cleanup

docs/concepts/flows.mdx

@@ -48,8 +48,6 @@ class ExampleFlow(Flow):
             ],
         )
 
-        #TODO: NEED TO ADD AN EXAMPLE AGENT IN HERE AS WELL.
-
         random_city = response["choices"][0]["message"]["content"]
         # Store the city in our state
         self.state["city"] = random_city
@@ -547,6 +545,97 @@ 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 LiteAgent to Flows
+
+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
+from typing import List, cast
+from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
+from pydantic import BaseModel, Field
+from crewai.flow.flow import Flow, listen, start
+from crewai.lite_agent import LiteAgent
+
+# 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 = None
+
+class MarketResearchFlow(Flow[MarketResearchState]):
+    @start()
+    def initialize_research(self):
+        print(f"Starting market research for {self.state.product}")
+
+    @listen(initialize_research)
+    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.",
+            llm="gpt-4o",
+            tools=[WebsiteSearchTool()],
+            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 = analyst.kickoff(query)
+        self.state.analysis = cast(MarketAnalysis, result.pydantic)
+        return result.pydantic
+
+    @listen(analyze_market)
+    def present_results(self):
+        analysis = self.state.analysis
+        if analysis is None:
+            print("No analysis results available")
+            return
+
+        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
+flow = MarketResearchFlow()
+result = flow.kickoff(inputs={"product": "AI-powered chatbots"})
+```
+
+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**: 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
 
 Creating a flow with multiple crews in CrewAI is straightforward.

docs/concepts/lite-agent.mdx

@@ -26,7 +26,67 @@ Think of a LiteAgent as a specialized worker that excels at individual tasks. Wh
 
 ## Creating a LiteAgent
 
-Here's a simple example of creating and using a LiteAgent in a flow:
+Here's a simple example of creating and using a standalone LiteAgent:
+
+```python
+from typing import List, cast
+
+from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
+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=[WebsiteSearchTool()],
+    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

examples/flow_lite_agent.py

@@ -20,7 +20,6 @@ class MarketResearchState(BaseModel):
     analysis: MarketAnalysis | None = None
 
 
-# Create a flow class
 class MarketResearchFlow(Flow[MarketResearchState]):
     @start()
     def initialize_research(self):

examples/lite_agent_example_2.py

@@ -0,0 +1,46 @@
+from typing import List, cast
+
+from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
+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=[WebsiteSearchTool()],
+    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}")

src/crewai/agents/parser.py

@@ -87,7 +87,18 @@ class CrewAgentParser:
             r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
         )
         action_match = re.search(regex, text, re.DOTALL)
-        if action_match:
+        if includes_answer:
+            final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
+            # Check whether the final answer ends with triple backticks.
+            if final_answer.endswith("```"):
+                # Count occurrences of triple backticks in the final answer.
+                count = final_answer.count("```")
+                # If count is odd then it's an unmatched trailing set; remove it.
+                if count % 2 != 0:
+                    final_answer = final_answer[:-3].rstrip()
+            return AgentFinish(thought, final_answer, text)
+
+        elif action_match:
             if includes_answer:
                 raise OutputParserException(
                     f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}"
@@ -102,17 +113,6 @@ class CrewAgentParser:
 
             return AgentAction(thought, clean_action, safe_tool_input, text)
 
-        elif includes_answer:
-            final_answer = text.split(FINAL_ANSWER_ACTION)[-1].strip()
-            # Check whether the final answer ends with triple backticks.
-            if final_answer.endswith("```"):
-                # Count occurrences of triple backticks in the final answer.
-                count = final_answer.count("```")
-                # If count is odd then it's an unmatched trailing set; remove it.
-                if count % 2 != 0:
-                    final_answer = final_answer[:-3].rstrip()
-            return AgentFinish(thought, final_answer, text)
-
         if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
             raise OutputParserException(
                 f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",

src/crewai/lite_agent.py

@@ -395,6 +395,7 @@ class LiteAgent(BaseModel):
                         callbacks=self._callbacks,
                         printer=self._printer,
                     )
+
                     # Emit LLM call completed event
                     crewai_event_bus.emit(
                         self,