chore: remove unsupported crew attributes from docs

.github/workflows/tests.yml

@@ -12,9 +12,6 @@ jobs:
   tests:
     runs-on: ubuntu-latest
     timeout-minutes: 15
-    strategy:
-      matrix:
-        python-version: ['3.10', '3.11', '3.12']
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
@@ -24,8 +21,9 @@ jobs:
         with:
           enable-cache: true
 
-      - name: Set up Python ${{ matrix.python-version }}
-        run: uv python install ${{ matrix.python-version }}
+
+      - name: Set up Python
+        run: uv python install 3.12.8
 
       - name: Install the project
         run: uv sync --dev --all-extras

docs/concepts/flows.mdx

@@ -545,20 +545,16 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
 
 When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
 
-## Adding Agents to Flows
+## Adding LiteAgent to Flows
 
-Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
+LiteAgents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use a LiteAgent within a flow to perform market research:
 
 ```python
-import asyncio
-from typing import Any, Dict, List
-
-from crewai_tools import SerperDevTool
+from typing import List, cast
+from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
 from pydantic import BaseModel, Field
-
-from crewai.agent import Agent
 from crewai.flow.flow import Flow, listen, start
-
+from crewai.lite_agent import LiteAgent
 
 # Define a structured output format
 class MarketAnalysis(BaseModel):
@@ -566,30 +562,28 @@ class MarketAnalysis(BaseModel):
     market_size: str = Field(description="Estimated market size")
     competitors: List[str] = Field(description="Major competitors in the space")
 
-
 # Define flow state
 class MarketResearchState(BaseModel):
     product: str = ""
     analysis: MarketAnalysis | None = None
 
-
-# Create a flow class
 class MarketResearchFlow(Flow[MarketResearchState]):
     @start()
-    def initialize_research(self) -> Dict[str, Any]:
+    def initialize_research(self):
         print(f"Starting market research for {self.state.product}")
-        return {"product": self.state.product}
 
     @listen(initialize_research)
-    async def analyze_market(self) -> Dict[str, Any]:
-        # Create an Agent for market research
-        analyst = Agent(
+    def analyze_market(self):
+        # Create a LiteAgent for market research
+        analyst = LiteAgent(
             role="Market Research Analyst",
             goal=f"Analyze the market for {self.state.product}",
             backstory="You are an experienced market analyst with expertise in "
             "identifying market trends and opportunities.",
-            tools=[SerperDevTool()],
+            llm="gpt-4o",
+            tools=[WebsiteSearchTool()],
             verbose=True,
+            response_format=MarketAnalysis,
         )
 
         # Define the research query
@@ -598,65 +592,49 @@ class MarketResearchFlow(Flow[MarketResearchState]):
         1. Key market trends
         2. Market size
         3. Major competitors
-
+        
         Format your response according to the specified structure.
         """
 
-        # Execute the analysis with structured output format
-        result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
-        if result.pydantic:
-            print("result", result.pydantic)
-        else:
-            print("result", result)
-
-        # Return the analysis to update the state
-        return {"analysis": result.pydantic}
+        # Execute the analysis
+        result = analyst.kickoff(query)
+        self.state.analysis = cast(MarketAnalysis, result.pydantic)
+        return result.pydantic
 
     @listen(analyze_market)
-    def present_results(self, analysis) -> None:
+    def present_results(self):
+        analysis = self.state.analysis
+        if analysis is None:
+            print("No analysis results available")
+            return
+
         print("\nMarket Analysis Results")
         print("=====================")
 
-        if isinstance(analysis, dict):
-            # If we got a dict with 'analysis' key, extract the actual analysis object
-            market_analysis = analysis.get("analysis")
-        else:
-            market_analysis = analysis
+        print("\nKey Market Trends:")
+        for trend in analysis.key_trends:
+            print(f"- {trend}")
 
-        if market_analysis and isinstance(market_analysis, MarketAnalysis):
-            print("\nKey Market Trends:")
-            for trend in market_analysis.key_trends:
-                print(f"- {trend}")
-
-            print(f"\nMarket Size: {market_analysis.market_size}")
-
-            print("\nMajor Competitors:")
-            for competitor in market_analysis.competitors:
-                print(f"- {competitor}")
-        else:
-            print("No structured analysis data available.")
-            print("Raw analysis:", analysis)
+        print(f"\nMarket Size: {analysis.market_size}")
 
+        print("\nMajor Competitors:")
+        for competitor in analysis.competitors:
+            print(f"- {competitor}")
 
 # Usage example
-async def run_flow():
-    flow = MarketResearchFlow()
-    result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
-    return result
-
-
-# Run the flow
-if __name__ == "__main__":
-    asyncio.run(run_flow())
+flow = MarketResearchFlow()
+result = flow.kickoff(inputs={"product": "AI-powered chatbots"})
 ```
 
-This example demonstrates several key features of using Agents in flows:
+This example demonstrates several key features of using LiteAgents in flows:
 
 1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
 
 2. **State Management**: The flow state (`MarketResearchState`) maintains context between steps and stores both inputs and outputs.
 
-3. **Tool Integration**: Agents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
+3. **Tool Integration**: LiteAgents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
+
+If you want to learn more about LiteAgents, check out the [LiteAgent](/concepts/lite-agent) page.
 
 ## Adding Crews to Flows
 

docs/concepts/lite-agent.mdx

@@ -0,0 +1,242 @@
+---
+title: LiteAgent
+description: A lightweight, single-purpose agent for simple autonomous tasks within the CrewAI framework.
+icon: feather
+---
+
+## Overview
+
+A `LiteAgent` is a streamlined version of CrewAI's Agent, designed for simpler, standalone tasks that don't require the full complexity of a crew-based workflow. It's perfect for quick automations, single-purpose tasks, or when you need a lightweight solution.
+
+<Tip>
+  Think of a LiteAgent as a specialized worker that excels at individual tasks.
+  While regular Agents are team players in a crew, LiteAgents are solo
+  performers optimized for specific operations.
+</Tip>
+
+## LiteAgent Attributes
+
+| Attribute                        | Parameter         | Type                   | Description                                                     |
+| :------------------------------- | :---------------- | :--------------------- | :-------------------------------------------------------------- |
+| **Role**                         | `role`            | `str`                  | Defines the agent's function and expertise.                     |
+| **Goal**                         | `goal`            | `str`                  | The specific objective that guides the agent's actions.         |
+| **Backstory**                    | `backstory`       | `str`                  | Provides context and personality to the agent.                  |
+| **LLM** _(optional)_             | `llm`             | `Union[str, LLM, Any]` | Language model powering the agent. Defaults to "gpt-4".         |
+| **Tools** _(optional)_           | `tools`           | `List[BaseTool]`       | Capabilities available to the agent. Defaults to an empty list. |
+| **Verbose** _(optional)_         | `verbose`         | `bool`                 | Enable detailed execution logs. Default is False.               |
+| **Response Format** _(optional)_ | `response_format` | `Type[BaseModel]`      | Pydantic model for structured output. Optional.                 |
+
+## Creating a LiteAgent
+
+Here's a simple example of creating and using a standalone LiteAgent:
+
+```python
+from typing import List, cast
+
+from crewai_tools import SerperDevTool
+from pydantic import BaseModel, Field
+
+from crewai.lite_agent import LiteAgent
+
+
+# Define a structured output format
+class MovieReview(BaseModel):
+    title: str = Field(description="The title of the movie")
+    rating: float = Field(description="Rating out of 10")
+    pros: List[str] = Field(description="List of positive aspects")
+    cons: List[str] = Field(description="List of negative aspects")
+
+
+# Create a LiteAgent
+critic = LiteAgent(
+    role="Movie Critic",
+    goal="Provide insightful movie reviews",
+    backstory="You are an experienced film critic known for balanced, thoughtful reviews.",
+    tools=[SerperDevTool()],
+    verbose=True,
+    response_format=MovieReview,
+)
+
+# Use the agent
+query = """
+Review the movie 'Inception'. Include:
+1. Your rating out of 10
+2. Key positive aspects
+3. Areas that could be improved
+"""
+
+result = critic.kickoff(query)
+
+
+# Access the structured output
+review = cast(MovieReview, result.pydantic)
+print(f"\nMovie Review: {review.title}")
+print(f"Rating: {review.rating}/10")
+print("\nPros:")
+for pro in review.pros:
+    print(f"- {pro}")
+print("\nCons:")
+for con in review.cons:
+    print(f"- {con}")
+
+```
+
+This example demonstrates the core features of a LiteAgent:
+
+- Structured output using Pydantic models
+- Tool integration with WebSearchTool
+- Simple execution with `kickoff()`
+- Easy access to both raw and structured results
+
+## Using LiteAgent in a Flow
+
+For more complex scenarios, you can integrate LiteAgents into a Flow. Here's an example of a market research flow:
+
+````python
+from typing import List
+from pydantic import BaseModel, Field
+from crewai.flow.flow import Flow, start, listen
+from crewai.lite_agent import LiteAgent
+from crewai.tools import WebSearchTool
+
+# Define a structured output format
+class MarketAnalysis(BaseModel):
+    key_trends: List[str] = Field(description="List of identified market trends")
+    market_size: str = Field(description="Estimated market size")
+    competitors: List[str] = Field(description="Major competitors in the space")
+
+# Define flow state
+class MarketResearchState(BaseModel):
+    product: str = ""
+    analysis: MarketAnalysis = None
+
+# Create a flow class
+class MarketResearchFlow(Flow[MarketResearchState]):
+    @start()
+    def initialize_research(self, product: str):
+        print(f"Starting market research for {product}")
+        self.state.product = product
+
+    @listen(initialize_research)
+    async def analyze_market(self):
+        # Create a LiteAgent for market research
+        analyst = LiteAgent(
+            role="Market Research Analyst",
+            goal=f"Analyze the market for {self.state.product}",
+            backstory="You are an experienced market analyst with expertise in "
+                     "identifying market trends and opportunities.",
+            tools=[WebSearchTool()],
+            verbose=True,
+            response_format=MarketAnalysis
+        )
+
+        # Define the research query
+        query = f"""
+        Research the market for {self.state.product}. Include:
+        1. Key market trends
+        2. Market size
+        3. Major competitors
+
+        Format your response according to the specified structure.
+        """
+
+        # Execute the analysis
+        result = await analyst.kickoff_async(query)
+        self.state.analysis = result.pydantic
+        return result.pydantic
+
+    @listen(analyze_market)
+    def present_results(self):
+        analysis = self.state.analysis
+        print("\nMarket Analysis Results")
+        print("=====================")
+
+        print("\nKey Market Trends:")
+        for trend in analysis.key_trends:
+            print(f"- {trend}")
+
+        print(f"\nMarket Size: {analysis.market_size}")
+
+        print("\nMajor Competitors:")
+        for competitor in analysis.competitors:
+            print(f"- {competitor}")
+
+# Usage example
+import asyncio
+
+async def run_flow():
+    flow = MarketResearchFlow()
+    result = await flow.kickoff(inputs={"product": "AI-powered chatbots"})
+    return result
+
+# Run the flow
+if __name__ == "__main__":
+    asyncio.run(run_flow())
+
+## Key Features
+
+### 1. Simplified Setup
+Unlike regular Agents, LiteAgents are designed for quick setup and standalone operation. They don't require crew configuration or task management.
+
+### 2. Structured Output
+LiteAgents support Pydantic models for response formatting, making it easy to get structured, type-safe data from your agent's operations.
+
+### 3. Tool Integration
+Just like regular Agents, LiteAgents can use tools to enhance their capabilities:
+```python
+from crewai.tools import SerperDevTool, CalculatorTool
+
+agent = LiteAgent(
+    role="Research Assistant",
+    goal="Find and analyze information",
+    tools=[SerperDevTool(), CalculatorTool()],
+    verbose=True
+)
+````
+
+### 4. Async Support
+
+LiteAgents support asynchronous execution through the `kickoff_async` method, making them suitable for non-blocking operations in your application.
+
+## Response Formatting
+
+LiteAgents support structured output through Pydantic models using the `response_format` parameter. This feature ensures type safety and consistent output structure, making it easier to work with agent responses in your application.
+
+### Basic Usage
+
+```python
+from pydantic import BaseModel, Field
+
+class SearchResult(BaseModel):
+    title: str = Field(description="The title of the found content")
+    summary: str = Field(description="A brief summary of the content")
+    relevance_score: float = Field(description="Relevance score from 0 to 1")
+
+agent = LiteAgent(
+    role="Search Specialist",
+    goal="Find and summarize relevant information",
+    response_format=SearchResult
+)
+
+result = await agent.kickoff_async("Find information about quantum computing")
+print(f"Title: {result.pydantic.title}")
+print(f"Summary: {result.pydantic.summary}")
+print(f"Relevance: {result.pydantic.relevance_score}")
+```
+
+### Handling Responses
+
+When using `response_format`, the agent's response will be available in two forms:
+
+1. **Raw Response**: Access the unstructured string response
+
+   ```python
+   result = await agent.kickoff_async("Analyze the market")
+   print(result.raw)  # Original LLM response
+   ```
+
+2. **Structured Response**: Access the parsed Pydantic model
+   ```python
+   print(result.pydantic)  # Parsed response as Pydantic model
+   print(result.pydantic.dict())  # Convert to dictionary
+   ```

docs/docs.json

@@ -66,6 +66,7 @@
               "concepts/tasks",
               "concepts/crews",
               "concepts/flows",
+              "concepts/lite-agent",
               "concepts/knowledge",
               "concepts/llms",
               "concepts/processes",
@@ -76,7 +77,9 @@
               "concepts/testing",
               "concepts/cli",
               "concepts/tools",
-              "concepts/event-listener"
+              "concepts/event-listener",
+              "concepts/langchain-tools",
+              "concepts/llamaindex-tools"
             ]
           },
           {
@@ -95,9 +98,7 @@
               "how-to/kickoff-async",
               "how-to/kickoff-for-each",
               "how-to/replay-tasks-from-latest-crew-kickoff",
-              "how-to/conditional-tasks",
-              "how-to/langchain-tools",
-              "how-to/llamaindex-tools"
+              "how-to/conditional-tasks"
             ]
           },
           {
@@ -196,11 +197,6 @@
           "anchor": "Community",
           "href": "https://community.crewai.com",
           "icon": "discourse"
-        },
-        {
-          "anchor": "Tutorials",
-          "href": "https://www.youtube.com/@crewAIInc",
-          "icon": "youtube"
         }
       ]
     }
@@ -235,4 +231,4 @@
       "reddit": "https://www.reddit.com/r/crewAIInc/"
     }
   }
-}
+}

docs/installation.mdx

@@ -4,21 +4,6 @@ description: Get started with CrewAI - Install, configure, and build your first
 icon: wrench
 ---
 
-## Video Tutorial
-Watch this video tutorial for a step-by-step demonstration of the installation process:
-
-<iframe
-  width="100%"
-  height="400"
-  src="https://www.youtube.com/embed/-kSOTtYzgEw"
-  title="CrewAI Installation Guide"
-  frameborder="0"
-  style={{ borderRadius: '10px' }}
-  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-  allowfullscreen
-></iframe>
-
-## Text Tutorial
 <Note>
   **Python Version Requirements**
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.114.0"
+version = "0.108.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<3.13"

src/crewai/__init__.py

@@ -17,7 +17,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.114.0"
+__version__ = "0.108.0"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,6 +1,7 @@
+import re
 import shutil
 import subprocess
-from typing import Any, Dict, List, Literal, Optional, Sequence, Type, Union
+from typing import Any, Dict, List, Literal, Optional, Sequence, Union
 
 from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
@@ -10,7 +11,6 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
-from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.llm import BaseLLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.security import Fingerprint
@@ -449,74 +449,3 @@ class Agent(BaseAgent):
 
     def set_fingerprint(self, fingerprint: Fingerprint):
         self.security_config.fingerprint = fingerprint
-
-    def kickoff(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        response_format: Optional[Type[Any]] = None,
-    ) -> LiteAgentOutput:
-        """
-        Execute the agent with the given messages using a LiteAgent instance.
-
-        This method is useful when you want to use the Agent configuration but
-        with the simpler and more direct execution flow of LiteAgent.
-
-        Args:
-            messages: Either a string query or a list of message dictionaries.
-                     If a string is provided, it will be converted to a user message.
-                     If a list is provided, each dict should have 'role' and 'content' keys.
-            response_format: Optional Pydantic model for structured output.
-
-        Returns:
-            LiteAgentOutput: The result of the agent execution.
-        """
-        lite_agent = LiteAgent(
-            role=self.role,
-            goal=self.goal,
-            backstory=self.backstory,
-            llm=self.llm,
-            tools=self.tools or [],
-            max_iterations=self.max_iter,
-            max_execution_time=self.max_execution_time,
-            respect_context_window=self.respect_context_window,
-            verbose=self.verbose,
-            response_format=response_format,
-            i18n=self.i18n,
-        )
-
-        return lite_agent.kickoff(messages)
-
-    async def kickoff_async(
-        self,
-        messages: Union[str, List[Dict[str, str]]],
-        response_format: Optional[Type[Any]] = None,
-    ) -> LiteAgentOutput:
-        """
-        Execute the agent asynchronously with the given messages using a LiteAgent instance.
-
-        This is the async version of the kickoff method.
-
-        Args:
-            messages: Either a string query or a list of message dictionaries.
-                     If a string is provided, it will be converted to a user message.
-                     If a list is provided, each dict should have 'role' and 'content' keys.
-            response_format: Optional Pydantic model for structured output.
-
-        Returns:
-            LiteAgentOutput: The result of the agent execution.
-        """
-        lite_agent = LiteAgent(
-            role=self.role,
-            goal=self.goal,
-            backstory=self.backstory,
-            llm=self.llm,
-            tools=self.tools or [],
-            max_iterations=self.max_iter,
-            max_execution_time=self.max_execution_time,
-            respect_context_window=self.respect_context_window,
-            verbose=self.verbose,
-            response_format=response_format,
-            i18n=self.i18n,
-        )
-
-        return await lite_agent.kickoff_async(messages)

src/crewai/cli/templates/crew/main.py

@@ -60,7 +60,7 @@ def test():
         "current_year": str(datetime.now().year)
     }
     try:
-        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), eval_llm=sys.argv[2], inputs=inputs)
+        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while testing the crew: {e}")

src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.114.0,<1.0.0"
+    "crewai[tools]>=0.108.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.114.0,<1.0.0",
+    "crewai[tools]>=0.108.0,<1.0.0",
 ]
 
 [project.scripts]

src/crewai/cli/templates/tool/pyproject.toml

@@ -5,7 +5,7 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<3.13"
 dependencies = [
-    "crewai[tools]>=0.114.0"
+    "crewai[tools]>=0.108.0"
 ]
 
 [tool.crewai]

src/crewai/flow/flow.py

@@ -1043,7 +1043,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             import traceback
 
             traceback.print_exc()
-            raise
 
     def _log_flow_event(
         self, message: str, color: str = "yellow", level: str = "info"

src/crewai/lite_agent.py

@@ -1,4 +1,6 @@
 import asyncio
+import json
+import re
 import uuid
 from datetime import datetime
 from typing import Any, Callable, Dict, List, Optional, Type, Union, cast

src/crewai/memory/external/external_memory.py

@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Any, Dict, Optional
+from typing import TYPE_CHECKING, Any, Dict, Optional, Self
 
 from crewai.memory.external.external_memory_item import ExternalMemoryItem
 from crewai.memory.memory import Memory
@@ -52,7 +52,7 @@ class ExternalMemory(Memory):
     def reset(self) -> None:
         self.storage.reset()
 
-    def set_crew(self, crew: Any) -> "ExternalMemory":
+    def set_crew(self, crew: Any) -> Self:
         super().set_crew(crew)
 
         if not self.storage:

src/crewai/memory/memory.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Self
 
 from pydantic import BaseModel
 
@@ -38,6 +38,6 @@ class Memory(BaseModel):
             query=query, limit=limit, score_threshold=score_threshold
         )
 
-    def set_crew(self, crew: Any) -> "Memory":
+    def set_crew(self, crew: Any) -> Self:
         self.crew = crew
         return self

src/crewai/tools/base_tool.py

@@ -244,13 +244,9 @@ def to_langchain(
     return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
 
 
-def tool(*args, result_as_answer=False):
+def tool(*args):
     """
     Decorator to create a tool from a function.
-    
-    Args:
-        *args: Positional arguments, either the function to decorate or the tool name.
-        result_as_answer: Flag to indicate if the tool result should be used as the final agent answer.
     """
 
     def _make_with_name(tool_name: str) -> Callable:
@@ -276,7 +272,6 @@ def tool(*args, result_as_answer=False):
                 description=f.__doc__,
                 func=f,
                 args_schema=args_schema,
-                result_as_answer=result_as_answer,
             )
 
         return _make_tool

tests/cassettes/test_lite_agent_created_with_correct_parameters[False].yaml

@@ -1,486 +0,0 @@
-interactions:
-- request:
-    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test Backstory\nYour
-      personal goal is: Test Goal\n\nYou ONLY have access to the following tools,
-      and should NEVER make up tools that are not listed here:\n\nTool Name: search_web\nTool
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tests/test_lite_agent.py

@@ -4,8 +4,8 @@ from typing import cast
 import pytest
 from pydantic import BaseModel, Field
 
-from crewai import LLM, Agent
-from crewai.lite_agent import LiteAgent, LiteAgentOutput
+from crewai import LLM
+from crewai.lite_agent import LiteAgent
 from crewai.tools import BaseTool
 from crewai.utilities.events import crewai_event_bus
 from crewai.utilities.events.tool_usage_events import ToolUsageStartedEvent
@@ -63,74 +63,12 @@ class ResearchResult(BaseModel):
     sources: list[str] = Field(description="List of sources used")
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.mark.parametrize("verbose", [True, False])
-def test_lite_agent_created_with_correct_parameters(monkeypatch, verbose):
-    """Test that LiteAgent is created with the correct parameters when Agent.kickoff() is called."""
-    # Create a test agent with specific parameters
-    llm = LLM(model="gpt-4o-mini")
-    custom_tools = [WebSearchTool(), CalculatorTool()]
-    max_iter = 10
-    max_execution_time = 300
-
-    agent = Agent(
-        role="Test Agent",
-        goal="Test Goal",
-        backstory="Test Backstory",
-        llm=llm,
-        tools=custom_tools,
-        max_iter=max_iter,
-        max_execution_time=max_execution_time,
-        verbose=verbose,
-    )
-
-    # Create a mock to capture the created LiteAgent
-    created_lite_agent = None
-    original_lite_agent = LiteAgent
-
-    # Define a mock LiteAgent class that captures its arguments
-    class MockLiteAgent(original_lite_agent):
-        def __init__(self, **kwargs):
-            nonlocal created_lite_agent
-            created_lite_agent = kwargs
-            super().__init__(**kwargs)
-
-    # Patch the LiteAgent class
-    monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
-
-    # Call kickoff to create the LiteAgent
-    agent.kickoff("Test query")
-
-    # Verify all parameters were passed correctly
-    assert created_lite_agent is not None
-    assert created_lite_agent["role"] == "Test Agent"
-    assert created_lite_agent["goal"] == "Test Goal"
-    assert created_lite_agent["backstory"] == "Test Backstory"
-    assert created_lite_agent["llm"] == llm
-    assert len(created_lite_agent["tools"]) == 2
-    assert isinstance(created_lite_agent["tools"][0], WebSearchTool)
-    assert isinstance(created_lite_agent["tools"][1], CalculatorTool)
-    assert created_lite_agent["max_iterations"] == max_iter
-    assert created_lite_agent["max_execution_time"] == max_execution_time
-    assert created_lite_agent["verbose"] == verbose
-    assert created_lite_agent["response_format"] is None
-
-    # Test with a response_format
-    monkeypatch.setattr("crewai.agent.LiteAgent", MockLiteAgent)
-
-    class TestResponse(BaseModel):
-        test_field: str
-
-    agent.kickoff("Test query", response_format=TestResponse)
-    assert created_lite_agent["response_format"] == TestResponse
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_lite_agent_with_tools():
-    """Test that Agent can use tools."""
+    """Test that LiteAgent can use tools."""
     # Create a LiteAgent with tools
     llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
+    agent = LiteAgent(
         role="Research Assistant",
         goal="Find information about the population of Tokyo",
         backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
@@ -168,7 +106,7 @@ def test_lite_agent_with_tools():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_lite_agent_structured_output():
-    """Test that Agent can return a simple structured output."""
+    """Test that LiteAgent can return a simple structured output."""
 
     class SimpleOutput(BaseModel):
         """Simple structure for agent outputs."""
@@ -179,18 +117,18 @@ def test_lite_agent_structured_output():
     web_search_tool = WebSearchTool()
 
     llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
+    agent = LiteAgent(
         role="Info Gatherer",
         goal="Provide brief information",
         backstory="You gather and summarize information quickly.",
         llm=llm,
         tools=[web_search_tool],
         verbose=True,
+        response_format=SimpleOutput,
     )
 
     result = agent.kickoff(
-        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence",
-        response_format=SimpleOutput,
+        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
     )
 
     print(f"\n=== Agent Result Type: {type(result)}")
@@ -217,7 +155,7 @@ def test_lite_agent_structured_output():
 def test_lite_agent_returns_usage_metrics():
     """Test that LiteAgent returns usage metrics."""
     llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
+    agent = LiteAgent(
         role="Research Assistant",
         goal="Find information about the population of Tokyo",
         backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
@@ -232,26 +170,3 @@ def test_lite_agent_returns_usage_metrics():
 
     assert result.usage_metrics is not None
     assert result.usage_metrics["total_tokens"] > 0
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-@pytest.mark.asyncio
-async def test_lite_agent_returns_usage_metrics_async():
-    """Test that LiteAgent returns usage metrics when run asynchronously."""
-    llm = LLM(model="gpt-4o-mini")
-    agent = Agent(
-        role="Research Assistant",
-        goal="Find information about the population of Tokyo",
-        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
-        llm=llm,
-        tools=[WebSearchTool()],
-        verbose=True,
-    )
-
-    result = await agent.kickoff_async(
-        "What is the population of Tokyo? Return your strucutred output in JSON format with the following fields: summary, confidence"
-    )
-    assert isinstance(result, LiteAgentOutput)
-    assert "21 million" in result.raw or "37 million" in result.raw
-    assert result.usage_metrics is not None
-    assert result.usage_metrics["total_tokens"] > 0

tests/tools/test_base_tool.py

@@ -100,25 +100,3 @@ def test_default_cache_function_is_true():
     my_tool = MyCustomTool()
     # Assert all the right attributes were defined
     assert my_tool.cache_function()
-
-
-def test_result_as_answer_in_tool_decorator():
-    @tool("Tool with result as answer", result_as_answer=True)
-    def my_tool_with_result_as_answer(question: str) -> str:
-        """This tool will return its result as the final answer."""
-        return question
-    
-    assert my_tool_with_result_as_answer.result_as_answer is True
-    
-    converted_tool = my_tool_with_result_as_answer.to_structured_tool()
-    assert converted_tool.result_as_answer is True
-    
-    @tool("Tool with default result_as_answer")
-    def my_tool_with_default(question: str) -> str:
-        """This tool uses the default result_as_answer value."""
-        return question
-    
-    assert my_tool_with_default.result_as_answer is False
-    
-    converted_tool = my_tool_with_default.to_structured_tool()
-    assert converted_tool.result_as_answer is False

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-    "python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'darwin'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'darwin')",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Darwin' and sys_platform == 'linux'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system == 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system == 'Darwin' and sys_platform != 'darwin' and sys_platform != 'linux')",
-    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'darwin') or (python_full_version >= '3.12.4' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
+    "python_full_version >= '3.12.4' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 
 [[package]]
@@ -344,7 +321,7 @@ name = "build"
 version = "1.2.2.post1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "os_name == 'nt'" },
+    { name = "colorama", marker = "(os_name == 'nt' and platform_machine != 'aarch64' and sys_platform == 'linux') or (os_name == 'nt' and sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "importlib-metadata", marker = "python_full_version < '3.10.2'" },
     { name = "packaging" },
     { name = "pyproject-hooks" },
@@ -579,7 +556,7 @@ name = "click"
 version = "8.1.8"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
 wheels = [
@@ -630,7 +607,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.114.0"
+version = "0.108.0"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -745,6 +722,7 @@ requires-dist = [
     { name = "tomli-w", specifier = ">=1.1.0" },
     { name = "uv", specifier = ">=0.4.25" },
 ]
+provides-extras = ["tools", "embeddings", "agentops", "fastembed", "pdfplumber", "pandas", "openpyxl", "mem0", "docling", "aisuite"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -2518,7 +2496,7 @@ version = "1.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "click" },
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
     { name = "ghp-import" },
     { name = "jinja2" },
     { name = "markdown" },
@@ -2699,7 +2677,7 @@ version = "2.10.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "pygments" },
-    { name = "pywin32", marker = "platform_system == 'Windows'" },
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
     { name = "tqdm" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3a/93/80ac75c20ce54c785648b4ed363c88f148bf22637e10c9863db4fbe73e74/mpire-2.10.2.tar.gz", hash = "sha256:f66a321e93fadff34585a4bfa05e95bd946cf714b442f51c529038eb45773d97", size = 271270 }
@@ -2946,7 +2924,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.1.0.70"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9f/fd/713452cd72343f682b1c7b9321e23829f00b842ceaedcda96e742ea0b0b3/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f", size = 664752741 },
@@ -2973,9 +2951,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.4.5.107"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
-    { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/bc/1d/8de1e5c67099015c834315e333911273a8c6aaba78923dd1d1e25fc5f217/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd", size = 124161928 },
@@ -2986,7 +2964,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.1.0.106"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
+    { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/65/5b/cfaeebf25cd9fdec14338ccb16f6b2c4c7fa9163aefcf057d86b9cc248bb/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c", size = 195958278 },
@@ -2997,7 +2975,6 @@ name = "nvidia-nccl-cu12"
 version = "2.20.5"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c1/bb/d09dda47c881f9ff504afd6f9ca4f502ded6d8fc2f572cacc5e39da91c28/nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01", size = 176238458 },
     { url = "https://files.pythonhosted.org/packages/4b/2a/0a131f572aa09f741c30ccd45a8e56316e8be8dfc7bc19bf0ab7cfef7b19/nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56", size = 176249402 },
 ]
 
@@ -3007,7 +2984,6 @@ version = "12.6.85"
 source = { registry = "https://pypi.org/simple" }
 wheels = [
     { url = "https://files.pythonhosted.org/packages/9d/d7/c5383e47c7e9bf1c99d5bd2a8c935af2b6d705ad831a7ec5c97db4d82f4f/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a", size = 19744971 },
-    { url = "https://files.pythonhosted.org/packages/31/db/dc71113d441f208cdfe7ae10d4983884e13f464a6252450693365e166dcf/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cf4eaa7d4b6b543ffd69d6abfb11efdeb2db48270d94dfd3a452c24150829e41", size = 19270338 },
 ]
 
 [[package]]
@@ -3525,7 +3501,7 @@ name = "portalocker"
 version = "2.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "pywin32", marker = "platform_system == 'Windows'" },
+    { name = "pywin32", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ed/d3/c6c64067759e87af98cc668c1cc75171347d0f1577fab7ca3749134e3cd4/portalocker-2.10.1.tar.gz", hash = "sha256:ef1bf844e878ab08aee7e40184156e1151f228f103aa5c6bd0724cc330960f8f", size = 40891 }
 wheels = [
@@ -5032,19 +5008,19 @@ dependencies = [
     { name = "fsspec" },
     { name = "jinja2" },
     { name = "networkx" },
-    { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
-    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "sympy" },
-    { name = "triton", marker = "platform_machine == 'x86_64' and platform_system == 'Linux'" },
+    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "typing-extensions" },
 ]
 wheels = [
@@ -5091,7 +5067,7 @@ name = "tqdm"
 version = "4.66.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "colorama", marker = "platform_system == 'Windows'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/83/6ba9844a41128c62e810fddddd72473201f3eacde02046066142a2d96cc5/tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad", size = 169504 }
 wheels = [
@@ -5133,7 +5109,7 @@ name = "triton"
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 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "filelock", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" },
+    { name = "filelock", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
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