Merge branch 'main' into devin/1738752192-fix-memory-reset-openai-dependency

* docs: fix long term memory class name in examples

- Replace EnhanceLongTermMemory with LongTermMemory to match actual implementation
- Update code examples to show correct usage
- Fixes #2026

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

* docs: improve memory examples with imports, types and security

- Add proper import statements
- Add type hints for better readability
- Add descriptive comments for each memory type
- Add security considerations section
- Add configuration examples section
- Use environment variables for storage paths

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

* Update memory.mdx

* Update memory.mdx

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Joe Moura <joao@crewai.com>
Co-authored-by: João Moura <joaomdmoura@gmail.com>

README.md

@@ -1,10 +1,18 @@
 <div align="center">
 
-![Logo of CrewAI, two people rowing on a boat](./docs/crewai_logo.png)
+![Logo of CrewAI](./docs/crewai_logo.png)
 
 # **CrewAI**
 
-🤖 **CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
+**CrewAI**: Production-grade framework for orchestrating sophisticated AI agent systems. From simple automations to complex real-world applications, CrewAI provides precise control and deep customization. By fostering collaborative intelligence through flexible, production-ready architecture, CrewAI empowers agents to work together seamlessly, tackling complex business challenges with predictable, consistent results.
+
+**CrewAI Enterprise**
+Want to plan, build (+ no code), deploy, monitor and interare your agents: [CrewAI Enterprise](https://www.crewai.com/enterprise). Designed for complex, real-world applications, our enterprise solution offers:
+
+- **Seamless Integrations**
+- **Scalable & Secure Deployment**
+- **Actionable Insights**
+- **24/7 Support**
 
 <h3>
 
@@ -190,7 +198,7 @@ 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
@@ -392,7 +400,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             goal="Gather and validate supporting market data",
             backstory="You excel at finding and correlating multiple data sources"
         )
-        
+
         analysis_task = Task(
             description="Analyze {sector} sector data for the past {timeframe}",
             expected_output="Detailed market analysis with confidence score",
@@ -403,7 +411,7 @@ class AdvancedAnalysisFlow(Flow[MarketState]):
             expected_output="Corroborating evidence and potential contradictions",
             agent=researcher
         )
-        
+
         # Demonstrate crew autonomy
         analysis_crew = Crew(
             agents=[analyst, researcher],

docs/concepts/crews.mdx

@@ -23,14 +23,14 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
 | **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
 | **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
-| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                          |
-| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
-| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
-| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |
+| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                             |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                         |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                      |
 | **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
 | **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
 | **Share Crew** _(optional)_           | `share_crew`           | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models.                                                                                                      |
-| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **Output Log File** _(optional)_      | `output_log_file`      | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`.                                                                      |
 | **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
 | **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
 | **Planning** *(optional)*             | `planning`             | Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.                                                     |
@@ -240,6 +240,23 @@ print(f"Tasks Output: {crew_output.tasks_output}")
 print(f"Token Usage: {crew_output.token_usage}")
 ```
 
+## Accessing Crew Logs
+
+You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
+In case of `True(Boolean)` will save as `logs.txt`.
+
+In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
+
+```python Code
+# Save crew logs
+crew = Crew(output_log_file = True)  # Logs will be saved as logs.txt
+crew = Crew(output_log_file = file_name)  # Logs will be saved as file_name.txt
+crew = Crew(output_log_file = file_name.txt)  # Logs will be saved as file_name.txt
+crew = Crew(output_log_file = file_name.json)  # Logs will be saved as file_name.json
+```
+
+
+
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
@@ -279,9 +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/flows.mdx

@@ -232,18 +232,18 @@ class UnstructuredExampleFlow(Flow):
     def first_method(self):
         # The state automatically includes an 'id' field
         print(f"State ID: {self.state['id']}")
-        self.state.message = "Hello from structured flow"
-        self.state.counter = 0
+        self.state['counter'] = 0
+        self.state['message'] = "Hello from structured flow"
 
     @listen(first_method)
     def second_method(self):
-        self.state.counter += 1
-        self.state.message += " - updated"
+        self.state['counter'] += 1
+        self.state['message'] += " - updated"
 
     @listen(second_method)
     def third_method(self):
-        self.state.counter += 1
-        self.state.message += " - updated again"
+        self.state['counter'] += 1
+        self.state['message'] += " - updated again"
 
         print(f"State after third_method: {self.state}")
 

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>
@@ -457,26 +463,32 @@ Learn how to get the most out of your LLM configuration:
 
   <Accordion title="Google">
     ```python Code
-    # Option 1. Gemini accessed with an API key.
+    # Option 1: Gemini accessed with an API key.
     # https://ai.google.dev/gemini-api/docs/api-key
     GEMINI_API_KEY=<your-api-key>
 
-    # Option 2. Vertex AI IAM credentials for Gemini, Anthropic, and anything in the Model Garden.
+    # Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
     # https://cloud.google.com/vertex-ai/generative-ai/docs/overview
     ```
 
-    ## GET CREDENTIALS 
+    Get credentials:
+    ```python Code
+    import json
+
     file_path = 'path/to/vertex_ai_service_account.json'
 
     # Load the JSON file
     with open(file_path, 'r') as file:
         vertex_credentials = json.load(file)
 
-    # Convert to JSON string
+    # Convert the credentials to a JSON string
     vertex_credentials_json = json.dumps(vertex_credentials)
+    ```
 
     Example usage:
     ```python Code
+    from crewai import LLM
+
     llm = LLM(
         model="gemini/gemini-1.5-pro-latest",
         temperature=0.7,
@@ -691,8 +703,53 @@ 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

@@ -58,41 +58,107 @@ my_crew = Crew(
 ### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
 
 ```python Code
-from crewai import Crew, Agent, Task, Process
+from crewai import Crew, Process
+from crewai.memory import LongTermMemory, ShortTermMemory, EntityMemory
+from crewai.memory.storage import LTMSQLiteStorage, RAGStorage
+from typing import List, Optional
 
 # Assemble your crew with memory capabilities
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process="Process.sequential",
-    memory=True,
-    long_term_memory=EnhanceLongTermMemory(
+my_crew: Crew = Crew(
+    agents = [...],
+    tasks = [...],
+    process = Process.sequential,
+    memory = True,
+    # Long-term memory for persistent storage across sessions
+    long_term_memory = LongTermMemory(
         storage=LTMSQLiteStorage(
-            db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
+            db_path="/my_crew1/long_term_memory_storage.db"
         )
     ),
-    short_term_memory=EnhanceShortTermMemory(
-        storage=CustomRAGStorage(
-            crew_name="my_crew",
-            storage_type="short_term",
-            data_dir="//my_data_dir",
-            model=embedder["model"],
-            dimension=embedder["dimension"],
+    # Short-term memory for current context using RAG
+    short_term_memory = ShortTermMemory(
+        storage = RAGStorage(
+                embedder_config={
+                    "provider": "openai",
+                    "config": {
+                        "model": 'text-embedding-3-small'
+                    }
+                },
+                type="short_term",
+                path="/my_crew1/"
+            )
         ),
     ),
-    entity_memory=EnhanceEntityMemory(
-        storage=CustomRAGStorage(
-            crew_name="my_crew",
-            storage_type="entities",
-            data_dir="//my_data_dir",
-            model=embedder["model"],
-            dimension=embedder["dimension"],
-        ),
+    # Entity memory for tracking key information about entities
+    entity_memory = EntityMemory(
+        storage=RAGStorage(
+            embedder_config={
+                "provider": "openai",
+                "config": {
+                    "model": 'text-embedding-3-small'
+                }
+            },
+            type="short_term",
+            path="/my_crew1/"
+        )
     ),
     verbose=True,
 )
 ```
 
+## Security Considerations
+
+When configuring memory storage:
+- Use environment variables for storage paths (e.g., `CREWAI_STORAGE_DIR`)
+- Never hardcode sensitive information like database credentials
+- Consider access permissions for storage directories
+- Use relative paths when possible to maintain portability
+
+Example using environment variables:
+```python
+import os
+from crewai import Crew
+from crewai.memory import LongTermMemory
+from crewai.memory.storage import LTMSQLiteStorage
+
+# Configure storage path using environment variable
+storage_path = os.getenv("CREWAI_STORAGE_DIR", "./storage")
+crew = Crew(
+    memory=True,
+    long_term_memory=LongTermMemory(
+        storage=LTMSQLiteStorage(
+            db_path="{storage_path}/memory.db".format(storage_path=storage_path)
+        )
+    )
+)
+```
+
+## Configuration Examples
+
+### Basic Memory Configuration
+```python
+from crewai import Crew
+from crewai.memory import LongTermMemory
+
+# Simple memory configuration
+crew = Crew(memory=True)  # Uses default storage locations
+```
+
+### Custom Storage Configuration
+```python
+from crewai import Crew
+from crewai.memory import LongTermMemory
+from crewai.memory.storage import LTMSQLiteStorage
+
+# Configure custom storage paths
+crew = Crew(
+    memory=True,
+    long_term_memory=LongTermMemory(
+        storage=LTMSQLiteStorage(db_path="./memory.db")
+    )
+)
+```
+
 ## Integrating Mem0 for Enhanced User Memory
 
 [Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
@@ -185,7 +251,12 @@ 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={
+        "provider": "openai",
+        "config": {
+            "model": 'text-embedding-3-small'
+        }
+    }
 )
 ```
 
@@ -224,7 +295,7 @@ my_crew = Crew(
         "provider": "google",
         "config": {
             "api_key": "<YOUR_API_KEY>",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
         }
     }
 )
@@ -242,13 +313,15 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=OpenAIEmbeddingFunction(
-        api_key="YOUR_API_KEY",
-        api_base="YOUR_API_BASE_PATH",
-        api_type="azure",
-        api_version="YOUR_API_VERSION",
-        model_name="text-embedding-3-small"
-    )
+    embedder={
+        "provider": "openai",
+        "config": {
+            "api_key": "YOUR_API_KEY",
+            "api_base": "YOUR_API_BASE_PATH",
+            "api_version": "YOUR_API_VERSION",
+            "model_name": 'text-embedding-3-small'
+        }
+    }
 )
 ```
 
@@ -264,12 +337,15 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder=GoogleVertexEmbeddingFunction(
-        project_id="YOUR_PROJECT_ID",
-        region="YOUR_REGION",
-        api_key="YOUR_API_KEY",
-        model_name="textembedding-gecko"
-    )
+    embedder={
+        "provider": "vertexai",
+        "config": {
+            "project_id"="YOUR_PROJECT_ID",
+            "region"="YOUR_REGION",
+            "api_key"="YOUR_API_KEY",
+            "model_name"="textembedding-gecko"
+        }
+    }
 )
 ```
 
@@ -288,7 +364,7 @@ my_crew = Crew(
         "provider": "cohere",
         "config": {
             "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
         }
     }
 )
@@ -308,7 +384,7 @@ my_crew = Crew(
         "provider": "voyageai",
         "config": {
             "api_key": "YOUR_API_KEY",
-            "model_name": "<model_name>"
+            "model": "<model_name>"
         }
     }
 )
@@ -358,6 +434,33 @@ my_crew = Crew(
 )
 ```
 
+### Adding Custom Embedding Function
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+from chromadb import Documents, EmbeddingFunction, Embeddings
+
+# Create a custom embedding function
+class CustomEmbedder(EmbeddingFunction):
+    def __call__(self, input: Documents) -> Embeddings:
+        # generate embeddings
+        return [1, 2, 3] # this is a dummy embedding
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "custom",
+        "config": {
+            "embedder": CustomEmbedder()
+        }
+    }
+)
+```
+
 ### Resetting Memory
 
 ```shell

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

docs/how-to/human-input-on-execution.mdx

@@ -60,12 +60,12 @@ writer = Agent(
 # Create tasks for your agents
 task1 = Task(
     description=(
-        "Conduct a comprehensive analysis of the latest advancements in AI in 2024. "
+        "Conduct a comprehensive analysis of the latest advancements in AI in 2025. "
         "Identify key trends, breakthrough technologies, and potential industry impacts. "
         "Compile your findings in a detailed report. "
         "Make sure to check with a human if the draft is good before finalizing your answer."
     ),
-    expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
+    expected_output='A comprehensive full report on the latest AI advancements in 2025, leave nothing out',
     agent=researcher,
     human_input=True
 )
@@ -76,7 +76,7 @@ task2 = Task(
         "Your post should be informative yet accessible, catering to a tech-savvy audience. "
         "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
     ),
-    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
+    expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2025',
     agent=writer,
     human_input=True
 )

docs/how-to/mlflow-observability.mdx

@@ -0,0 +1,206 @@
+---
+title: Agent Monitoring with MLflow
+description: Quickly start monitoring your Agents with MLflow.
+icon: bars-staggered
+---
+
+# MLflow Overview
+
+[MLflow](https://mlflow.org/) is an open-source platform to assist machine learning practitioners and teams in handling the complexities of the machine learning process.
+
+It provides a tracing feature that enhances LLM observability in your Generative AI applications by capturing detailed information about the execution of your application’s services. 
+Tracing provides a way to record the inputs, outputs, and metadata associated with each intermediate step of a request, enabling you to easily pinpoint the source of bugs and unexpected behaviors.
+
+![Overview of MLflow crewAI tracing usage](/images/mlflow-tracing.gif)
+
+### Features
+
+- **Tracing Dashboard**: Monitor activities of your crewAI agents with detailed dashboards that include inputs, outputs and metadata of spans.
+- **Automated Tracing**: A fully automated integration with crewAI, which can be enabled by running `mlflow.crewai.autolog()`. 
+- **Manual Trace Instrumentation with minor efforts**: Customize trace instrumentation through MLflow's high-level fluent APIs such as decorators, function wrappers and context managers.
+- **OpenTelemetry Compatibility**: MLflow Tracing supports exporting traces to an OpenTelemetry Collector, which can then be used to export traces to various backends such as Jaeger, Zipkin, and AWS X-Ray.
+- **Package and Deploy Agents**: Package and deploy your crewAI agents to an inference server with a variety of deployment targets.
+- **Securely Host LLMs**: Host multiple LLM from various providers in one unified endpoint through MFflow gateway.
+- **Evaluation**: Evaluate your crewAI agents with a wide range of metrics using a convenient API `mlflow.evaluate()`.
+
+## Setup Instructions
+
+<Steps>
+    <Step title="Install MLflow package">
+      ```shell
+      # The crewAI integration is available in mlflow>=2.19.0
+      pip install mlflow
+      ```
+    </Step>
+    <Step title="Start MFflow tracking server">
+      ```shell
+      # This process is optional, but it is recommended to use MLflow tracking server for better visualization and broader features.
+      mlflow server
+      ```
+    </Step>
+    <Step title="Initialize MLflow in Your Application">
+      Add the following two lines to your application code:
+
+      ```python
+      import mlflow
+
+      mlflow.crewai.autolog()
+
+      # Optional: Set a tracking URI and an experiment name if you have a tracking server
+      mlflow.set_tracking_uri("http://localhost:5000")
+      mlflow.set_experiment("CrewAI")
+      ```
+      
+      Example Usage for tracing CrewAI Agents:
+
+      ```python
+      from crewai import Agent, Crew, Task
+      from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
+      from crewai_tools import SerperDevTool, WebsiteSearchTool
+
+      from textwrap import dedent
+
+      content = "Users name is John. He is 30 years old and lives in San Francisco."
+      string_source = StringKnowledgeSource(
+          content=content, metadata={"preference": "personal"}
+      )
+
+      search_tool = WebsiteSearchTool()
+
+
+      class TripAgents:
+          def city_selection_agent(self):
+              return Agent(
+                  role="City Selection Expert",
+                  goal="Select the best city based on weather, season, and prices",
+                  backstory="An expert in analyzing travel data to pick ideal destinations",
+                  tools=[
+                      search_tool,
+                  ],
+                  verbose=True,
+              )
+
+          def local_expert(self):
+              return Agent(
+                  role="Local Expert at this city",
+                  goal="Provide the BEST insights about the selected city",
+                  backstory="""A knowledgeable local guide with extensive information
+              about the city, it's attractions and customs""",
+                  tools=[search_tool],
+                  verbose=True,
+              )
+
+
+      class TripTasks:
+          def identify_task(self, agent, origin, cities, interests, range):
+              return Task(
+                  description=dedent(
+                      f"""
+                      Analyze and select the best city for the trip based
+                      on specific criteria such as weather patterns, seasonal
+                      events, and travel costs. This task involves comparing
+                      multiple cities, considering factors like current weather
+                      conditions, upcoming cultural or seasonal events, and
+                      overall travel expenses.
+                      Your final answer must be a detailed
+                      report on the chosen city, and everything you found out
+                      about it, including the actual flight costs, weather
+                      forecast and attractions.
+
+                      Traveling from: {origin}
+                      City Options: {cities}
+                      Trip Date: {range}
+                      Traveler Interests: {interests}
+                  """
+                  ),
+                  agent=agent,
+                  expected_output="Detailed report on the chosen city including flight costs, weather forecast, and attractions",
+              )
+
+          def gather_task(self, agent, origin, interests, range):
+              return Task(
+                  description=dedent(
+                      f"""
+                      As a local expert on this city you must compile an
+                      in-depth guide for someone traveling there and wanting
+                      to have THE BEST trip ever!
+                      Gather information about key attractions, local customs,
+                      special events, and daily activity recommendations.
+                      Find the best spots to go to, the kind of place only a
+                      local would know.
+                      This guide should provide a thorough overview of what
+                      the city has to offer, including hidden gems, cultural
+                      hotspots, must-visit landmarks, weather forecasts, and
+                      high level costs.
+                      The final answer must be a comprehensive city guide,
+                      rich in cultural insights and practical tips,
+                      tailored to enhance the travel experience.
+
+                      Trip Date: {range}
+                      Traveling from: {origin}
+                      Traveler Interests: {interests}
+                  """
+                  ),
+                  agent=agent,
+                  expected_output="Comprehensive city guide including hidden gems, cultural hotspots, and practical travel tips",
+              )
+
+
+      class TripCrew:
+          def __init__(self, origin, cities, date_range, interests):
+              self.cities = cities
+              self.origin = origin
+              self.interests = interests
+              self.date_range = date_range
+
+          def run(self):
+              agents = TripAgents()
+              tasks = TripTasks()
+
+              city_selector_agent = agents.city_selection_agent()
+              local_expert_agent = agents.local_expert()
+
+              identify_task = tasks.identify_task(
+                  city_selector_agent,
+                  self.origin,
+                  self.cities,
+                  self.interests,
+                  self.date_range,
+              )
+              gather_task = tasks.gather_task(
+                  local_expert_agent, self.origin, self.interests, self.date_range
+              )
+
+              crew = Crew(
+                  agents=[city_selector_agent, local_expert_agent],
+                  tasks=[identify_task, gather_task],
+                  verbose=True,
+                  memory=True,
+                  knowledge={
+                      "sources": [string_source],
+                      "metadata": {"preference": "personal"},
+                  },
+              )
+
+              result = crew.kickoff()
+              return result
+
+
+      trip_crew = TripCrew("California", "Tokyo", "Dec 12 - Dec 20", "sports")
+      result = trip_crew.run()
+
+      print(result)
+      ```
+      Refer to [MLflow Tracing Documentation](https://mlflow.org/docs/latest/llms/tracing/index.html) for more configurations and use cases.
+    </Step>
+    <Step title="Visualize Activities of Agents">
+      Now traces for your crewAI agents are captured by MLflow. 
+      Let's visit MLflow tracking server to view the traces and get insights into your Agents.
+
+      Open `127.0.0.1:5000` on your browser to visit MLflow tracking server. 
+      <Frame caption="MLflow Tracing Dashboard">
+        <img src="/images/mlflow1.png" alt="MLflow tracing example with crewai" />
+      </Frame>
+    </Step>
+</Steps> 
+

docs/how-to/multimodal-agents.mdx

@@ -45,6 +45,7 @@ image_analyst = Agent(
 # Create a task for image analysis
 task = Task(
     description="Analyze the product image at https://example.com/product.jpg and provide a detailed description",
+    expected_output="A detailed description of the product image",
     agent=image_analyst
 )
 
@@ -81,6 +82,7 @@ inspection_task = Task(
     3. Compliance with standards
     Provide a detailed report highlighting any issues found.
     """,
+    expected_output="A detailed report highlighting any issues found",
     agent=expert_analyst
 )
 

docs/memory.md

@@ -0,0 +1,45 @@
+# Memory in CrewAI
+
+CrewAI provides a robust memory system that allows agents to retain and recall information from previous interactions.
+
+## Configuring Embedding Providers
+
+CrewAI supports multiple embedding providers for memory functionality:
+
+- OpenAI (default) - Requires `OPENAI_API_KEY`
+- Ollama - Requires `CREWAI_OLLAMA_URL` (defaults to "http://localhost:11434/api/embeddings")
+
+### Environment Variables
+
+Configure the embedding provider using these environment variables:
+
+- `CREWAI_EMBEDDING_PROVIDER`: Provider name (default: "openai")
+- `CREWAI_EMBEDDING_MODEL`: Model name (default: "text-embedding-3-small")
+- `CREWAI_OLLAMA_URL`: URL for Ollama API (when using Ollama provider)
+
+### Example Configuration
+
+```python
+# Using OpenAI (default)
+os.environ["OPENAI_API_KEY"] = "your-api-key"
+
+# Using Ollama
+os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
+os.environ["CREWAI_EMBEDDING_MODEL"] = "llama2"  # or any other model supported by your Ollama instance
+os.environ["CREWAI_OLLAMA_URL"] = "http://localhost:11434/api/embeddings"  # optional, this is the default
+```
+
+## Memory Usage
+
+When an agent has memory enabled, it can access and store information from previous interactions:
+
+```python
+agent = Agent(
+    role="Researcher",
+    goal="Research AI topics",
+    backstory="You're an AI researcher",
+    memory=True  # Enable memory for this agent
+)
+```
+
+The memory system uses embeddings to store and retrieve relevant information, allowing agents to maintain context across multiple interactions and tasks.

docs/mint.json

@@ -101,6 +101,7 @@
         "how-to/conditional-tasks",
         "how-to/agentops-observability",
         "how-to/langtrace-observability",
+        "how-to/mlflow-observability",
         "how-to/openlit-observability",
         "how-to/portkey-observability"
       ]

docs/quickstart.mdx

@@ -58,7 +58,7 @@ Follow the steps below to get crewing! 🚣‍♂️
       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
@@ -195,10 +195,10 @@ Follow the steps below to get crewing! 🚣‍♂️
 
   <CodeGroup>
     ```markdown output/report.md
-    # Comprehensive Report on the Rise and Impact of AI Agents in 2024
+    # Comprehensive Report on the Rise and Impact of AI Agents in 2025
 
     ## 1. Introduction to AI Agents
-    In 2024, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
+    In 2025, Artificial Intelligence (AI) agents are at the forefront of innovation across various industries. As intelligent systems that can perform tasks typically requiring human cognition, AI agents are paving the way for significant advancements in operational efficiency, decision-making, and overall productivity within sectors like Human Resources (HR) and Finance. This report aims to detail the rise of AI agents, their frameworks, applications, and potential implications on the workforce.
 
     ## 2. Benefits of AI Agents
     AI agents bring numerous advantages that are transforming traditional work environments. Key benefits include:
@@ -252,7 +252,7 @@ Follow the steps below to get crewing! 🚣‍♂️
     To stay competitive and harness the full potential of AI agents, organizations must remain vigilant about latest developments in AI technology and consider continuous learning and adaptation in their strategic planning.
 
     ## 8. Conclusion
-    The emergence of AI agents is undeniably reshaping the workplace landscape in 2024. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
+    The emergence of AI agents is undeniably reshaping the workplace landscape in 5. With their ability to automate tasks, enhance efficiency, and improve decision-making, AI agents are critical in driving operational success. Organizations must embrace and adapt to AI developments to thrive in an increasingly digital business environment.
     ```
   </CodeGroup>
   </Step>

docs/tools/filewritetool.mdx

@@ -8,9 +8,9 @@ icon: file-pen
 
 ## Description
 
-The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files. 
+The `FileWriterTool` is a component of the crewai_tools package, designed to simplify the process of writing content to files with cross-platform compatibility (Windows, Linux, macOS). 
 It is particularly useful in scenarios such as generating reports, saving logs, creating configuration files, and more. 
-This tool supports creating new directories if they don't exist, making it easier to organize your output.
+This tool handles path differences across operating systems, supports UTF-8 encoding, and automatically creates directories if they don't exist, making it easier to organize your output reliably across different platforms.
 
 ## Installation
 
@@ -43,6 +43,8 @@ print(result)
 
 ## Conclusion
 
-By integrating the `FileWriterTool` into your crews, the agents can execute the process of writing content to files and creating directories. 
-This tool is essential for tasks that require saving output data, creating structured file systems, and more. By adhering to the setup and usage guidelines provided, 
-incorporating this tool into projects is straightforward and efficient.
+By integrating the `FileWriterTool` into your crews, the agents can reliably write content to files across different operating systems. 
+This tool is essential for tasks that require saving output data, creating structured file systems, and handling cross-platform file operations. 
+It's particularly recommended for Windows users who may encounter file writing issues with standard Python file operations.
+
+By adhering to the setup and usage guidelines provided, incorporating this tool into projects is straightforward and ensures consistent file writing behavior across all platforms.

mkdocs.yml

@@ -152,6 +152,7 @@ nav:
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
     - Agent Monitoring with OpenLIT: 'how-to/openlit-Observability.md'
+    - Agent Monitoring with MLflow: 'how-to/mlflow-Observability.md'
   - Tools Docs:
     - Browserbase Web Loader: 'tools/BrowserbaseLoadTool.md'
     - Code Docs RAG Search: 'tools/CodeDocsSearchTool.md'

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.100.0"
+version = "0.100.1"
 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"
@@ -11,7 +11,7 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.4.2",
     "openai>=1.13.3",
-    "litellm==1.59.8",
+    "litellm==1.60.2",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",

src/crewai/__init__.py

@@ -14,7 +14,7 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.100.0"
+__version__ = "0.100.1"
 __all__ = [
     "Agent",
     "Crew",

src/crewai/agent.py

@@ -1,3 +1,4 @@
+import os
 import shutil
 import subprocess
 from typing import Any, Dict, List, Literal, Optional, Union
@@ -7,6 +8,7 @@ from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 from crewai.agents import CacheHandler
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
+from crewai.cli.constants import ENV_VARS, LITELLM_PARAMS
 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
@@ -19,7 +21,6 @@ from crewai.tools.base_tool import Tool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.converter import generate_model_description
-from crewai.utilities.llm_utils import create_llm
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
@@ -61,7 +62,6 @@ class Agent(BaseAgent):
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
             knowledge_sources: Knowledge sources for the agent.
-            embedder: Embedder configuration for the agent.
     """
 
     _times_executed: int = PrivateAttr(default=0)
@@ -85,7 +85,7 @@ class Agent(BaseAgent):
     llm: Union[str, InstanceOf[LLM], Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Union[str, InstanceOf[LLM], Any]] = Field(
+    function_calling_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
     system_template: Optional[str] = Field(
@@ -123,19 +123,105 @@ class Agent(BaseAgent):
         default="safe",
         description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
     )
-    embedder: Optional[Dict[str, Any]] = Field(
+    embedder_config: Optional[Dict[str, Any]] = Field(
         default=None,
         description="Embedder configuration for the agent.",
     )
+    knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
+        default=None,
+        description="Knowledge sources for the agent.",
+    )
+    _knowledge: Optional[Knowledge] = PrivateAttr(
+        default=None,
+    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
         self._set_knowledge()
         self.agent_ops_agent_name = self.role
+        unaccepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
-        self.llm = create_llm(self.llm)
-        if self.function_calling_llm and not isinstance(self.function_calling_llm, LLM):
-            self.function_calling_llm = create_llm(self.function_calling_llm)
+        # Handle different cases for self.llm
+        if isinstance(self.llm, str):
+            # If it's a string, create an LLM instance
+            self.llm = LLM(model=self.llm)
+        elif isinstance(self.llm, LLM):
+            # If it's already an LLM instance, keep it as is
+            pass
+        elif self.llm is None:
+            # Determine the model name from environment variables or use default
+            model_name = (
+                os.environ.get("OPENAI_MODEL_NAME")
+                or os.environ.get("MODEL")
+                or "gpt-4o-mini"
+            )
+            llm_params = {"model": model_name}
+
+            api_base = os.environ.get("OPENAI_API_BASE") or os.environ.get(
+                "OPENAI_BASE_URL"
+            )
+            if api_base:
+                llm_params["base_url"] = api_base
+
+            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
+
+            # Iterate over all environment variables to find matching API keys or use defaults
+            for provider, env_vars in ENV_VARS.items():
+                if provider == set_provider:
+                    for env_var in env_vars:
+                        # Check if the environment variable is set
+                        key_name = env_var.get("key_name")
+                        if key_name and key_name not in unaccepted_attributes:
+                            env_value = os.environ.get(key_name)
+                            if env_value:
+                                key_name = key_name.lower()
+                                for pattern in LITELLM_PARAMS:
+                                    if pattern in key_name:
+                                        key_name = pattern
+                                        break
+                                llm_params[key_name] = env_value
+                        # Check for default values if the environment variable is not set
+                        elif env_var.get("default", False):
+                            for key, value in env_var.items():
+                                if key not in ["prompt", "key_name", "default"]:
+                                    # Only add default if the key is already set in os.environ
+                                    if key in os.environ:
+                                        llm_params[key] = value
+
+            self.llm = LLM(**llm_params)
+        else:
+            # For any other type, attempt to extract relevant attributes
+            llm_params = {
+                "model": getattr(self.llm, "model_name", None)
+                or getattr(self.llm, "deployment_name", None)
+                or str(self.llm),
+                "temperature": getattr(self.llm, "temperature", None),
+                "max_tokens": getattr(self.llm, "max_tokens", None),
+                "logprobs": getattr(self.llm, "logprobs", None),
+                "timeout": getattr(self.llm, "timeout", None),
+                "max_retries": getattr(self.llm, "max_retries", None),
+                "api_key": getattr(self.llm, "api_key", None),
+                "base_url": getattr(self.llm, "base_url", None),
+                "organization": getattr(self.llm, "organization", None),
+            }
+            # Remove None values to avoid passing unnecessary parameters
+            llm_params = {k: v for k, v in llm_params.items() if v is not None}
+            self.llm = LLM(**llm_params)
+
+        # Similar handling for function_calling_llm
+        if self.function_calling_llm:
+            if isinstance(self.function_calling_llm, str):
+                self.function_calling_llm = LLM(model=self.function_calling_llm)
+            elif not isinstance(self.function_calling_llm, LLM):
+                self.function_calling_llm = LLM(
+                    model=getattr(self.function_calling_llm, "model_name", None)
+                    or getattr(self.function_calling_llm, "deployment_name", None)
+                    or str(self.function_calling_llm)
+                )
 
         if not self.agent_executor:
             self._setup_agent_executor()
@@ -157,11 +243,19 @@ class Agent(BaseAgent):
                 if isinstance(self.knowledge_sources, list) and all(
                     isinstance(k, BaseKnowledgeSource) for k in self.knowledge_sources
                 ):
-                    self.knowledge = Knowledge(
+                    # Validate embedding configuration based on provider
+                    from crewai.utilities.constants import DEFAULT_EMBEDDING_PROVIDER
+                    provider = os.getenv("CREWAI_EMBEDDING_PROVIDER", DEFAULT_EMBEDDING_PROVIDER)
+                    
+                    if provider == "openai" and not os.getenv("OPENAI_API_KEY"):
+                        raise ValueError("Please provide an OpenAI API key via OPENAI_API_KEY environment variable")
+                    elif provider == "ollama" and not os.getenv("CREWAI_OLLAMA_URL", "http://localhost:11434/api/embeddings"):
+                        raise ValueError("Please provide Ollama URL via CREWAI_OLLAMA_URL environment variable")
+                    
+                    self._knowledge = Knowledge(
                         sources=self.knowledge_sources,
-                        embedder=self.embedder,
+                        embedder_config=self.embedder_config,
                         collection_name=knowledge_agent_name,
-                        storage=self.knowledge_storage or None,
                     )
         except (TypeError, ValueError) as e:
             raise ValueError(f"Invalid Knowledge Configuration: {str(e)}")
@@ -220,8 +314,8 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        if self.knowledge:
-            agent_knowledge_snippets = self.knowledge.query([task.prompt()])
+        if self._knowledge:
+            agent_knowledge_snippets = self._knowledge.query([task.prompt()])
             if agent_knowledge_snippets:
                 agent_knowledge_context = extract_knowledge_context(
                     agent_knowledge_snippets
@@ -254,9 +348,6 @@ class Agent(BaseAgent):
                 }
             )["output"]
         except Exception as e:
-            if e.__class__.__module__.startswith("litellm"):
-                # Do not retry on litellm errors
-                raise e
             self._times_executed += 1
             if self._times_executed > self.max_retry_limit:
                 raise e
@@ -331,7 +422,6 @@ class Agent(BaseAgent):
 
     def get_multimodal_tools(self) -> List[Tool]:
         from crewai.tools.agent_tools.add_image_tool import AddImageTool
-
         return [AddImageTool()]
 
     def get_code_execution_tools(self):

src/crewai/agents/agent_builder/base_agent.py

@@ -24,6 +24,7 @@ from crewai.tools import BaseTool
 from crewai.tools.base_tool import Tool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
+from crewai.utilities.converter import Converter
 
 T = TypeVar("T", bound="BaseAgent")
 
@@ -42,7 +43,7 @@ class BaseAgent(ABC, BaseModel):
         max_rpm (Optional[int]): Maximum number of requests per minute for the agent execution.
         allow_delegation (bool): Allow delegation of tasks to agents.
         tools (Optional[List[Any]]): Tools at the agent's disposal.
-        max_iter (Optional[int]): Maximum iterations for an agent to execute a task.
+        max_iter (int): Maximum iterations for an agent to execute a task.
         agent_executor (InstanceOf): An instance of the CrewAgentExecutor class.
         llm (Any): Language model that will run the agent.
         crew (Any): Crew to which the agent belongs.
@@ -112,9 +113,9 @@ class BaseAgent(ABC, BaseModel):
         description="Enable agent to delegate and ask questions among each other.",
     )
     tools: Optional[List[Any]] = Field(
-        default_factory=list, description="Tools at agents' disposal"
+        default_factory=lambda: [], description="Tools at agents' disposal"
     )
-    max_iter: Optional[int] = Field(
+    max_iter: int = Field(
         default=25, description="Maximum iterations for an agent to execute a task"
     )
     agent_executor: InstanceOf = Field(
@@ -125,11 +126,12 @@ class BaseAgent(ABC, BaseModel):
     )
     crew: Any = Field(default=None, description="Crew to which the agent belongs.")
     i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
-    cache_handler: InstanceOf[CacheHandler] = Field(
+    cache_handler: Optional[InstanceOf[CacheHandler]] = Field(
         default=None, description="An instance of the CacheHandler class."
     )
     tools_handler: InstanceOf[ToolsHandler] = Field(
-        default=None, description="An instance of the ToolsHandler class."
+        default_factory=ToolsHandler,
+        description="An instance of the ToolsHandler class.",
     )
     max_tokens: Optional[int] = Field(
         default=None, description="Maximum number of tokens for the agent's execution."
@@ -254,7 +256,7 @@ class BaseAgent(ABC, BaseModel):
     @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
 

src/crewai/cli/reset_memories_command.py

@@ -2,11 +2,7 @@ import subprocess
 
 import click
 
-from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
-from crewai.memory.entity.entity_memory import EntityMemory
-from crewai.memory.long_term.long_term_memory import LongTermMemory
-from crewai.memory.short_term.short_term_memory import ShortTermMemory
-from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+from crewai.cli.utils import get_crew
 
 
 def reset_memories_command(
@@ -30,30 +26,35 @@ def reset_memories_command(
     """
 
     try:
+        crew = get_crew()
+        if not crew:
+            raise ValueError("No crew found.")
         if all:
-            ShortTermMemory().reset()
-            EntityMemory().reset()
-            LongTermMemory().reset()
-            TaskOutputStorageHandler().reset()
-            KnowledgeStorage().reset()
+            crew.reset_memories(command_type="all")
             click.echo("All memories have been reset.")
-        else:
-            if long:
-                LongTermMemory().reset()
-                click.echo("Long term memory has been reset.")
+            return
 
-            if short:
-                ShortTermMemory().reset()
-                click.echo("Short term memory has been reset.")
-            if entity:
-                EntityMemory().reset()
-                click.echo("Entity memory has been reset.")
-            if kickoff_outputs:
-                TaskOutputStorageHandler().reset()
-                click.echo("Latest Kickoff outputs stored has been reset.")
-            if knowledge:
-                KnowledgeStorage().reset()
-                click.echo("Knowledge has been reset.")
+        if not any([long, short, entity, kickoff_outputs, knowledge]):
+            click.echo(
+                "No memory type specified. Please specify at least one type to reset."
+            )
+            return
+
+        if long:
+            crew.reset_memories(command_type="long")
+            click.echo("Long term memory has been reset.")
+        if short:
+            crew.reset_memories(command_type="short")
+            click.echo("Short term memory has been reset.")
+        if entity:
+            crew.reset_memories(command_type="entity")
+            click.echo("Entity memory has been reset.")
+        if kickoff_outputs:
+            crew.reset_memories(command_type="kickoff_outputs")
+            click.echo("Latest Kickoff outputs stored has been reset.")
+        if knowledge:
+            crew.reset_memories(command_type="knowledge")
+            click.echo("Knowledge has been reset.")
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while resetting the memories: {e}", err=True)

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]>=0.100.0,<1.0.0"
+    "crewai[tools]>=0.100.1,<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]>=0.100.0,<1.0.0",
+    "crewai[tools]>=0.100.1,<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]>=0.100.0"
+    "crewai[tools]>=0.100.1"
 ]
 
 [tool.crewai]

src/crewai/cli/utils.py

@@ -9,6 +9,7 @@ import tomli
 from rich.console import Console
 
 from crewai.cli.constants import ENV_VARS
+from crewai.crew import Crew
 
 if sys.version_info >= (3, 11):
     import tomllib
@@ -247,3 +248,64 @@ def write_env_file(folder_path, env_vars):
     with open(env_file_path, "w") as file:
         for key, value in env_vars.items():
             file.write(f"{key}={value}\n")
+
+
+def get_crew(crew_path: str = "crew.py", require: bool = False) -> Crew | None:
+    """Get the crew instance from the crew.py file."""
+    try:
+        import importlib.util
+        import os
+
+        for root, _, files in os.walk("."):
+            if "crew.py" in files:
+                crew_path = os.path.join(root, "crew.py")
+                try:
+                    spec = importlib.util.spec_from_file_location(
+                        "crew_module", crew_path
+                    )
+                    if not spec or not spec.loader:
+                        continue
+                    module = importlib.util.module_from_spec(spec)
+                    try:
+                        sys.modules[spec.name] = module
+                        spec.loader.exec_module(module)
+
+                        for attr_name in dir(module):
+                            attr = getattr(module, attr_name)
+                            try:
+                                if callable(attr) and hasattr(attr, "crew"):
+                                    crew_instance = attr().crew()
+                                    return crew_instance
+
+                            except Exception as e:
+                                print(f"Error processing attribute {attr_name}: {e}")
+                                continue
+
+                    except Exception as exec_error:
+                        print(f"Error executing module: {exec_error}")
+                        import traceback
+
+                        print(f"Traceback: {traceback.format_exc()}")
+
+                except (ImportError, AttributeError) as e:
+                    if require:
+                        console.print(
+                            f"Error importing crew from {crew_path}: {str(e)}",
+                            style="bold red",
+                        )
+                        continue
+
+                break
+
+        if require:
+            console.print("No valid Crew instance found in crew.py", style="bold red")
+            raise SystemExit
+        return None
+
+    except Exception as e:
+        if require:
+            console.print(
+                f"Unexpected error while loading crew: {str(e)}", style="bold red"
+            )
+            raise SystemExit
+        return None

src/crewai/crew.py

@@ -1,12 +1,10 @@
 import asyncio
 import json
-import re
 import uuid
 import warnings
 from concurrent.futures import Future
-from copy import copy as shallow_copy
 from hashlib import md5
-from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -18,7 +16,6 @@ from pydantic import (
     field_validator,
     model_validator,
 )
-from pydantic_core import PydanticCustomError
 
 from crewai.agent import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -183,9 +180,9 @@ class Crew(BaseModel):
         default=None,
         description="Path to the prompt json file to be used for the crew.",
     )
-    output_log_file: Optional[str] = Field(
+    output_log_file: Optional[Union[bool, str]] = Field(
         default=None,
-        description="output_log_file",
+        description="Path to the log file to be saved",
     )
     planning: Optional[bool] = Field(
         default=False,
@@ -293,7 +290,7 @@ class Crew(BaseModel):
                 ):
                     self.knowledge = Knowledge(
                         sources=self.knowledge_sources,
-                        embedder_config=self.embedder,
+                        embedder=self.embedder,
                         collection_name="crew",
                     )
 
@@ -380,6 +377,22 @@ class Crew(BaseModel):
 
         return self
 
+    @model_validator(mode="after")
+    def validate_must_have_non_conditional_task(self) -> "Crew":
+        """Ensure that a crew has at least one non-conditional task."""
+        if not self.tasks:
+            return self
+        non_conditional_count = sum(
+            1 for task in self.tasks if not isinstance(task, ConditionalTask)
+        )
+        if non_conditional_count == 0:
+            raise PydanticCustomError(
+                "only_conditional_tasks",
+                "Crew must include at least one non-conditional task",
+                {},
+            )
+        return self
+
     @model_validator(mode="after")
     def validate_first_task(self) -> "Crew":
         """Ensure the first task is not a ConditionalTask."""
@@ -439,6 +452,8 @@ class Crew(BaseModel):
                         )
         return self
 
+
+
     @property
     def key(self) -> str:
         source = [agent.key for agent in self.agents] + [
@@ -681,12 +696,7 @@ class Crew(BaseModel):
                 manager.tools = []
                 raise Exception("Manager agent should not have tools")
         else:
-            self.manager_llm = (
-                getattr(self.manager_llm, "model_name", None)
-                or getattr(self.manager_llm, "model", None)
-                or getattr(self.manager_llm, "deployment_name", None)
-                or self.manager_llm
-            )
+            self.manager_llm = create_llm(self.manager_llm)
             manager = Agent(
                 role=i18n.retrieve("hierarchical_manager_agent", "role"),
                 goal=i18n.retrieve("hierarchical_manager_agent", "goal"),
@@ -746,6 +756,7 @@ class Crew(BaseModel):
                     task, task_outputs, futures, task_index, was_replayed
                 )
                 if skipped_task_output:
+                    task_outputs.append(skipped_task_output)
                     continue
 
             if task.async_execution:
@@ -769,7 +780,7 @@ class Crew(BaseModel):
                     context=context,
                     tools=tools_for_task,
                 )
-                task_outputs = [task_output]
+                task_outputs.append(task_output)
                 self._process_task_result(task, task_output)
                 self._store_execution_log(task, task_output, task_index, was_replayed)
 
@@ -790,7 +801,7 @@ class Crew(BaseModel):
             task_outputs = self._process_async_tasks(futures, was_replayed)
             futures.clear()
 
-        previous_output = task_outputs[task_index - 1] if task_outputs else None
+        previous_output = task_outputs[-1] if task_outputs else None
         if previous_output is not None and not task.should_execute(previous_output):
             self._logger.log(
                 "debug",
@@ -912,11 +923,15 @@ class Crew(BaseModel):
             )
 
     def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
-        if len(task_outputs) != 1:
-            raise ValueError(
-                "Something went wrong. Kickoff should return only one task output."
-            )
-        final_task_output = task_outputs[0]
+        if not task_outputs:
+            raise ValueError("No task outputs available to create crew output.")
+            
+        # Filter out empty outputs and get the last valid one as the main output
+        valid_outputs = [t for t in task_outputs if t.raw]
+        if not valid_outputs:
+            raise ValueError("No valid task outputs available to create crew output.")
+        final_task_output = valid_outputs[-1]
+            
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
         token_usage = self.calculate_usage_metrics()
@@ -925,7 +940,7 @@ class Crew(BaseModel):
             raw=final_task_output.raw,
             pydantic=final_task_output.pydantic,
             json_dict=final_task_output.json_dict,
-            tasks_output=[task.output for task in self.tasks if task.output],
+            tasks_output=task_outputs,
             token_usage=token_usage,
         )
 
@@ -1152,3 +1167,80 @@ class Crew(BaseModel):
 
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"
+
+    def reset_memories(self, command_type: str) -> None:
+        """Reset specific or all memories for the crew.
+
+        Args:
+            command_type: Type of memory to reset.
+                Valid options: 'long', 'short', 'entity', 'knowledge',
+                'kickoff_outputs', or 'all'
+
+        Raises:
+            ValueError: If an invalid command type is provided.
+            RuntimeError: If memory reset operation fails.
+        """
+        VALID_TYPES = frozenset(
+            ["long", "short", "entity", "knowledge", "kickoff_outputs", "all"]
+        )
+
+        if command_type not in VALID_TYPES:
+            raise ValueError(
+                f"Invalid command type. Must be one of: {', '.join(sorted(VALID_TYPES))}"
+            )
+
+        try:
+            if command_type == "all":
+                self._reset_all_memories()
+            else:
+                self._reset_specific_memory(command_type)
+
+            self._logger.log("info", f"{command_type} memory has been reset")
+
+        except Exception as e:
+            error_msg = f"Failed to reset {command_type} memory: {str(e)}"
+            self._logger.log("error", error_msg)
+            raise RuntimeError(error_msg) from e
+
+    def _reset_all_memories(self) -> None:
+        """Reset all available memory systems."""
+        memory_systems = [
+            ("short term", self._short_term_memory),
+            ("entity", self._entity_memory),
+            ("long term", self._long_term_memory),
+            ("task output", self._task_output_handler),
+            ("knowledge", self.knowledge),
+        ]
+
+        for name, system in memory_systems:
+            if system is not None:
+                try:
+                    system.reset()
+                except Exception as e:
+                    raise RuntimeError(f"Failed to reset {name} memory") from e
+
+    def _reset_specific_memory(self, memory_type: str) -> None:
+        """Reset a specific memory system.
+
+        Args:
+            memory_type: Type of memory to reset
+
+        Raises:
+            RuntimeError: If the specified memory system fails to reset
+        """
+        reset_functions = {
+            "long": (self._long_term_memory, "long term"),
+            "short": (self._short_term_memory, "short term"),
+            "entity": (self._entity_memory, "entity"),
+            "knowledge": (self.knowledge, "knowledge"),
+            "kickoff_outputs": (self._task_output_handler, "task output"),
+        }
+
+        memory_system, name = reset_functions[memory_type]
+        if memory_system is None:
+            raise RuntimeError(f"{name} memory system is not initialized")
+
+        try:
+            memory_system.reset()
+        except Exception as e:
+            raise RuntimeError(f"Failed to reset {name} memory") from e

src/crewai/crews/crew_output.py

@@ -1,7 +1,9 @@
 import json
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional
 
 from pydantic import BaseModel, Field
+from pydantic.main import IncEx
+from typing_extensions import Literal
 
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
@@ -21,16 +23,45 @@ class CrewOutput(BaseModel):
     tasks_output: list[TaskOutput] = Field(
         description="Output of each task", default=[]
     )
-    token_usage: UsageMetrics = Field(description="Processed token summary", default={})
+    token_usage: UsageMetrics = Field(description="Processed token summary", default_factory=UsageMetrics)
 
-    @property
-    def json(self) -> Optional[str]:
-        if self.tasks_output[-1].output_format != OutputFormat.JSON:
+    def model_json(self) -> str:
+        """Get the JSON representation of the output."""
+        if self.tasks_output and self.tasks_output[-1].output_format != OutputFormat.JSON:
             raise ValueError(
                 "No JSON output found in the final task. Please make sure to set the output_json property in the final task in your crew."
             )
+        return json.dumps(self.json_dict) if self.json_dict else "{}"
 
-        return json.dumps(self.json_dict)
+    def model_dump_json(
+        self,
+        *,
+        indent: Optional[int] = None,
+        include: Optional[IncEx] = None,
+        exclude: Optional[IncEx] = None,
+        context: Optional[Any] = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal["none", "warn", "error"] = False,
+        serialize_as_any: bool = False,
+    ) -> str:
+        """Override model_dump_json to handle custom JSON output."""
+        return super().model_dump_json(
+            indent=indent,
+            include=include,
+            exclude=exclude,
+            context=context,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            round_trip=round_trip,
+            warnings=warnings,
+            serialize_as_any=serialize_as_any,
+        )
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert json_output and pydantic_output to a dictionary."""

src/crewai/flow/flow.py

@@ -600,7 +600,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
             ```
         """
         try:
-            if not hasattr(self, '_state'):
+            if not hasattr(self, "_state"):
                 return ""
 
             if isinstance(self._state, dict):
@@ -706,26 +706,31 @@ class Flow(Generic[T], metaclass=FlowMeta):
             inputs: Optional dictionary containing input values and potentially a state ID to restore
         """
         # Handle state restoration if ID is provided in inputs
-        if inputs and 'id' in inputs and self._persistence is not None:
-            restore_uuid = inputs['id']
+        if inputs and "id" in inputs and self._persistence is not None:
+            restore_uuid = inputs["id"]
             stored_state = self._persistence.load_state(restore_uuid)
 
             # Override the id in the state if it exists in inputs
-            if 'id' in inputs:
+            if "id" in inputs:
                 if isinstance(self._state, dict):
-                    self._state['id'] = inputs['id']
+                    self._state["id"] = inputs["id"]
                 elif isinstance(self._state, BaseModel):
-                    setattr(self._state, 'id', inputs['id'])
+                    setattr(self._state, "id", inputs["id"])
 
             if stored_state:
-                self._log_flow_event(f"Loading flow state from memory for UUID: {restore_uuid}", color="yellow")
+                self._log_flow_event(
+                    f"Loading flow state from memory for UUID: {restore_uuid}",
+                    color="yellow",
+                )
                 # Restore the state
                 self._restore_state(stored_state)
             else:
-                self._log_flow_event(f"No flow state found for UUID: {restore_uuid}", color="red")
+                self._log_flow_event(
+                    f"No flow state found for UUID: {restore_uuid}", color="red"
+                )
 
             # Apply any additional inputs after restoration
-            filtered_inputs = {k: v for k, v in inputs.items() if k != 'id'}
+            filtered_inputs = {k: v for k, v in inputs.items() if k != "id"}
             if filtered_inputs:
                 self._initialize_state(filtered_inputs)
 
@@ -737,9 +742,11 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 flow_name=self.__class__.__name__,
             ),
         )
-        self._log_flow_event(f"Flow started with ID: {self.flow_id}", color="bold_magenta")
+        self._log_flow_event(
+            f"Flow started with ID: {self.flow_id}", color="bold_magenta"
+        )
 
-        if inputs is not None and 'id' not in inputs:
+        if inputs is not None and "id" not in inputs:
             self._initialize_state(inputs)
 
         return asyncio.run(self.kickoff_async())
@@ -984,7 +991,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             traceback.print_exc()
 
-    def _log_flow_event(self, message: str, color: str = "yellow", level: str = "info") -> None:
+    def _log_flow_event(
+        self, message: str, color: str = "yellow", level: str = "info"
+    ) -> None:
         """Centralized logging method for flow events.
 
         This method provides a consistent interface for logging flow-related events,

src/crewai/knowledge/embedder/fastembed.py

@@ -47,7 +47,7 @@ class FastEmbed(BaseEmbedder):
             cache_dir=str(cache_dir) if cache_dir else None,
         )
 
-    def embed_chunks(self, chunks: List[str]) -> List[np.ndarray]:
+    def embed_chunks(self, chunks: List[str]) -> np.ndarray:
         """
         Generate embeddings for a list of text chunks
 
@@ -55,12 +55,12 @@ class FastEmbed(BaseEmbedder):
             chunks: List of text chunks to embed
 
         Returns:
-            List of embeddings
+            Array of embeddings
         """
         embeddings = list(self.model.embed(chunks))
-        return embeddings
+        return np.stack(embeddings)
 
-    def embed_texts(self, texts: List[str]) -> List[np.ndarray]:
+    def embed_texts(self, texts: List[str]) -> np.ndarray:
         """
         Generate embeddings for a list of texts
 
@@ -68,10 +68,10 @@ class FastEmbed(BaseEmbedder):
             texts: List of texts to embed
 
         Returns:
-            List of embeddings
+            Array of embeddings
         """
         embeddings = list(self.model.embed(texts))
-        return embeddings
+        return np.stack(embeddings)
 
     def embed_text(self, text: str) -> np.ndarray:
         """

src/crewai/knowledge/knowledge.py

@@ -67,3 +67,9 @@ class Knowledge(BaseModel):
                 source.add()
         except Exception as e:
             raise e
+
+    def reset(self) -> None:
+        if self.storage:
+            self.storage.reset()
+        else:
+            raise ValueError("Storage is not initialized.")

src/crewai/knowledge/storage/knowledge_storage.py

@@ -154,9 +154,15 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 filtered_ids.append(doc_id)
 
             # If we have no metadata at all, set it to None
-            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
-                None if all(m is None for m in filtered_metadata) else filtered_metadata
-            )
+            final_metadata: Optional[List[Dict[str, Union[str, int, float, bool]]]] = None
+            if not all(m is None for m in filtered_metadata):
+                final_metadata = []
+                for m in filtered_metadata:
+                    if m is not None:
+                        filtered_m = {k: v for k, v in m.items() if isinstance(v, (str, int, float, bool))}
+                        final_metadata.append(filtered_m)
+                    else:
+                        final_metadata.append({"empty": True})
 
             self.collection.upsert(
                 documents=filtered_docs,

src/crewai/llm.py

@@ -5,15 +5,17 @@ import sys
 import threading
 import warnings
 from contextlib import contextmanager
-from typing import Any, Dict, List, Optional, Union, cast
+from typing import Any, Dict, List, Literal, Optional, Type, Union, cast
 
 from dotenv import load_dotenv
+from pydantic import BaseModel
 
 with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
     from litellm import Choices, get_supported_openai_params
     from litellm.types.utils import ModelResponse
+    from litellm.utils import supports_response_schema
 
 
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
@@ -128,7 +130,7 @@ class LLM:
         presence_penalty: Optional[float] = None,
         frequency_penalty: Optional[float] = None,
         logit_bias: Optional[Dict[int, float]] = None,
-        response_format: Optional[Dict[str, Any]] = None,
+        response_format: Optional[Type[BaseModel]] = None,
         seed: Optional[int] = None,
         logprobs: Optional[int] = None,
         top_logprobs: Optional[int] = None,
@@ -137,6 +139,7 @@ class LLM:
         api_version: Optional[str] = None,
         api_key: Optional[str] = None,
         callbacks: List[Any] = [],
+        reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None,
         **kwargs,
     ):
         self.model = model
@@ -159,7 +162,9 @@ class LLM:
         self.api_key = api_key
         self.callbacks = callbacks
         self.context_window_size = 0
+        self.reasoning_effort = reasoning_effort
         self.additional_params = kwargs
+        self.is_anthropic = self._is_anthropic_model(model)
 
         litellm.drop_params = True
 
@@ -174,55 +179,88 @@ class LLM:
         self.set_callbacks(callbacks)
         self.set_env_callbacks()
 
+    def _is_anthropic_model(self, model: str) -> bool:
+        """Determine if the model is from Anthropic provider.
+        
+        Args:
+            model: The model identifier string.
+            
+        Returns:
+            bool: True if the model is from Anthropic, False otherwise.
+        """
+        ANTHROPIC_PREFIXES = ('anthropic/', 'claude-', 'claude/')
+        return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
+
     def call(
         self,
         messages: Union[str, List[Dict[str, str]]],
         tools: Optional[List[dict]] = None,
         callbacks: Optional[List[Any]] = None,
         available_functions: Optional[Dict[str, Any]] = None,
-    ) -> str:
-        """
-        High-level llm call method that:
-          1) Accepts either a string or a list of messages
-          2) Converts string input to the required message format
-          3) Calls litellm.completion
-          4) Handles function/tool calls if any
-          5) Returns the final text response or tool result
-
-        Parameters:
-        - messages (Union[str, List[Dict[str, str]]]): The input messages for the LLM.
-          - If a string is provided, it will be converted into a message list with a single entry.
-          - If a list of dictionaries is provided, each dictionary should have 'role' and 'content' keys.
-        - tools (Optional[List[dict]]): A list of tool schemas for function calling.
-        - callbacks (Optional[List[Any]]): A list of callback functions to be executed.
-        - available_functions (Optional[Dict[str, Any]]): A dictionary mapping function names to actual Python functions.
-
+    ) -> Union[str, Any]:
+        """High-level LLM call method.
+        
+        Args:
+            messages: Input messages for the LLM.
+                     Can be a string or list of message dictionaries.
+                     If string, it will be converted to a single user message.
+                     If list, each dict must have 'role' and 'content' keys.
+            tools: Optional list of tool schemas for function calling.
+                  Each tool should define its name, description, and parameters.
+            callbacks: Optional list of callback functions to be executed
+                      during and after the LLM call.
+            available_functions: Optional dict mapping function names to callables
+                               that can be invoked by the LLM.
+        
         Returns:
-        - str: The final text response from the LLM or the result of a tool function call.
-
+            Union[str, Any]: Either a text response from the LLM (str) or
+                           the result of a tool function call (Any).
+        
+        Raises:
+            TypeError: If messages format is invalid
+            ValueError: If response format is not supported
+            LLMContextLengthExceededException: If input exceeds model's context limit
+        
         Examples:
-        ---------
-        # Example 1: Using a string input
-        response = llm.call("Return the name of a random city in the world.")
-        print(response)
-
-        # Example 2: Using a list of messages
-        messages = [{"role": "user", "content": "What is the capital of France?"}]
-        response = llm.call(messages)
-        print(response)
+            # Example 1: Simple string input
+            >>> response = llm.call("Return the name of a random city.")
+            >>> print(response)
+            "Paris"
+            
+            # Example 2: Message list with system and user messages
+            >>> messages = [
+            ...     {"role": "system", "content": "You are a geography expert"},
+            ...     {"role": "user", "content": "What is France's capital?"}
+            ... ]
+            >>> response = llm.call(messages)
+            >>> print(response)
+            "The capital of France is Paris."
         """
+        # Validate parameters before proceeding with the call.
+        self._validate_call_params()
+
         if isinstance(messages, str):
             messages = [{"role": "user", "content": messages}]
 
+        # For O1 models, system messages are not supported.
+        # Convert any system messages into assistant messages.
+        if "o1" in self.model.lower():
+            for message in messages:
+                if message.get("role") == "system":
+                    message["role"] = "assistant"
+
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
                 self.set_callbacks(callbacks)
 
             try:
-                # --- 1) Prepare the parameters for the completion call
+                # --- 1) Format messages according to provider requirements
+                formatted_messages = self._format_messages_for_provider(messages)
+
+                # --- 2) Prepare the parameters for the completion call
                 params = {
                     "model": self.model,
-                    "messages": messages,
+                    "messages": formatted_messages,
                     "timeout": self.timeout,
                     "temperature": self.temperature,
                     "top_p": self.top_p,
@@ -242,6 +280,7 @@ class LLM:
                     "api_key": self.api_key,
                     "stream": False,
                     "tools": tools,
+                    "reasoning_effort": self.reasoning_effort,
                     **self.additional_params,
                 }
 
@@ -309,6 +348,68 @@ class LLM:
                     logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
+    def _format_messages_for_provider(self, messages: List[Dict[str, str]]) -> List[Dict[str, str]]:
+        """Format messages according to provider requirements.
+        
+        Args:
+            messages: List of message dictionaries with 'role' and 'content' keys.
+                     Can be empty or None.
+        
+        Returns:
+            List of formatted messages according to provider requirements.
+            For Anthropic models, ensures first message has 'user' role.
+        
+        Raises:
+            TypeError: If messages is None or contains invalid message format.
+        """
+        if messages is None:
+            raise TypeError("Messages cannot be None")
+            
+        # Validate message format first
+        for msg in messages:
+            if not isinstance(msg, dict) or "role" not in msg or "content" not in msg:
+                raise TypeError("Invalid message format. Each message must be a dict with 'role' and 'content' keys")
+            
+        if not self.is_anthropic:
+            return messages
+            
+        # Anthropic requires messages to start with 'user' role
+        if not messages or messages[0]["role"] == "system":
+            # If first message is system or empty, add a placeholder user message
+            return [{"role": "user", "content": "."}, *messages]
+                
+        return messages
+
+    def _get_custom_llm_provider(self) -> str:
+        """
+        Derives the custom_llm_provider from the model string.
+        - For example, if the model is "openrouter/deepseek/deepseek-chat", returns "openrouter".
+        - If the model is "gemini/gemini-1.5-pro", returns "gemini".
+        - If there is no '/', defaults to "openai".
+        """
+        if "/" in self.model:
+            return self.model.split("/")[0]
+        return "openai"
+
+    def _validate_call_params(self) -> None:
+        """
+        Validate parameters before making a call. Currently this only checks if
+        a response_format is provided and whether the model supports it.
+        The custom_llm_provider is dynamically determined from the model:
+          - E.g., "openrouter/deepseek/deepseek-chat" yields "openrouter"
+          - "gemini/gemini-1.5-pro" yields "gemini"
+          - If no slash is present, "openai" is assumed.
+        """
+        provider = self._get_custom_llm_provider()
+        if self.response_format is not None and not supports_response_schema(
+            model=self.model,
+            custom_llm_provider=provider,
+        ):
+            raise ValueError(
+                f"The model {self.model} does not support response_format for provider '{provider}'. "
+                "Please remove response_format or use a supported model."
+            )
+
     def supports_function_calling(self) -> bool:
         try:
             params = get_supported_openai_params(model=self.model)

src/crewai/memory/entity/entity_memory.py

@@ -1,3 +1,7 @@
+from typing import Optional
+
+from pydantic import PrivateAttr
+
 from crewai.memory.entity.entity_memory_item import EntityMemoryItem
 from crewai.memory.memory import Memory
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -10,13 +14,15 @@ class EntityMemory(Memory):
     Inherits from the Memory class.
     """
 
-    def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
-            self.memory_provider = crew.memory_config.get("provider")
-        else:
-            self.memory_provider = None
+    _memory_provider: Optional[str] = PrivateAttr()
 
-        if self.memory_provider == "mem0":
+    def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
+        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
+            memory_provider = crew.memory_config.get("provider")
+        else:
+            memory_provider = None
+
+        if memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
             except ImportError:
@@ -36,11 +42,13 @@ class EntityMemory(Memory):
                     path=path,
                 )
             )
-        super().__init__(storage)
+
+        super().__init__(storage=storage)
+        self._memory_provider = memory_provider
 
     def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         """Saves an entity item into the SQLite storage."""
-        if self.memory_provider == "mem0":
+        if self._memory_provider == "mem0":
             data = f"""
             Remember details about the following entity:
             Name: {item.name}

src/crewai/memory/long_term/long_term_memory.py

@@ -17,7 +17,7 @@ class LongTermMemory(Memory):
     def __init__(self, storage=None, path=None):
         if not storage:
             storage = LTMSQLiteStorage(db_path=path) if path else LTMSQLiteStorage()
-        super().__init__(storage)
+        super().__init__(storage=storage)
 
     def save(self, item: LongTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         metadata = item.metadata

src/crewai/memory/memory.py

@@ -1,15 +1,19 @@
 from typing import Any, Dict, List, Optional
 
-from crewai.memory.storage.rag_storage import RAGStorage
+from pydantic import BaseModel
 
 
-class Memory:
+class Memory(BaseModel):
     """
     Base class for memory, now supporting agent tags and generic metadata.
     """
 
-    def __init__(self, storage: RAGStorage):
-        self.storage = storage
+    embedder_config: Optional[Dict[str, Any]] = None
+
+    storage: Any
+
+    def __init__(self, storage: Any, **data: Any):
+        super().__init__(storage=storage, **data)
 
     def save(
         self,

src/crewai/memory/short_term/short_term_memory.py

@@ -1,5 +1,7 @@
 from typing import Any, Dict, Optional
 
+from pydantic import PrivateAttr
+
 from crewai.memory.memory import Memory
 from crewai.memory.short_term.short_term_memory_item import ShortTermMemoryItem
 from crewai.memory.storage.rag_storage import RAGStorage
@@ -14,13 +16,15 @@ class ShortTermMemory(Memory):
     MemoryItem instances.
     """
 
-    def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
-            self.memory_provider = crew.memory_config.get("provider")
-        else:
-            self.memory_provider = None
+    _memory_provider: Optional[str] = PrivateAttr()
 
-        if self.memory_provider == "mem0":
+    def __init__(self, crew=None, embedder_config=None, storage=None, path=None):
+        if crew and hasattr(crew, "memory_config") and crew.memory_config is not None:
+            memory_provider = crew.memory_config.get("provider")
+        else:
+            memory_provider = None
+
+        if memory_provider == "mem0":
             try:
                 from crewai.memory.storage.mem0_storage import Mem0Storage
             except ImportError:
@@ -39,7 +43,8 @@ class ShortTermMemory(Memory):
                     path=path,
                 )
             )
-        super().__init__(storage)
+        super().__init__(storage=storage)
+        self._memory_provider = memory_provider
 
     def save(
         self,
@@ -48,7 +53,7 @@ class ShortTermMemory(Memory):
         agent: Optional[str] = None,
     ) -> None:
         item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
-        if self.memory_provider == "mem0":
+        if self._memory_provider == "mem0":
             item.data = f"Remember the following insights from Agent run: {item.data}"
 
         super().save(value=item.data, metadata=item.metadata, agent=item.agent)

src/crewai/memory/storage/base_rag_storage.py

@@ -13,7 +13,7 @@ class BaseRAGStorage(ABC):
         self,
         type: str,
         allow_reset: bool = True,
-        embedder_config: Optional[Any] = None,
+        embedder_config: Optional[Dict[str, Any]] = None,
         crew: Any = None,
     ):
         self.type = type

src/crewai/memory/storage/rag_storage.py

@@ -6,12 +6,17 @@ import shutil
 import uuid
 from typing import Any, Dict, List, Optional
 
-from chromadb.api import ClientAPI
+from chromadb.api import ClientAPI, Collection
+from chromadb.api.types import Documents, Embeddings, Metadatas
 
 from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities import EmbeddingConfigurator
 from crewai.utilities.constants import MAX_FILE_NAME_LENGTH
 from crewai.utilities.paths import db_storage_path
+from crewai.utilities.exceptions.embedding_exceptions import (
+    EmbeddingConfigurationError,
+    EmbeddingInitializationError
+)
 
 
 @contextlib.contextmanager
@@ -32,15 +37,24 @@ def suppress_logging(
 
 
 class RAGStorage(BaseRAGStorage):
-    """
-    Extends Storage to handle embeddings for memory entries, improving
-    search efficiency.
+    """RAG-based Storage implementation using ChromaDB for vector storage and retrieval.
+
+    This class extends BaseRAGStorage to handle embeddings for memory entries,
+    improving search efficiency through vector similarity.
+
+    Attributes:
+        app: ChromaDB client instance
+        collection: ChromaDB collection for storing embeddings
+        type: Type of memory storage
+        allow_reset: Whether memory reset is allowed
+        path: Custom storage path for the database
     """
 
     app: ClientAPI | None = None
+    collection: Any = None
 
     def __init__(
-        self, type, allow_reset=True, embedder_config=None, crew=None, path=None
+        self, type: str, allow_reset: bool = True, embedder_config: Dict[str, Any] | None = None, crew: Any = None, path: str | None = None
     ):
         super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
@@ -50,7 +64,6 @@ class RAGStorage(BaseRAGStorage):
         self.storage_file_name = self._build_storage_file_name(type, agents)
 
         self.type = type
-
         self.allow_reset = allow_reset
         self.path = path
         self._initialize_app()
@@ -59,26 +72,36 @@ class RAGStorage(BaseRAGStorage):
         configurator = EmbeddingConfigurator()
         self.embedder_config = configurator.configure_embedder(self.embedder_config)
 
-    def _initialize_app(self):
+    def _initialize_app(self) -> None:
+        """Initialize the ChromaDB client and collection.
+
+        Raises:
+            RuntimeError: If ChromaDB client initialization fails
+            EmbeddingConfigurationError: If embedding configuration is invalid
+            EmbeddingInitializationError: If embedding function fails to initialize
+        """
         import chromadb
         from chromadb.config import Settings
 
         self._set_embedder_config()
-        chroma_client = chromadb.PersistentClient(
-            path=self.path if self.path else self.storage_file_name,
-            settings=Settings(allow_reset=self.allow_reset),
-        )
-
-        self.app = chroma_client
-
         try:
-            self.collection = self.app.get_collection(
-                name=self.type, embedding_function=self.embedder_config
-            )
-        except Exception:
-            self.collection = self.app.create_collection(
-                name=self.type, embedding_function=self.embedder_config
+            self.app = chromadb.PersistentClient(
+                path=self.path if self.path else self.storage_file_name,
+                settings=Settings(allow_reset=self.allow_reset),
             )
+            if not self.app:
+                raise RuntimeError("Failed to initialize ChromaDB client")
+
+            try:
+                self.collection = self.app.get_collection(
+                    name=self.type, embedding_function=self.embedder_config
+                )
+            except Exception:
+                self.collection = self.app.create_collection(
+                    name=self.type, embedding_function=self.embedder_config
+                )
+        except Exception as e:
+            raise RuntimeError(f"Failed to initialize ChromaDB: {str(e)}")
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -101,12 +124,21 @@ class RAGStorage(BaseRAGStorage):
         return f"{base_path}/{file_name}"
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+        """Save a value with metadata to the memory storage.
+
+        Args:
+            value: The text content to store
+            metadata: Additional metadata for the stored content
+
+        Raises:
+            EmbeddingInitializationError: If embedding generation fails
+        """
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
         try:
             self._generate_embedding(value, metadata)
         except Exception as e:
-            logging.error(f"Error during {self.type} save: {str(e)}")
+            raise EmbeddingInitializationError(self.type, str(e))
 
     def search(
         self,
@@ -114,7 +146,18 @@ class RAGStorage(BaseRAGStorage):
         limit: int = 3,
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
-    ) -> List[Any]:
+    ) -> List[Dict[str, Any]]:
+        """Search for similar content in memory.
+
+        Args:
+            query: The search query text
+            limit: Maximum number of results to return
+            filter: Optional filter criteria
+            score_threshold: Minimum similarity score threshold
+
+        Returns:
+            List of matching results with metadata and scores
+        """
         if not hasattr(self, "app"):
             self._initialize_app()
 
@@ -138,37 +181,50 @@ class RAGStorage(BaseRAGStorage):
             logging.error(f"Error during {self.type} search: {str(e)}")
             return []
 
-    def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> None:  # type: ignore
+    def _generate_embedding(self, text: str, metadata: Optional[Dict[str, Any]] = None) -> Any:
+        """Generate and store embeddings for the given text.
+
+        Args:
+            text: The text to generate embeddings for
+            metadata: Optional additional metadata to store with the embeddings
+
+        Returns:
+            Any: The generated embedding or None if only storing
+        """
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
 
-        self.collection.add(
-            documents=[text],
-            metadatas=[metadata or {}],
-            ids=[str(uuid.uuid4())],
-        )
+        try:
+            self.collection.add(
+                documents=[text],
+                metadatas=[metadata or {}],
+                ids=[str(uuid.uuid4())],
+            )
+            return None
+        except Exception as e:
+            raise EmbeddingInitializationError(self.type, f"Failed to generate embedding: {str(e)}")
 
     def reset(self) -> None:
+        """Reset the memory storage by clearing the database and removing files.
+
+        Raises:
+            RuntimeError: If memory reset fails and allow_reset is False
+            EmbeddingConfigurationError: If embedding configuration is invalid during reinitialization
+        """
         try:
             if self.app:
                 self.app.reset()
-                shutil.rmtree(f"{db_storage_path()}/{self.type}")
+                storage_path = self.path if self.path else db_storage_path()
+                db_dir = os.path.join(storage_path, self.type)
+                if os.path.exists(db_dir):
+                    shutil.rmtree(db_dir)
                 self.app = None
                 self.collection = None
         except Exception as e:
             if "attempt to write a readonly database" in str(e):
-                # Ignore this specific error
+                # Ignore this specific error as it's expected in some environments
                 pass
             else:
-                raise Exception(
-                    f"An error occurred while resetting the {self.type} memory: {e}"
-                )
-
-    def _create_default_embedding_function(self):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
-
-        return OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-        )
+                if not self.allow_reset:
+                    raise RuntimeError(f"Failed to reset {self.type} memory: {str(e)}")
+                logging.error(f"Error during {self.type} memory reset: {str(e)}")

src/crewai/task.py

@@ -9,11 +9,13 @@ from copy import copy
 from hashlib import md5
 from pathlib import Path
 from typing import (
+    AbstractSet,
     Any,
     Callable,
     ClassVar,
     Dict,
     List,
+    Mapping,
     Optional,
     Set,
     Tuple,
@@ -109,7 +111,7 @@ class Task(BaseModel):
         description="Task output, it's final result after being executed", default=None
     )
     tools: Optional[List[BaseTool]] = Field(
-        default_factory=list,
+        default_factory=lambda: [],
         description="Tools the agent is limited to use for this task.",
     )
     id: UUID4 = Field(
@@ -125,7 +127,7 @@ class Task(BaseModel):
         description="A converter class used to export structured output",
         default=None,
     )
-    processed_by_agents: Set[str] = Field(default_factory=set)
+    processed_by_agents: Set[str] = Field(default_factory=lambda: set())
     guardrail: Optional[Callable[[TaskOutput], Tuple[bool, Any]]] = Field(
         default=None,
         description="Function to validate task output before proceeding to next task",
@@ -423,6 +425,10 @@ class Task(BaseModel):
         if self.callback:
             self.callback(self.output)
 
+        crew = self.agent.crew  # type: ignore[union-attr]
+        if crew and crew.task_callback and crew.task_callback != self.callback:
+            crew.task_callback(self.output)
+
         if self._execution_span:
             self._telemetry.task_ended(self._execution_span, self, agent.crew)
             self._execution_span = None
@@ -602,37 +608,56 @@ class Task(BaseModel):
         self.delegations += 1
 
     def copy(
+        self,
+        *,
+        include: Optional[AbstractSet[int] | AbstractSet[str] | Mapping[int, Any] | Mapping[str, Any]] = None,
+        exclude: Optional[AbstractSet[int] | AbstractSet[str] | Mapping[int, Any] | Mapping[str, Any]] = None,
+        update: Optional[Dict[str, Any]] = None,
+        deep: bool = False,
+    ) -> "Task":
+        """Create a copy of the Task."""
+        exclude_set = {"id", "agent", "context", "tools"}
+        if exclude:
+            if isinstance(exclude, (AbstractSet, set)):
+                exclude_set.update(str(x) for x in exclude)
+            elif isinstance(exclude, Mapping):
+                exclude_set.update(str(x) for x in exclude.keys())
+        
+        copied_task = super().copy(
+            include=include,
+            exclude=exclude_set,
+            update=update,
+            deep=deep,
+        )
+        
+        copied_task.id = uuid.uuid4()
+        copied_task.agent = None
+        copied_task.context = None
+        copied_task.tools = []
+        
+        return copied_task
+
+    def copy_with_agents(
         self, agents: List["BaseAgent"], task_mapping: Dict[str, "Task"]
     ) -> "Task":
-        """Create a deep copy of the Task."""
-        exclude = {
-            "id",
-            "agent",
-            "context",
-            "tools",
-        }
-
-        copied_data = self.model_dump(exclude=exclude)
-        copied_data = {k: v for k, v in copied_data.items() if v is not None}
-
-        cloned_context = (
-            [task_mapping[context_task.key] for context_task in self.context]
-            if self.context
-            else None
-        )
+        """Create a copy of the Task with agent references."""
+        copied_task = self.copy()
 
         def get_agent_by_role(role: str) -> Union["BaseAgent", None]:
             return next((agent for agent in agents if agent.role == role), None)
 
-        cloned_agent = get_agent_by_role(self.agent.role) if self.agent else None
-        cloned_tools = copy(self.tools) if self.tools else []
+        if self.agent:
+            copied_task.agent = get_agent_by_role(self.agent.role)
 
-        copied_task = Task(
-            **copied_data,
-            context=cloned_context,
-            agent=cloned_agent,
-            tools=cloned_tools,
-        )
+        if self.context:
+            copied_task.context = [
+                task_mapping[context_task.key]
+                for context_task in self.context
+                if context_task.key in task_mapping
+            ]
+
+        if self.tools:
+            copied_task.tools = copy(self.tools)
 
         return copied_task
 
@@ -670,19 +695,32 @@ class Task(BaseModel):
             return OutputFormat.PYDANTIC
         return OutputFormat.RAW
 
-    def _save_file(self, result: Any) -> None:
+    def _save_file(self, result: Union[Dict, str, Any]) -> None:
         """Save task output to a file.
 
+        Note:
+            For cross-platform file writing, especially on Windows, consider using FileWriterTool
+            from the crewai_tools package:
+                pip install 'crewai[tools]'
+                from crewai_tools import FileWriterTool
+
         Args:
             result: The result to save to the file. Can be a dict or any stringifiable object.
 
         Raises:
             ValueError: If output_file is not set
-            RuntimeError: If there is an error writing to the file
+            RuntimeError: If there is an error writing to the file. For cross-platform
+                compatibility, especially on Windows, use FileWriterTool from crewai_tools
+                package.
         """
         if self.output_file is None:
             raise ValueError("output_file is not set.")
 
+        FILEWRITER_RECOMMENDATION = (
+            "For cross-platform file writing, especially on Windows, "
+            "use FileWriterTool from crewai_tools package."
+        )
+
         try:
             resolved_path = Path(self.output_file).expanduser().resolve()
             directory = resolved_path.parent
@@ -698,7 +736,12 @@ class Task(BaseModel):
                 else:
                     file.write(str(result))
         except (OSError, IOError) as e:
-            raise RuntimeError(f"Failed to save output file: {e}")
+            raise RuntimeError(
+                "\n".join([
+                    f"Failed to save output file: {e}",
+                    FILEWRITER_RECOMMENDATION
+                ])
+            )
         return None
 
     def __repr__(self):

src/crewai/tasks/task_output.py

@@ -1,7 +1,9 @@
 import json
-from typing import Any, Dict, Optional
+from typing import Any, Callable, Dict, Optional
 
 from pydantic import BaseModel, Field, model_validator
+from pydantic.main import IncEx
+from typing_extensions import Literal
 
 from crewai.tasks.output_format import OutputFormat
 
@@ -34,8 +36,8 @@ class TaskOutput(BaseModel):
         self.summary = f"{excerpt}..."
         return self
 
-    @property
-    def json(self) -> Optional[str]:
+    def model_json(self) -> str:
+        """Get the JSON representation of the output."""
         if self.output_format != OutputFormat.JSON:
             raise ValueError(
                 """
@@ -44,8 +46,37 @@ class TaskOutput(BaseModel):
                 please make sure to set the output_json property for the task
                 """
             )
+        return json.dumps(self.json_dict) if self.json_dict else "{}"
 
-        return json.dumps(self.json_dict)
+    def model_dump_json(
+        self,
+        *,
+        indent: Optional[int] = None,
+        include: Optional[IncEx] = None,
+        exclude: Optional[IncEx] = None,
+        context: Optional[Any] = None,
+        by_alias: bool = False,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+        round_trip: bool = False,
+        warnings: bool | Literal["none", "warn", "error"] = False,
+        serialize_as_any: bool = False,
+    ) -> str:
+        """Override model_dump_json to handle custom JSON output."""
+        return super().model_dump_json(
+            indent=indent,
+            include=include,
+            exclude=exclude,
+            context=context,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+            round_trip=round_trip,
+            warnings=warnings,
+            serialize_as_any=serialize_as_any,
+        )
 
     def to_dict(self) -> Dict[str, Any]:
         """Convert json_output and pydantic_output to a dictionary."""

src/crewai/tools/agent_tools/add_image_tool.py

@@ -7,11 +7,11 @@ from crewai.utilities import I18N
 
 i18n = I18N()
 
+
 class AddImageToolSchema(BaseModel):
     image_url: str = Field(..., description="The URL or path of the image to add")
     action: Optional[str] = Field(
-        default=None,
-        description="Optional context or question about the image"
+        default=None, description="Optional context or question about the image"
     )
 
 
@@ -36,10 +36,7 @@ class AddImageTool(BaseTool):
                 "image_url": {
                     "url": image_url,
                 },
-            }
+            },
         ]
 
-        return {
-            "role": "user",
-            "content": content
-        }
+        return {"role": "user", "content": content}

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -82,12 +82,12 @@ class BaseAgentTool(BaseTool):
             available_agents = [agent.role for agent in self.agents]
             logger.debug(f"Available agents: {available_agents}")
 
-            agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
+            matching_agents = [
                 available_agent
                 for available_agent in self.agents
                 if self.sanitize_agent_name(available_agent.role) == sanitized_name
             ]
-            logger.debug(f"Found {len(agent)} matching agents for role '{sanitized_name}'")
+            logger.debug(f"Found {len(matching_agents)} matching agents for role '{sanitized_name}'")
         except (AttributeError, ValueError) as e:
             # Handle specific exceptions that might occur during role name processing
             return self.i18n.errors("agent_tool_unexisting_coworker").format(
@@ -97,7 +97,7 @@ class BaseAgentTool(BaseTool):
                 error=str(e)
             )
 
-        if not agent:
+        if not matching_agents:
             # No matching agent found after sanitization
             return self.i18n.errors("agent_tool_unexisting_coworker").format(
                 coworkers="\n".join(
@@ -106,19 +106,19 @@ class BaseAgentTool(BaseTool):
                 error=f"No agent found with role '{sanitized_name}'"
             )
 
-        agent = agent[0]
+        selected_agent = matching_agents[0]
         try:
             task_with_assigned_agent = Task(
                 description=task,
-                agent=agent,
-                expected_output=agent.i18n.slice("manager_request"),
-                i18n=agent.i18n,
+                agent=selected_agent,
+                expected_output=selected_agent.i18n.slice("manager_request"),
+                i18n=selected_agent.i18n,
             )
-            logger.debug(f"Created task for agent '{self.sanitize_agent_name(agent.role)}': {task}")
-            return agent.execute_task(task_with_assigned_agent, context)
+            logger.debug(f"Created task for agent '{self.sanitize_agent_name(selected_agent.role)}': {task}")
+            return selected_agent.execute_task(task_with_assigned_agent, context)
         except Exception as e:
             # Handle task creation or execution errors
             return self.i18n.errors("agent_tool_execution_error").format(
-                agent_role=self.sanitize_agent_name(agent.role),
+                agent_role=self.sanitize_agent_name(selected_agent.role),
                 error=str(e)
             )

src/crewai/tools/base_tool.py

@@ -1,40 +1,36 @@
-import warnings
 from abc import ABC, abstractmethod
 from inspect import signature
-from typing import Any, Callable, Type, get_args, get_origin
+from typing import Any, Callable, Dict, Optional, Type, Tuple, get_args, get_origin
 
-from pydantic import (
-    BaseModel,
-    ConfigDict,
-    Field,
-    PydanticDeprecatedSince20,
-    create_model,
-    validator,
-)
+from pydantic import BaseModel, ConfigDict, Field, create_model, validator
+from pydantic.fields import FieldInfo
 from pydantic import BaseModel as PydanticBaseModel
 
 from crewai.tools.structured_tool import CrewStructuredTool
 
-# Ignore all "PydanticDeprecatedSince20" warnings globally
-warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
-
+def _create_model_fields(fields: Dict[str, Tuple[Any, FieldInfo]]) -> Dict[str, Any]:
+    """Helper function to create model fields with proper type hints."""
+    return {name: (annotation, field) for name, (annotation, field) in fields.items()}
 
 class BaseTool(BaseModel, ABC):
+    """Base class for all tools."""
+
     class _ArgsSchemaPlaceholder(PydanticBaseModel):
         pass
 
-    model_config = ConfigDict()
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+    func: Optional[Callable] = None
 
     name: str
     """The unique name of the tool that clearly communicates its purpose."""
     description: str
     """Used to tell the model how/when/why to use the tool."""
-    args_schema: Type[PydanticBaseModel] = Field(default_factory=_ArgsSchemaPlaceholder)
+    args_schema: Type[PydanticBaseModel] = Field(default=_ArgsSchemaPlaceholder)
     """The schema for the arguments that the tool accepts."""
     description_updated: bool = False
     """Flag to check if the description has been updated."""
     cache_function: Callable = lambda _args=None, _result=None: True
-    """Function that will be used to determine if the tool should be cached, should return a boolean. If None, the tool will be cached."""
+    """Function that will be used to determine if the tool should be cached."""
     result_as_answer: bool = False
     """Flag to check if the tool should be the final agent answer."""
 
@@ -57,7 +53,6 @@ class BaseTool(BaseModel, ABC):
 
     def model_post_init(self, __context: Any) -> None:
         self._generate_description()
-
         super().model_post_init(__context)
 
     def run(
@@ -87,50 +82,7 @@ class BaseTool(BaseModel, ABC):
             result_as_answer=self.result_as_answer,
         )
 
-    @classmethod
-    def from_langchain(cls, tool: Any) -> "BaseTool":
-        """Create a Tool instance from a CrewStructuredTool.
-
-        This method takes a CrewStructuredTool object and converts it into a
-        Tool instance. It ensures that the provided tool has a callable 'func'
-        attribute and infers the argument schema if not explicitly provided.
-        """
-        if not hasattr(tool, "func") or not callable(tool.func):
-            raise ValueError("The provided tool must have a callable 'func' attribute.")
-
-        args_schema = getattr(tool, "args_schema", None)
-
-        if args_schema is None:
-            # Infer args_schema from the function signature if not provided
-            func_signature = signature(tool.func)
-            annotations = func_signature.parameters
-            args_fields = {}
-            for name, param in annotations.items():
-                if name != "self":
-                    param_annotation = (
-                        param.annotation if param.annotation != param.empty else Any
-                    )
-                    field_info = Field(
-                        default=...,
-                        description="",
-                    )
-                    args_fields[name] = (param_annotation, field_info)
-            if args_fields:
-                args_schema = create_model(f"{tool.name}Input", **args_fields)
-            else:
-                # Create a default schema with no fields if no parameters are found
-                args_schema = create_model(
-                    f"{tool.name}Input", __base__=PydanticBaseModel
-                )
-
-        return cls(
-            name=getattr(tool, "name", "Unnamed Tool"),
-            description=getattr(tool, "description", ""),
-            func=tool.func,
-            args_schema=args_schema,
-        )
-
-    def _set_args_schema(self):
+    def _set_args_schema(self) -> None:
         if self.args_schema is None:
             class_name = f"{self.__class__.__name__}Schema"
             self.args_schema = type(
@@ -145,7 +97,7 @@ class BaseTool(BaseModel, ABC):
                 },
             )
 
-    def _generate_description(self):
+    def _generate_description(self) -> None:
         args_schema = {
             name: {
                 "description": field.description,
@@ -179,79 +131,25 @@ class BaseTool(BaseModel, ABC):
 
 
 class Tool(BaseTool):
-    """The function that will be executed when the tool is called."""
+    """Tool class that wraps a function."""
 
     func: Callable
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    def __init__(self, **kwargs):
+        if "func" not in kwargs:
+            raise ValueError("Tool requires a 'func' argument")
+        super().__init__(**kwargs)
 
     def _run(self, *args: Any, **kwargs: Any) -> Any:
         return self.func(*args, **kwargs)
 
-    @classmethod
-    def from_langchain(cls, tool: Any) -> "Tool":
-        """Create a Tool instance from a CrewStructuredTool.
 
-        This method takes a CrewStructuredTool object and converts it into a
-        Tool instance. It ensures that the provided tool has a callable 'func'
-        attribute and infers the argument schema if not explicitly provided.
-
-        Args:
-            tool (Any): The CrewStructuredTool object to be converted.
-
-        Returns:
-            Tool: A new Tool instance created from the provided CrewStructuredTool.
-
-        Raises:
-            ValueError: If the provided tool does not have a callable 'func' attribute.
-        """
-        if not hasattr(tool, "func") or not callable(tool.func):
-            raise ValueError("The provided tool must have a callable 'func' attribute.")
-
-        args_schema = getattr(tool, "args_schema", None)
-
-        if args_schema is None:
-            # Infer args_schema from the function signature if not provided
-            func_signature = signature(tool.func)
-            annotations = func_signature.parameters
-            args_fields = {}
-            for name, param in annotations.items():
-                if name != "self":
-                    param_annotation = (
-                        param.annotation if param.annotation != param.empty else Any
-                    )
-                    field_info = Field(
-                        default=...,
-                        description="",
-                    )
-                    args_fields[name] = (param_annotation, field_info)
-            if args_fields:
-                args_schema = create_model(f"{tool.name}Input", **args_fields)
-            else:
-                # Create a default schema with no fields if no parameters are found
-                args_schema = create_model(
-                    f"{tool.name}Input", __base__=PydanticBaseModel
-                )
-
-        return cls(
-            name=getattr(tool, "name", "Unnamed Tool"),
-            description=getattr(tool, "description", ""),
-            func=tool.func,
-            args_schema=args_schema,
-        )
-
-
-def to_langchain(
-    tools: list[BaseTool | CrewStructuredTool],
-) -> list[CrewStructuredTool]:
-    return [t.to_structured_tool() if isinstance(t, BaseTool) else t for t in tools]
-
-
-def tool(*args):
-    """
-    Decorator to create a tool from a function.
-    """
+def tool(*args: Any) -> Any:
+    """Decorator to create a tool from a function."""
 
     def _make_with_name(tool_name: str) -> Callable:
-        def _make_tool(f: Callable) -> BaseTool:
+        def _make_tool(f: Callable) -> Tool:
             if f.__doc__ is None:
                 raise ValueError("Function must have a docstring")
             if f.__annotations__ is None:

src/crewai/tools/structured_tool.py

@@ -2,9 +2,14 @@ from __future__ import annotations
 
 import inspect
 import textwrap
-from typing import Any, Callable, Optional, Union, get_type_hints
+from typing import Any, Callable, Dict, Optional, Tuple, Union, get_type_hints
 
-from pydantic import BaseModel, Field, create_model
+from pydantic import BaseModel, ConfigDict, Field, create_model
+from pydantic.fields import FieldInfo
+
+def _create_model_fields(fields: Dict[str, Tuple[Any, FieldInfo]]) -> Dict[str, Any]:
+    """Helper function to create model fields with proper type hints."""
+    return {name: (annotation, field) for name, (annotation, field) in fields.items()}
 
 from crewai.utilities.logger import Logger
 
@@ -142,7 +147,8 @@ class CrewStructuredTool:
 
         # Create model
         schema_name = f"{name.title()}Schema"
-        return create_model(schema_name, **fields)
+        model_fields = _create_model_fields(fields)
+        return create_model(schema_name, __base__=BaseModel, **model_fields)
 
     def _validate_function_signature(self) -> None:
         """Validate that the function signature matches the args schema."""

src/crewai/translations/en.json

@@ -15,7 +15,7 @@
     "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfies the expected criteria, use the EXACT format below:\n\n```\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n```",
     "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nHere is the expected format I must follow:\n\n```\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n```\n This Thought/Action/Action Input/Result process can repeat N times. Once I know the final answer, I must return the following format:\n\n```\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described\n\n```",
     "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
-    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
+    "expected_output": "\nThis is the expected criteria for your final answer: {expected_output}\nyou MUST return the actual complete content as the final answer, not a summary.",
     "human_feedback": "You got human feedback on your work, re-evaluate it and give a new Final Answer when ready.\n {human_feedback}",
     "getting_input": "This is the agent's final answer: {final_answer}\n\n",
     "summarizer_system_message": "You are a helpful assistant that summarizes text.",

src/crewai/utilities/constants.py

@@ -4,3 +4,7 @@ DEFAULT_SCORE_THRESHOLD = 0.35
 KNOWLEDGE_DIRECTORY = "knowledge"
 MAX_LLM_RETRY = 3
 MAX_FILE_NAME_LENGTH = 255
+
+# Default embedding configuration
+DEFAULT_EMBEDDING_PROVIDER = "openai"
+DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small"

src/crewai/utilities/embedding_configurator.py

@@ -1,9 +1,15 @@
 import os
-from typing import Any, Dict, cast
+from typing import Any, Dict, List, Optional, cast
 
 from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api.types import validate_embedding_function
 
+from crewai.utilities.exceptions.embedding_exceptions import (
+    EmbeddingConfigurationError,
+    EmbeddingProviderError,
+    EmbeddingInitializationError
+)
+
 
 class EmbeddingConfigurator:
     def __init__(self):
@@ -14,7 +20,6 @@ class EmbeddingConfigurator:
             "vertexai": self._configure_vertexai,
             "google": self._configure_google,
             "cohere": self._configure_cohere,
-            "voyageai": self._configure_voyageai,
             "bedrock": self._configure_bedrock,
             "huggingface": self._configure_huggingface,
             "watson": self._configure_watson,
@@ -22,7 +27,7 @@ class EmbeddingConfigurator:
 
     def configure_embedder(
         self,
-        embedder_config: Dict[str, Any] | None = None,
+        embedder_config: Optional[Dict[str, Any]] = None,
     ) -> EmbeddingFunction:
         """Configures and returns an embedding function based on the provided config."""
         if embedder_config is None:
@@ -37,41 +42,47 @@ class EmbeddingConfigurator:
                 validate_embedding_function(provider)
                 return provider
             except Exception as e:
-                raise ValueError(f"Invalid custom embedding function: {str(e)}")
+                raise EmbeddingConfigurationError(f"Invalid custom embedding function: {str(e)}")
 
-        if provider not in self.embedding_functions:
-            raise Exception(
-                f"Unsupported embedding provider: {provider}, supported providers: {list(self.embedding_functions.keys())}"
-            )
-        return self.embedding_functions[provider](config, model_name)
+        if not provider or provider not in self.embedding_functions:
+            raise EmbeddingProviderError(str(provider), list(self.embedding_functions.keys()))
+
+        try:
+            return self.embedding_functions[str(provider)](config, model_name)
+        except Exception as e:
+            raise EmbeddingInitializationError(str(provider), str(e))
 
     @staticmethod
-    def _create_default_embedding_function():
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
-
-        return OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-        )
+    def _create_default_embedding_function() -> EmbeddingFunction:
+        from crewai.utilities.constants import DEFAULT_EMBEDDING_PROVIDER, DEFAULT_EMBEDDING_MODEL
+        
+        provider = os.getenv("CREWAI_EMBEDDING_PROVIDER", DEFAULT_EMBEDDING_PROVIDER)
+        model = os.getenv("CREWAI_EMBEDDING_MODEL", DEFAULT_EMBEDDING_MODEL)
+        
+        if provider == "openai":
+            api_key = os.getenv("OPENAI_API_KEY")
+            if not api_key:
+                raise EmbeddingConfigurationError("OpenAI API key is required but not provided")
+            from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
+            return OpenAIEmbeddingFunction(api_key=api_key, model_name=model)
+        elif provider == "ollama":
+            from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
+            url = os.getenv("CREWAI_OLLAMA_URL", "http://localhost:11434/api/embeddings")
+            return OllamaEmbeddingFunction(url=url, model_name=model)
+        else:
+            raise EmbeddingProviderError(provider, ["openai", "ollama"])
 
     @staticmethod
-    def _configure_openai(config, model_name):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
-
+    def _configure_openai(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
         return OpenAIEmbeddingFunction(
             api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
             model_name=model_name,
         )
 
     @staticmethod
-    def _configure_azure(config, model_name):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
-
+    def _configure_azure(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.openai_embedding_function import OpenAIEmbeddingFunction
         return OpenAIEmbeddingFunction(
             api_key=config.get("api_key"),
             api_base=config.get("api_base"),
@@ -81,90 +92,62 @@ class EmbeddingConfigurator:
         )
 
     @staticmethod
-    def _configure_ollama(config, model_name):
-        from chromadb.utils.embedding_functions.ollama_embedding_function import (
-            OllamaEmbeddingFunction,
-        )
-
+    def _configure_ollama(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.ollama_embedding_function import OllamaEmbeddingFunction
         return OllamaEmbeddingFunction(
             url=config.get("url", "http://localhost:11434/api/embeddings"),
             model_name=model_name,
         )
 
     @staticmethod
-    def _configure_vertexai(config, model_name):
-        from chromadb.utils.embedding_functions.google_embedding_function import (
-            GoogleVertexEmbeddingFunction,
-        )
-
+    def _configure_vertexai(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.google_embedding_function import GoogleVertexEmbeddingFunction
         return GoogleVertexEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_google(config, model_name):
-        from chromadb.utils.embedding_functions.google_embedding_function import (
-            GoogleGenerativeAiEmbeddingFunction,
-        )
-
+    def _configure_google(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.google_embedding_function import GoogleGenerativeAiEmbeddingFunction
         return GoogleGenerativeAiEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_cohere(config, model_name):
-        from chromadb.utils.embedding_functions.cohere_embedding_function import (
-            CohereEmbeddingFunction,
-        )
-
+    def _configure_cohere(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.cohere_embedding_function import CohereEmbeddingFunction
         return CohereEmbeddingFunction(
             model_name=model_name,
             api_key=config.get("api_key"),
         )
 
     @staticmethod
-    def _configure_voyageai(config, model_name):
-        from chromadb.utils.embedding_functions.voyageai_embedding_function import (
-            VoyageAIEmbeddingFunction,
-        )
-
-        return VoyageAIEmbeddingFunction(
-            model_name=model_name,
-            api_key=config.get("api_key"),
-        )
-
-    @staticmethod
-    def _configure_bedrock(config, model_name):
-        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-            AmazonBedrockEmbeddingFunction,
-        )
-
+    def _configure_bedrock(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import AmazonBedrockEmbeddingFunction
         return AmazonBedrockEmbeddingFunction(
             session=config.get("session"),
         )
 
     @staticmethod
-    def _configure_huggingface(config, model_name):
-        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-            HuggingFaceEmbeddingServer,
-        )
-
+    def _configure_huggingface(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
+        from chromadb.utils.embedding_functions.huggingface_embedding_function import HuggingFaceEmbeddingServer
         return HuggingFaceEmbeddingServer(
             url=config.get("api_url"),
         )
 
     @staticmethod
-    def _configure_watson(config, model_name):
+    def _configure_watson(config: Dict[str, Any], model_name: str) -> EmbeddingFunction:
         try:
             import ibm_watsonx_ai.foundation_models as watson_models
             from ibm_watsonx_ai import Credentials
             from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
         except ImportError as e:
-            raise ImportError(
-                "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
-            ) from e
+            raise EmbeddingConfigurationError(
+                "IBM Watson dependencies are not installed. Please install them to use Watson embedding.",
+                provider="watson"
+            )
 
         class WatsonEmbeddingFunction(EmbeddingFunction):
             def __call__(self, input: Documents) -> Embeddings:
@@ -189,7 +172,6 @@ class EmbeddingConfigurator:
                     embeddings = embedding.embed_documents(input)
                     return cast(Embeddings, embeddings)
                 except Exception as e:
-                    print("Error during Watson embedding:", e)
-                    raise e
+                    raise EmbeddingInitializationError("watson", str(e))
 
         return WatsonEmbeddingFunction()

src/crewai/utilities/exceptions/embedding_exceptions.py

@@ -0,0 +1,20 @@
+from typing import List, Optional
+
+
+class EmbeddingConfigurationError(Exception):
+    def __init__(self, message: str, provider: Optional[str] = None):
+        self.message = message
+        self.provider = provider
+        super().__init__(self.message)
+
+
+class EmbeddingProviderError(EmbeddingConfigurationError):
+    def __init__(self, provider: str, supported_providers: List[str]):
+        message = f"Unsupported embedding provider: {provider}, supported providers: {supported_providers}"
+        super().__init__(message, provider)
+
+
+class EmbeddingInitializationError(EmbeddingConfigurationError):
+    def __init__(self, provider: str, error: str):
+        message = f"Failed to initialize embedding function for provider {provider}: {error}"
+        super().__init__(message, provider)

src/crewai/utilities/file_handler.py

@@ -1,30 +1,64 @@
+import json
 import os
 import pickle
 from datetime import datetime
+from typing import Union
 
 
 class FileHandler:
-    """take care of file operations, currently it only logs messages to a file"""
+    """Handler for file operations supporting both JSON and text-based logging.
+    
+    Args:
+        file_path (Union[bool, str]): Path to the log file or boolean flag
+    """
 
-    def __init__(self, file_path):
-        if isinstance(file_path, bool):
+    def __init__(self, file_path: Union[bool, str]):
+        self._initialize_path(file_path)
+        
+    def _initialize_path(self, file_path: Union[bool, str]):
+        if file_path is True:  # File path is boolean True
             self._path = os.path.join(os.curdir, "logs.txt")
-        elif isinstance(file_path, str):
-            self._path = file_path
+        
+        elif isinstance(file_path, str):  # File path is a string
+            if file_path.endswith((".json", ".txt")):
+                self._path = file_path  # No modification if the file ends with .json or .txt
+            else:
+                self._path = file_path + ".txt"  # Append .txt if the file doesn't end with .json or .txt
+        
         else:
-            raise ValueError("file_path must be either a boolean or a string.")
-
+            raise ValueError("file_path must be a string or boolean.")  # Handle the case where file_path isn't valid
+        
     def log(self, **kwargs):
-        now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        message = (
-            f"{now}: "
-            + ", ".join([f'{key}="{value}"' for key, value in kwargs.items()])
-            + "\n"
-        )
-        with open(self._path, "a", encoding="utf-8") as file:
-            file.write(message + "\n")
+        try:
+            now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            log_entry = {"timestamp": now, **kwargs}
 
+            if self._path.endswith(".json"):
+                # Append log in JSON format
+                with open(self._path, "a", encoding="utf-8") as file:
+                    # If the file is empty, start with a list; else, append to it
+                    try:
+                        # Try reading existing content to avoid overwriting
+                        with open(self._path, "r", encoding="utf-8") as read_file:
+                            existing_data = json.load(read_file)
+                            existing_data.append(log_entry)
+                    except (json.JSONDecodeError, FileNotFoundError):
+                        # If no valid JSON or file doesn't exist, start with an empty list
+                        existing_data = [log_entry]
+                    
+                    with open(self._path, "w", encoding="utf-8") as write_file:
+                        json.dump(existing_data, write_file, indent=4)
+                        write_file.write("\n")
+            
+            else:
+                # Append log in plain text format
+                message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n"
+                with open(self._path, "a", encoding="utf-8") as file:
+                    file.write(message)
 
+        except Exception as e:
+            raise ValueError(f"Failed to log message: {str(e)}")
+        
 class PickleHandler:
     def __init__(self, file_name: str) -> None:
         """

tests/agent_test.py

@@ -1183,7 +1183,7 @@ def test_agent_max_retry_limit():
             [
                 mock.call(
                     {
-                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
+                        "input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,
@@ -1191,7 +1191,7 @@ def test_agent_max_retry_limit():
                 ),
                 mock.call(
                     {
-                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
+                        "input": "Say the word: Hi\n\nThis is the expected criteria for your final answer: The word: Hi\nyou MUST return the actual complete content as the final answer, not a summary.",
                         "tool_names": "",
                         "tools": "",
                         "ask_for_human_input": True,

tests/cassettes/test_deepseek_r1_with_open_router.yaml

@@ -0,0 +1,100 @@
+interactions:
+- request:
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+      "What is the capital of France?"}], "stop": [], "stream": false}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '139'
+      host:
+      - openrouter.ai
+      http-referer:
+      - https://litellm.ai
+      user-agent:
+      - litellm/1.60.2
+      x-title:
+      - liteLLM
+    method: POST
+    uri: https://openrouter.ai/api/v1/chat/completions
+  response:
+    content: "\n         \n\n         \n\n         \n\n         \n\n         \n\n
+      \        \n\n         \n\n         \n\n         \n\n         \n\n         \n\n
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tests/cassettes/test_o3_mini_reasoning_effort_high.yaml

@@ -0,0 +1,107 @@
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tests/cassettes/test_o3_mini_reasoning_effort_low.yaml

@@ -0,0 +1,102 @@
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tests/cassettes/test_o3_mini_reasoning_effort_medium.yaml

@@ -0,0 +1,102 @@
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+      x-ratelimit-limit-tokens:
+      - '150000000'
+      x-ratelimit-remaining-requests:
+      - '29999'
+      x-ratelimit-remaining-tokens:
+      - '149999974'
+      x-ratelimit-reset-requests:
+      - 2ms
+      x-ratelimit-reset-tokens:
+      - 0s
+      x-request-id:
+      - req_bfd83679e674c3894991477f1fb043b2
+    http_version: HTTP/1.1
+    status_code: 200
+version: 1

tests/cli/cli_test.py

@@ -55,72 +55,83 @@ def test_train_invalid_string_iterations(train_crew, runner):
     )
 
 
-@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
-@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
-@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
-@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
-def test_reset_all_memories(
-    MockTaskOutputStorageHandler,
-    MockLongTermMemory,
-    MockEntityMemory,
-    MockShortTermMemory,
-    runner,
-):
-    result = runner.invoke(reset_memories, ["--all"])
-    MockShortTermMemory().reset.assert_called_once()
-    MockEntityMemory().reset.assert_called_once()
-    MockLongTermMemory().reset.assert_called_once()
-    MockTaskOutputStorageHandler().reset.assert_called_once()
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_all_memories(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
+    result = runner.invoke(reset_memories, ["-a"])
 
+    mock_crew.reset_memories.assert_called_once_with(command_type="all")
     assert result.output == "All memories have been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
-def test_reset_short_term_memories(MockShortTermMemory, runner):
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_short_term_memories(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
     result = runner.invoke(reset_memories, ["-s"])
-    MockShortTermMemory().reset.assert_called_once()
+
+    mock_crew.reset_memories.assert_called_once_with(command_type="short")
     assert result.output == "Short term memory has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
-def test_reset_entity_memories(MockEntityMemory, runner):
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_entity_memories(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
     result = runner.invoke(reset_memories, ["-e"])
-    MockEntityMemory().reset.assert_called_once()
+
+    mock_crew.reset_memories.assert_called_once_with(command_type="entity")
     assert result.output == "Entity memory has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
-def test_reset_long_term_memories(MockLongTermMemory, runner):
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_long_term_memories(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
     result = runner.invoke(reset_memories, ["-l"])
-    MockLongTermMemory().reset.assert_called_once()
+
+    mock_crew.reset_memories.assert_called_once_with(command_type="long")
     assert result.output == "Long term memory has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
-def test_reset_kickoff_outputs(MockTaskOutputStorageHandler, runner):
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_kickoff_outputs(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
     result = runner.invoke(reset_memories, ["-k"])
-    MockTaskOutputStorageHandler().reset.assert_called_once()
+
+    mock_crew.reset_memories.assert_called_once_with(command_type="kickoff_outputs")
     assert result.output == "Latest Kickoff outputs stored has been reset.\n"
 
 
-@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
-@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
-def test_reset_multiple_memory_flags(MockShortTermMemory, MockLongTermMemory, runner):
-    result = runner.invoke(
-        reset_memories,
-        [
-            "-s",
-            "-l",
-        ],
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_multiple_memory_flags(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
+    result = runner.invoke(reset_memories, ["-s", "-l"])
+
+    # Check that reset_memories was called twice with the correct arguments
+    assert mock_crew.reset_memories.call_count == 2
+    mock_crew.reset_memories.assert_has_calls(
+        [mock.call(command_type="long"), mock.call(command_type="short")]
     )
-    MockShortTermMemory().reset.assert_called_once()
-    MockLongTermMemory().reset.assert_called_once()
     assert (
         result.output
         == "Long term memory has been reset.\nShort term memory has been reset.\n"
     )
 
 
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_knowledge(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
+    result = runner.invoke(reset_memories, ["--knowledge"])
+
+    mock_crew.reset_memories.assert_called_once_with(command_type="knowledge")
+    assert result.output == "Knowledge has been reset.\n"
+
+
 def test_reset_no_memory_flags(runner):
     result = runner.invoke(
         reset_memories,

tests/config/tasks.yaml

@@ -2,7 +2,7 @@ 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

tests/conftest.py

@@ -1,37 +1,30 @@
 # conftest.py
 import os
-import tempfile
-from pathlib import Path
-
 import pytest
 from dotenv import load_dotenv
 
 load_result = load_dotenv(override=True)
 
 @pytest.fixture(autouse=True)
-def setup_test_environment():
-    """Set up test environment with a temporary directory for SQLite storage."""
-    with tempfile.TemporaryDirectory() as temp_dir:
-        # Create the directory with proper permissions
-        storage_dir = Path(temp_dir) / "crewai_test_storage"
-        storage_dir.mkdir(parents=True, exist_ok=True)
-        
-        # Validate that the directory was created successfully
-        if not storage_dir.exists() or not storage_dir.is_dir():
-            raise RuntimeError(f"Failed to create test storage directory: {storage_dir}")
-        
-        # Verify directory permissions
-        try:
-            # Try to create a test file to verify write permissions
-            test_file = storage_dir / ".permissions_test"
-            test_file.touch()
-            test_file.unlink()
-        except (OSError, IOError) as e:
-            raise RuntimeError(f"Test storage directory {storage_dir} is not writable: {e}")
-        
-        # Set environment variable to point to the test storage directory
-        os.environ["CREWAI_STORAGE_DIR"] = str(storage_dir)
-        
-        yield
-        
-        # Cleanup is handled automatically when tempfile context exits
+def setup_test_env():
+    """Configure test environment to use Ollama as the default embedding provider."""
+    # Store original environment variables
+    original_env = {
+        "CREWAI_EMBEDDING_PROVIDER": os.environ.get("CREWAI_EMBEDDING_PROVIDER"),
+        "CREWAI_EMBEDDING_MODEL": os.environ.get("CREWAI_EMBEDDING_MODEL"),
+        "CREWAI_OLLAMA_URL": os.environ.get("CREWAI_OLLAMA_URL"),
+    }
+    
+    # Set test environment
+    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
+    os.environ["CREWAI_EMBEDDING_MODEL"] = "llama2"
+    os.environ["CREWAI_OLLAMA_URL"] = "http://localhost:11434/api/embeddings"
+    
+    yield
+    
+    # Restore original environment
+    for key, value in original_env.items():
+        if value is None:
+            os.environ.pop(key, None)
+        else:
+            os.environ[key] = value

tests/crew_test.py

@@ -49,6 +49,39 @@ writer = Agent(
 )
 
 
+def test_crew_with_only_conditional_tasks_raises_error():
+    """Test that creating a crew with only conditional tasks raises an error."""
+    def condition_func(task_output: TaskOutput) -> bool:
+        return True
+
+    conditional1 = ConditionalTask(
+        description="Conditional task 1",
+        expected_output="Output 1",
+        agent=researcher,
+        condition=condition_func,
+    )
+    conditional2 = ConditionalTask(
+        description="Conditional task 2",
+        expected_output="Output 2",
+        agent=researcher,
+        condition=condition_func,
+    )
+    conditional3 = ConditionalTask(
+        description="Conditional task 3",
+        expected_output="Output 3",
+        agent=researcher,
+        condition=condition_func,
+    )
+
+    with pytest.raises(
+        pydantic_core._pydantic_core.ValidationError,
+        match="Crew must include at least one non-conditional task",
+    ):
+        Crew(
+            agents=[researcher],
+            tasks=[conditional1, conditional2, conditional3],
+        )
+
 def test_crew_config_conditional_requirement():
     with pytest.raises(ValueError):
         Crew(process=Process.sequential)
@@ -1917,6 +1950,77 @@ def test_task_callback_on_crew():
         assert isinstance(args[0], TaskOutput)
 
 
+def test_task_callback_both_on_task_and_crew():
+    from unittest.mock import MagicMock, patch
+    mock_callback_on_task = MagicMock()
+    mock_callback_on_crew = MagicMock()
+
+    researcher_agent = 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,
+    )
+
+    list_ideas = 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 important events.",
+        agent=researcher_agent,
+        async_execution=True,
+        callback=mock_callback_on_task,
+    )
+
+    crew = Crew(
+        agents=[researcher_agent],
+        process=Process.sequential,
+        tasks=[list_ideas],
+        task_callback=mock_callback_on_crew,
+    )
+
+    with patch.object(Agent, "execute_task") as execute:
+        execute.return_value = "ok"
+        crew.kickoff()
+
+        assert list_ideas.callback is not None
+        mock_callback_on_task.assert_called_once_with(list_ideas.output)
+        mock_callback_on_crew.assert_called_once_with(list_ideas.output)
+
+
+def test_task_same_callback_both_on_task_and_crew():
+    from unittest.mock import MagicMock, patch
+
+    mock_callback = MagicMock()
+
+    researcher_agent = 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,
+    )
+
+    list_ideas = 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 important events.",
+        agent=researcher_agent,
+        async_execution=True,
+        callback=mock_callback,
+    )
+
+    crew = Crew(
+        agents=[researcher_agent],
+        process=Process.sequential,
+        tasks=[list_ideas],
+        task_callback=mock_callback,
+    )
+
+    with patch.object(Agent, "execute_task") as execute:
+        execute.return_value = "ok"
+        crew.kickoff()
+
+        assert list_ideas.callback is not None
+        mock_callback.assert_called_once_with(list_ideas.output)
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_tools_with_custom_caching():
     from unittest.mock import patch
@@ -1989,6 +2093,195 @@ def test_tools_with_custom_caching():
             assert result.raw == "3"
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_conditional_task_uses_last_output():
+    """Test that conditional tasks use the last task output for condition evaluation."""
+    task1 = Task(
+        description="First task",
+        expected_output="First output",
+        agent=researcher,
+    )
+    def condition_fails(task_output: TaskOutput) -> bool:
+        # This condition will never be met
+        return "never matches" in task_output.raw.lower()
+    
+    def condition_succeeds(task_output: TaskOutput) -> bool:
+        # This condition will match first task's output
+        return "first success" in task_output.raw.lower()
+    
+    conditional_task1 = ConditionalTask(
+        description="Second task - conditional that fails condition",
+        expected_output="Second output",
+        agent=researcher,
+        condition=condition_fails,
+    )
+    
+    conditional_task2 = ConditionalTask(
+        description="Third task - conditional that succeeds using first task output",
+        expected_output="Third output",
+        agent=writer,
+        condition=condition_succeeds,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, conditional_task1, conditional_task2],
+    )
+
+    # Mock outputs for tasks
+    mock_first = TaskOutput(
+        description="First task output",
+        raw="First success output",  # Will be used by third task's condition
+        agent=researcher.role,
+    )
+    mock_skipped = TaskOutput(
+        description="Second task output",
+        raw="",  # Empty output since condition fails
+        agent=researcher.role,
+    )
+    mock_third = TaskOutput(
+        description="Third task output",
+        raw="Third task executed",  # Output when condition succeeds using first task output
+        agent=writer.role,
+    )
+    
+    # Set up mocks for task execution and conditional logic
+    with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
+        # First conditional fails, second succeeds
+        mock_should_execute.side_effect = [False, True]
+        
+        with patch.object(Task, "execute_sync") as mock_execute:
+            mock_execute.side_effect = [mock_first, mock_third]
+            result = crew.kickoff()
+            
+            # Verify execution behavior
+            assert mock_execute.call_count == 2  # Only first and third tasks execute
+            assert mock_should_execute.call_count == 2  # Both conditionals checked
+            
+            # Verify outputs collection
+            assert len(result.tasks_output) == 3
+            assert result.tasks_output[0].raw == "First success output"  # First task succeeded
+            assert result.tasks_output[1].raw == ""  # Second task skipped (condition failed)
+            assert result.tasks_output[2].raw == "Third task executed"  # Third task used first task's output
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_conditional_tasks_result_collection():
+    """Test that task outputs are properly collected based on execution status."""
+    task1 = Task(
+        description="Normal task that always executes",
+        expected_output="First output",
+        agent=researcher,
+    )
+    
+    def condition_never_met(task_output: TaskOutput) -> bool:
+        return "never matches" in task_output.raw.lower()
+    
+    def condition_always_met(task_output: TaskOutput) -> bool:
+        return "success" in task_output.raw.lower()
+    
+    task2 = ConditionalTask(
+        description="Conditional task that never executes",
+        expected_output="Second output",
+        agent=researcher,
+        condition=condition_never_met,
+    )
+    
+    task3 = ConditionalTask(
+        description="Conditional task that always executes",
+        expected_output="Third output",
+        agent=writer,
+        condition=condition_always_met,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2, task3],
+    )
+
+    # Mock outputs for different execution paths
+    mock_success = TaskOutput(
+        description="Success output",
+        raw="Success output",  # Triggers third task's condition
+        agent=researcher.role,
+    )
+    mock_skipped = TaskOutput(
+        description="Skipped output",
+        raw="",  # Empty output for skipped task
+        agent=researcher.role,
+    )
+    mock_conditional = TaskOutput(
+        description="Conditional output",
+        raw="Conditional task executed",
+        agent=writer.role,
+    )
+    
+    # Set up mocks for task execution and conditional logic
+    with patch.object(ConditionalTask, "should_execute") as mock_should_execute:
+        # First conditional fails, second succeeds
+        mock_should_execute.side_effect = [False, True]
+        
+        with patch.object(Task, "execute_sync") as mock_execute:
+            mock_execute.side_effect = [mock_success, mock_conditional]
+            result = crew.kickoff()
+            
+            # Verify execution behavior
+            assert mock_execute.call_count == 2  # Only first and third tasks execute
+            assert mock_should_execute.call_count == 2  # Both conditionals checked
+            
+            # Verify task output collection
+            assert len(result.tasks_output) == 3
+            assert result.tasks_output[0].raw == "Success output"      # Normal task executed
+            assert result.tasks_output[1].raw == ""                    # Second task skipped
+            assert result.tasks_output[2].raw == "Conditional task executed"  # Third task executed
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_multiple_conditional_tasks():
+    """Test that having multiple conditional tasks in sequence works correctly."""
+    task1 = Task(
+        description="Initial research task",
+        expected_output="Research output",
+        agent=researcher,
+    )
+    
+    def condition1(task_output: TaskOutput) -> bool:
+        return "success" in task_output.raw.lower()
+    
+    def condition2(task_output: TaskOutput) -> bool:
+        return "proceed" in task_output.raw.lower()
+    
+    task2 = ConditionalTask(
+        description="First conditional task",
+        expected_output="Conditional output 1",
+        agent=writer,
+        condition=condition1,
+    )
+    
+    task3 = ConditionalTask(
+        description="Second conditional task",
+        expected_output="Conditional output 2",
+        agent=writer,
+        condition=condition2,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2, task3],
+    )
+
+    # Mock different task outputs to test conditional logic
+    mock_success = TaskOutput(
+        description="Mock success",
+        raw="Success and proceed output",
+        agent=researcher.role,
+    )
+    
+    # Set up mocks for task execution
+    with patch.object(Task, "execute_sync", return_value=mock_success) as mock_execute:
+        result = crew.kickoff()
+        # Verify all tasks were executed (no IndexError)
+        assert mock_execute.call_count == 3
+        assert len(result.tasks_output) == 3
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_using_contextual_memory():
     from unittest.mock import patch

tests/llm_test.py

@@ -1,7 +1,9 @@
+import os
 from time import sleep
 from unittest.mock import MagicMock, patch
 
 import pytest
+from pydantic import BaseModel
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.llm import LLM
@@ -202,3 +204,170 @@ def test_llm_passes_additional_params():
 
         # Check the result from llm.call
         assert result == "Test response"
+
+
+def test_get_custom_llm_provider_openrouter():
+    llm = LLM(model="openrouter/deepseek/deepseek-chat")
+    assert llm._get_custom_llm_provider() == "openrouter"
+
+
+def test_get_custom_llm_provider_gemini():
+    llm = LLM(model="gemini/gemini-1.5-pro")
+    assert llm._get_custom_llm_provider() == "gemini"
+
+
+def test_get_custom_llm_provider_openai():
+    llm = LLM(model="gpt-4")
+    assert llm._get_custom_llm_provider() == "openai"
+
+
+def test_validate_call_params_supported():
+    class DummyResponse(BaseModel):
+        a: int
+
+    # Patch supports_response_schema to simulate a supported model.
+    with patch("crewai.llm.supports_response_schema", return_value=True):
+        llm = LLM(
+            model="openrouter/deepseek/deepseek-chat", response_format=DummyResponse
+        )
+        # Should not raise any error.
+        llm._validate_call_params()
+
+
+def test_validate_call_params_not_supported():
+    class DummyResponse(BaseModel):
+        a: int
+
+    # Patch supports_response_schema to simulate an unsupported model.
+    with patch("crewai.llm.supports_response_schema", return_value=False):
+        llm = LLM(model="gemini/gemini-1.5-pro", response_format=DummyResponse)
+        with pytest.raises(ValueError) as excinfo:
+            llm._validate_call_params()
+        assert "does not support response_format" in str(excinfo.value)
+
+
+def test_validate_call_params_no_response_format():
+    # When no response_format is provided, no validation error should occur.
+    llm = LLM(model="gemini/gemini-1.5-pro", response_format=None)
+    llm._validate_call_params()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_o3_mini_reasoning_effort_high():
+    llm = LLM(
+        model="o3-mini",
+        reasoning_effort="high",
+    )
+    result = llm.call("What is the capital of France?")
+    assert isinstance(result, str)
+    assert "Paris" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_o3_mini_reasoning_effort_low():
+    llm = LLM(
+        model="o3-mini",
+        reasoning_effort="low",
+    )
+    result = llm.call("What is the capital of France?")
+    assert isinstance(result, str)
+    assert "Paris" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_o3_mini_reasoning_effort_medium():
+    llm = LLM(
+        model="o3-mini",
+        reasoning_effort="medium",
+    )
+    result = llm.call("What is the capital of France?")
+    assert isinstance(result, str)
+    assert "Paris" in result
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.fixture
+def anthropic_llm():
+    """Fixture providing an Anthropic LLM instance."""
+    return LLM(model="anthropic/claude-3-sonnet")
+
+@pytest.fixture
+def system_message():
+    """Fixture providing a system message."""
+    return {"role": "system", "content": "test"}
+
+@pytest.fixture
+def user_message():
+    """Fixture providing a user message."""
+    return {"role": "user", "content": "test"}
+
+def test_anthropic_message_formatting_edge_cases(anthropic_llm):
+    """Test edge cases for Anthropic message formatting."""
+    # Test None messages
+    with pytest.raises(TypeError, match="Messages cannot be None"):
+        anthropic_llm._format_messages_for_provider(None)
+        
+    # Test empty message list
+    formatted = anthropic_llm._format_messages_for_provider([])
+    assert len(formatted) == 1
+    assert formatted[0]["role"] == "user"
+    assert formatted[0]["content"] == "."
+    
+    # Test invalid message format
+    with pytest.raises(TypeError, match="Invalid message format"):
+        anthropic_llm._format_messages_for_provider([{"invalid": "message"}])
+
+def test_anthropic_model_detection():
+    """Test Anthropic model detection with various formats."""
+    models = [
+        ("anthropic/claude-3", True),
+        ("claude-instant", True),
+        ("claude/v1", True),
+        ("gpt-4", False),
+        ("", False),
+        ("anthropomorphic", False),  # Should not match partial words
+    ]
+    
+    for model, expected in models:
+        llm = LLM(model=model)
+        assert llm.is_anthropic == expected, f"Failed for model: {model}"
+
+def test_anthropic_message_formatting(anthropic_llm, system_message, user_message):
+    """Test Anthropic message formatting with fixtures."""
+    # Test when first message is system
+    formatted = anthropic_llm._format_messages_for_provider([system_message])
+    assert len(formatted) == 2
+    assert formatted[0]["role"] == "user"
+    assert formatted[0]["content"] == "."
+    assert formatted[1] == system_message
+
+    # Test when first message is already user
+    formatted = anthropic_llm._format_messages_for_provider([user_message])
+    assert len(formatted) == 1
+    assert formatted[0] == user_message
+
+    # Test with empty message list
+    formatted = anthropic_llm._format_messages_for_provider([])
+    assert len(formatted) == 1
+    assert formatted[0]["role"] == "user"
+    assert formatted[0]["content"] == "."
+
+    # Test with non-Anthropic model (should not modify messages)
+    non_anthropic_llm = LLM(model="gpt-4")
+    formatted = non_anthropic_llm._format_messages_for_provider([system_message])
+    assert len(formatted) == 1
+    assert formatted[0] == system_message
+
+
+def test_deepseek_r1_with_open_router():
+    if not os.getenv("OPEN_ROUTER_API_KEY"):
+        pytest.skip("OPEN_ROUTER_API_KEY not set; skipping test.")
+
+    llm = LLM(
+        model="openrouter/deepseek/deepseek-r1",
+        base_url="https://openrouter.ai/api/v1",
+        api_key=os.getenv("OPEN_ROUTER_API_KEY"),
+    )
+    result = llm.call("What is the capital of France?")
+    assert isinstance(result, str)
+    assert "Paris" in result

tests/memory/test_memory_reset.py

@@ -0,0 +1,91 @@
+import os
+import tempfile
+import pytest
+from crewai.memory import ShortTermMemory, LongTermMemory, EntityMemory
+from crewai.utilities.exceptions.embedding_exceptions import (
+    EmbeddingConfigurationError,
+    EmbeddingProviderError
+)
+from crewai.utilities import EmbeddingConfigurator
+
+@pytest.fixture
+def temp_db_dir():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        yield tmpdir
+
+def test_memory_reset_with_ollama(temp_db_dir):
+    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
+    os.environ["CREWAI_EMBEDDING_MODEL"] = "llama2"
+    
+    memories = [
+        ShortTermMemory(path=temp_db_dir),
+        LongTermMemory(path=temp_db_dir),
+        EntityMemory(path=temp_db_dir)
+    ]
+    for memory in memories:
+        memory.reset()
+
+def test_memory_reset_with_openai(temp_db_dir):
+    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "openai"
+    os.environ["CREWAI_EMBEDDING_MODEL"] = "text-embedding-3-small"
+    
+    memories = [
+        ShortTermMemory(path=temp_db_dir),
+        LongTermMemory(path=temp_db_dir),
+        EntityMemory(path=temp_db_dir)
+    ]
+    for memory in memories:
+        memory.reset()
+
+def test_memory_reset_with_invalid_provider(temp_db_dir):
+    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "invalid_provider"
+    with pytest.raises(EmbeddingProviderError):
+        memories = [
+            ShortTermMemory(path=temp_db_dir),
+            LongTermMemory(path=temp_db_dir),
+            EntityMemory(path=temp_db_dir)
+        ]
+        for memory in memories:
+            memory.reset()
+
+def test_memory_reset_with_invalid_configuration(temp_db_dir):
+    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "openai"
+    os.environ.pop("OPENAI_API_KEY", None)
+    
+    with pytest.raises(EmbeddingConfigurationError):
+        memories = [
+            ShortTermMemory(path=temp_db_dir),
+            LongTermMemory(path=temp_db_dir),
+            EntityMemory(path=temp_db_dir)
+        ]
+        for memory in memories:
+            memory.reset()
+
+def test_memory_reset_with_missing_ollama_url(temp_db_dir):
+    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
+    os.environ.pop("CREWAI_OLLAMA_URL", None)
+    # Should use default URL when CREWAI_OLLAMA_URL is not set
+    memories = [
+        ShortTermMemory(path=temp_db_dir),
+        LongTermMemory(path=temp_db_dir),
+        EntityMemory(path=temp_db_dir)
+    ]
+    for memory in memories:
+        memory.reset()
+
+def test_memory_reset_with_custom_path(temp_db_dir):
+    os.environ["CREWAI_EMBEDDING_PROVIDER"] = "ollama"
+    custom_path = os.path.join(temp_db_dir, "custom")
+    os.makedirs(custom_path, exist_ok=True)
+    
+    memories = [
+        ShortTermMemory(path=custom_path),
+        LongTermMemory(path=custom_path),
+        EntityMemory(path=custom_path)
+    ]
+    for memory in memories:
+        memory.reset()
+        
+    assert not os.path.exists(os.path.join(custom_path, "short_term"))
+    assert not os.path.exists(os.path.join(custom_path, "long_term"))
+    assert not os.path.exists(os.path.join(custom_path, "entity"))

tests/task_test.py

@@ -723,14 +723,14 @@ def test_interpolate_inputs():
     )
 
     task.interpolate_inputs_and_add_conversation_history(
-        inputs={"topic": "AI", "date": "2024"}
+        inputs={"topic": "AI", "date": "2025"}
     )
     assert (
         task.description
         == "Give me a list of 5 interesting ideas about AI to explore for an article, what makes them unique and interesting."
     )
     assert task.expected_output == "Bullet point list of 5 interesting ideas about AI."
-    assert task.output_file == "/tmp/AI/output_2024.txt"
+    assert task.output_file == "/tmp/AI/output_2025.txt"
 
     task.interpolate_inputs_and_add_conversation_history(
         inputs={"topic": "ML", "date": "2025"}

uv.lock

@@ -649,7 +649,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.100.0"
+version = "0.100.1"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -740,7 +740,7 @@ requires-dist = [
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "json5", specifier = ">=0.10.0" },
     { name = "jsonref", specifier = ">=1.1.0" },
-    { name = "litellm", specifier = "==1.59.8" },
+    { name = "litellm", specifier = "==1.60.2" },
     { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
     { name = "openai", specifier = ">=1.13.3" },
     { name = "openpyxl", specifier = ">=3.1.5" },
@@ -2374,7 +2374,7 @@ wheels = [
 
 [[package]]
 name = "litellm"
-version = "1.59.8"
+version = "1.60.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -2389,9 +2389,9 @@ dependencies = [
     { name = "tiktoken" },
     { name = "tokenizers" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/86/b0/c8ec06bd1c87a92d6d824008982b3c82b450d7bd3be850a53913f1ac4907/litellm-1.59.8.tar.gz", hash = "sha256:9d645cc4460f6a9813061f07086648c4c3d22febc8e1f21c663f2b7750d90512", size = 6428607 }
+sdist = { url = "https://files.pythonhosted.org/packages/94/8f/704cdb0fdbdd49dc5062a39ae5f1a8f308ae0ffd746df6e0137fc1776b8a/litellm-1.60.2.tar.gz", hash = "sha256:a8170584fcfd6f5175201d869e61ccd8a40ffe3264fc5e53c5b805ddf8a6e05a", size = 6447447 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b9/38/889da058f566ef9ea321aafa25e423249492cf2a508dfdc0e5acfcf04526/litellm-1.59.8-py3-none-any.whl", hash = "sha256:2473914bd2343485a185dfe7eedb12ee5fda32da3c9d9a8b73f6966b9b20cf39", size = 6716233 },
+    { url = "https://files.pythonhosted.org/packages/8a/ba/0eaec9aee9f99fdf46ef1c0bddcfe7f5720b182f84f6ed27f13145d5ded2/litellm-1.60.2-py3-none-any.whl", hash = "sha256:1cb08cda04bf8c5ef3e690171a779979e4b16a5e3a24cd8dc1f198e7f198d5c4", size = 6746809 },
 ]
 
 [[package]]
@@ -3185,7 +3185,7 @@ wheels = [
 
 [[package]]
 name = "openai"
-version = "1.59.6"
+version = "1.61.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -3197,9 +3197,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/2e/7a/07fbe7bdabffd0a5be1bfe5903a02c4fff232e9acbae894014752a8e4def/openai-1.59.6.tar.gz", hash = "sha256:c7670727c2f1e4473f62fea6fa51475c8bc098c9ffb47bfb9eef5be23c747934", size = 344915 }
+sdist = { url = "https://files.pythonhosted.org/packages/32/2a/b3fa8790be17d632f59d4f50257b909a3f669036e5195c1ae55737274620/openai-1.61.0.tar.gz", hash = "sha256:216f325a24ed8578e929b0f1b3fb2052165f3b04b0461818adaa51aa29c71f8a", size = 350174 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/70/45/6de8e5fd670c804b29c777e4716f1916741c71604d5c7d952eee8432f7d3/openai-1.59.6-py3-none-any.whl", hash = "sha256:b28ed44eee3d5ebe1a3ea045ee1b4b50fea36ecd50741aaa5ce5a5559c900cb6", size = 454817 },
+    { url = "https://files.pythonhosted.org/packages/93/76/70c5ad6612b3e4c89fa520266bbf2430a89cae8bd87c1e2284698af5927e/openai-1.61.0-py3-none-any.whl", hash = "sha256:e8c512c0743accbdbe77f3429a1490d862f8352045de8dc81969301eb4a4f666", size = 460623 },
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 [[package]]