Merge branch 'main' into Branch_1984

docs/concepts/crews.mdx

@@ -296,9 +296,9 @@ print(result)
 Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
 
 - `kickoff()`: Starts the execution process according to the defined process flow.
-- `kickoff_for_each()`: Executes tasks for each agent individually.
+- `kickoff_for_each()`: Executes tasks sequentially for each provided input event or item in the collection.
 - `kickoff_async()`: Initiates the workflow asynchronously.
-- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
+- `kickoff_for_each_async()`: Executes tasks concurrently for each provided input event or item, leveraging asynchronous processing.
 
 ```python Code
 # Start the crew's task execution

docs/concepts/llms.mdx

@@ -38,6 +38,7 @@ Here's a detailed breakdown of supported models and their capabilities, you can
     | GPT-4 | 8,192 tokens | High-accuracy tasks, complex reasoning |
     | GPT-4 Turbo | 128,000 tokens | Long-form content, document analysis |
     | GPT-4o & GPT-4o-mini | 128,000 tokens | Cost-effective large context processing |
+    | o3-mini | 200,000 tokens | Fast reasoning, complex reasoning |
 
     <Note>
       1 token ≈ 4 characters in English. For example, 8,192 tokens ≈ 32,768 characters or about 6,000 words.
@@ -162,7 +163,8 @@ Here's a detailed breakdown of supported models and their capabilities, you can
   <Tab title="Others">
     | Provider | Context Window | Key Features |
     |----------|---------------|--------------|
-    | Deepseek Chat | 128,000 tokens | Specialized in technical discussions |
+    | Deepseek Chat | 64,000 tokens | Specialized in technical discussions |
+    | Deepseek R1 | 64,000 tokens | Affordable reasoning model |
     | Claude 3 | Up to 200K tokens | Strong reasoning, code understanding |
     | Gemma Series | 8,192 tokens | Efficient, smaller-scale tasks |
 
@@ -296,6 +298,10 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
         # llm: sambanova/Meta-Llama-3.1-8B-Instruct
         # llm: sambanova/BioMistral-7B
         # llm: sambanova/Falcon-180B
+
+        # Open Router Models - Affordable reasoning
+        # llm: openrouter/deepseek/deepseek-r1
+        # llm: openrouter/deepseek/deepseek-chat
     ```
 
     <Info>
@@ -691,8 +697,53 @@ Learn how to get the most out of your LLM configuration:
       - Support for long context windows
     </Info>
   </Accordion>
+
+  <Accordion title="Open Router">
+    ```python Code
+    OPENROUTER_API_KEY=<your-api-key>
+    ```
+    
+    Example usage:
+    ```python Code
+    llm = LLM(
+        model="openrouter/deepseek/deepseek-r1",
+        base_url="https://openrouter.ai/api/v1",
+        api_key=OPENROUTER_API_KEY
+    )
+    ```
+
+    <Info>
+      Open Router models:
+      - openrouter/deepseek/deepseek-r1
+      - openrouter/deepseek/deepseek-chat
+    </Info>
+  </Accordion>
 </AccordionGroup>
 
+## Structured LLM Calls
+
+CrewAI supports structured responses from LLM calls by allowing you to define a `response_format` using a Pydantic model. This enables the framework to automatically parse and validate the output, making it easier to integrate the response into your application without manual post-processing.
+
+For example, you can define a Pydantic model to represent the expected response structure and pass it as the `response_format` when instantiating the LLM. The model will then be used to convert the LLM output into a structured Python object.
+
+```python Code
+from crewai import LLM
+
+class Dog(BaseModel):
+    name: str
+    age: int
+    breed: str
+
+
+llm = LLM(model="gpt-4o", response_format=Dog)
+
+response = llm.call(
+    "Analyze the following messages and return the name, age, and breed. "
+    "Meet Kona! She is 3 years old and is a black german shepherd."
+)
+print(response)
+```
+
 ## Common Issues and Solutions
 
 <Tabs>

docs/concepts/memory.mdx

@@ -185,7 +185,7 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
+    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model="text-embedding-3-small"),
 )
 ```
 
@@ -224,7 +224,7 @@ my_crew = Crew(
         "provider": "google",
         "config": {
             "api_key": "<YOUR_API_KEY>",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
         }
     }
 )
@@ -247,7 +247,7 @@ my_crew = Crew(
         api_base="YOUR_API_BASE_PATH",
         api_type="azure",
         api_version="YOUR_API_VERSION",
-        model_name="text-embedding-3-small"
+        model="text-embedding-3-small"
     )
 )
 ```
@@ -268,7 +268,7 @@ my_crew = Crew(
         project_id="YOUR_PROJECT_ID",
         region="YOUR_REGION",
         api_key="YOUR_API_KEY",
-        model_name="textembedding-gecko"
+        model="textembedding-gecko"
     )
 )
 ```
@@ -288,7 +288,7 @@ my_crew = Crew(
         "provider": "cohere",
         "config": {
             "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
         }
     }
 )
@@ -308,7 +308,7 @@ my_crew = Crew(
         "provider": "voyageai",
         "config": {
             "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
         }
     }
 )

docs/concepts/planning.mdx

@@ -81,8 +81,8 @@ my_crew.kickoff()
 
 3. **Collect Data:**
 
-   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
-   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
+   - Search for the latest papers, articles, and reports published in 2024 and early 2025.
+   - Use keywords like "Large Language Models 2025", "AI LLM advancements", "AI ethics 2025", etc.
 
 4. **Analyze Findings:**
 

docs/concepts/tasks.mdx

@@ -69,7 +69,7 @@ research_task:
   description: >
     Conduct a thorough research about {topic}
     Make sure you find any interesting and relevant information given
-    the current year is 2024.
+    the current year is 2025.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
   agent: researcher
@@ -155,7 +155,7 @@ research_task = Task(
     description="""
         Conduct a thorough research about AI Agents.
         Make sure you find any interesting and relevant information given
-        the current year is 2024.
+        the current year is 2025.
     """,
     expected_output="""
         A list with 10 bullet points of the most relevant information about AI Agents