents.agent_adapters.base_agent_adapter import BaseAgentAdapter
-from crewai.tools import BaseTool
-from crewai.utilities.token_counter_callback import TokenProcess
-
-
-# Concrete implementation for testing
-class ConcreteAgentAdapter(BaseAgentAdapter):
-    def configure_tools(
-        self, tools: Optional[List[BaseTool]] = None, **kwargs: Any
-    ) -> None:
-        # Simple implementation for testing
-        self.tools = tools or []
-
-    def execute_task(
-        self,
-        task: Any,
-        context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
-    ) -> str:
-        # Dummy implementation needed due to BaseAgent inheritance
-        return "Task executed"
-
-    def create_agent_executor(self, tools: Optional[List[BaseTool]] = None) -> Any:
-        # Dummy implementation
-        return None
-
-    def get_delegation_tools(
-        self, tools: List[BaseTool], tool_map: Optional[Dict[str, BaseTool]]
-    ) -> List[BaseTool]:
-        # Dummy implementation
-        return []
-
-    def _parse_output(self, agent_output: Any, token_process: TokenProcess):
-        # Dummy implementation
-        pass
-
-    def get_output_converter(self, tools: Optional[List[BaseTool]] = None) -> Any:
-        # Dummy implementation
-        return None
-
-
-def test_base_agent_adapter_initialization():
-    """Test initialization of the concrete agent adapter."""
-    adapter = ConcreteAgentAdapter(
-        role="test role", goal="test goal", backstory="test backstory"
-    )
-    assert isinstance(adapter, BaseAgent)
-    assert isinstance(adapter, BaseAgentAdapter)
-    assert adapter.role == "test role"
-    assert adapter._agent_config is None
-    assert adapter.adapted_structured_output is False
-
-
-def test_base_agent_adapter_initialization_with_config():
-    """Test initialization with agent_config."""
-    config = {"model": "gpt-4"}
-    adapter = ConcreteAgentAdapter(
-        agent_config=config,
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-    )
-    assert adapter._agent_config == config
-
-
-def test_configure_tools_method_exists():
-    """Test that configure_tools method exists and can be called."""
-    adapter = ConcreteAgentAdapter(
-        role="test role", goal="test goal", backstory="test backstory"
-    )
-    # Create dummy tools if needed, or pass None
-    tools = []
-    adapter.configure_tools(tools)
-    assert hasattr(adapter, "tools")
-    assert adapter.tools == tools
-
-
-def test_configure_structured_output_method_exists():
-    """Test that configure_structured_output method exists and can be called."""
-    adapter = ConcreteAgentAdapter(
-        role="test role", goal="test goal", backstory="test backstory"
-    )
-
-    # Define a dummy structure or pass None/Any
-    class DummyOutput(BaseModel):
-        data: str
-
-    structured_output = DummyOutput
-    adapter.configure_structured_output(structured_output)
-    # Add assertions here if configure_structured_output modifies state
-    # For now, just ensuring it runs without error is sufficient
-    pass
-
-
-def test_base_agent_adapter_inherits_base_agent():
-    """Test that BaseAgentAdapter inherits from BaseAgent."""
-    assert issubclass(BaseAgentAdapter, BaseAgent)
-
-
-class ConcreteAgentAdapterWithoutRequiredMethods(BaseAgentAdapter):
-    pass
-
-
-def test_base_agent_adapter_fails_without_required_methods():
-    """Test that BaseAgentAdapter fails without required methods."""
-    with pytest.raises(TypeError):
-        ConcreteAgentAdapterWithoutRequiredMethods()  # type: ignore

tests/agents/agent_adapters/test_base_tool_adapter.py

@@ -1,94 +0,0 @@
-from typing import Any, List
-from unittest.mock import Mock
-
-import pytest
-
-from crewai.agents.agent_adapters.base_tool_adapter import BaseToolAdapter
-from crewai.tools.base_tool import BaseTool
-
-
-class ConcreteToolAdapter(BaseToolAdapter):
-    def configure_tools(self, tools: List[BaseTool]) -> None:
-        self.converted_tools = [f"converted_{tool.name}" for tool in tools]
-
-
-@pytest.fixture
-def mock_tool_1():
-    tool = Mock(spec=BaseTool)
-    tool.name = "Mock Tool 1"
-    return tool
-
-
-@pytest.fixture
-def mock_tool_2():
-    tool = Mock(spec=BaseTool)
-    tool.name = "MockTool2"
-    return tool
-
-
-@pytest.fixture
-def tools_list(mock_tool_1, mock_tool_2):
-    return [mock_tool_1, mock_tool_2]
-
-
-def test_initialization_with_tools(tools_list):
-    adapter = ConcreteToolAdapter(tools=tools_list)
-    assert adapter.original_tools == tools_list
-    assert adapter.converted_tools == []  # Conversion happens in configure_tools
-
-
-def test_initialization_without_tools():
-    adapter = ConcreteToolAdapter()
-    assert adapter.original_tools == []
-    assert adapter.converted_tools == []
-
-
-def test_configure_tools(tools_list):
-    adapter = ConcreteToolAdapter()
-    adapter.configure_tools(tools_list)
-    assert adapter.converted_tools == ["converted_Mock Tool 1", "converted_MockTool2"]
-    assert adapter.original_tools == []  # original_tools is only set in init
-
-    adapter_with_init_tools = ConcreteToolAdapter(tools=tools_list)
-    adapter_with_init_tools.configure_tools(tools_list)
-    assert adapter_with_init_tools.converted_tools == [
-        "converted_Mock Tool 1",
-        "converted_MockTool2",
-    ]
-    assert adapter_with_init_tools.original_tools == tools_list
-
-
-def test_tools_method(tools_list):
-    adapter = ConcreteToolAdapter()
-    adapter.configure_tools(tools_list)
-    assert adapter.tools() == ["converted_Mock Tool 1", "converted_MockTool2"]
-
-
-def test_tools_method_empty():
-    adapter = ConcreteToolAdapter()
-    assert adapter.tools() == []
-
-
-def test_sanitize_tool_name_with_spaces():
-    adapter = ConcreteToolAdapter()
-    assert adapter.sanitize_tool_name("Tool With Spaces") == "Tool_With_Spaces"
-
-
-def test_sanitize_tool_name_without_spaces():
-    adapter = ConcreteToolAdapter()
-    assert adapter.sanitize_tool_name("ToolWithoutSpaces") == "ToolWithoutSpaces"
-
-
-def test_sanitize_tool_name_empty():
-    adapter = ConcreteToolAdapter()
-    assert adapter.sanitize_tool_name("") == ""
-
-
-class ConcreteToolAdapterWithoutRequiredMethods(BaseToolAdapter):
-    pass
-
-
-def test_tool_adapted_fails_without_required_methods():
-    """Test that BaseToolAdapter fails without required methods."""
-    with pytest.raises(TypeError):
-        ConcreteToolAdapterWithoutRequiredMethods()  # type: ignore

tests/agents/agent_builder/base_agent_test.py

@@ -18,6 +18,9 @@ class MockAgent(BaseAgent):
 
     def create_agent_executor(self, tools=None) -> None: ...
 
+    def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
+        return []
+
     def get_delegation_tools(self, agents: List["BaseAgent"]): ...
 
     def get_output_converter(

tests/cassettes/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold.yaml

@@ -1,330 +0,0 @@
-interactions:
-- request:
-    body: '{"input": ["Brandon''s favorite color is red and he likes Mexican food."],
-      "model": "text-embedding-3-small", "encoding_format": "base64"}'
-    headers:
-      accept:
-      - application/json
-      accept-encoding:
-      - gzip, deflate, zstd
-      connection:
-      - keep-alive
-      content-length:
-      - '137'
-      content-type:
-      - application/json
-      host:
-      - api.openai.com
-      user-agent:
-      - OpenAI/Python 1.68.2
-      x-stainless-arch:
-      - arm64
-      x-stainless-async:
-      - 'false'
-      x-stainless-lang:
-      - python
-      x-stainless-os:
-      - MacOS
-      x-stainless-package-version:
-      - 1.68.2
-      x-stainless-read-timeout:
-      - '600'
-      x-stainless-retry-count:
-      - '0'
-      x-stainless-runtime:
-      - CPython
-      x-stainless-runtime-version:
-      - 3.12.9
-    method: POST
-    uri: https://api.openai.com/v1/embeddings
-  response:
-    body:
-      string: !!binary |
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tests/cassettes/test_task_with_max_execution_time_exceeded.yaml

@@ -1,81 +0,0 @@
-interactions:
-- request:
-    body: '{"contents": [{"role": "user", "parts": [{"text": "\nCurrent Task: Give
-      me a list of 5 interesting ideas to explore for an article, what makes them
-      unique and interesting.\n\nThis is the expected criteria for your final answer:
-      Bullet point list of 5 interesting ideas.\nyou MUST return the actual complete
-      content as the final answer, not a summary.\n\nBegin! This is VERY important
-      to you, use the tools available and give your best Final Answer, your job depends
-      on it!\n\nThought:"}]}], "system_instruction": {"parts": [{"text": "You are
-      Researcher. You''re an expert researcher, specialized in technology, software
-      engineering, AI and startups. You work as a freelancer and are now working on
-      doing research and analysis for a new customer.\nYour personal goal is: Make
-      the best research and analysis on content about AI and AI agents. Use the tool
-      provided to you.\nYou ONLY have access to the following tools, and should NEVER
-      make up tools that are not listed here:\n\nTool Name: what amazing tool\nTool
-      Arguments: {}\nTool Description: My tool\n\nIMPORTANT: Use the following format
-      in your response:\n\n```\nThought: you should always think about what to do\nAction:
-      the action to take, only one name of [what amazing tool], just the name, exactly
-      as it''s written.\nAction Input: the input to the action, just a simple JSON
-      object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
-      the result of the action\n```\n\nOnce all necessary information is gathered,
-      return the following format:\n\n```\nThought: I now know the final answer\nFinal
-      Answer: the final answer to the original input question\n```"}]}, "generationConfig":
-      {"temperature": 0.7, "stop_sequences": ["\nObservation:"]}}'
-    headers:
-      accept:
-      - '*/*'
-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      content-length:
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-      content-type:
-      - application/json
-      host:
-      - generativelanguage.googleapis.com
-      user-agent:
-      - litellm/1.60.2
-    method: POST
-    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-pro-latest:generateContent
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tests/task_test.py

@@ -3,7 +3,6 @@
 import hashlib
 import json
 import os
-import time
 from functools import partial
 from typing import Tuple, Union
 from unittest.mock import MagicMock, patch
@@ -1369,90 +1368,3 @@ def test_interpolate_valid_types():
     assert parsed["optional"] is None
     assert parsed["nested"]["flag"] is True
     assert parsed["nested"]["empty"] is None
-
-
-def test_task_with_no_max_execution_time():
-    researcher = Agent(
-    role="Researcher",
-    goal="Make the best research and analysis on content about AI and AI agents",
-    backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-    allow_delegation=False,
-    max_execution_time=None
-    )
-
-    task = Task(
-        description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 interesting ideas.",
-        agent=researcher,
-    )
-
-    with patch.object(Agent, "_execute_without_timeout", return_value = "ok") as execute:
-        result = task.execute_sync(agent=researcher)
-        assert result.raw == "ok"
-        execute.assert_called_once()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_with_max_execution_time():
-    from crewai.tools import tool
-    """Test that execution raises TimeoutError when max_execution_time is exceeded."""
-
-    @tool("what amazing tool", result_as_answer=True)
-    def my_tool() -> str:
-        "My tool"
-        time.sleep(1)
-        return "okay"
-
-    researcher = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents. Use the tool provided to you.",
-        backstory=(
-            "You're an expert researcher, specialized in technology, software engineering, AI and startups. "
-            "You work as a freelancer and are now working on doing research and analysis for a new customer."
-        ),
-        allow_delegation=False,
-        tools=[my_tool],
-        max_execution_time=4
-    )
-
-    task = Task(
-        description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 interesting ideas.",
-        agent=researcher,
-    )
-
-    result = task.execute_sync(agent=researcher)
-    assert result.raw == "okay"
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_with_max_execution_time_exceeded():
-    from crewai.tools import tool
-    """Test that execution raises TimeoutError when max_execution_time is exceeded."""
-
-    @tool("what amazing tool", result_as_answer=True)
-    def my_tool() -> str:
-        "My tool"
-        time.sleep(10)
-        return "okay"
-
-    researcher = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents. Use the tool provided to you.",
-        backstory=(
-            "You're an expert researcher, specialized in technology, software engineering, AI and startups. "
-            "You work as a freelancer and are now working on doing research and analysis for a new customer."
-        ),
-        allow_delegation=False,
-        tools=[my_tool],
-        max_execution_time=1
-    )
-
-    task = Task(
-        description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 interesting ideas.",
-        agent=researcher,
-    )
-
-    with pytest.raises(TimeoutError):
-        task.execute_sync(agent=researcher)

tests/test_templating.py

@@ -1,91 +0,0 @@
-import datetime
-from typing import Dict, List
-
-import pytest
-
-from crewai.agent import Agent
-from crewai.task import Task
-
-
-class TestTemplating:
-    def test_task_interpolation(self):
-        task = Task(
-            description="Research about {topic} and provide {count} key points",
-            expected_output="A list of {count} key points about {topic}"
-        )
-        
-        inputs = {"topic": "AI", "count": 5}
-        task.interpolate_inputs(inputs)
-        
-        assert task.description == "Research about AI and provide 5 key points"
-        assert task.expected_output == "A list of 5 key points about AI"
-        
-        task = Task(
-            description="Research about {topics[0]} and {topics[1]}",
-            expected_output="Analysis of {{data.main_theme}}"
-        )
-        
-        inputs = {
-            "topics": ["AI", "Machine Learning"],
-            "data": {"main_theme": "Technology Trends"}
-        }
-        
-        task.interpolate_inputs(inputs)
-        
-        assert task.description == "Research about AI and Machine Learning"
-        assert task.expected_output == "Analysis of Technology Trends"
-    
-    def test_agent_interpolation(self):
-        agent = Agent(
-            role="{industry} Researcher",
-            goal="Research {count} key developments in {industry}",
-            backstory="You are a senior researcher in the {industry} field with {experience} years of experience"
-        )
-        
-        inputs = {"industry": "Healthcare", "count": 5, "experience": 10}
-        agent.interpolate_inputs(inputs)
-        
-        assert agent.role == "Healthcare Researcher"
-        assert agent.goal == "Research 5 key developments in Healthcare"
-        assert agent.backstory == "You are a senior researcher in the Healthcare field with 10 years of experience"
-        
-        agent = Agent(
-            role="{{specialties[0]}} and {{specialties[1]}} Specialist",
-            goal="Analyze trends in {{fields.primary}} sector",
-            backstory="Expert in {{fields.primary}} and {{fields.secondary}}"
-        )
-        
-        inputs = {
-            "specialties": ["AI", "Data Science"],
-            "fields": {"primary": "Healthcare", "secondary": "Finance"}
-        }
-        
-        agent.interpolate_inputs(inputs)
-        
-        assert agent.role == "AI and Data Science Specialist"
-        assert agent.goal == "Analyze trends in Healthcare sector"
-        assert agent.backstory == "Expert in Healthcare and Finance"
-    
-    def test_conditional_templating(self):
-        task = Task(
-            description="{% if priority == 'high' %}URGENT: {% endif %}Research {topic}",
-            expected_output="A report on {topic}"
-        )
-        
-        inputs = {"topic": "AI", "priority": "high"}
-        task.interpolate_inputs(inputs)
-        assert task.description == "URGENT: Research AI"
-        
-        inputs = {"topic": "AI", "priority": "low"}
-        task.interpolate_inputs(inputs)
-        assert task.description == "Research AI"
-    
-    def test_loop_templating(self):
-        task = Task(
-            description="Research the following topics: {% for topic in topics %}{{topic}}{% if not loop.last %}, {% endif %}{% endfor %}",
-            expected_output="A report on multiple topics"
-        )
-        
-        inputs = {"topics": ["AI", "Machine Learning", "Data Science"]}
-        task.interpolate_inputs(inputs)
-        assert task.description == "Research the following topics: AI, Machine Learning, Data Science"

tests/utilities/test_jinja_templating.py

@@ -1,84 +0,0 @@
-import datetime
-from typing import Any, Dict, List
-
-import pytest
-from pydantic import BaseModel
-
-from crewai.utilities.jinja_templating import render_template, to_jinja_template
-
-
-class Person(BaseModel):
-    name: str
-    age: int
-    
-    def __str__(self):
-        return f"{self.name} ({self.age})"
-
-class TestJinjaTemplating:
-    def test_to_jinja_template(self):
-        assert to_jinja_template("Hello {name}!") == "Hello {{name}}!"
-        
-        assert to_jinja_template("Hello {{name}}!") == "Hello {{name}}!"
-        
-        assert to_jinja_template("Hello {name} and {{title}}!") == "Hello {{name}} and {{title}}!"
-        
-        assert to_jinja_template("") == ""
-        
-        assert to_jinja_template("Hello world!") == "Hello world!"
-    
-    def test_render_template_simple_types(self):
-        inputs = {"name": "John", "age": 30, "active": True, "height": 1.85}
-        
-        assert render_template("Hello {name}!", inputs) == "Hello John!"
-        assert render_template("Age: {age}", inputs) == "Age: 30"
-        assert render_template("Active: {active}", inputs) == "Active: True"
-        assert render_template("Height: {height}", inputs) == "Height: 1.85"
-        
-        assert render_template("{name} is {age} years old", inputs) == "John is 30 years old"
-    
-    def test_render_template_container_types(self):
-        inputs = {
-            "items": ["apple", "banana", "orange"],
-            "person": {"name": "John", "age": 30}
-        }
-        
-        assert render_template("First item: {{items[0]}}", inputs) == "First item: apple"
-        
-        assert render_template("Person name: {{person.name}}", inputs) == "Person name: John"
-        
-        assert render_template(
-            "Items: {% for item in items %}{{item}}{% if not loop.last %}, {% endif %}{% endfor %}",
-            inputs
-        ) == "Items: apple, banana, orange"
-        
-        assert render_template(
-            "{% if items|length > 2 %}Many items{% else %}Few items{% endif %}",
-            inputs
-        ) == "Many items"
-    
-    def test_render_template_datetime(self):
-        today = datetime.datetime.now()
-        inputs = {"today": today}
-        
-        assert render_template("Today: {{today|date}}", inputs) == f"Today: {today.strftime('%Y-%m-%d')}"
-        
-        assert render_template("Today: {{today|date('%d/%m/%Y')}}", inputs) == f"Today: {today.strftime('%d/%m/%Y')}"
-    
-    def test_render_template_custom_objects(self):
-        person = Person(name="John", age=30)
-        inputs = {"person": person}
-        
-        assert render_template("Person: {person}", inputs) == "Person: John (30)"
-        
-        assert render_template("Person name: {{person.name}}", inputs) == "Person name: John"
-    
-    def test_render_template_error_handling(self):
-        inputs = {"name": "John"}
-        
-        with pytest.raises(KeyError) as excinfo:
-            render_template("Hello {age}!", inputs)
-        assert "Template variable 'age' not found" in str(excinfo.value)
-        
-        with pytest.raises(ValueError) as excinfo:
-            render_template("Hello {name}!", {})
-        assert "Inputs dictionary cannot be empty" in str(excinfo.value)

tests/utilities/test_string_utils.py

@@ -1,8 +1,6 @@
-import datetime
 from typing import Any, Dict, List, Union
 
 import pytest
-from pydantic import BaseModel
 
 from crewai.utilities.string_utils import interpolate_only
 
@@ -187,96 +185,3 @@ class TestInterpolateOnly:
             interpolate_only(template, inputs)
 
         assert "inputs dictionary cannot be empty" in str(excinfo.value).lower()
-        
-    
-    def test_container_types_list_access(self):
-        """Test accessing list items with Jinja2 syntax."""
-        template = "First item: {{items[0]}}, Second item: {{items[1]}}"
-        inputs = {
-            "items": ["apple", "banana", "orange"]
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "First item: apple, Second item: banana"
-    
-    def test_container_types_dict_access(self):
-        """Test accessing dictionary items with Jinja2 syntax."""
-        template = "Name: {{person.name}}, Age: {{person.age}}"
-        inputs = {
-            "person": {"name": "John", "age": 30}
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "Name: John, Age: 30"
-    
-    def test_conditional_statements(self):
-        """Test conditional statements with Jinja2 syntax."""
-        template = "{% if priority == 'high' %}URGENT: {% endif %}Task: {task}"
-        
-        inputs_high = {
-            "task": "Fix bug",
-            "priority": "high"
-        }
-        result_high = interpolate_only(template, inputs_high)
-        assert result_high == "URGENT: Task: Fix bug"
-        
-        inputs_low = {
-            "task": "Fix bug",
-            "priority": "low"
-        }
-        result_low = interpolate_only(template, inputs_low)
-        assert result_low == "Task: Fix bug"
-    
-    def test_loop_statements(self):
-        """Test loop statements with Jinja2 syntax."""
-        template = "Items: {% for item in items %}{{item}}{% if not loop.last %}, {% endif %}{% endfor %}"
-        inputs = {
-            "items": ["apple", "banana", "orange"]
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "Items: apple, banana, orange"
-    
-    def test_datetime_formatting(self):
-        """Test datetime formatting with Jinja2 filters."""
-        today = datetime.datetime(2024, 4, 20)
-        inputs = {"today": today}
-        
-        template = "Date: {{today|date}}"
-        result = interpolate_only(template, inputs)
-        assert result == "Date: 2024-04-20"
-        
-        template = "Date: {{today|date('%d/%m/%Y')}}"
-        result = interpolate_only(template, inputs)
-        assert result == "Date: 20/04/2024"
-    
-    def test_custom_objects(self):
-        """Test custom objects with Jinja2 syntax."""
-        class Person(BaseModel):
-            name: str
-            age: int
-            
-            def __str__(self):
-                return f"{self.name} ({self.age})"
-        
-        person = Person(name="John", age=30)
-        inputs = {"person": person}
-        
-        template = "Person: {person}"
-        result = interpolate_only(template, inputs)
-        assert result == "Person: John (30)"
-        
-        template = "Name: {{person.name}}, Age: {{person.age}}"
-        result = interpolate_only(template, inputs)
-        assert result == "Name: John, Age: 30"
-    
-    def test_mixed_syntax(self):
-        """Test mixed CrewAI and Jinja2 syntax."""
-        template = "Hello {name}! Items: {% for item in items %}{{item}}{% if not loop.last %}, {% endif %}{% endfor %}"
-        inputs = {
-            "name": "John",
-            "items": ["apple", "banana", "orange"]
-        }
-        
-        result = interpolate_only(template, inputs)
-        assert result == "Hello John! Items: apple, banana, orange"