Cassettes

* upgrade chroma and adjust embedder function generator

* >= version

* linted

.github/security.md

@@ -0,0 +1,19 @@
+CrewAI takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organization.
+If you believe you have found a security vulnerability in any CrewAI product or service, please report it to us as described below.
+
+ ## Reporting a Vulnerability
+ Please do not report security vulnerabilities through public GitHub issues.
+ To report a vulnerability, please email us at security@crewai.com.
+ Please include the requested information listed below so that we can triage your report more quickly
+
+ - Type of issue (e.g. SQL injection, cross-site scripting, etc.)
+ - Full paths of source file(s) related to the manifestation of the issue
+ - The location of the affected source code (tag/branch/commit or direct URL)
+ - Any special configuration required to reproduce the issue
+ - Step-by-step instructions to reproduce the issue (please include screenshots if needed)
+ - Proof-of-concept or exploit code (if possible)
+ - Impact of the issue, including how an attacker might exploit the issue
+
+ Once we have received your report, we will respond to you at the email address you provide. If the issue is confirmed, we will release a patch as soon as possible depending on the complexity of the issue.
+
+ At this time, we are not offering a bug bounty program. Any rewards will be at our discretion.

.github/workflows/security-checker.yml

@@ -19,5 +19,5 @@ jobs:
         run: pip install bandit
 
       - name: Run Bandit
-        run: bandit -c pyproject.toml -r src/ -lll
+        run: bandit -c pyproject.toml -r src/ -ll
 

.gitignore

@@ -17,3 +17,7 @@ rc-tests/*
 temp/*
 .vscode/*
 crew_tasks_output.json
+.codesight
+.mypy_cache
+.ruff_cache
+.venv

README.md

@@ -351,7 +351,7 @@ pre-commit install
 ### Running Tests
 
 ```bash
-uvx pytest
+uv run pytest .
 ```
 
 ### Running static type checks

docs/concepts/agents.mdx

@@ -31,16 +31,17 @@ Think of an agent as a member of a team, with specific skills and a particular j
 | **Max RPM** *(optional)*   | `max_rpm`  | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`.                                                                               |
 | **Max Execution Time** *(optional)*   | `max_execution_time`  | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time.                                                                     |
 | **Verbose** *(optional)*   | `verbose`  | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`.                                                                                              |
-| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.
+| **Allow Delegation** *(optional)* | `allow_delegation`  | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.                                                                                                      |
 | **Step Callback** *(optional)* | `step_callback`  | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`.                                                               |
 | **Cache** *(optional)*     | `cache`  | Indicates if the agent should use a cache for tool usage. Default is `True`.                                                                                                                                                                  |
 | **System Template** *(optional)*     | `system_template`  | Specifies the system format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Prompt Template** *(optional)*     | `prompt_template`  | Specifies the prompt format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Response Template** *(optional)*     | `response_template`  | Specifies the response format for the agent. Default is `None`.                                                                                                                                                                  |
 | **Allow Code Execution** *(optional)*     | `allow_code_execution`  | Enable code execution for the agent. Default is `False`.                                                                                                                                                                  |
-| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
+| **Max Retry Limit** *(optional)*     | `max_retry_limit`  | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.                                                                                                                                                                  |
 | **Use System Prompt** *(optional)*     | `use_system_prompt`  | Adds the ability to not use system prompt (to support o1 models). Default is `True`.                                                                                                                                                                  |
 | **Respect Context Window** *(optional)*     | `respect_context_window`  | Summary strategy to avoid overflowing the context window. Default is `True`.                                                                                                                                                                  |
+| **Code Execution Mode** *(optional)* | `code_execution_mode` | Determines the mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution on the host machine). Default is `safe`.                                                                                                          |
 
 ## Creating an agent
 
@@ -83,6 +84,7 @@ agent = Agent(
   max_retry_limit=2,  # Optional
   use_system_prompt=True,  # Optional
   respect_context_window=True,  # Optional
+  code_execution_mode='safe',  # Optional, defaults to 'safe'
 )
 ```
 
@@ -156,4 +158,4 @@ crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
 ## Conclusion
 
 Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, 
-you can create sophisticated AI systems that leverage the power of collaborative intelligence.
+you can create sophisticated AI systems that leverage the power of collaborative intelligence. The `code_execution_mode` attribute provides flexibility in how agents execute code, allowing for both secure and direct execution options.

docs/concepts/cli.mdx

@@ -6,7 +6,7 @@ icon: terminal
 
 # CrewAI CLI Documentation
 
-The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews and pipelines.
+The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
 
 ## Installation
 
@@ -146,3 +146,34 @@ crewai run
 Make sure to run these commands from the directory where your CrewAI project is set up. 
 Some commands may require additional configuration or setup within your project structure.
 </Note>
+
+
+### 9. API Keys
+
+When running ```crewai create crew``` command, the CLI will first show you the top 5 most common LLM providers and ask you to select one.
+
+Once you've selected an LLM provider, you will be prompted for API keys.
+
+#### Initial API key providers
+
+The CLI will initially prompt for API keys for the following services:
+
+* OpenAI
+* Groq
+* Anthropic
+* Google Gemini
+
+When you select a provider, the CLI will prompt you to enter your API key.
+
+#### Other Options
+
+If you select option 6, you will be able to select from a list of LiteLLM supported providers.
+
+When you select a provider, the CLI will prompt you to enter the Key name and the API key.
+
+See the following link for each provider's key name:
+
+* [LiteLLM Providers](https://docs.litellm.ai/docs/providers)
+
+
+

docs/concepts/crews.mdx

@@ -22,7 +22,8 @@ A crew in crewAI represents a collaborative group of agents working together to
 | **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution. Defaults to `None`.                                                                                                                                                                     |
 | **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). Defaults to `False`.                                                                                                                                                       |
+| **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`.                                                                                                                                       |

docs/concepts/flows.mdx

@@ -18,68 +18,71 @@ Flows allow you to create structured, event-driven workflows. They provide a sea
 
 4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
 
+5. **Input Flexibility**: Flows can accept inputs to initialize or update their state, with different handling for structured and unstructured state management.
+
 ## Getting Started
 
 Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
 
-```python Code
-import asyncio
+### Passing Inputs to Flows
 
+Flows can accept inputs to initialize or update their state before execution. The way inputs are handled depends on whether the flow uses structured or unstructured state management.
+
+#### Structured State Management
+
+In structured state management, the flow's state is defined using a Pydantic `BaseModel`. Inputs must match the model's schema, and any updates will overwrite the default values.
+
+```python
 from crewai.flow.flow import Flow, listen, start
-from litellm import completion
+from pydantic import BaseModel
 
+class ExampleState(BaseModel):
+    counter: int = 0
+    message: str = ""
 
-class ExampleFlow(Flow):
-    model = "gpt-4o-mini"
-
+class StructuredExampleFlow(Flow[ExampleState]):
     @start()
-    def generate_city(self):
-        print("Starting flow")
+    def first_method(self):
+        # Implementation
 
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": "Return the name of a random city in the world.",
-                },
-            ],
-        )
+flow = StructuredExampleFlow()
+flow.kickoff(inputs={"counter": 10})
+```
 
-        random_city = response["choices"][0]["message"]["content"]
-        print(f"Random City: {random_city}")
+In this example, the `counter` is initialized to `10`, while `message` retains its default value.
 
-        return random_city
+#### Unstructured State Management
 
-    @listen(generate_city)
-    def generate_fun_fact(self, random_city):
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Tell me a fun fact about {random_city}",
-                },
-            ],
-        )
+In unstructured state management, the flow's state is a dictionary. You can pass any dictionary to update the state.
 
-        fun_fact = response["choices"][0]["message"]["content"]
-        return fun_fact
+```python
+from crewai.flow.flow import Flow, listen, start
 
+class UnstructuredExampleFlow(Flow):
+    @start()
+    def first_method(self):
+        # Implementation
 
-async def main():
-    flow = ExampleFlow()
-    result = await flow.kickoff()
+flow = UnstructuredExampleFlow()
+flow.kickoff(inputs={"counter": 5, "message": "Initial message"})
+```
 
-    print(f"Generated fun fact: {result}")
+Here, both `counter` and `message` are updated based on the provided inputs.
 
-asyncio.run(main())
+**Note:** Ensure that inputs for structured state management adhere to the defined schema to avoid validation errors.
+
+### Example Flow
+
+```python
+# Existing example code
 ```
 
 In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
 
 When you run the Flow, it will generate a random city and then generate a fun fact about that city. The output will be printed to the console.
 
+**Note:** Ensure you have set up your `.env` file to store your `OPENAI_API_KEY`. This key is necessary for authenticating requests to the OpenAI API.
+
 ### @start()
 
 The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
@@ -94,14 +97,14 @@ The `@listen()` decorator can be used in several ways:
 
 1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
 
-   ```python Code
+   ```python
    @listen("generate_city")
    def generate_fun_fact(self, random_city):
        # Implementation
    ```
 
 2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
-   ```python Code
+   ```python
    @listen(generate_city)
    def generate_fun_fact(self, random_city):
        # Implementation
@@ -118,8 +121,7 @@ When you run a Flow, the final output is determined by the last method that comp
 Here's how you can access the final output:
 
 <CodeGroup>
-```python Code
-import asyncio
+```python
 from crewai.flow.flow import Flow, listen, start
 
 class OutputExampleFlow(Flow):
@@ -131,26 +133,23 @@ class OutputExampleFlow(Flow):
     def second_method(self, first_output):
         return f"Second method received: {first_output}"
 
-async def main():
-    flow = OutputExampleFlow()
-    final_output = await flow.kickoff()
-    print("---- Final Output ----")
-    print(final_output)
+flow = OutputExampleFlow()
+final_output = flow.kickoff()
 
-asyncio.run(main())
+print("---- Final Output ----")
+print(final_output)
 ```
 
-``` text Output
+```text
 ---- Final Output ----
 Second method received: Output from first_method
 ```
 
 </CodeGroup>
 
-In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow. 
+In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow.
 The `kickoff()` method will return the final output, which is then printed to the console.
 
-
 #### Accessing and Updating State
 
 In addition to retrieving the final output, you can also access and update the state within your Flow. The state can be used to store and share data between different methods in the Flow. After the Flow has run, you can access the state to retrieve any information that was added or updated during the execution.
@@ -159,8 +158,7 @@ Here's an example of how to update and access the state:
 
 <CodeGroup>
 
-```python Code
-import asyncio
+```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
@@ -181,45 +179,41 @@ class StateExampleFlow(Flow[ExampleState]):
         self.state.counter += 1
         return self.state.message
 
-async def main():
-    flow = StateExampleFlow()
-    final_output = await flow.kickoff()
-    print(f"Final Output: {final_output}")
-    print("Final State:")
-    print(flow.state)
-
-asyncio.run(main())
+flow = StateExampleFlow()
+final_output = flow.kickoff()
+print(f"Final Output: {final_output}")
+print("Final State:")
+print(flow.state)
 ```
 
-``` text Output
+```text
 Final Output: Hello from first_method - updated by second_method
 Final State:
 counter=2 message='Hello from first_method - updated by second_method'
 ```
+
 </CodeGroup>
 
-In this example, the state is updated by both `first_method` and `second_method`. 
+In this example, the state is updated by both `first_method` and `second_method`.
 After the Flow has run, you can access the final state to see the updates made by these methods.
 
-By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems, 
+By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems,
 while also maintaining and accessing the state throughout the Flow's execution.
 
 ## Flow State Management
 
-Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management, 
+Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management,
 allowing developers to choose the approach that best fits their application's needs.
 
 ### Unstructured State Management
 
-In unstructured state management, all state is stored in the `state` attribute of the `Flow` class. 
+In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
 This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
 
-```python Code
-import asyncio
-
+```python
 from crewai.flow.flow import Flow, listen, start
 
-class UntructuredExampleFlow(Flow):
+class UnstructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
@@ -238,13 +232,8 @@ class UntructuredExampleFlow(Flow):
 
         print(f"State after third_method: {self.state}")
 
-
-async def main():
-    flow = UntructuredExampleFlow()
-    await flow.kickoff()
-
-
-asyncio.run(main())
+flow = UnstructuredExampleFlow()
+flow.kickoff()
 ```
 
 **Key Points:**
@@ -254,21 +243,17 @@ asyncio.run(main())
 
 ### Structured State Management
 
-Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow. 
+Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
 By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
 
-```python Code
-import asyncio
-
+```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
-
 class ExampleState(BaseModel):
     counter: int = 0
     message: str = ""
 
-
 class StructuredExampleFlow(Flow[ExampleState]):
 
     @start()
@@ -287,13 +272,8 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
         print(f"State after third_method: {self.state}")
 
-
-async def main():
-    flow = StructuredExampleFlow()
-    await flow.kickoff()
-
-
-asyncio.run(main())
+flow = StructuredExampleFlow()
+flow.kickoff()
 ```
 
 **Key Points:**
@@ -325,8 +305,7 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger
 
 <CodeGroup>
 
-```python Code
-import asyncio
+```python
 from crewai.flow.flow import Flow, listen, or_, start
 
 class OrExampleFlow(Flow):
@@ -343,23 +322,18 @@ class OrExampleFlow(Flow):
     def logger(self, result):
         print(f"Logger: {result}")
 
-
-async def main():
-    flow = OrExampleFlow()
-    await flow.kickoff()
-
-
-asyncio.run(main())
+flow = OrExampleFlow()
+flow.kickoff()
 ```
 
-``` text Output
+```text
 Logger: Hello from the start method
 Logger: Hello from the second method
 ```
 
 </CodeGroup>
 
-When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`. 
+When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`.
 The `or_` function is used to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
 
 ### Conditional Logic: `and`
@@ -368,8 +342,7 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge
 
 <CodeGroup>
 
-```python Code
-import asyncio
+```python
 from crewai.flow.flow import Flow, and_, listen, start
 
 class AndExampleFlow(Flow):
@@ -387,34 +360,28 @@ class AndExampleFlow(Flow):
         print("---- Logger ----")
         print(self.state)
 
-
-async def main():
-    flow = AndExampleFlow()
-    await flow.kickoff()
-
-
-asyncio.run(main())
+flow = AndExampleFlow()
+flow.kickoff()
 ```
 
-``` text Output
+```text
 ---- Logger ----
 {'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
 ```
 
 </CodeGroup>
 
-When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output. 
+When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output.
 The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
 
 ### Router
 
-The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method. 
+The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method.
 You can specify different routes based on the output of the method, allowing you to control the flow of execution dynamically.
 
 <CodeGroup>
 
-```python Code
-import asyncio
+```python
 import random
 from crewai.flow.flow import Flow, listen, router, start
 from pydantic import BaseModel
@@ -445,16 +412,11 @@ class RouterFlow(Flow[ExampleState]):
     def fourth_method(self):
         print("Fourth method running")
 
-
-async def main():
-    flow = RouterFlow()
-    await flow.kickoff()
-
-
-asyncio.run(main())
+flow = RouterFlow()
+flow.kickoff()
 ```
 
-``` text Output
+```text
 Starting the structured flow
 Third method running
 Fourth method running
@@ -462,16 +424,16 @@ Fourth method running
 
 </CodeGroup>
 
-In the above example, the `start_method` generates a random boolean value and sets it in the state. 
-The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean. 
-If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`. 
+In the above example, the `start_method` generates a random boolean value and sets it in the state.
+The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean.
+If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`.
 The `third_method` and `fourth_method` listen to the output of the `second_method` and execute based on the returned value.
 
 When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
 
 ## Adding Crews to Flows
 
-Creating a flow with multiple crews in CrewAI is straightforward. 
+Creating a flow with multiple crews in CrewAI is straightforward.
 
 You can generate a new CrewAI project that includes all the scaffolding needed to create a flow with multiple crews by running the following command:
 
@@ -485,22 +447,21 @@ This command will generate a new CrewAI project with the necessary folder struct
 
 After running the `crewai create flow name_of_flow` command, you will see a folder structure similar to the following:
 
-| Directory/File                   | Description                                                       |
-|:---------------------------------|:------------------------------------------------------------------|
-| `name_of_flow/`                  | Root directory for the flow.                                      |
-| ├── `crews/`                     | Contains directories for specific crews.                          |
-| │   └── `poem_crew/`             | Directory for the "poem_crew" with its configurations and scripts.|
-| │       ├── `config/`            | Configuration files directory for the "poem_crew".                |
-| │       │   ├── `agents.yaml`    | YAML file defining the agents for "poem_crew".                    |
-| │       │   └── `tasks.yaml`     | YAML file defining the tasks for "poem_crew".                     |
-| │       ├── `poem_crew.py`       | Script for "poem_crew" functionality.                             |
-| ├── `tools/`                     | Directory for additional tools used in the flow.                  |
-| │   └── `custom_tool.py`         | Custom tool implementation.                                       |
-| ├── `main.py`                    | Main script for running the flow.                                 |
-| ├── `README.md`                  | Project description and instructions.                             |
-| ├── `pyproject.toml`             | Configuration file for project dependencies and settings.         |
-| └── `.gitignore`                 | Specifies files and directories to ignore in version control.     |
-
+| Directory/File         | Description                                                        |
+| :--------------------- | :----------------------------------------------------------------- |
+| `name_of_flow/`        | Root directory for the flow.                                       |
+| ├── `crews/`           | Contains directories for specific crews.                           |
+| │ └── `poem_crew/`     | Directory for the "poem_crew" with its configurations and scripts. |
+| │ ├── `config/`        | Configuration files directory for the "poem_crew".                 |
+| │ │ ├── `agents.yaml`  | YAML file defining the agents for "poem_crew".                     |
+| │ │ └── `tasks.yaml`   | YAML file defining the tasks for "poem_crew".                      |
+| │ ├── `poem_crew.py`   | Script for "poem_crew" functionality.                              |
+| ├── `tools/`           | Directory for additional tools used in the flow.                   |
+| │ └── `custom_tool.py` | Custom tool implementation.                                        |
+| ├── `main.py`          | Main script for running the flow.                                  |
+| ├── `README.md`        | Project description and instructions.                              |
+| ├── `pyproject.toml`   | Configuration file for project dependencies and settings.          |
+| └── `.gitignore`       | Specifies files and directories to ignore in version control.      |
 
 ### Building Your Crews
 
@@ -518,9 +479,8 @@ The `main.py` file is where you create your flow and connect the crews together.
 
 Here's an example of how you can connect the `poem_crew` in the `main.py` file:
 
-```python Code
+```python
 #!/usr/bin/env python
-import asyncio
 from random import randint
 
 from pydantic import BaseModel
@@ -536,14 +496,12 @@ class PoemFlow(Flow[PoemState]):
     @start()
     def generate_sentence_count(self):
         print("Generating sentence count")
-        # Generate a number between 1 and 5
         self.state.sentence_count = randint(1, 5)
 
     @listen(generate_sentence_count)
     def generate_poem(self):
         print("Generating poem")
-        poem_crew = PoemCrew().crew()
-        result = poem_crew.kickoff(inputs={"sentence_count": self.state.sentence_count})
+        result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count})
 
         print("Poem generated", result.raw)
         self.state.poem = result.raw
@@ -554,18 +512,17 @@ class PoemFlow(Flow[PoemState]):
         with open("poem.txt", "w") as f:
             f.write(self.state.poem)
 
-async def run():
-    """
-    Run the flow.
-    """
+def kickoff():
     poem_flow = PoemFlow()
-    await poem_flow.kickoff()
+    poem_flow.kickoff()
 
-def main():
-    asyncio.run(run())
+
+def plot():
+    poem_flow = PoemFlow()
+    poem_flow.plot()
 
 if __name__ == "__main__":
-    main()
+    kickoff()
 ```
 
 In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method.
@@ -587,17 +544,53 @@ source .venv/bin/activate
 After activating the virtual environment, you can run the flow by executing one of the following commands:
 
 ```bash
-crewai flow run
+crewai flow kickoff
 ```
 
 or
 
 ```bash
-uv run run_flow
+uv run kickoff
 ```
 
 The flow will execute, and you should see the output in the console.
 
+
+### Adding Additional Crews Using the CLI
+
+Once you have created your initial flow, you can easily add additional crews to your project using the CLI. This allows you to expand your flow's capabilities by integrating new crews without starting from scratch.
+
+To add a new crew to your existing flow, use the following command:
+
+```bash
+crewai flow add-crew <crew_name>
+```
+
+This command will create a new directory for your crew within the `crews` folder of your flow project. It will include the necessary configuration files and a crew definition file, similar to the initial setup.
+
+#### Folder Structure
+
+After adding a new crew, your folder structure will look like this:
+
+| Directory/File         | Description                                                        |
+| :--------------------- | :----------------------------------------------------------------- |
+| `name_of_flow/`        | Root directory for the flow.                                       |
+| ├── `crews/`           | Contains directories for specific crews.                           |
+| │   ├── `poem_crew/`   | Directory for the "poem_crew" with its configurations and scripts. |
+| │   │   ├── `config/`  | Configuration files directory for the "poem_crew".                 |
+| │   │   │  ├── `agents.yaml` | YAML file defining the agents for "poem_crew".                     |
+| │   │   │  └── `tasks.yaml`  | YAML file defining the tasks for "poem_crew".                      |
+| │   │   └── `poem_crew.py`    | Script for "poem_crew" functionality.                              |
+| └── `name_of_crew/`       | Directory for the new crew.                                          |
+|     ├── `config/`         | Configuration files directory for the new crew.                     |
+|     │   ├── `agents.yaml` | YAML file defining the agents for the new crew.                     |
+|     │   └── `tasks.yaml`  | YAML file defining the tasks for the new crew.                      |
+|     └── `name_of_crew.py` | Script for the new crew functionality.                              |
+
+You can then customize the `agents.yaml` and `tasks.yaml` files to define the agents and tasks for your new crew. The `name_of_crew.py` file will contain the crew's logic, which you can modify to suit your needs.
+
+By using the CLI to add additional crews, you can efficiently build complex AI workflows that leverage multiple crews working together.
+
 ## Plot Flows
 
 Visualizing your AI workflows can provide valuable insights into the structure and execution paths of your flows. CrewAI offers a powerful visualization tool that allows you to generate interactive plots of your flows, making it easier to understand and optimize your AI workflows.
@@ -614,7 +607,7 @@ CrewAI provides two convenient methods to generate plots of your flows:
 
 If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
 
-```python Code
+```python
 # Assuming you have a flow instance
 flow.plot("my_flow_plot")
 ```
@@ -637,13 +630,114 @@ The generated plot will display nodes representing the tasks in your flow, with
 
 By visualizing your flows, you can gain a clearer understanding of the workflow's structure, making it easier to debug, optimize, and communicate your AI processes to others.
 
-### Conclusion
 
-Plotting your flows is a powerful feature of CrewAI that enhances your ability to design and manage complex AI workflows. Whether you choose to use the `plot()` method or the command line, generating plots will provide you with a visual representation of your workflows, aiding in both development and presentation.
+## Advanced
+
+In this section, we explore more complex use cases of CrewAI Flows, starting with a self-evaluation loop. This pattern is crucial for developing AI systems that can iteratively improve their outputs through feedback.
+
+### 1) Self-Evaluation Loop
+
+The self-evaluation loop is a powerful pattern that allows AI workflows to automatically assess and refine their outputs. This example demonstrates how to set up a flow that generates content, evaluates it, and iterates based on feedback until the desired quality is achieved.
+
+#### Overview
+
+The self-evaluation loop involves two main Crews:
+
+1. **ShakespeareanXPostCrew**: Generates a Shakespearean-style post on a given topic.
+2. **XPostReviewCrew**: Evaluates the generated post, providing feedback on its validity and quality.
+
+The process iterates until the post meets the criteria or a maximum retry limit is reached. This approach ensures high-quality outputs through iterative refinement.
+
+#### Importance
+
+This pattern is essential for building robust AI systems that can adapt and improve over time. By automating the evaluation and feedback loop, developers can ensure that their AI workflows produce reliable and high-quality results.
+
+#### Main Code Highlights
+
+Below is the `main.py` file for the self-evaluation loop flow:
+
+```python
+from typing import Optional
+from crewai.flow.flow import Flow, listen, router, start
+from pydantic import BaseModel
+from self_evaluation_loop_flow.crews.shakespeare_crew.shakespeare_crew import (
+    ShakespeareanXPostCrew,
+)
+from self_evaluation_loop_flow.crews.x_post_review_crew.x_post_review_crew import (
+    XPostReviewCrew,
+)
+
+class ShakespeareXPostFlowState(BaseModel):
+    x_post: str = ""
+    feedback: Optional[str] = None
+    valid: bool = False
+    retry_count: int = 0
+
+class ShakespeareXPostFlow(Flow[ShakespeareXPostFlowState]):
+
+    @start("retry")
+    def generate_shakespeare_x_post(self):
+        print("Generating Shakespearean X post")
+        topic = "Flying cars"
+        result = (
+            ShakespeareanXPostCrew()
+            .crew()
+            .kickoff(inputs={"topic": topic, "feedback": self.state.feedback})
+        )
+        print("X post generated", result.raw)
+        self.state.x_post = result.raw
+
+    @router(generate_shakespeare_x_post)
+    def evaluate_x_post(self):
+        if self.state.retry_count > 3:
+            return "max_retry_exceeded"
+        result = XPostReviewCrew().crew().kickoff(inputs={"x_post": self.state.x_post})
+        self.state.valid = result["valid"]
+        self.state.feedback = result["feedback"]
+        print("valid", self.state.valid)
+        print("feedback", self.state.feedback)
+        self.state.retry_count += 1
+        if self.state.valid:
+            return "complete"
+        return "retry"
+
+    @listen("complete")
+    def save_result(self):
+        print("X post is valid")
+        print("X post:", self.state.x_post)
+        with open("x_post.txt", "w") as file:
+            file.write(self.state.x_post)
+
+    @listen("max_retry_exceeded")
+    def max_retry_exceeded_exit(self):
+        print("Max retry count exceeded")
+        print("X post:", self.state.x_post)
+        print("Feedback:", self.state.feedback)
+
+def kickoff():
+    shakespeare_flow = ShakespeareXPostFlow()
+    shakespeare_flow.kickoff()
+
+def plot():
+    shakespeare_flow = ShakespeareXPostFlow()
+    shakespeare_flow.plot()
+
+if __name__ == "__main__":
+    kickoff()
+```
+
+#### Code Highlights
+
+- **Retry Mechanism**: The flow uses a retry mechanism to regenerate the post if it doesn't meet the criteria, up to a maximum of three retries.
+- **Feedback Loop**: Feedback from the `XPostReviewCrew` is used to refine the post iteratively.
+- **State Management**: The flow maintains state using a Pydantic model, ensuring type safety and clarity.
+
+For a complete example and further details, please refer to the [Self Evaluation Loop Flow repository](https://github.com/crewAIInc/crewAI-examples/tree/main/self_evaluation_loop_flow).
+
 
 ## Next Steps
 
-If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are four specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
+If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are five specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
 
 1. **Email Auto Responder Flow**: This example demonstrates an infinite loop where a background job continually runs to automate email responses. It's a great use case for tasks that need to be performed repeatedly without manual intervention. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow)
 
@@ -653,17 +747,19 @@ If you're interested in exploring additional examples of flows, we have a variet
 
 4. **Meeting Assistant Flow**: This flow demonstrates how to broadcast one event to trigger multiple follow-up actions. For instance, after a meeting is completed, the flow can update a Trello board, send a Slack message, and save the results. It's a great example of handling multiple outcomes from a single event, making it ideal for comprehensive task management and notification systems. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow)
 
+5. **Self Evaluation Loop Flow**: This flow demonstrates a self-evaluation loop where AI workflows automatically assess and refine their outputs through feedback. It involves generating content, evaluating it, and iterating until the desired quality is achieved. This pattern is crucial for developing robust AI systems that can adapt and improve over time. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/self_evaluation_loop_flow)
+
 By exploring these examples, you can gain insights into how to leverage CrewAI Flows for various use cases, from automating repetitive tasks to managing complex, multi-step processes with dynamic decision-making and human feedback.
 
 Also, check out our YouTube video on how to use flows in CrewAI below!
 
-<iframe 
-    width="560" 
-    height="315" 
-    src="https://www.youtube.com/embed/MTb5my6VOT8" 
-    title="YouTube video player" 
-    frameborder="0" 
-    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" 
-    referrerpolicy="strict-origin-when-cross-origin" 
-    allowfullscreen
-></iframe>    
+<iframe
+  width="560"
+  height="315"
+  src="https://www.youtube.com/embed/MTb5my6VOT8"
+  title="YouTube video player"
+  frameborder="0"
+  allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+  referrerpolicy="strict-origin-when-cross-origin"
+  allowfullscreen
+></iframe>

docs/concepts/llms.mdx

@@ -25,50 +25,148 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
 - `OPENAI_API_BASE`
 - `OPENAI_API_KEY`
 
-### 2. String Identifier
+### 2. Updating YAML files
 
-```python Code
-agent = Agent(llm="gpt-4o", ...)
+You can update the `agents.yml` file to refer to the LLM you want to use:
+
+```yaml Code
+researcher:
+    role: Research Specialist
+    goal: Conduct comprehensive research and analysis to gather relevant information,
+        synthesize findings, and produce well-documented insights.
+    backstory: A dedicated research professional with years of experience in academic
+        investigation, literature review, and data analysis, known for thorough and
+        methodical approaches to complex research questions.
+    verbose: true
+    llm: openai/gpt-4o
+    # llm: azure/gpt-4o-mini
+    # llm: gemini/gemini-pro
+    # llm: anthropic/claude-3-5-sonnet-20240620
+    # llm: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
+    # llm: mistral/mistral-large-latest
+    # llm: ollama/llama3:70b
+    # llm: groq/llama-3.2-90b-vision-preview
+    # llm: watsonx/meta-llama/llama-3-1-70b-instruct
+    # ...
 ```
 
-### 3. LLM Instance
+Keep in mind that you will need to set certain ENV vars depending on the model you are
+using to account for the credentials or set a custom LLM object like described below.
+Here are some of the required ENV vars for some of the LLM integrations:
 
-List of [more providers](https://docs.litellm.ai/docs/providers).
+<AccordionGroup>
+    <Accordion title="OpenAI">
+    ```python Code
+    OPENAI_API_KEY=<your-api-key>
+    OPENAI_API_BASE=<optional-custom-base-url>
+    OPENAI_MODEL_NAME=<openai-model-name>
+    OPENAI_ORGANIZATION=<your-org-id>       # OPTIONAL
+    OPENAI_API_BASE=<openaiai-api-base>     # OPTIONAL
+    ```
+    </Accordion>
 
-```python Code
-from crewai import LLM
+    <Accordion title="Anthropic">
+    ```python Code
+    ANTHROPIC_API_KEY=<your-api-key>
+    ```
+    </Accordion>
 
-llm = LLM(model="gpt-4", temperature=0.7)
-agent = Agent(llm=llm, ...)
-```
+    <Accordion title="Google">
+    ```python Code
+    GEMINI_API_KEY=<your-api-key>
+    ```
+    </Accordion>
 
-### 4. Custom LLM Objects
+    <Accordion title="Azure">
+    ```python Code
+    AZURE_API_KEY=<your-api-key>                    # "my-azure-api-key"
+    AZURE_API_BASE=<your-resource-url>              # "https://example-endpoint.openai.azure.com"
+    AZURE_API_VERSION=<api-version>                 # "2023-05-15"
+    AZURE_AD_TOKEN=<your-azure-ad-token>            # Optional
+    AZURE_API_TYPE=<your-azure-api-type>            # Optional
+    ```
+    </Accordion>
+
+    <Accordion title="AWS Bedrock">
+    ```python Code
+    AWS_ACCESS_KEY_ID=<your-access-key>
+    AWS_SECRET_ACCESS_KEY=<your-secret-key>
+    AWS_DEFAULT_REGION=<your-region>
+    ```
+    </Accordion>
+
+    <Accordion title="Mistral">
+    ```python Code
+    MISTRAL_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="Groq">
+    ```python Code
+    GROQ_API_KEY=<your-api-key>
+    ```
+    </Accordion>
+
+    <Accordion title="IBM watsonx.ai">
+    ```python Code
+    WATSONX_URL=<your-url>                          # (required) Base URL of your WatsonX instance
+    WATSONX_APIKEY=<your-apikey>                    # (required) IBM cloud API key
+    WATSONX_TOKEN=<your-token>                      # (required) IAM auth token (alternative to APIKEY)
+    WATSONX_PROJECT_ID=<your-project-id>            # (optional) Project ID of your WatsonX instance
+    WATSONX_DEPLOYMENT_SPACE_ID=<your-space-id>     # (optional) ID of deployment space for deployed models
+    ```
+    </Accordion>
+</AccordionGroup>
+
+### 3. Custom LLM Objects
 
 Pass a custom LLM implementation or object from another library.
 
+See below for examples.
+
+<Tabs>
+    <Tab title="String Identifier">
+    ```python Code
+    agent = Agent(llm="gpt-4o", ...)
+    ```
+    </Tab>
+
+    <Tab title="LLM Instance">
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(model="gpt-4", temperature=0.7)
+    agent = Agent(llm=llm, ...)
+    ```
+    </Tab>
+</Tabs>
+
 ## Connecting to OpenAI-Compatible LLMs
 
 You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
 
-1. Using environment variables:
+<Tabs>
+    <Tab title="Using Environment Variables">
+    ```python Code
+    import os
 
-```python Code
-import os
+    os.environ["OPENAI_API_KEY"] = "your-api-key"
+    os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
+    ```
+    </Tab>
+    <Tab title="Using LLM Class Attributes">
+    ```python Code
+    from crewai import LLM
 
-os.environ["OPENAI_API_KEY"] = "your-api-key"
-os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
-```
-
-2. Using LLM class attributes:
-
-```python Code
-llm = LLM(
-    model="custom-model-name",
-    api_key="your-api-key",
-    base_url="https://api.your-provider.com/v1"
-)
-agent = Agent(llm=llm, ...)
-```
+    llm = LLM(
+        model="custom-model-name",
+        api_key="your-api-key",
+        base_url="https://api.your-provider.com/v1"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Tab>
+</Tabs>
 
 ## LLM Configuration Options
 
@@ -95,43 +193,188 @@ When configuring an LLM for your agent, you have access to a wide range of param
 | **api_key**          | `str`             | Your API key for authentication.                                                                 |
 
 
-Example:
+These are examples of how to configure LLMs for your agent.
 
-```python Code
-llm = LLM(
-    model="gpt-4",
-    temperature=0.8,
-    max_tokens=150,
-    top_p=0.9,
-    frequency_penalty=0.1,
-    presence_penalty=0.1,
-    stop=["END"],
-    seed=42,
-    base_url="https://api.openai.com/v1",
-    api_key="your-api-key-here"
-)
-agent = Agent(llm=llm, ...)
-```
-## Using Ollama (Local LLMs)
+<AccordionGroup>
+    <Accordion title="OpenAI">
 
-crewAI supports using Ollama for running open-source models locally:
+    ```python Code
+    from crewai import LLM
 
-1. Install Ollama: [ollama.ai](https://ollama.ai/)
-2. Run a model: `ollama run llama2`
-3. Configure agent:
+    llm = LLM(
+        model="gpt-4",
+        temperature=0.8,
+        max_tokens=150,
+        top_p=0.9,
+        frequency_penalty=0.1,
+        presence_penalty=0.1,
+        stop=["END"],
+        seed=42,
+        base_url="https://api.openai.com/v1",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
 
-```python Code
-agent = Agent(
-    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
-    ...
-)
-```
+    <Accordion title="Cerebras">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="cerebras/llama-3.1-70b",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Ollama (Local LLMs)">
+
+    CrewAI supports using Ollama for running open-source models locally:
+
+    1. Install Ollama: [ollama.ai](https://ollama.ai/)
+    2. Run a model: `ollama run llama2`
+    3. Configure agent:
+
+    ```python Code
+    from crewai import LLM
+
+    agent = Agent(
+        llm=LLM(
+            model="ollama/llama3.1",
+            base_url="http://localhost:11434"
+        ),
+        ...
+    )
+    ```
+    </Accordion>
+
+    <Accordion title="Groq">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="groq/llama3-8b-8192",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Anthropic">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Fireworks AI">
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Gemini">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-1.5-pro-002",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Perplexity AI (pplx-api)">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="perplexity/mistral-7b-instruct",
+        base_url="https://api.perplexity.ai/v1",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="IBM watsonx.ai">
+    You can use IBM Watson by seeting the following ENV vars:
+
+    ```python Code
+    WATSONX_URL=<your-url>
+    WATSONX_APIKEY=<your-apikey>
+    WATSONX_PROJECT_ID=<your-project-id>
+    ```
+
+    You can then define your agents llms by updating the `agents.yml`
+
+    ```yaml Code
+    researcher:
+        role: Research Specialist
+        goal: Conduct comprehensive research and analysis to gather relevant information,
+            synthesize findings, and produce well-documented insights.
+        backstory: A dedicated research professional with years of experience in academic
+            investigation, literature review, and data analysis, known for thorough and
+            methodical approaches to complex research questions.
+        verbose: true
+        llm: watsonx/meta-llama/llama-3-1-70b-instruct
+    ```
+
+    You can also set up agents more dynamically as a base level LLM instance, like bellow:
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="watsonx/ibm/granite-13b-chat-v2",
+        base_url="https://api.watsonx.ai/v1",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Hugging Face">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
+        api_key="your-api-key-here",
+        base_url="your_api_endpoint"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+</AccordionGroup>
 
 ## Changing the Base API URL
 
 You can change the base API URL for any LLM provider by setting the `base_url` parameter:
 
 ```python Code
+from crewai import LLM
+
 llm = LLM(
     model="custom-model-name",
     base_url="https://api.your-provider.com/v1",

docs/concepts/memory.mdx

@@ -18,6 +18,7 @@ reason, and learn from past interactions.
 | **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
 | **Entity Memory**    | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
 | **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
+| **User Memory**      | Stores user-specific information and preferences, enhancing personalization and user experience. |
 
 ## How Memory Systems Empower Agents
 
@@ -34,7 +35,7 @@ By default, the memory system is disabled, and you can ensure it is active by se
 The memory will use OpenAI embeddings by default, but you can change it by setting `embedder` to a different model. 
 It's also possible to initialize the memory instance with your own instance.
 
-The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using the EmbedChain package.
+The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG.
 The **Long-Term Memory** uses SQLite3 to store task results. Currently, there is no way to override these storage implementations.
 The data storage files are saved into a platform-specific location found using the appdirs package,
 and the name of the project can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
@@ -92,6 +93,47 @@ my_crew = Crew(
 )
 ```
 
+## Integrating Mem0 for Enhanced User Memory
+
+[Mem0](https://mem0.ai/) is a self-improving memory layer for LLM applications, enabling personalized AI experiences. 
+
+To include user-specific memory you can get your API key [here](https://app.mem0.ai/dashboard/api-keys) and refer the [docs](https://docs.mem0.ai/platform/quickstart#4-1-create-memories) for adding user preferences.
+
+
+```python Code
+import os
+from crewai import Crew, Process
+from mem0 import MemoryClient
+
+# Set environment variables for Mem0
+os.environ["MEM0_API_KEY"] = "m0-xx"
+
+# Step 1: Record preferences based on past conversation or user input
+client = MemoryClient()
+messages = [
+    {"role": "user", "content": "Hi there! I'm planning a vacation and could use some advice."},
+    {"role": "assistant", "content": "Hello! I'd be happy to help with your vacation planning. What kind of destination do you prefer?"},
+    {"role": "user", "content": "I am more of a beach person than a mountain person."},
+    {"role": "assistant", "content": "That's interesting. Do you like hotels or Airbnb?"},
+    {"role": "user", "content": "I like Airbnb more."},
+]
+client.add(messages, user_id="john")
+
+# Step 2: Create a Crew with User Memory
+
+crew = Crew(
+    agents=[...],
+    tasks=[...],
+    verbose=True,
+    process=Process.sequential,
+    memory=True,
+    memory_config={
+        "provider": "mem0",
+        "config": {"user_id": "john"},
+    },
+)
+```
+
 
 ## Additional Embedding Providers
 
@@ -113,6 +155,42 @@ my_crew = Crew(
     }
 )
 ```
+Alternatively, you can directly pass the OpenAIEmbeddingFunction to the embedder parameter.
+
+Example:
+```python Code
+from crewai import Crew, Agent, Task, Process
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder=OpenAIEmbeddingFunction(api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"),
+)
+```
+
+### Using Ollama embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "ollama",
+        "config": {
+            "model": "mxbai-embed-large"
+        }
+    }
+)
+```
 
 ### Using Google AI embeddings
 
@@ -128,9 +206,8 @@ my_crew = Crew(
     embedder={
         "provider": "google",
         "config": {
-            "model": 'models/embedding-001',
-            "task_type": "retrieval_document",
-            "title": "Embeddings for Embedchain"
+            "api_key": "<YOUR_API_KEY>",
+            "model_name": "<model_name>"
         }
     }
 )
@@ -139,6 +216,7 @@ my_crew = Crew(
 ### Using Azure OpenAI embeddings
 
 ```python Code
+from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
@@ -147,36 +225,20 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "azure_openai",
-        "config": {
-            "model": 'text-embedding-ada-002',
-            "deployment_name": "your_embedding_model_deployment_name"
-        }
-    }
-)
-```
-
-### Using GPT4ALL embeddings
-
-```python Code
-from crewai import Crew, Agent, Task, Process
-
-my_crew = Crew(
-    agents=[...],
-    tasks=[...],
-    process=Process.sequential,
-    memory=True,
-    verbose=True,
-    embedder={
-        "provider": "gpt4all"
-    }
+    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"
+    )
 )
 ```
 
 ### Using Vertex AI embeddings
 
 ```python Code
+from chromadb.utils.embedding_functions import GoogleVertexEmbeddingFunction
 from crewai import Crew, Agent, Task, Process
 
 my_crew = Crew(
@@ -185,12 +247,12 @@ my_crew = Crew(
     process=Process.sequential,
     memory=True,
     verbose=True,
-    embedder={
-        "provider": "vertexai",
-        "config": {
-            "model": 'textembedding-gecko'
-        }
-    }
+    embedder=GoogleVertexEmbeddingFunction(
+        project_id="YOUR_PROJECT_ID",
+        region="YOUR_REGION",
+        api_key="YOUR_API_KEY",
+        model_name="textembedding-gecko"
+    )
 )
 ```
 
@@ -208,8 +270,52 @@ my_crew = Crew(
     embedder={
         "provider": "cohere",
         "config": {
-            "model": "embed-english-v3.0",
-            "vector_dimension": 1024
+            "api_key": "YOUR_API_KEY",
+            "model_name": "<model_name>"
+        }
+    }
+)
+```
+### Using HuggingFace embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "huggingface",
+        "config": {
+            "api_url": "<api_url>",
+        }
+    }
+)
+```
+
+### Using Watson embeddings
+
+```python Code
+from crewai import Crew, Agent, Task, Process
+
+# Note: Ensure you have installed and imported `ibm_watsonx_ai` for Watson embeddings to work.
+
+my_crew = Crew(
+    agents=[...],
+    tasks=[...],
+    process=Process.sequential,
+    memory=True,
+    verbose=True,
+    embedder={
+        "provider": "watson",
+        "config": {
+            "model": "<model_name>",
+            "api_url": "<api_url>",
+            "api_key": "<YOUR_API_KEY>",
+            "project_id": "<YOUR_PROJECT_ID>",
         }
     }
 )

docs/concepts/tools.mdx

@@ -5,13 +5,14 @@ icon: screwdriver-wrench
 ---
 
 ## Introduction
-CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. 
+
+CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
 This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
 
 ## What is a Tool?
 
-A tool in CrewAI is a skill or function that agents can utilize to perform various actions. 
-This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), 
+A tool in CrewAI is a skill or function that agents can utilize to perform various actions.
+This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools),
 enabling everything from simple searches to complex interactions and effective teamwork among agents.
 
 ## Key Characteristics of Tools
@@ -103,57 +104,53 @@ crew.kickoff()
 
 Here is a list of the available tools and their descriptions:
 
-| Tool                        | Description                                                                                   |
-| :-------------------------- | :-------------------------------------------------------------------------------------------- |
-| **BrowserbaseLoadTool**     | A tool for interacting with and extracting data from web browsers.                            |
-| **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents. |
-| **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
-| **ComposioTool**            | Enables use of Composio tools.                                                                |
-| **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
-| **DALL-E Tool**             | A tool for generating images using the DALL-E API.                                            |
-| **DirectorySearchTool**     | A RAG tool for searching within directories, useful for navigating through file systems.      |
-| **DOCXSearchTool**          | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.         |
-| **DirectoryReadTool**       | Facilitates reading and processing of directory structures and their contents.                |
-| **EXASearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
-| **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
-| **FirecrawlSearchTool**     | A tool to search webpages using Firecrawl and return the results.                             |
-| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl.                                               |
-| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages URL using Firecrawl and returning its contents.              |
-| **GithubSearchTool**        | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
-| **SerperDevTool**           | A specialized tool for development purposes, with specific functionalities under development. |
-| **TXTSearchTool**           | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.     |
-| **JSONSearchTool**          | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
-| **LlamaIndexTool**          | Enables the use of LlamaIndex tools.                                                          |
-| **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
-| **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
-| **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
-| **Vision Tool**             | A tool for generating images using the DALL-E API.                                            |
-| **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                |
-| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.   |
-| **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                |
-| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                  |
-| **XMLSearchTool**           | A RAG tool designed for searching within XML files, suitable for structured data formats.     |
-| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis.          |
-| **YoutubeVideoSearchTool**  | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.         |
+| Tool                             | Description                                                                                    |
+| :------------------------------- | :--------------------------------------------------------------------------------------------- |
+| **BrowserbaseLoadTool**          | A tool for interacting with and extracting data from web browsers.                             |
+| **CodeDocsSearchTool**           | A RAG tool optimized for searching through code documentation and related technical documents. |
+| **CodeInterpreterTool**          | A tool for interpreting python code.                                                           |
+| **ComposioTool**                 | Enables use of Composio tools.                                                                 |
+| **CSVSearchTool**                | A RAG tool designed for searching within CSV files, tailored to handle structured data.        |
+| **DALL-E Tool**                  | A tool for generating images using the DALL-E API.                                             |
+| **DirectorySearchTool**          | A RAG tool for searching within directories, useful for navigating through file systems.       |
+| **DOCXSearchTool**               | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.          |
+| **DirectoryReadTool**            | Facilitates reading and processing of directory structures and their contents.                 |
+| **EXASearchTool**                | A tool designed for performing exhaustive searches across various data sources.                |
+| **FileReadTool**                 | Enables reading and extracting data from files, supporting various file formats.               |
+| **FirecrawlSearchTool**          | A tool to search webpages using Firecrawl and return the results.                              |
+| **FirecrawlCrawlWebsiteTool**    | A tool for crawling webpages using Firecrawl.                                                  |
+| **FirecrawlScrapeWebsiteTool**   | A tool for scraping webpages URL using Firecrawl and returning its contents.                   |
+| **GithubSearchTool**             | A RAG tool for searching within GitHub repositories, useful for code and documentation search. |
+| **SerperDevTool**                | A specialized tool for development purposes, with specific functionalities under development.  |
+| **TXTSearchTool**                | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.      |
+| **JSONSearchTool**               | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
+| **LlamaIndexTool**               | Enables the use of LlamaIndex tools.                                                           |
+| **MDXSearchTool**                | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.       |
+| **PDFSearchTool**                | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
+| **PGSearchTool**                 | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
+| **Vision Tool**                  | A tool for generating images using the DALL-E API.                                             |
+| **RagTool**                      | A general-purpose RAG tool capable of handling various data sources and types.                 |
+| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.         |
+| **ScrapeWebsiteTool**            | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
+| **WebsiteSearchTool**            | A RAG tool for searching website content, optimized for web data extraction.                   |
+| **XMLSearchTool**                | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
+| **YoutubeChannelSearchTool**     | A RAG tool for searching within YouTube channels, useful for video content analysis.           |
+| **YoutubeVideoSearchTool**       | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
 
 ## Creating your own Tools
 
 <Tip>
-  Developers can craft `custom tools` tailored for their agent’s needs or utilize pre-built options.
+  Developers can craft `custom tools` tailored for their agent’s needs or
+  utilize pre-built options.
 </Tip>
 
-To create your own CrewAI tools you will need to install our extra tools package:
-
-```bash
-pip install 'crewai[tools]'
-```
-
-Once you do that there are two main ways for one to create a CrewAI tool:
+There are two main ways for one to create a CrewAI tool:
 
 ### Subclassing `BaseTool`
 
 ```python Code
-from crewai_tools import BaseTool
+from crewai.tools import BaseTool
+
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
@@ -167,7 +164,7 @@ class MyCustomTool(BaseTool):
 ### Utilizing the `tool` Decorator
 
 ```python Code
-from crewai_tools import tool
+from crewai.tools import tool
 @tool("Name of my tool")
 def my_tool(question: str) -> str:
     """Clear description for what this tool is useful for, your agent will need this information to use it."""
@@ -178,11 +175,13 @@ def my_tool(question: str) -> str:
 ### Custom Caching Mechanism
 
 <Tip>
-  Tools can optionally implement a `cache_function` to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic.
+  Tools can optionally implement a `cache_function` to fine-tune caching
+  behavior. This function determines when to cache results based on specific
+  conditions, offering granular control over caching logic.
 </Tip>
 
 ```python Code
-from crewai_tools import tool
+from crewai.tools import tool
 
 @tool
 def multiplication_tool(first_number: int, second_number: int) -> str:
@@ -208,6 +207,6 @@ writer1 = Agent(
 
 ## Conclusion
 
-Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively. 
-When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, 
-caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.
+Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
+When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling,
+caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

docs/how-to/create-custom-tools.mdx

@@ -6,28 +6,27 @@ icon: hammer
 
 ## Creating and Utilizing Tools in CrewAI
 
-This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools, 
-incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, 
+This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools,
+incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
 enabling agents to perform a wide range of actions.
 
-### Prerequisites
-
-Before creating your own tools, ensure you have the crewAI extra tools package installed:
-
-```bash
-pip install 'crewai[tools]'
-```
-
 ### Subclassing `BaseTool`
 
-To create a personalized tool, inherit from `BaseTool` and define the necessary attributes and the `_run` method.
+To create a personalized tool, inherit from `BaseTool` and define the necessary attributes, including the `args_schema` for input validation, and the `_run` method.
 
 ```python Code
-from crewai_tools import BaseTool
+from typing import Type
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+class MyToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+    argument: str = Field(..., description="Description of the argument.")
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = "What this tool does. It's vital for effective utilization."
+    args_schema: Type[BaseModel] = MyToolInput
 
     def _run(self, argument: str) -> str:
         # Your tool's logic here
@@ -40,7 +39,7 @@ Alternatively, you can use the tool decorator `@tool`. This approach allows you
 offering a concise and efficient way to create specialized tools tailored to your needs.
 
 ```python Code
-from crewai_tools import tool
+from crewai.tools import tool
 
 @tool("Tool Name")
 def my_simple_tool(question: str) -> str:
@@ -66,5 +65,5 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
 cached_tool.cache_function = my_cache_strategy
 ```
 
-By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes, 
+By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
 you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents.

docs/quickstart.mdx

@@ -330,4 +330,4 @@ This will clear the crew's memory, allowing for a fresh start.
 
 ## Deploying Your Project
 
-The easiest way to deploy your crew is through [CrewAI Enterprise](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
+The easiest way to deploy your crew is through [CrewAI Enterprise](http://app.crewai.com/), where you can deploy your crew in a few clicks.

docs/tools/visiontool.mdx

@@ -8,13 +8,13 @@ icon: eye
 
 ## Description
 
-This tool is used to extract text from images. When passed to the agent it will extract the text from the image and then use it to generate a response, report or any other output. 
+This tool is used to extract text from images. When passed to the agent it will extract the text from the image and then use it to generate a response, report or any other output.
 The URL or the PATH of the image should be passed to the Agent.
 
-
 ## Installation
 
 Install the crewai_tools package
+
 ```shell
 pip install 'crewai[tools]'
 ```
@@ -44,7 +44,6 @@ def researcher(self) -> Agent:
 
 The VisionTool requires the following arguments:
 
-| Argument       | Type     | Description                                                                                                                         |
-|:---------------|:---------|:-------------------------------------------------------------------------------------------------------------------------------------|
-| **image_path** | `string` | **Mandatory**. The path to the image file from which text needs to be extracted.                                                |
-
+| Argument           | Type     | Description                                                                      |
+| :----------------- | :------- | :------------------------------------------------------------------------------- |
+| **image_path_url** | `string` | **Mandatory**. The path to the image file from which text needs to be extracted. |

poetry.lock

@@ -1597,12 +1597,12 @@ files = [
 google-auth = ">=2.14.1,<3.0.dev0"
 googleapis-common-protos = ">=1.56.2,<2.0.dev0"
 grpcio = [
-    {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
     {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
 ]
 grpcio-status = [
-    {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
     {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
 ]
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0"
@@ -4286,8 +4286,8 @@ files = [
 
 [package.dependencies]
 numpy = [
-    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
     {version = ">=1.22.4", markers = "python_version < \"3.11\""},
+    {version = ">=1.23.2", markers = "python_version == \"3.11\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "crewai"
-version = "0.70.1"
+version = "0.80.0"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
@@ -16,19 +16,19 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.12.1",
+    "crewai-tools>=0.14.0",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
     "jsonref>=1.1.0",
-    "agentops>=0.3.0",
-    "embedchain>=0.1.114",
     "json-repair>=0.25.2",
     "auth0-python>=4.7.1",
     "litellm>=1.44.22",
     "pyvis>=0.3.2",
-    "uv>=0.4.18",
+    "uv>=0.4.25",
     "tomli-w>=1.1.0",
+    "tomli>=2.0.2",
+    "chromadb>=0.5.18",
 ]
 
 [project.urls]
@@ -37,8 +37,9 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.12.1"]
+tools = ["crewai-tools>=0.14.0"]
 agentops = ["agentops>=0.3.0"]
+mem0 = ["mem0ai>=0.1.29"]
 
 [tool.uv]
 dev-dependencies = [
@@ -52,7 +53,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.12.1",
+    "crewai-tools>=0.14.0",
     "pytest>=8.0.0",
     "pytest-vcr>=1.0.2",
     "python-dotenv>=1.0.0",

src/crewai/__init__.py

@@ -14,5 +14,5 @@ warnings.filterwarnings(
     category=UserWarning,
     module="pydantic.main",
 )
-__version__ = "0.70.1"
+__version__ = "0.80.0"
 __all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]

src/crewai/agent.py

@@ -1,15 +1,18 @@
 import os
-from inspect import signature
-from typing import Any, List, Optional, Union
+import shutil
+import subprocess
+from typing import Any, List, Literal, Optional, Union
 
 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
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.tools.agent_tools import AgentTools
+from crewai.tools.agent_tools.agent_tools import AgentTools
+from crewai.tools import BaseTool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -112,10 +115,19 @@ class Agent(BaseAgent):
         default=2,
         description="Maximum number of retries for an agent to execute a task when an error occurs.",
     )
+    code_execution_mode: Literal["safe", "unsafe"] = Field(
+        default="safe",
+        description="Mode for code execution: 'safe' (using Docker) or 'unsafe' (direct execution).",
+    )
 
     @model_validator(mode="after")
     def post_init_setup(self):
         self.agent_ops_agent_name = self.role
+        unnacepted_attributes = [
+            "AWS_ACCESS_KEY_ID",
+            "AWS_SECRET_ACCESS_KEY",
+            "AWS_REGION_NAME",
+        ]
 
         # Handle different cases for self.llm
         if isinstance(self.llm, str):
@@ -125,8 +137,12 @@ class Agent(BaseAgent):
             # If it's already an LLM instance, keep it as is
             pass
         elif self.llm is None:
-            # If it's None, use environment variables or default
-            model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
+            # 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(
@@ -135,9 +151,44 @@ class Agent(BaseAgent):
             if api_base:
                 llm_params["base_url"] = api_base
 
-            api_key = os.environ.get("OPENAI_API_KEY")
-            if api_key:
-                llm_params["api_key"] = api_key
+            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:
+                        if env_var["key_name"] in unnacepted_attributes:
+                            continue
+                        # Check if the environment variable is set
+                        if "key_name" in env_var:
+                            env_value = os.environ.get(env_var["key_name"])
+                            if env_value:
+                                # Map key names containing "API_KEY" to "api_key"
+                                key_name = (
+                                    "api_key"
+                                    if "API_KEY" in env_var["key_name"]
+                                    else env_var["key_name"]
+                                )
+                                # Map key names containing "API_BASE" to "api_base"
+                                key_name = (
+                                    "api_base"
+                                    if "API_BASE" in env_var["key_name"]
+                                    else key_name
+                                )
+                                # Map key names containing "API_VERSION" to "api_version"
+                                key_name = (
+                                    "api_version"
+                                    if "API_VERSION" in env_var["key_name"]
+                                    else key_name
+                                )
+                                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:
@@ -173,6 +224,9 @@ class Agent(BaseAgent):
         if not self.agent_executor:
             self._setup_agent_executor()
 
+        if self.allow_code_execution:
+            self._validate_docker_installation()
+
         return self
 
     def _setup_agent_executor(self):
@@ -184,7 +238,7 @@ class Agent(BaseAgent):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -208,9 +262,11 @@ class Agent(BaseAgent):
 
         if self.crew and self.crew.memory:
             contextual_memory = ContextualMemory(
+                self.crew.memory_config,
                 self.crew._short_term_memory,
                 self.crew._long_term_memory,
                 self.crew._entity_memory,
+                self.crew._user_memory,
             )
             memory = contextual_memory.build_context_for_task(task, context)
             if memory.strip() != "":
@@ -251,7 +307,9 @@ class Agent(BaseAgent):
 
         return result
 
-    def create_agent_executor(self, tools=None, task=None) -> None:
+    def create_agent_executor(
+        self, tools: Optional[List[BaseTool]] = None, task=None
+    ) -> None:
         """Create an agent executor for the agent.
 
         Returns:
@@ -308,7 +366,9 @@ class Agent(BaseAgent):
         try:
             from crewai_tools import CodeInterpreterTool
 
-            return [CodeInterpreterTool()]
+            # Set the unsafe_mode based on the code_execution_mode attribute
+            unsafe_mode = self.code_execution_mode == "unsafe"
+            return [CodeInterpreterTool(unsafe_mode=unsafe_mode)]
         except ModuleNotFoundError:
             self._logger.log(
                 "info", "Coding tools not available. Install crewai_tools. "
@@ -322,7 +382,7 @@ class Agent(BaseAgent):
         tools_list = []
         try:
             # tentatively try to import from crewai_tools import BaseTool as CrewAITool
-            from crewai_tools import BaseTool as CrewAITool
+            from crewai.tools import BaseTool as CrewAITool
 
             for tool in tools:
                 if isinstance(tool, CrewAITool):
@@ -381,33 +441,42 @@ class Agent(BaseAgent):
 
         return description
 
-    def _render_text_description_and_args(self, tools: List[Any]) -> str:
+    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
         """Render the tool name, description, and args in plain text.
 
-        Output will be in the format of:
+            Output will be in the format of:
 
-        .. code-block:: markdown
+            .. code-block:: markdown
 
             search: This tool is used for search, args: {"query": {"type": "string"}}
             calculator: This tool is used for math, \
-    args: {"expression": {"type": "string"}}
+            args: {"expression": {"type": "string"}}
         """
         tool_strings = []
         for tool in tools:
-            args_schema = str(tool.args)
-            if hasattr(tool, "func") and tool.func:
-                sig = signature(tool.func)
-                description = (
-                    f"Tool Name: {tool.name}{sig}\nTool Description: {tool.description}"
-                )
-            else:
-                description = (
-                    f"Tool Name: {tool.name}\nTool Description: {tool.description}"
-                )
-            tool_strings.append(f"{description}\nTool Arguments: {args_schema}")
+            tool_strings.append(tool.description)
 
         return "\n".join(tool_strings)
 
+    def _validate_docker_installation(self) -> None:
+        """Check if Docker is installed and running."""
+        if not shutil.which("docker"):
+            raise RuntimeError(
+                f"Docker is not installed. Please install Docker to use code execution with agent: {self.role}"
+            )
+
+        try:
+            subprocess.run(
+                ["docker", "info"],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            )
+        except subprocess.CalledProcessError:
+            raise RuntimeError(
+                f"Docker is not running. Please start Docker to use code execution with agent: {self.role}"
+            )
+
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/agents/agent_builder/base_agent.py

@@ -18,6 +18,7 @@ from pydantic_core import PydanticCustomError
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.agents.cache.cache_handler import CacheHandler
 from crewai.agents.tools_handler import ToolsHandler
+from crewai.tools import BaseTool
 from crewai.utilities import I18N, Logger, RPMController
 from crewai.utilities.config import process_config
 
@@ -49,11 +50,11 @@ class BaseAgent(ABC, BaseModel):
 
 
     Methods:
-        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = None) -> str:
+        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[BaseTool]] = None) -> str:
             Abstract method to execute a task.
         create_agent_executor(tools=None) -> None:
             Abstract method to create an agent executor.
-        _parse_tools(tools: List[Any]) -> List[Any]:
+        _parse_tools(tools: List[BaseTool]) -> List[Any]:
             Abstract method to parse tools.
         get_delegation_tools(agents: List["BaseAgent"]):
             Abstract method to set the agents task tools for handling delegation and question asking to other agents in crew.
@@ -105,7 +106,7 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: Optional[List[Any]] = Field(
+    tools: Optional[List[BaseTool]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: Optional[int] = Field(
@@ -188,7 +189,7 @@ class BaseAgent(ABC, BaseModel):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         pass
 
@@ -197,11 +198,11 @@ class BaseAgent(ABC, BaseModel):
         pass
 
     @abstractmethod
-    def _parse_tools(self, tools: List[Any]) -> List[Any]:
+    def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[Any]:
+    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]:
         """Set the task tools that init BaseAgenTools class."""
         pass
 

src/crewai/agents/agent_builder/base_agent_executor_mixin.py

@@ -17,7 +17,7 @@ if TYPE_CHECKING:
 
 class CrewAgentExecutorMixin:
     crew: Optional["Crew"]
-    crew_agent: Optional["BaseAgent"]
+    agent: Optional["BaseAgent"]
     task: Optional["Task"]
     iterations: int
     have_forced_answer: bool
@@ -33,9 +33,9 @@ class CrewAgentExecutorMixin:
         """Create and save a short-term memory item if conditions are met."""
         if (
             self.crew
-            and self.crew_agent
+            and self.agent
             and self.task
-            and "Action: Delegate work to coworker" not in output.log
+            and "Action: Delegate work to coworker" not in output.text
         ):
             try:
                 if (
@@ -43,11 +43,11 @@ class CrewAgentExecutorMixin:
                     and self.crew._short_term_memory
                 ):
                     self.crew._short_term_memory.save(
-                        value=output.log,
+                        value=output.text,
                         metadata={
                             "observation": self.task.description,
                         },
-                        agent=self.crew_agent.role,
+                        agent=self.agent.role,
                     )
             except Exception as e:
                 print(f"Failed to add to short term memory: {e}")
@@ -61,18 +61,18 @@ class CrewAgentExecutorMixin:
             and self.crew._long_term_memory
             and self.crew._entity_memory
             and self.task
-            and self.crew_agent
+            and self.agent
         ):
             try:
-                ltm_agent = TaskEvaluator(self.crew_agent)
-                evaluation = ltm_agent.evaluate(self.task, output.log)
+                ltm_agent = TaskEvaluator(self.agent)
+                evaluation = ltm_agent.evaluate(self.task, output.text)
 
                 if isinstance(evaluation, ConverterError):
                     return
 
                 long_term_memory = LongTermMemoryItem(
                     task=self.task.description,
-                    agent=self.crew_agent.role,
+                    agent=self.agent.role,
                     quality=evaluation.quality,
                     datetime=str(time.time()),
                     expected_output=self.task.expected_output,

src/crewai/agents/agent_builder/utilities/base_token_process.py

@@ -4,6 +4,7 @@ from crewai.types.usage_metrics import UsageMetrics
 class TokenProcess:
     total_tokens: int = 0
     prompt_tokens: int = 0
+    cached_prompt_tokens: int = 0
     completion_tokens: int = 0
     successful_requests: int = 0
 
@@ -15,6 +16,9 @@ class TokenProcess:
         self.completion_tokens = self.completion_tokens + tokens
         self.total_tokens = self.total_tokens + tokens
 
+    def sum_cached_prompt_tokens(self, tokens: int):
+        self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
+
     def sum_successful_requests(self, requests: int):
         self.successful_requests = self.successful_requests + requests
 
@@ -22,6 +26,7 @@ class TokenProcess:
         return UsageMetrics(
             total_tokens=self.total_tokens,
             prompt_tokens=self.prompt_tokens,
+            cached_prompt_tokens=self.cached_prompt_tokens,
             completion_tokens=self.completion_tokens,
             successful_requests=self.successful_requests,
         )

src/crewai/agents/crew_agent_executor.py

@@ -2,6 +2,7 @@ import json
 import re
 from typing import Any, Dict, List, Union
 
+from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
     FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE,
@@ -29,7 +30,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         llm: Any,
         task: Any,
         crew: Any,
-        agent: Any,
+        agent: BaseAgent,
         prompt: dict[str, str],
         max_iter: int,
         tools: List[Any],
@@ -103,7 +104,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
 
             if self.crew and self.crew._train:
                 self._handle_crew_training_output(formatted_answer)
-
+        self._create_short_term_memory(formatted_answer)
+        self._create_long_term_memory(formatted_answer)
         return {"output": formatted_answer.output}
 
     def _invoke_loop(self, formatted_answer=None):
@@ -115,6 +117,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         callbacks=self.callbacks,
                     )
 
+                    if answer is None or answer == "":
+                        self._printer.print(
+                            content="Received None or empty response from LLM call.",
+                            color="red",
+                        )
+                        raise ValueError(
+                            "Invalid response from LLM call - None or empty."
+                        )
+
                     if not self.use_stop_words:
                         try:
                             self._format_answer(answer)
@@ -134,25 +145,26 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         formatted_answer.result = action_result
                         self._show_logs(formatted_answer)
 
-                        if self.step_callback:
-                            self.step_callback(formatted_answer)
+                    if self.step_callback:
+                        self.step_callback(formatted_answer)
 
-                        if self._should_force_answer():
-                            if self.have_forced_answer:
-                                return AgentFinish(
-                                    output=self._i18n.errors(
-                                        "force_final_answer_error"
-                                    ).format(formatted_answer.text),
-                                    text=formatted_answer.text,
-                                )
-                            else:
-                                formatted_answer.text += (
-                                    f'\n{self._i18n.errors("force_final_answer")}'
-                                )
-                                self.have_forced_answer = True
-                        self.messages.append(
-                            self._format_msg(formatted_answer.text, role="assistant")
-                        )
+                    if self._should_force_answer():
+                        if self.have_forced_answer:
+                            return AgentFinish(
+                                thought="",
+                                output=self._i18n.errors(
+                                    "force_final_answer_error"
+                                ).format(formatted_answer.text),
+                                text=formatted_answer.text,
+                            )
+                        else:
+                            formatted_answer.text += (
+                                f'\n{self._i18n.errors("force_final_answer")}'
+                            )
+                            self.have_forced_answer = True
+                    self.messages.append(
+                        self._format_msg(formatted_answer.text, role="assistant")
+                    )
 
         except OutputParserException as e:
             self.messages.append({"role": "user", "content": e.error})
@@ -176,6 +188,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return formatted_answer
 
     def _show_start_logs(self):
+        if self.agent is None:
+            raise ValueError("Agent cannot be None")
         if self.agent.verbose or (
             hasattr(self, "crew") and getattr(self.crew, "verbose", False)
         ):
@@ -188,6 +202,8 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             )
 
     def _show_logs(self, formatted_answer: Union[AgentAction, AgentFinish]):
+        if self.agent is None:
+            raise ValueError("Agent cannot be None")
         if self.agent.verbose or (
             hasattr(self, "crew") and getattr(self.crew, "verbose", False)
         ):
@@ -306,7 +322,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self, result: AgentFinish, human_feedback: str | None = None
     ) -> None:
         """Function to handle the process of the training data."""
-        agent_id = str(self.agent.id)
+        agent_id = str(self.agent.id)  # type: ignore
 
         # Load training data
         training_handler = CrewTrainingHandler(TRAINING_DATA_FILE)
@@ -339,7 +355,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 "initial_output": result.output,
                 "human_feedback": human_feedback,
                 "agent": agent_id,
-                "agent_role": self.agent.role,
+                "agent_role": self.agent.role,  # type: ignore
             }
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
@@ -370,4 +386,5 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         return CrewAgentParser(agent=self.agent).parse(answer)
 
     def _format_msg(self, prompt: str, role: str = "user") -> Dict[str, str]:
+        prompt = prompt.rstrip()
         return {"role": role, "content": prompt}

src/crewai/agents/tools_handler.py

@@ -1,6 +1,6 @@
 from typing import Any, Optional, Union
 
-from ..tools.cache_tools import CacheTools
+from ..tools.cache_tools.cache_tools import CacheTools
 from ..tools.tool_calling import InstructorToolCalling, ToolCalling
 from .cache.cache_handler import CacheHandler
 

src/crewai/cli/add_crew_to_flow.py

@@ -0,0 +1,70 @@
+from pathlib import Path
+
+import click
+
+from crewai.cli.utils import copy_template
+
+
+def add_crew_to_flow(crew_name: str) -> None:
+    """Add a new crew to the current flow."""
+    # Check if pyproject.toml exists in the current directory
+    if not Path("pyproject.toml").exists():
+        print("This command must be run from the root of a flow project.")
+        raise click.ClickException(
+            "This command must be run from the root of a flow project."
+        )
+
+    # Determine the flow folder based on the current directory
+    flow_folder = Path.cwd()
+    crews_folder = flow_folder / "src" / flow_folder.name / "crews"
+
+    if not crews_folder.exists():
+        print("Crews folder does not exist in the current flow.")
+        raise click.ClickException("Crews folder does not exist in the current flow.")
+
+    # Create the crew within the flow's crews directory
+    create_embedded_crew(crew_name, parent_folder=crews_folder)
+
+    click.echo(
+        f"Crew {crew_name} added to the current flow successfully!",
+    )
+
+
+def create_embedded_crew(crew_name: str, parent_folder: Path) -> None:
+    """Create a new crew within an existing flow project."""
+    folder_name = crew_name.replace(" ", "_").replace("-", "_").lower()
+    class_name = crew_name.replace("_", " ").replace("-", " ").title().replace(" ", "")
+
+    crew_folder = parent_folder / folder_name
+
+    if crew_folder.exists():
+        if not click.confirm(
+            f"Crew {folder_name} already exists. Do you want to override it?"
+        ):
+            click.secho("Operation cancelled.", fg="yellow")
+            return
+        click.secho(f"Overriding crew {folder_name}...", fg="green", bold=True)
+    else:
+        click.secho(f"Creating crew {folder_name}...", fg="green", bold=True)
+        crew_folder.mkdir(parents=True)
+
+    # Create config and crew.py files
+    config_folder = crew_folder / "config"
+    config_folder.mkdir(exist_ok=True)
+
+    templates_dir = Path(__file__).parent / "templates" / "crew"
+    config_template_files = ["agents.yaml", "tasks.yaml"]
+    crew_template_file = f"{folder_name}.py"  # Updated file name
+
+    for file_name in config_template_files:
+        src_file = templates_dir / "config" / file_name
+        dst_file = config_folder / file_name
+        copy_template(src_file, dst_file, crew_name, class_name, folder_name)
+
+    src_file = templates_dir / "crew.py"
+    dst_file = crew_folder / crew_template_file
+    copy_template(src_file, dst_file, crew_name, class_name, folder_name)
+
+    click.secho(
+        f"Crew {crew_name} added to the flow successfully!", fg="green", bold=True
+    )

src/crewai/cli/authentication/main.py

@@ -34,7 +34,9 @@ class AuthenticationCommand:
             "scope": "openid",
             "audience": AUTH0_AUDIENCE,
         }
-        response = requests.post(url=self.DEVICE_CODE_URL, data=device_code_payload)
+        response = requests.post(
+            url=self.DEVICE_CODE_URL, data=device_code_payload, timeout=20
+        )
         response.raise_for_status()
         return response.json()
 
@@ -54,7 +56,7 @@ class AuthenticationCommand:
 
         attempts = 0
         while True and attempts < 5:
-            response = requests.post(self.TOKEN_URL, data=token_payload)
+            response = requests.post(self.TOKEN_URL, data=token_payload, timeout=30)
             token_data = response.json()
 
             if response.status_code == 200:

src/crewai/cli/cli.py

@@ -3,6 +3,7 @@ from typing import Optional
 import click
 import pkg_resources
 
+from crewai.cli.add_crew_to_flow import add_crew_to_flow
 from crewai.cli.create_crew import create_crew
 from crewai.cli.create_flow import create_flow
 from crewai.cli.create_pipeline import create_pipeline
@@ -14,11 +15,11 @@ from .authentication.main import AuthenticationCommand
 from .deploy.main import DeployCommand
 from .evaluate_crew import evaluate_crew
 from .install_crew import install_crew
+from .kickoff_flow import kickoff_flow
 from .plot_flow import plot_flow
 from .replay_from_task import replay_task_command
 from .reset_memories_command import reset_memories_command
 from .run_crew import run_crew
-from .run_flow import run_flow
 from .tools.main import ToolCommand
 from .train_crew import train_crew
 from .update_crew import update_crew
@@ -32,10 +33,12 @@ def crewai():
 @crewai.command()
 @click.argument("type", type=click.Choice(["crew", "pipeline", "flow"]))
 @click.argument("name")
-def create(type, name):
+@click.option("--provider", type=str, help="The provider to use for the crew")
+@click.option("--skip_provider", is_flag=True, help="Skip provider validation")
+def create(type, name, provider, skip_provider=False):
     """Create a new crew, pipeline, or flow."""
     if type == "crew":
-        create_crew(name)
+        create_crew(name, provider, skip_provider)
     elif type == "pipeline":
         create_pipeline(name)
     elif type == "flow":
@@ -176,10 +179,16 @@ def test(n_iterations: int, model: str):
     evaluate_crew(n_iterations, model)
 
 
-@crewai.command()
-def install():
+@crewai.command(
+    context_settings=dict(
+        ignore_unknown_options=True,
+        allow_extra_args=True,
+    )
+)
+@click.pass_context
+def install(context):
     """Install the Crew."""
-    install_crew()
+    install_crew(context.args)
 
 
 @crewai.command()
@@ -304,11 +313,11 @@ def flow():
     pass
 
 
-@flow.command(name="run")
+@flow.command(name="kickoff")
 def flow_run():
-    """Run the Flow."""
+    """Kickoff the Flow."""
     click.echo("Running the Flow")
-    run_flow()
+    kickoff_flow()
 
 
 @flow.command(name="plot")
@@ -318,5 +327,13 @@ def flow_plot():
     plot_flow()
 
 
+@flow.command(name="add-crew")
+@click.argument("crew_name")
+def flow_add_crew(crew_name):
+    """Add a crew to an existing flow."""
+    click.echo(f"Adding crew {crew_name} to the flow")
+    add_crew_to_flow(crew_name)
+
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/config.py

@@ -0,0 +1,38 @@
+import json
+from pathlib import Path
+from pydantic import BaseModel, Field
+from typing import Optional
+
+DEFAULT_CONFIG_PATH = Path.home() / ".config" / "crewai" / "settings.json"
+
+class Settings(BaseModel):
+    tool_repository_username: Optional[str] = Field(None, description="Username for interacting with the Tool Repository")
+    tool_repository_password: Optional[str] = Field(None, description="Password for interacting with the Tool Repository")
+    config_path: Path = Field(default=DEFAULT_CONFIG_PATH, exclude=True)
+
+    def __init__(self, config_path: Path = DEFAULT_CONFIG_PATH, **data):
+        """Load Settings from config path"""
+        config_path.parent.mkdir(parents=True, exist_ok=True)
+
+        file_data = {}
+        if config_path.is_file():
+            try:
+                with config_path.open("r") as f:
+                    file_data = json.load(f)
+            except json.JSONDecodeError:
+                file_data = {}
+
+        merged_data = {**file_data, **data}
+        super().__init__(config_path=config_path, **merged_data)
+
+    def dump(self) -> None:
+        """Save current settings to settings.json"""
+        if self.config_path.is_file():
+            with self.config_path.open("r") as f:
+                existing_data = json.load(f)
+        else:
+            existing_data = {}
+
+        updated_data = {**existing_data, **self.model_dump(exclude_unset=True)}
+        with self.config_path.open("w") as f:
+            json.dump(updated_data, f, indent=4)

src/crewai/cli/constants.py

@@ -0,0 +1,168 @@
+ENV_VARS = {
+    "openai": [
+        {
+            "prompt": "Enter your OPENAI API key (press Enter to skip)",
+            "key_name": "OPENAI_API_KEY",
+        }
+    ],
+    "anthropic": [
+        {
+            "prompt": "Enter your ANTHROPIC API key (press Enter to skip)",
+            "key_name": "ANTHROPIC_API_KEY",
+        }
+    ],
+    "gemini": [
+        {
+            "prompt": "Enter your GEMINI API key (press Enter to skip)",
+            "key_name": "GEMINI_API_KEY",
+        }
+    ],
+    "groq": [
+        {
+            "prompt": "Enter your GROQ API key (press Enter to skip)",
+            "key_name": "GROQ_API_KEY",
+        }
+    ],
+    "watson": [
+        {
+            "prompt": "Enter your WATSONX URL (press Enter to skip)",
+            "key_name": "WATSONX_URL",
+        },
+        {
+            "prompt": "Enter your WATSONX API Key (press Enter to skip)",
+            "key_name": "WATSONX_APIKEY",
+        },
+        {
+            "prompt": "Enter your WATSONX Project Id (press Enter to skip)",
+            "key_name": "WATSONX_PROJECT_ID",
+        },
+    ],
+    "ollama": [
+        {
+            "default": True,
+            "API_BASE": "http://localhost:11434",
+        }
+    ],
+    "bedrock": [
+        {
+            "prompt": "Enter your AWS Access Key ID (press Enter to skip)",
+            "key_name": "AWS_ACCESS_KEY_ID",
+        },
+        {
+            "prompt": "Enter your AWS Secret Access Key (press Enter to skip)",
+            "key_name": "AWS_SECRET_ACCESS_KEY",
+        },
+        {
+            "prompt": "Enter your AWS Region Name (press Enter to skip)",
+            "key_name": "AWS_REGION_NAME",
+        },
+    ],
+    "azure": [
+        {
+            "prompt": "Enter your Azure deployment name (must start with 'azure/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your AZURE API key (press Enter to skip)",
+            "key_name": "AZURE_API_KEY",
+        },
+        {
+            "prompt": "Enter your AZURE API base URL (press Enter to skip)",
+            "key_name": "AZURE_API_BASE",
+        },
+        {
+            "prompt": "Enter your AZURE API version (press Enter to skip)",
+            "key_name": "AZURE_API_VERSION",
+        },
+    ],
+    "cerebras": [
+        {
+            "prompt": "Enter your Cerebras model name (must start with 'cerebras/')",
+            "key_name": "model",
+        },
+        {
+            "prompt": "Enter your Cerebras API version (press Enter to skip)",
+            "key_name": "CEREBRAS_API_KEY",
+        },
+    ],
+}
+
+
+PROVIDERS = [
+    "openai",
+    "anthropic",
+    "gemini",
+    "groq",
+    "ollama",
+    "watson",
+    "bedrock",
+    "azure",
+    "cerebras",
+]
+
+MODELS = {
+    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
+    "anthropic": [
+        "claude-3-5-sonnet-20240620",
+        "claude-3-sonnet-20240229",
+        "claude-3-opus-20240229",
+        "claude-3-haiku-20240307",
+    ],
+    "gemini": [
+        "gemini/gemini-1.5-flash",
+        "gemini/gemini-1.5-pro",
+        "gemini/gemini-gemma-2-9b-it",
+        "gemini/gemini-gemma-2-27b-it",
+    ],
+    "groq": [
+        "groq/llama-3.1-8b-instant",
+        "groq/llama-3.1-70b-versatile",
+        "groq/llama-3.1-405b-reasoning",
+        "groq/gemma2-9b-it",
+        "groq/gemma-7b-it",
+    ],
+    "ollama": ["ollama/llama3.1", "ollama/mixtral"],
+    "watson": [
+        "watsonx/google/flan-t5-xxl",
+        "watsonx/google/flan-ul2",
+        "watsonx/bigscience/mt0-xxl",
+        "watsonx/eleutherai/gpt-neox-20b",
+        "watsonx/ibm/mpt-7b-instruct2",
+        "watsonx/bigcode/starcoder",
+        "watsonx/meta-llama/llama-2-70b-chat",
+        "watsonx/meta-llama/llama-2-13b-chat",
+        "watsonx/ibm/granite-13b-instruct-v1",
+        "watsonx/ibm/granite-13b-chat-v1",
+        "watsonx/google/flan-t5-xl",
+        "watsonx/ibm/granite-13b-chat-v2",
+        "watsonx/ibm/granite-13b-instruct-v2",
+        "watsonx/elyza/elyza-japanese-llama-2-7b-instruct",
+        "watsonx/ibm-mistralai/mixtral-8x7b-instruct-v01-q",
+    ],
+    "bedrock": [
+        "bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
+        "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
+        "bedrock/anthropic.claude-3-haiku-20240307-v1:0",
+        "bedrock/anthropic.claude-3-opus-20240229-v1:0",
+        "bedrock/anthropic.claude-v2:1",
+        "bedrock/anthropic.claude-v2",
+        "bedrock/anthropic.claude-instant-v1",
+        "bedrock/meta.llama3-1-405b-instruct-v1:0",
+        "bedrock/meta.llama3-1-70b-instruct-v1:0",
+        "bedrock/meta.llama3-1-8b-instruct-v1:0",
+        "bedrock/meta.llama3-70b-instruct-v1:0",
+        "bedrock/meta.llama3-8b-instruct-v1:0",
+        "bedrock/amazon.titan-text-lite-v1",
+        "bedrock/amazon.titan-text-express-v1",
+        "bedrock/cohere.command-text-v14",
+        "bedrock/ai21.j2-mid-v1",
+        "bedrock/ai21.j2-ultra-v1",
+        "bedrock/ai21.jamba-instruct-v1:0",
+        "bedrock/meta.llama2-13b-chat-v1",
+        "bedrock/meta.llama2-70b-chat-v1",
+        "bedrock/mistral.mistral-7b-instruct-v0:2",
+        "bedrock/mistral.mixtral-8x7b-instruct-v0:1",
+    ],
+}
+
+JSON_URL = "https://raw.githubusercontent.com/BerriAI/litellm/main/model_prices_and_context_window.json"

src/crewai/cli/create_crew.py

@@ -1,12 +1,19 @@
+import shutil
+import sys
 from pathlib import Path
 
 import click
 
-from crewai.cli.utils import copy_template
+from crewai.cli.constants import ENV_VARS, MODELS
+from crewai.cli.provider import (
+    get_provider_data,
+    select_model,
+    select_provider,
+)
+from crewai.cli.utils import copy_template, load_env_vars, write_env_file
 
 
-def create_crew(name, parent_folder=None):
-    """Create a new crew."""
+def create_folder_structure(name, parent_folder=None):
     folder_name = name.replace(" ", "_").replace("-", "_").lower()
     class_name = name.replace("_", " ").replace("-", " ").title().replace(" ", "")
 
@@ -15,32 +22,151 @@ def create_crew(name, parent_folder=None):
     else:
         folder_path = Path(folder_name)
 
+    if folder_path.exists():
+        if not click.confirm(
+            f"Folder {folder_name} already exists. Do you want to override it?"
+        ):
+            click.secho("Operation cancelled.", fg="yellow")
+            sys.exit(0)
+        click.secho(f"Overriding folder {folder_name}...", fg="green", bold=True)
+        shutil.rmtree(folder_path)  # Delete the existing folder and its contents
+
     click.secho(
         f"Creating {'crew' if parent_folder else 'folder'} {folder_name}...",
         fg="green",
         bold=True,
     )
 
-    if not folder_path.exists():
-        folder_path.mkdir(parents=True)
-        (folder_path / "tests").mkdir(exist_ok=True)
-        if not parent_folder:
-            (folder_path / "src" / folder_name).mkdir(parents=True)
-            (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
-            (folder_path / "src" / folder_name / "config").mkdir(parents=True)
-            with open(folder_path / ".env", "w") as file:
-                file.write("OPENAI_API_KEY=YOUR_API_KEY")
-    else:
-        click.secho(
-            f"\tFolder {folder_name} already exists. Please choose a different name.",
-            fg="red",
-        )
-        return
+    folder_path.mkdir(parents=True)
+    (folder_path / "tests").mkdir(exist_ok=True)
+    if not parent_folder:
+        (folder_path / "src" / folder_name).mkdir(parents=True)
+        (folder_path / "src" / folder_name / "tools").mkdir(parents=True)
+        (folder_path / "src" / folder_name / "config").mkdir(parents=True)
+
+    return folder_path, folder_name, class_name
+
+
+def copy_template_files(folder_path, name, class_name, parent_folder):
+    package_dir = Path(__file__).parent
+    templates_dir = package_dir / "templates" / "crew"
+
+    root_template_files = (
+        [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else []
+    )
+    tools_template_files = ["tools/custom_tool.py", "tools/__init__.py"]
+    config_template_files = ["config/agents.yaml", "config/tasks.yaml"]
+    src_template_files = (
+        ["__init__.py", "main.py", "crew.py"] if not parent_folder else ["crew.py"]
+    )
+
+    for file_name in root_template_files:
+        src_file = templates_dir / file_name
+        dst_file = folder_path / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_path.name)
+
+    src_folder = (
+        folder_path / "src" / folder_path.name if not parent_folder else folder_path
+    )
+
+    for file_name in src_template_files:
+        src_file = templates_dir / file_name
+        dst_file = src_folder / file_name
+        copy_template(src_file, dst_file, name, class_name, folder_path.name)
+
+    if not parent_folder:
+        for file_name in tools_template_files + config_template_files:
+            src_file = templates_dir / file_name
+            dst_file = src_folder / file_name
+            copy_template(src_file, dst_file, name, class_name, folder_path.name)
+
+
+def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
+    folder_path, folder_name, class_name = create_folder_structure(name, parent_folder)
+    env_vars = load_env_vars(folder_path)
+    if not skip_provider:
+        if not provider:
+            provider_models = get_provider_data()
+            if not provider_models:
+                return
+
+        existing_provider = None
+        for provider, env_keys in ENV_VARS.items():
+            if any(
+                "key_name" in details and details["key_name"] in env_vars
+                for details in env_keys
+            ):
+                existing_provider = provider
+                break
+
+        if existing_provider:
+            if not click.confirm(
+                f"Found existing environment variable configuration for {existing_provider.capitalize()}. Do you want to override it?"
+            ):
+                click.secho("Keeping existing provider configuration.", fg="yellow")
+                return
+
+        provider_models = get_provider_data()
+        if not provider_models:
+            return
+
+        while True:
+            selected_provider = select_provider(provider_models)
+            if selected_provider is None:  # User typed 'q'
+                click.secho("Exiting...", fg="yellow")
+                sys.exit(0)
+            if selected_provider:  # Valid selection
+                break
+            click.secho(
+                "No provider selected. Please try again or press 'q' to exit.", fg="red"
+            )
+
+        # Check if the selected provider has predefined models
+        if selected_provider in MODELS and MODELS[selected_provider]:
+            while True:
+                selected_model = select_model(selected_provider, provider_models)
+                if selected_model is None:  # User typed 'q'
+                    click.secho("Exiting...", fg="yellow")
+                    sys.exit(0)
+                if selected_model:  # Valid selection
+                    break
+                click.secho(
+                    "No model selected. Please try again or press 'q' to exit.",
+                    fg="red",
+                )
+            env_vars["MODEL"] = selected_model
+
+        # Check if the selected provider requires API keys
+        if selected_provider in ENV_VARS:
+            provider_env_vars = ENV_VARS[selected_provider]
+            for details in provider_env_vars:
+                if details.get("default", False):
+                    # Automatically add default key-value pairs
+                    for key, value in details.items():
+                        if key not in ["prompt", "key_name", "default"]:
+                            env_vars[key] = value
+                elif "key_name" in details:
+                    # Prompt for non-default key-value pairs
+                    prompt = details["prompt"]
+                    key_name = details["key_name"]
+                    api_key_value = click.prompt(prompt, default="", show_default=False)
+
+                    if api_key_value.strip():
+                        env_vars[key_name] = api_key_value
+
+        if env_vars:
+            write_env_file(folder_path, env_vars)
+            click.secho("API keys and model saved to .env file", fg="green")
+        else:
+            click.secho(
+                "No API keys provided. Skipping .env file creation.", fg="yellow"
+            )
+
+        click.secho(f"Selected model: {env_vars.get('MODEL', 'N/A')}", fg="green")
 
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"
 
-    # List of template files to copy
     root_template_files = (
         [".gitignore", "pyproject.toml", "README.md"] if not parent_folder else []
     )

src/crewai/cli/install_crew.py

@@ -3,12 +3,13 @@ import subprocess
 import click
 
 
-def install_crew() -> None:
+def install_crew(proxy_options: list[str]) -> None:
     """
     Install the crew by running the UV command to lock and install.
     """
     try:
-        subprocess.run(["uv", "sync"], check=True, capture_output=False, text=True)
+        command = ["uv", "sync"] + proxy_options
+        subprocess.run(command, check=True, capture_output=False, text=True)
 
     except subprocess.CalledProcessError as e:
         click.echo(f"An error occurred while running the crew: {e}", err=True)

src/crewai/cli/kickoff_flow.py

@@ -3,11 +3,11 @@ import subprocess
 import click
 
 
-def run_flow() -> None:
+def kickoff_flow() -> None:
     """
-    Run the flow by running a command in the UV environment.
+    Kickoff the flow by running a command in the UV environment.
     """
-    command = ["uv", "run", "run_flow"]
+    command = ["uv", "run", "kickoff"]
 
     try:
         result = subprocess.run(command, capture_output=False, text=True, check=True)

src/crewai/cli/plot_flow.py

@@ -7,7 +7,7 @@ def plot_flow() -> None:
     """
     Plot the flow by running a command in the UV environment.
     """
-    command = ["uv", "run", "plot_flow"]
+    command = ["uv", "run", "plot"]
 
     try:
         result = subprocess.run(command, capture_output=False, text=True, check=True)

src/crewai/cli/provider.py

@@ -0,0 +1,227 @@
+import json
+import time
+from collections import defaultdict
+from pathlib import Path
+
+import click
+import requests
+
+from crewai.cli.constants import JSON_URL, MODELS, PROVIDERS
+
+
+def select_choice(prompt_message, choices):
+    """
+    Presents a list of choices to the user and prompts them to select one.
+
+    Args:
+    - prompt_message (str): The message to display to the user before presenting the choices.
+    - choices (list): A list of options to present to the user.
+
+    Returns:
+    - str: The selected choice from the list, or None if the user chooses to quit.
+    """
+
+    provider_models = get_provider_data()
+    if not provider_models:
+        return
+    click.secho(prompt_message, fg="cyan")
+    for idx, choice in enumerate(choices, start=1):
+        click.secho(f"{idx}. {choice}", fg="cyan")
+    click.secho("q. Quit", fg="cyan")
+
+    while True:
+        choice = click.prompt(
+            "Enter the number of your choice or 'q' to quit", type=str
+        )
+
+        if choice.lower() == "q":
+            return None
+
+        try:
+            selected_index = int(choice) - 1
+            if 0 <= selected_index < len(choices):
+                return choices[selected_index]
+        except ValueError:
+            pass
+
+        click.secho(
+            "Invalid selection. Please select a number between 1 and 6 or 'q' to quit.",
+            fg="red",
+        )
+
+
+def select_provider(provider_models):
+    """
+    Presents a list of providers to the user and prompts them to select one.
+
+    Args:
+    - provider_models (dict): A dictionary of provider models.
+
+    Returns:
+    - str: The selected provider
+    - None: If user explicitly quits
+    """
+    predefined_providers = [p.lower() for p in PROVIDERS]
+    all_providers = sorted(set(predefined_providers + list(provider_models.keys())))
+
+    provider = select_choice(
+        "Select a provider to set up:", predefined_providers + ["other"]
+    )
+    if provider is None:  # User typed 'q'
+        return None
+
+    if provider == "other":
+        provider = select_choice("Select a provider from the full list:", all_providers)
+        if provider is None:  # User typed 'q'
+            return None
+
+    return provider.lower() if provider else False
+
+
+def select_model(provider, provider_models):
+    """
+    Presents a list of models for a given provider to the user and prompts them to select one.
+
+    Args:
+    - provider (str): The provider for which to select a model.
+    - provider_models (dict): A dictionary of provider models.
+
+    Returns:
+    - str: The selected model, or None if the operation is aborted or an invalid selection is made.
+    """
+    predefined_providers = [p.lower() for p in PROVIDERS]
+
+    if provider in predefined_providers:
+        available_models = MODELS.get(provider, [])
+    else:
+        available_models = provider_models.get(provider, [])
+
+    if not available_models:
+        click.secho(f"No models available for provider '{provider}'.", fg="red")
+        return None
+
+    selected_model = select_choice(
+        f"Select a model to use for {provider.capitalize()}:", available_models
+    )
+    return selected_model
+
+
+def load_provider_data(cache_file, cache_expiry):
+    """
+    Loads provider data from a cache file if it exists and is not expired. If the cache is expired or corrupted, it fetches the data from the web.
+
+    Args:
+    - cache_file (Path): The path to the cache file.
+    - cache_expiry (int): The cache expiry time in seconds.
+
+    Returns:
+    - dict or None: The loaded provider data or None if the operation fails.
+    """
+    current_time = time.time()
+    if (
+        cache_file.exists()
+        and (current_time - cache_file.stat().st_mtime) < cache_expiry
+    ):
+        data = read_cache_file(cache_file)
+        if data:
+            return data
+        click.secho(
+            "Cache is corrupted. Fetching provider data from the web...", fg="yellow"
+        )
+    else:
+        click.secho(
+            "Cache expired or not found. Fetching provider data from the web...",
+            fg="cyan",
+        )
+    return fetch_provider_data(cache_file)
+
+
+def read_cache_file(cache_file):
+    """
+    Reads and returns the JSON content from a cache file. Returns None if the file contains invalid JSON.
+
+    Args:
+    - cache_file (Path): The path to the cache file.
+
+    Returns:
+    - dict or None: The JSON content of the cache file or None if the JSON is invalid.
+    """
+    try:
+        with open(cache_file, "r") as f:
+            return json.load(f)
+    except json.JSONDecodeError:
+        return None
+
+
+def fetch_provider_data(cache_file):
+    """
+    Fetches provider data from a specified URL and caches it to a file.
+
+    Args:
+    - cache_file (Path): The path to the cache file.
+
+    Returns:
+    - dict or None: The fetched provider data or None if the operation fails.
+    """
+    try:
+        response = requests.get(JSON_URL, stream=True, timeout=60)
+        response.raise_for_status()
+        data = download_data(response)
+        with open(cache_file, "w") as f:
+            json.dump(data, f)
+        return data
+    except requests.RequestException as e:
+        click.secho(f"Error fetching provider data: {e}", fg="red")
+    except json.JSONDecodeError:
+        click.secho("Error parsing provider data. Invalid JSON format.", fg="red")
+    return None
+
+
+def download_data(response):
+    """
+    Downloads data from a given HTTP response and returns the JSON content.
+
+    Args:
+    - response (requests.Response): The HTTP response object.
+
+    Returns:
+    - dict: The JSON content of the response.
+    """
+    total_size = int(response.headers.get("content-length", 0))
+    block_size = 8192
+    data_chunks = []
+    with click.progressbar(
+        length=total_size, label="Downloading", show_pos=True
+    ) as progress_bar:
+        for chunk in response.iter_content(block_size):
+            if chunk:
+                data_chunks.append(chunk)
+                progress_bar.update(len(chunk))
+    data_content = b"".join(data_chunks)
+    return json.loads(data_content.decode("utf-8"))
+
+
+def get_provider_data():
+    """
+    Retrieves provider data from a cache file, filters out models based on provider criteria, and returns a dictionary of providers mapped to their models.
+
+    Returns:
+    - dict or None: A dictionary of providers mapped to their models or None if the operation fails.
+    """
+    cache_dir = Path.home() / ".crewai"
+    cache_dir.mkdir(exist_ok=True)
+    cache_file = cache_dir / "provider_cache.json"
+    cache_expiry = 24 * 3600
+
+    data = load_provider_data(cache_file, cache_expiry)
+    if not data:
+        return None
+
+    provider_models = defaultdict(list)
+    for model_name, properties in data.items():
+        provider = properties.get("litellm_provider", "").strip().lower()
+        if "http" in provider or provider == "other":
+            continue
+        if provider:
+            provider_models[provider].append(model_name)
+    return provider_models

src/crewai/cli/run_crew.py

@@ -1,10 +1,9 @@
 import subprocess
 
 import click
-import tomllib
 from packaging import version
 
-from crewai.cli.utils import get_crewai_version
+from crewai.cli.utils import get_crewai_version, read_toml
 
 
 def run_crew() -> None:
@@ -15,10 +14,9 @@ def run_crew() -> None:
     crewai_version = get_crewai_version()
     min_required_version = "0.71.0"
 
-    with open("pyproject.toml", "rb") as f:
-        data = tomllib.load(f)
+    pyproject_data = read_toml()
 
-    if data.get("tool", {}).get("poetry") and (
+    if pyproject_data.get("tool", {}).get("poetry") and (
         version.parse(crewai_version) < version.parse(min_required_version)
     ):
         click.secho(
@@ -26,7 +24,6 @@ def run_crew() -> None:
             f"Please run `crewai update` to update your pyproject.toml to use uv.",
             fg="red",
         )
-        print()
 
     try:
         subprocess.run(command, capture_output=False, text=True, check=True)
@@ -35,10 +32,7 @@ def run_crew() -> None:
         click.echo(f"An error occurred while running the crew: {e}", err=True)
         click.echo(e.output, err=True, nl=True)
 
-        with open("pyproject.toml", "rb") as f:
-            data = tomllib.load(f)
-
-        if data.get("tool", {}).get("poetry"):
+        if pyproject_data.get("tool", {}).get("poetry"):
             click.secho(
                 "It's possible that you are using an old version of crewAI that uses poetry, please run `crewai update` to update your pyproject.toml to use uv.",
                 fg="yellow",

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

@@ -8,9 +8,12 @@ from crewai.project import CrewBase, agent, crew, task
 # from crewai_tools import SerperDevTool
 
 @CrewBase
-class {{crew_name}}Crew():
+class {{crew_name}}():
 	"""{{crew_name}} crew"""
 
+	agents_config = 'config/agents.yaml'
+	tasks_config = 'config/tasks.yaml'
+
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(
@@ -48,4 +51,4 @@ class {{crew_name}}Crew():
 			process=Process.sequential,
 			verbose=True,
 			# process=Process.hierarchical, # In case you wanna use that instead https://docs.crewai.com/how-to/Hierarchical/
-		)
+		)

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

@@ -1,9 +1,13 @@
 #!/usr/bin/env python
 import sys
-from {{folder_name}}.crew import {{crew_name}}Crew
+import warnings
+
+from {{folder_name}}.crew import {{crew_name}}
+
+warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 # This main file is intended to be a way for you to run your
-# crew locally, so refrain from adding necessary logic into this file.
+# crew locally, so refrain from adding unnecessary logic into this file.
 # Replace with inputs you want to test with, it will automatically
 # interpolate any tasks and agents information
 
@@ -14,7 +18,7 @@ def run():
     inputs = {
         'topic': 'AI LLMs'
     }
-    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
+    {{crew_name}}().crew().kickoff(inputs=inputs)
 
 
 def train():
@@ -25,7 +29,7 @@ def train():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+        {{crew_name}}().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
@@ -35,7 +39,7 @@ def replay():
     Replay the crew execution from a specific task.
     """
     try:
-        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
+        {{crew_name}}().crew().replay(task_id=sys.argv[1])
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")
@@ -48,7 +52,7 @@ def test():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
+        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.67.1,<1.0.0"
+    "crewai[tools]>=0.80.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/crew/tools/custom_tool.py

@@ -1,11 +1,18 @@
-from crewai_tools import BaseTool
+from crewai.tools import BaseTool
+from typing import Type
+from pydantic import BaseModel, Field
 
 
+class MyCustomToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+    argument: str = Field(..., description="Description of the argument.")
+
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
         "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
+    args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
         # Implementation goes here

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

@@ -1,65 +1,53 @@
 #!/usr/bin/env python
-import asyncio
 from random import randint
 
 from pydantic import BaseModel
+
 from crewai.flow.flow import Flow, listen, start
+
 from .crews.poem_crew.poem_crew import PoemCrew
 
+
 class PoemState(BaseModel):
     sentence_count: int = 1
     poem: str = ""
 
+
 class PoemFlow(Flow[PoemState]):
 
     @start()
     def generate_sentence_count(self):
         print("Generating sentence count")
-        # Generate a number between 1 and 5
-        self.state.sentence_count = randint(1, 5)  
+        self.state.sentence_count = randint(1, 5)
 
     @listen(generate_sentence_count)
     def generate_poem(self):
         print("Generating poem")
-        print(f"State before poem: {self.state}")
-        result = PoemCrew().crew().kickoff(inputs={"sentence_count": self.state.sentence_count})
-        
+        result = (
+            PoemCrew()
+            .crew()
+            .kickoff(inputs={"sentence_count": self.state.sentence_count})
+        )
+
         print("Poem generated", result.raw)
         self.state.poem = result.raw
-        
-        print(f"State after generate_poem: {self.state}")
 
     @listen(generate_poem)
     def save_poem(self):
         print("Saving poem")
-        print(f"State before save_poem: {self.state}")
         with open("poem.txt", "w") as f:
             f.write(self.state.poem)
-        print(f"State after save_poem: {self.state}")
 
-async def run_flow():
-    """
-    Run the flow.
-    """
+
+def kickoff():
     poem_flow = PoemFlow()
-    await poem_flow.kickoff()
+    poem_flow.kickoff()
 
-async def plot_flow():
-    """
-    Plot the flow.
-    """
+
+def plot():
     poem_flow = PoemFlow()
     poem_flow.plot()
 
 
-def main():
-    asyncio.run(run_flow())
-
-
-def plot():
-    asyncio.run(plot_flow())
-
-
-
 if __name__ == "__main__":
-    main()
+    kickoff()

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

@@ -5,14 +5,12 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.67.1,<1.0.0",
-    "asyncio"
+    "crewai[tools]>=0.80.0,<1.0.0",
 ]
 
 [project.scripts]
-{{folder_name}} = "{{folder_name}}.main:main"
-run_flow = "{{folder_name}}.main:main"
-plot_flow = "{{folder_name}}.main:plot"
+kickoff = "{{folder_name}}.main:kickoff"
+plot = "{{folder_name}}.main:plot"
 
 [build-system]
 requires = ["hatchling"]

src/crewai/cli/templates/flow/tools/custom_tool.py

@@ -1,4 +1,13 @@
-from crewai_tools import BaseTool
+from typing import Type
+
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class MyCustomToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+
+    argument: str = Field(..., description="Description of the argument.")
 
 
 class MyCustomTool(BaseTool):
@@ -6,6 +15,7 @@ class MyCustomTool(BaseTool):
     description: str = (
         "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
+    args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
         # Implementation goes here

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

@@ -6,7 +6,7 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = ">=0.70.1,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.80.0,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

src/crewai/cli/templates/pipeline/tools/custom_tool.py

@@ -1,11 +1,18 @@
-from crewai_tools import BaseTool
+from typing import Type
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
 
 
+class MyCustomToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+    argument: str = Field(..., description="Description of the argument.")
+
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
         "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
+    args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
         # Implementation goes here

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = ["Your Name <you@example.com>"]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.67.1,<1.0.0"
+    "crewai[tools]>=0.80.0,<1.0.0"
 ]
 
 [project.scripts]

src/crewai/cli/templates/pipeline_router/tools/custom_tool.py

@@ -1,11 +1,18 @@
-from crewai_tools import BaseTool
+from typing import Type
+from crewai.tools import BaseTool
+from pydantic import BaseModel, Field
 
 
+class MyCustomToolInput(BaseModel):
+    """Input schema for MyCustomTool."""
+    argument: str = Field(..., description="Description of the argument.")
+
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
     description: str = (
         "Clear description for what this tool is useful for, you agent will need this information to use it."
     )
+    args_schema: Type[BaseModel] = MyCustomToolInput
 
     def _run(self, argument: str) -> str:
         # Implementation goes here

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

@@ -5,6 +5,6 @@ description = "Power up your crews with {{folder_name}}"
 readme = "README.md"
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.70.1"
+    "crewai[tools]>=0.80.0"
 ]
 

src/crewai/cli/templates/tool/src/{{folder_name}}/tool.py

@@ -1,4 +1,5 @@
-from crewai_tools import BaseTool
+from crewai.tools import BaseTool
+
 
 class {{class_name}}(BaseTool):
     name: str = "Name of my tool"

src/crewai/cli/tools/main.py

@@ -1,17 +1,15 @@
 import base64
 import os
-import platform
 import subprocess
 import tempfile
 from pathlib import Path
-from netrc import netrc
-import stat
 
 import click
 from rich.console import Console
 
 from crewai.cli import git
 from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.config import Settings
 from crewai.cli.utils import (
     get_project_description,
     get_project_name,
@@ -28,8 +26,6 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
     A class to handle tool repository related operations for CrewAI projects.
     """
 
-    BASE_URL = "https://app.crewai.com/pypi/"
-
     def __init__(self):
         BaseCommand.__init__(self)
         PlusAPIMixin.__init__(self, telemetry=self._telemetry)
@@ -155,39 +151,35 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             raise SystemExit
 
         login_response_json = login_response.json()
-        self._set_netrc_credentials(login_response_json["credential"])
+
+        settings = Settings()
+        settings.tool_repository_username = login_response_json["credential"]["username"]
+        settings.tool_repository_password = login_response_json["credential"]["password"]
+        settings.dump()
 
         console.print(
             "Successfully authenticated to the tool repository.", style="bold green"
         )
 
-    def _set_netrc_credentials(self, credentials, netrc_path=None):
-        if not netrc_path:
-            netrc_filename = "_netrc" if platform.system() == "Windows" else ".netrc"
-            netrc_path = Path.home() / netrc_filename
-            netrc_path.touch(mode=stat.S_IRUSR | stat.S_IWUSR, exist_ok=True)
-
-        netrc_instance = netrc(file=netrc_path)
-        netrc_instance.hosts["app.crewai.com"] = (credentials["username"], "", credentials["password"])
-
-        with open(netrc_path, 'w') as file:
-            file.write(str(netrc_instance))
-
-        console.print(f"Added credentials to {netrc_path}", style="bold green")
-
     def _add_package(self, tool_details):
         tool_handle = tool_details["handle"]
         repository_handle = tool_details["repository"]["handle"]
+        repository_url = tool_details["repository"]["url"]
+        index = f"{repository_handle}={repository_url}"
 
         add_package_command = [
             "uv",
             "add",
-            "--extra-index-url",
-            self.BASE_URL + repository_handle,
+            "--index",
+            index,
             tool_handle,
         ]
         add_package_result = subprocess.run(
-            add_package_command, capture_output=False, text=True, check=True
+            add_package_command,
+            capture_output=False,
+            env=self._build_env_with_credentials(repository_handle),
+            text=True,
+            check=True
         )
 
         if add_package_result.stderr:
@@ -206,3 +198,13 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
                 "[bold yellow]Tip:[/bold yellow] Navigate to a different directory and try again."
             )
             raise SystemExit
+
+    def _build_env_with_credentials(self, repository_handle: str):
+        repository_handle = repository_handle.upper().replace("-", "_")
+        settings = Settings()
+
+        env = os.environ.copy()
+        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
+        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
+
+        return env

src/crewai/cli/update_crew.py

@@ -1,7 +1,9 @@
+import os
 import shutil
 
 import tomli_w
-import tomllib
+
+from crewai.cli.utils import read_toml
 
 
 def update_crew() -> None:
@@ -17,10 +19,9 @@ def migrate_pyproject(input_file, output_file):
     And it will be used to migrate the pyproject.toml to the new format when uv is used.
     When the time comes that uv supports the new format, this function will be deprecated.
     """
-
+    poetry_data = {}
     # Read the input pyproject.toml
-    with open(input_file, "rb") as f:
-        pyproject = tomllib.load(f)
+    pyproject_data = read_toml()
 
     # Initialize the new project structure
     new_pyproject = {
@@ -29,30 +30,30 @@ def migrate_pyproject(input_file, output_file):
     }
 
     # Migrate project metadata
-    if "tool" in pyproject and "poetry" in pyproject["tool"]:
-        poetry = pyproject["tool"]["poetry"]
-        new_pyproject["project"]["name"] = poetry.get("name")
-        new_pyproject["project"]["version"] = poetry.get("version")
-        new_pyproject["project"]["description"] = poetry.get("description")
+    if "tool" in pyproject_data and "poetry" in pyproject_data["tool"]:
+        poetry_data = pyproject_data["tool"]["poetry"]
+        new_pyproject["project"]["name"] = poetry_data.get("name")
+        new_pyproject["project"]["version"] = poetry_data.get("version")
+        new_pyproject["project"]["description"] = poetry_data.get("description")
         new_pyproject["project"]["authors"] = [
             {
                 "name": author.split("<")[0].strip(),
                 "email": author.split("<")[1].strip(">").strip(),
             }
-            for author in poetry.get("authors", [])
+            for author in poetry_data.get("authors", [])
         ]
-        new_pyproject["project"]["requires-python"] = poetry.get("python")
+        new_pyproject["project"]["requires-python"] = poetry_data.get("python")
     else:
         # If it's already in the new format, just copy the project section
-        new_pyproject["project"] = pyproject.get("project", {})
+        new_pyproject["project"] = pyproject_data.get("project", {})
 
     # Migrate or copy dependencies
     if "dependencies" in new_pyproject["project"]:
         # If dependencies are already in the new format, keep them as is
         pass
-    elif "dependencies" in poetry:
+    elif poetry_data and "dependencies" in poetry_data:
         new_pyproject["project"]["dependencies"] = []
-        for dep, version in poetry["dependencies"].items():
+        for dep, version in poetry_data["dependencies"].items():
             if isinstance(version, dict):  # Handle extras
                 extras = ",".join(version.get("extras", []))
                 new_dep = f"{dep}[{extras}]"
@@ -66,10 +67,10 @@ def migrate_pyproject(input_file, output_file):
             new_pyproject["project"]["dependencies"].append(new_dep)
 
     # Migrate or copy scripts
-    if "scripts" in poetry:
-        new_pyproject["project"]["scripts"] = poetry["scripts"]
-    elif "scripts" in pyproject.get("project", {}):
-        new_pyproject["project"]["scripts"] = pyproject["project"]["scripts"]
+    if poetry_data and "scripts" in poetry_data:
+        new_pyproject["project"]["scripts"] = poetry_data["scripts"]
+    elif pyproject_data.get("project", {}) and "scripts" in pyproject_data["project"]:
+        new_pyproject["project"]["scripts"] = pyproject_data["project"]["scripts"]
     else:
         new_pyproject["project"]["scripts"] = {}
 
@@ -86,14 +87,23 @@ def migrate_pyproject(input_file, output_file):
         new_pyproject["project"]["scripts"]["run_crew"] = f"{module_name}.main:run"
 
     # Migrate optional dependencies
-    if "extras" in poetry:
-        new_pyproject["project"]["optional-dependencies"] = poetry["extras"]
+    if poetry_data and "extras" in poetry_data:
+        new_pyproject["project"]["optional-dependencies"] = poetry_data["extras"]
 
     # Backup the old pyproject.toml
     backup_file = "pyproject-old.toml"
     shutil.copy2(input_file, backup_file)
     print(f"Original pyproject.toml backed up as {backup_file}")
 
+    # Rename the poetry.lock file
+    lock_file = "poetry.lock"
+    lock_backup = "poetry-old.lock"
+    if os.path.exists(lock_file):
+        os.rename(lock_file, lock_backup)
+        print(f"Original poetry.lock renamed to {lock_backup}")
+    else:
+        print("No poetry.lock file found to rename.")
+
     # Write the new pyproject.toml
     with open(output_file, "wb") as f:
         tomli_w.dump(new_pyproject, f)

src/crewai/cli/utils.py

@@ -6,9 +6,11 @@ from functools import reduce
 from typing import Any, Dict, List
 
 import click
+import tomli
 from rich.console import Console
 
 from crewai.cli.authentication.utils import TokenManager
+from crewai.cli.constants import ENV_VARS
 
 if sys.version_info >= (3, 11):
     import tomllib
@@ -53,6 +55,13 @@ def simple_toml_parser(content):
     return result
 
 
+def read_toml(file_path: str = "pyproject.toml"):
+    """Read the content of a TOML file and return it as a dictionary."""
+    with open(file_path, "rb") as f:
+        toml_dict = tomli.load(f)
+    return toml_dict
+
+
 def parse_toml(content):
     if sys.version_info >= (3, 11):
         return tomllib.loads(content)
@@ -200,3 +209,76 @@ def tree_find_and_replace(directory, find, replace):
                 new_dirpath = os.path.join(path, new_dirname)
                 old_dirpath = os.path.join(path, dirname)
                 os.rename(old_dirpath, new_dirpath)
+
+
+def load_env_vars(folder_path):
+    """
+    Loads environment variables from a .env file in the specified folder path.
+
+    Args:
+    - folder_path (Path): The path to the folder containing the .env file.
+
+    Returns:
+    - dict: A dictionary of environment variables.
+    """
+    env_file_path = folder_path / ".env"
+    env_vars = {}
+    if env_file_path.exists():
+        with open(env_file_path, "r") as file:
+            for line in file:
+                key, _, value = line.strip().partition("=")
+                if key and value:
+                    env_vars[key] = value
+    return env_vars
+
+
+def update_env_vars(env_vars, provider, model):
+    """
+    Updates environment variables with the API key for the selected provider and model.
+
+    Args:
+    - env_vars (dict): Environment variables dictionary.
+    - provider (str): Selected provider.
+    - model (str): Selected model.
+
+    Returns:
+    - None
+    """
+    api_key_var = ENV_VARS.get(
+        provider,
+        [
+            click.prompt(
+                f"Enter the environment variable name for your {provider.capitalize()} API key",
+                type=str,
+            )
+        ],
+    )[0]
+
+    if api_key_var not in env_vars:
+        try:
+            env_vars[api_key_var] = click.prompt(
+                f"Enter your {provider.capitalize()} API key", type=str, hide_input=True
+            )
+        except click.exceptions.Abort:
+            click.secho("Operation aborted by the user.", fg="red")
+            return None
+    else:
+        click.secho(f"API key already exists for {provider.capitalize()}.", fg="yellow")
+
+    env_vars["MODEL"] = model
+    click.secho(f"Selected model: {model}", fg="green")
+    return env_vars
+
+
+def write_env_file(folder_path, env_vars):
+    """
+    Writes environment variables to a .env file in the specified folder.
+
+    Args:
+    - folder_path (Path): The path to the folder where the .env file will be written.
+    - env_vars (dict): A dictionary of environment variables to write.
+    """
+    env_file_path = folder_path / ".env"
+    with open(env_file_path, "w") as file:
+        for key, value in env_vars.items():
+            file.write(f"{key}={value}\n")

src/crewai/crew.py

@@ -5,7 +5,7 @@ import uuid
 import warnings
 from concurrent.futures import Future
 from hashlib import md5
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
 
 from pydantic import (
     UUID4,
@@ -27,17 +27,16 @@ from crewai.llm import LLM
 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.memory.user.user_memory import UserMemory
 from crewai.process import Process
 from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
-from crewai.tools.agent_tools import AgentTools
+from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import (
-    TRAINING_DATA_FILE,
-)
+from crewai.utilities.constants import TRAINING_DATA_FILE
 from crewai.utilities.evaluators.crew_evaluator_handler import CrewEvaluator
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.formatter import (
@@ -71,6 +70,7 @@ class Crew(BaseModel):
         manager_llm: The language model that will run manager agent.
         manager_agent: Custom agent that will be used as manager.
         memory: Whether the crew should use memory to store memories of it's execution.
+        memory_config: Configuration for the memory to be used for the crew.
         cache: Whether the crew should use a cache to store the results of the tools execution.
         function_calling_llm: The language model that will run the tool calling for all the agents.
         process: The process flow that the crew will follow (e.g., sequential, hierarchical).
@@ -94,6 +94,7 @@ class Crew(BaseModel):
     _short_term_memory: Optional[InstanceOf[ShortTermMemory]] = PrivateAttr()
     _long_term_memory: Optional[InstanceOf[LongTermMemory]] = PrivateAttr()
     _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
+    _user_memory: Optional[InstanceOf[UserMemory]] = PrivateAttr()
     _train: Optional[bool] = PrivateAttr(default=False)
     _train_iteration: Optional[int] = PrivateAttr()
     _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
@@ -114,6 +115,10 @@ class Crew(BaseModel):
         default=False,
         description="Whether the crew should use memory to store memories of it's execution",
     )
+    memory_config: Optional[Dict[str, Any]] = Field(
+        default=None,
+        description="Configuration for the memory to be used for the crew.",
+    )
     short_term_memory: Optional[InstanceOf[ShortTermMemory]] = Field(
         default=None,
         description="An Instance of the ShortTermMemory to be used by the Crew",
@@ -126,8 +131,12 @@ class Crew(BaseModel):
         default=None,
         description="An Instance of the EntityMemory to be used by the Crew",
     )
+    user_memory: Optional[InstanceOf[UserMemory]] = Field(
+        default=None,
+        description="An instance of the UserMemory to be used by the Crew to store/fetch memories of a specific user.",
+    )
     embedder: Optional[dict] = Field(
-        default={"provider": "openai"},
+        default=None,
         description="Configuration for the embedder to be used for the crew.",
     )
     usage_metrics: Optional[UsageMetrics] = Field(
@@ -154,6 +163,16 @@ class Crew(BaseModel):
         default=None,
         description="Callback to be executed after each task for all agents execution.",
     )
+    before_kickoff_callbacks: List[
+        Callable[[Optional[Dict[str, Any]]], Optional[Dict[str, Any]]]
+    ] = Field(
+        default_factory=list,
+        description="List of callbacks to be executed before crew kickoff. It may be used to adjust inputs before the crew is executed.",
+    )
+    after_kickoff_callbacks: List[Callable[[CrewOutput], CrewOutput]] = Field(
+        default_factory=list,
+        description="List of callbacks to be executed after crew kickoff. It may be used to adjust the output of the crew.",
+    )
     max_rpm: Optional[int] = Field(
         default=None,
         description="Maximum number of requests per minute for the crew execution to be respected.",
@@ -238,13 +257,22 @@ class Crew(BaseModel):
             self._short_term_memory = (
                 self.short_term_memory
                 if self.short_term_memory
-                else ShortTermMemory(crew=self, embedder_config=self.embedder)
+                else ShortTermMemory(
+                    crew=self,
+                    embedder_config=self.embedder,
+                )
             )
             self._entity_memory = (
                 self.entity_memory
                 if self.entity_memory
                 else EntityMemory(crew=self, embedder_config=self.embedder)
             )
+            if hasattr(self, "memory_config") and self.memory_config is not None:
+                self._user_memory = (
+                    self.user_memory if self.user_memory else UserMemory(crew=self)
+                )
+            else:
+                self._user_memory = None
         return self
 
     @model_validator(mode="after")
@@ -435,27 +463,32 @@ class Crew(BaseModel):
         self, n_iterations: int, filename: str, inputs: Optional[Dict[str, Any]] = {}
     ) -> None:
         """Trains the crew for a given number of iterations."""
-        self._setup_for_training(filename)
+        train_crew = self.copy()
+        train_crew._setup_for_training(filename)
 
         for n_iteration in range(n_iterations):
-            self._train_iteration = n_iteration
-            self.kickoff(inputs=inputs)
+            train_crew._train_iteration = n_iteration
+            train_crew.kickoff(inputs=inputs)
 
         training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
-        for agent in self.agents:
-            result = TaskEvaluator(agent).evaluate_training_data(
-                training_data=training_data, agent_id=str(agent.id)
-            )
+        for agent in train_crew.agents:
+            if training_data.get(str(agent.id)):
+                result = TaskEvaluator(agent).evaluate_training_data(
+                    training_data=training_data, agent_id=str(agent.id)
+                )
 
-            CrewTrainingHandler(filename).save_trained_data(
-                agent_id=str(agent.role), trained_data=result.model_dump()
-            )
+                CrewTrainingHandler(filename).save_trained_data(
+                    agent_id=str(agent.role), trained_data=result.model_dump()
+                )
 
     def kickoff(
         self,
         inputs: Optional[Dict[str, Any]] = None,
     ) -> CrewOutput:
+        for callback in self.before_kickoff_callbacks:
+            inputs = callback(inputs)
+
         """Starts the crew to work on its assigned tasks."""
         self._execution_span = self._telemetry.crew_execution_span(self, inputs)
         self._task_output_handler.reset()
@@ -498,6 +531,9 @@ class Crew(BaseModel):
                 f"The process '{self.process}' is not implemented yet."
             )
 
+        for callback in self.after_kickoff_callbacks:
+            result = callback(result)
+
         metrics += [agent._token_process.get_summary() for agent in self.agents]
 
         self.usage_metrics = UsageMetrics()
@@ -774,7 +810,9 @@ class Crew(BaseModel):
 
     def _log_task_start(self, task: Task, role: str = "None"):
         if self.output_log_file:
-            self._file_handler.log(task_name=task.name, task=task.description, agent=role, status="started")
+            self._file_handler.log(
+                task_name=task.name, task=task.description, agent=role, status="started"
+            )
 
     def _update_manager_tools(self, task: Task):
         if self.manager_agent:
@@ -796,7 +834,13 @@ class Crew(BaseModel):
     def _process_task_result(self, task: Task, output: TaskOutput) -> None:
         role = task.agent.role if task.agent is not None else "None"
         if self.output_log_file:
-            self._file_handler.log(task_name=task.name, task=task.description, agent=role, status="completed", output=output.raw)
+            self._file_handler.log(
+                task_name=task.name,
+                task=task.description,
+                agent=role,
+                status="completed",
+                output=output.raw,
+            )
 
     def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
         if len(task_outputs) != 1:
@@ -979,17 +1023,19 @@ class Crew(BaseModel):
         inputs: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
-        self._test_execution_span = self._telemetry.test_execution_span(
-            self,
+        test_crew = self.copy()
+
+        self._test_execution_span = test_crew._telemetry.test_execution_span(
+            test_crew,
             n_iterations,
             inputs,
             openai_model_name,  # type: ignore[arg-type]
         )  # type: ignore[arg-type]
-        evaluator = CrewEvaluator(self, openai_model_name)  # type: ignore[arg-type]
+        evaluator = CrewEvaluator(test_crew, openai_model_name)  # type: ignore[arg-type]
 
         for i in range(1, n_iterations + 1):
             evaluator.set_iteration(i)
-            self.kickoff(inputs=inputs)
+            test_crew.kickoff(inputs=inputs)
 
         evaluator.print_crew_evaluation_result()
 

src/crewai/flow/flow.py

@@ -1,10 +1,20 @@
-# flow.py
-
 import asyncio
 import inspect
-from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generic,
+    List,
+    Optional,
+    Set,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
@@ -120,6 +130,7 @@ class FlowMeta(type):
                 methods = attr_value.__trigger_methods__
                 condition_type = getattr(attr_value, "__condition_type__", "OR")
                 listeners[attr_name] = (condition_type, methods)
+
             elif hasattr(attr_value, "__is_router__"):
                 routers[attr_value.__router_for__] = attr_name
                 possible_returns = get_possible_return_constants(attr_value)
@@ -159,7 +170,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def __init__(self) -> None:
         self._methods: Dict[str, Callable] = {}
         self._state: T = self._create_initial_state()
-        self._completed_methods: Set[str] = set()
+        self._method_execution_counts: Dict[str, int] = {}
         self._pending_and_listeners: Dict[str, Set[str]] = {}
         self._method_outputs: List[Any] = []  # List to store all method outputs
 
@@ -190,7 +201,74 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """Returns the list of all outputs from executed methods."""
         return self._method_outputs
 
-    async def kickoff(self) -> Any:
+    def _initialize_state(self, inputs: Dict[str, Any]) -> None:
+        """
+        Initializes or updates the state with the provided inputs.
+
+        Args:
+            inputs: Dictionary of inputs to initialize or update the state.
+
+        Raises:
+            ValueError: If inputs do not match the structured state model.
+            TypeError: If state is neither a BaseModel instance nor a dictionary.
+        """
+        if isinstance(self._state, BaseModel):
+            # Structured state management
+            try:
+                # Define a function to create the dynamic class
+                def create_model_with_extra_forbid(
+                    base_model: Type[BaseModel],
+                ) -> Type[BaseModel]:
+                    class ModelWithExtraForbid(base_model):  # type: ignore
+                        model_config = base_model.model_config.copy()
+                        model_config["extra"] = "forbid"
+
+                    return ModelWithExtraForbid
+
+                # Create the dynamic class
+                ModelWithExtraForbid = create_model_with_extra_forbid(
+                    self._state.__class__
+                )
+
+                # Create a new instance using the combined state and inputs
+                self._state = cast(
+                    T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs})
+                )
+
+            except ValidationError as e:
+                raise ValueError(f"Invalid inputs for structured state: {e}") from e
+        elif isinstance(self._state, dict):
+            # Unstructured state management
+            self._state.update(inputs)
+        else:
+            raise TypeError("State must be a BaseModel instance or a dictionary.")
+
+    def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Starts the execution of the flow synchronously.
+
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
+        if inputs is not None:
+            self._initialize_state(inputs)
+        return asyncio.run(self.kickoff_async())
+
+    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Starts the execution of the flow asynchronously.
+
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
+        if inputs is not None:
+            self._initialize_state(inputs)
         if not self._start_methods:
             raise ValueError("No start method defined")
 
@@ -213,17 +291,27 @@ class Flow(Generic[T], metaclass=FlowMeta):
         else:
             return None  # Or raise an exception if no methods were executed
 
-    async def _execute_start_method(self, start_method: str) -> None:
-        result = await self._execute_method(self._methods[start_method])
-        await self._execute_listeners(start_method, result)
+    async def _execute_start_method(self, start_method_name: str) -> None:
+        result = await self._execute_method(
+            start_method_name, self._methods[start_method_name]
+        )
+        await self._execute_listeners(start_method_name, result)
 
-    async def _execute_method(self, method: Callable, *args: Any, **kwargs: Any) -> Any:
+    async def _execute_method(
+        self, method_name: str, method: Callable, *args: Any, **kwargs: Any
+    ) -> Any:
         result = (
             await method(*args, **kwargs)
             if asyncio.iscoroutinefunction(method)
             else method(*args, **kwargs)
         )
         self._method_outputs.append(result)  # Store the output
+
+        # Track method execution counts
+        self._method_execution_counts[method_name] = (
+            self._method_execution_counts.get(method_name, 0) + 1
+        )
+
         return result
 
     async def _execute_listeners(self, trigger_method: str, result: Any) -> None:
@@ -231,32 +319,39 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
         if trigger_method in self._routers:
             router_method = self._methods[self._routers[trigger_method]]
-            path = await self._execute_method(router_method)
-            # Use the path as the new trigger method
+            path = await self._execute_method(
+                self._routers[trigger_method], router_method
+            )
             trigger_method = path
 
-        for listener, (condition_type, methods) in self._listeners.items():
+        for listener_name, (condition_type, methods) in self._listeners.items():
             if condition_type == "OR":
                 if trigger_method in methods:
+                    # Schedule the listener without preventing re-execution
                     listener_tasks.append(
-                        self._execute_single_listener(listener, result)
+                        self._execute_single_listener(listener_name, result)
                     )
             elif condition_type == "AND":
-                if listener not in self._pending_and_listeners:
-                    self._pending_and_listeners[listener] = set()
-                self._pending_and_listeners[listener].add(trigger_method)
-                if set(methods) == self._pending_and_listeners[listener]:
+                # Initialize pending methods for this listener if not already done
+                if listener_name not in self._pending_and_listeners:
+                    self._pending_and_listeners[listener_name] = set(methods)
+                # Remove the trigger method from pending methods
+                self._pending_and_listeners[listener_name].discard(trigger_method)
+                if not self._pending_and_listeners[listener_name]:
+                    # All required methods have been executed
                     listener_tasks.append(
-                        self._execute_single_listener(listener, result)
+                        self._execute_single_listener(listener_name, result)
                     )
-                    del self._pending_and_listeners[listener]
+                    # Reset pending methods for this listener
+                    self._pending_and_listeners.pop(listener_name, None)
 
         # Run all listener tasks concurrently and wait for them to complete
-        await asyncio.gather(*listener_tasks)
+        if listener_tasks:
+            await asyncio.gather(*listener_tasks)
 
-    async def _execute_single_listener(self, listener: str, result: Any) -> None:
+    async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
         try:
-            method = self._methods[listener]
+            method = self._methods[listener_name]
             sig = inspect.signature(method)
             params = list(sig.parameters.values())
 
@@ -265,15 +360,19 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             if method_params:
                 # If listener expects parameters, pass the result
-                listener_result = await self._execute_method(method, result)
+                listener_result = await self._execute_method(
+                    listener_name, method, result
+                )
             else:
                 # If listener does not expect parameters, call without arguments
-                listener_result = await self._execute_method(method)
+                listener_result = await self._execute_method(listener_name, method)
 
             # Execute listeners of this listener
-            await self._execute_listeners(listener, listener_result)
+            await self._execute_listeners(listener_name, listener_result)
         except Exception as e:
-            print(f"[Flow._execute_single_listener] Error in method {listener}: {e}")
+            print(
+                f"[Flow._execute_single_listener] Error in method {listener_name}: {e}"
+            )
             import traceback
 
             traceback.print_exc()

src/crewai/llm.py

@@ -1,7 +1,10 @@
+import io
+import logging
+import sys
+import warnings
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Union
-import logging
-import warnings
+
 import litellm
 from litellm import get_supported_openai_params
 
@@ -9,9 +12,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededException,
 )
 
-import sys
-import io
-
 
 class FilteredStream(io.StringIO):
     def write(self, s):
@@ -118,12 +118,12 @@ class LLM:
 
         litellm.drop_params = True
         litellm.set_verbose = False
-        litellm.callbacks = callbacks
+        self.set_callbacks(callbacks)
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
-                litellm.callbacks = callbacks
+                self.set_callbacks(callbacks)
 
             try:
                 params = {
@@ -181,3 +181,15 @@ class LLM:
     def get_context_window_size(self) -> int:
         # Only using 75% of the context window size to avoid cutting the message in the middle
         return int(LLM_CONTEXT_WINDOW_SIZES.get(self.model, 8192) * 0.75)
+
+    def set_callbacks(self, callbacks: List[Any]):
+        callback_types = [type(callback) for callback in callbacks]
+        for callback in litellm.success_callback[:]:
+            if type(callback) in callback_types:
+                litellm.success_callback.remove(callback)
+
+        for callback in litellm._async_success_callback[:]:
+            if type(callback) in callback_types:
+                litellm._async_success_callback.remove(callback)
+
+        litellm.callbacks = callbacks

src/crewai/memory/__init__.py

@@ -1,5 +1,6 @@
 from .entity.entity_memory import EntityMemory
 from .long_term.long_term_memory import LongTermMemory
 from .short_term.short_term_memory import ShortTermMemory
+from .user.user_memory import UserMemory
 
-__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"]
+__all__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"]

src/crewai/memory/contextual/contextual_memory.py

@@ -1,13 +1,25 @@
-from typing import Optional
+from typing import Optional, Dict, Any
 
-from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
+from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
 
 
 class ContextualMemory:
-    def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
+    def __init__(
+        self,
+        memory_config: Optional[Dict[str, Any]],
+        stm: ShortTermMemory,
+        ltm: LongTermMemory,
+        em: EntityMemory,
+        um: UserMemory,
+    ):
+        if memory_config is not None:
+            self.memory_provider = memory_config.get("provider")
+        else:
+            self.memory_provider = None
         self.stm = stm
         self.ltm = ltm
         self.em = em
+        self.um = um
 
     def build_context_for_task(self, task, context) -> str:
         """
@@ -23,6 +35,8 @@ class ContextualMemory:
         context.append(self._fetch_ltm_context(task.description))
         context.append(self._fetch_stm_context(query))
         context.append(self._fetch_entity_context(query))
+        if self.memory_provider == "mem0":
+            context.append(self._fetch_user_context(query))
         return "\n".join(filter(None, context))
 
     def _fetch_stm_context(self, query) -> str:
@@ -31,7 +45,12 @@ class ContextualMemory:
         formatted as bullet points.
         """
         stm_results = self.stm.search(query)
-        formatted_results = "\n".join([f"- {result}" for result in stm_results])
+        formatted_results = "\n".join(
+            [
+                f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
+                for result in stm_results
+            ]
+        )
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
     def _fetch_ltm_context(self, task) -> Optional[str]:
@@ -60,6 +79,26 @@ class ContextualMemory:
         """
         em_results = self.em.search(query)
         formatted_results = "\n".join(
-            [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
+            [
+                f"- {result['memory'] if self.memory_provider == 'mem0' else result['context']}"
+                for result in em_results
+            ]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
         return f"Entities:\n{formatted_results}" if em_results else ""
+
+    def _fetch_user_context(self, query: str) -> str:
+        """
+        Fetches and formats relevant user information from User Memory.
+        Args:
+            query (str): The search query to find relevant user memories.
+        Returns:
+            str: Formatted user memories as bullet points, or an empty string if none found.
+        """
+        user_memories = self.um.search(query)
+        if not user_memories:
+            return ""
+
+        formatted_memories = "\n".join(
+            f"- {result['memory']}" for result in user_memories
+        )
+        return f"User memories/preferences:\n{formatted_memories}"

src/crewai/memory/entity/entity_memory.py

@@ -11,21 +11,43 @@ class EntityMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None):
-        storage = (
-            storage
-            if storage
-            else RAGStorage(
-                type="entities",
-                allow_reset=False,
-                embedder_config=embedder_config,
-                crew=crew,
+        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
+
+        if self.memory_provider == "mem0":
+            try:
+                from crewai.memory.storage.mem0_storage import Mem0Storage
+            except ImportError:
+                raise ImportError(
+                    "Mem0 is not installed. Please install it with `pip install mem0ai`."
+                )
+            storage = Mem0Storage(type="entities", crew=crew)
+        else:
+            storage = (
+                storage
+                if storage
+                else RAGStorage(
+                    type="entities",
+                    allow_reset=True,
+                    embedder_config=embedder_config,
+                    crew=crew,
+                )
             )
-        )
         super().__init__(storage)
 
     def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         """Saves an entity item into the SQLite storage."""
-        data = f"{item.name}({item.type}): {item.description}"
+        if self.memory_provider == "mem0":
+            data = f"""
+            Remember details about the following entity:
+            Name: {item.name}
+            Type: {item.type}
+            Entity Description: {item.description}
+            """
+        else:
+            data = f"{item.name}({item.type}): {item.description}"
         super().save(data, item.metadata)
 
     def reset(self) -> None:

src/crewai/memory/long_term/long_term_memory.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict
+from typing import Any, Dict, List
 
 from crewai.memory.long_term.long_term_memory_item import LongTermMemoryItem
 from crewai.memory.memory import Memory
@@ -28,7 +28,7 @@ class LongTermMemory(Memory):
             datetime=item.datetime,
         )
 
-    def search(self, task: str, latest_n: int = 3) -> Dict[str, Any]:
+    def search(self, task: str, latest_n: int = 3) -> List[Dict[str, Any]]:  # type: ignore # signature of "search" incompatible with supertype "Memory"
         return self.storage.load(task, latest_n)  # type: ignore # BUG?: "Storage" has no attribute "load"
 
     def reset(self) -> None:

src/crewai/memory/memory.py

@@ -1,6 +1,6 @@
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, List
 
-from crewai.memory.storage.interface import Storage
+from crewai.memory.storage.rag_storage import RAGStorage
 
 
 class Memory:
@@ -8,7 +8,7 @@ class Memory:
     Base class for memory, now supporting agent tags and generic metadata.
     """
 
-    def __init__(self, storage: Storage):
+    def __init__(self, storage: RAGStorage):
         self.storage = storage
 
     def save(
@@ -23,5 +23,12 @@ class Memory:
 
         self.storage.save(value, metadata)
 
-    def search(self, query: str) -> Dict[str, Any]:
-        return self.storage.search(query)
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ) -> List[Any]:
+        return self.storage.search(
+            query=query, limit=limit, score_threshold=score_threshold
+        )

src/crewai/memory/short_term/short_term_memory.py

@@ -14,13 +14,27 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None):
-        storage = (
-            storage
-            if storage
-            else RAGStorage(
-                type="short_term", embedder_config=embedder_config, crew=crew
+        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
+
+        if self.memory_provider == "mem0":
+            try:
+                from crewai.memory.storage.mem0_storage import Mem0Storage
+            except ImportError:
+                raise ImportError(
+                    "Mem0 is not installed. Please install it with `pip install mem0ai`."
+                )
+            storage = Mem0Storage(type="short_term", crew=crew)
+        else:
+            storage = (
+                storage
+                if storage
+                else RAGStorage(
+                    type="short_term", embedder_config=embedder_config, crew=crew
+                )
             )
-        )
         super().__init__(storage)
 
     def save(
@@ -30,11 +44,20 @@ class ShortTermMemory(Memory):
         agent: Optional[str] = None,
     ) -> None:
         item = ShortTermMemoryItem(data=value, metadata=metadata, agent=agent)
+        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)
 
-    def search(self, query: str, score_threshold: float = 0.35):
-        return self.storage.search(query=query, score_threshold=score_threshold)  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ):
+        return self.storage.search(
+            query=query, limit=limit, score_threshold=score_threshold
+        )  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
 
     def reset(self) -> None:
         try:

src/crewai/memory/storage/base_rag_storage.py

@@ -0,0 +1,76 @@
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
+
+
+class BaseRAGStorage(ABC):
+    """
+    Base class for RAG-based Storage implementations.
+    """
+
+    app: Any | None = None
+
+    def __init__(
+        self,
+        type: str,
+        allow_reset: bool = True,
+        embedder_config: Optional[Any] = None,
+        crew: Any = None,
+    ):
+        self.type = type
+        self.allow_reset = allow_reset
+        self.embedder_config = embedder_config
+        self.crew = crew
+        self.agents = self._initialize_agents()
+
+    def _initialize_agents(self) -> str:
+        if self.crew:
+            return "_".join(
+                [self._sanitize_role(agent.role) for agent in self.crew.agents]
+            )
+        return ""
+
+    @abstractmethod
+    def _sanitize_role(self, role: str) -> str:
+        """Sanitizes agent roles to ensure valid directory names."""
+        pass
+
+    @abstractmethod
+    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+        """Save a value with metadata to the storage."""
+        pass
+
+    @abstractmethod
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        filter: Optional[dict] = None,
+        score_threshold: float = 0.35,
+    ) -> List[Any]:
+        """Search for entries in the storage."""
+        pass
+
+    @abstractmethod
+    def reset(self) -> None:
+        """Reset the storage."""
+        pass
+
+    @abstractmethod
+    def _generate_embedding(
+        self, text: str, metadata: Optional[Dict[str, Any]] = None
+    ) -> Any:
+        """Generate an embedding for the given text and metadata."""
+        pass
+
+    @abstractmethod
+    def _initialize_app(self):
+        """Initialize the vector db."""
+        pass
+
+    def setup_config(self, config: Dict[str, Any]):
+        """Setup the config of the storage."""
+        pass
+
+    def initialize_client(self):
+        """Initialize the client of the storage. This should setup the app and the db collection"""
+        pass

src/crewai/memory/storage/interface.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict
+from typing import Any, Dict, List
 
 
 class Storage:
@@ -7,8 +7,10 @@ class Storage:
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         pass
 
-    def search(self, key: str) -> Dict[str, Any]:  # type: ignore
-        pass
+    def search(
+        self, query: str, limit: int, score_threshold: float
+    ) -> Dict[str, Any] | List[Any]:
+        return {}
 
     def reset(self) -> None:
         pass

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -70,7 +70,7 @@ class KickoffTaskOutputsSQLiteStorage:
                         task.expected_output,
                         json.dumps(output, cls=CrewJSONEncoder),
                         task_index,
-                        json.dumps(inputs),
+                        json.dumps(inputs, cls=CrewJSONEncoder),
                         was_replayed,
                     ),
                 )
@@ -103,7 +103,7 @@ class KickoffTaskOutputsSQLiteStorage:
                         else value
                     )
 
-                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"
+                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"  # nosec
                 values.append(task_index)
 
                 cursor.execute(query, tuple(values))

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -83,7 +83,7 @@ class LTMSQLiteStorage:
                     WHERE task_description = ?
                     ORDER BY datetime DESC, score ASC
                     LIMIT {latest_n}
-                """,
+                """,  # nosec
                     (task_description,),
                 )
                 rows = cursor.fetchall()

src/crewai/memory/storage/mem0_storage.py

@@ -0,0 +1,104 @@
+import os
+from typing import Any, Dict, List
+
+from mem0 import MemoryClient
+from crewai.memory.storage.interface import Storage
+
+
+class Mem0Storage(Storage):
+    """
+    Extends Storage to handle embedding and searching across entities using Mem0.
+    """
+
+    def __init__(self, type, crew=None):
+        super().__init__()
+
+        if type not in ["user", "short_term", "long_term", "entities"]:
+            raise ValueError("Invalid type for Mem0Storage. Must be 'user' or 'agent'.")
+
+        self.memory_type = type
+        self.crew = crew
+        self.memory_config = crew.memory_config
+
+        # User ID is required for user memory type "user" since it's used as a unique identifier for the user.
+        user_id = self._get_user_id()
+        if type == "user" and not user_id:
+            raise ValueError("User ID is required for user memory type")
+
+        # API key in memory config overrides the environment variable
+        mem0_api_key = self.memory_config.get("config", {}).get("api_key") or os.getenv(
+            "MEM0_API_KEY"
+        )
+        self.memory = MemoryClient(api_key=mem0_api_key)
+
+    def _sanitize_role(self, role: str) -> str:
+        """
+        Sanitizes agent roles to ensure valid directory names.
+        """
+        return role.replace("\n", "").replace(" ", "_").replace("/", "_")
+
+    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
+        user_id = self._get_user_id()
+        agent_name = self._get_agent_name()
+        if self.memory_type == "user":
+            self.memory.add(value, user_id=user_id, metadata={**metadata})
+        elif self.memory_type == "short_term":
+            agent_name = self._get_agent_name()
+            self.memory.add(
+                value, agent_id=agent_name, metadata={"type": "short_term", **metadata}
+            )
+        elif self.memory_type == "long_term":
+            agent_name = self._get_agent_name()
+            self.memory.add(
+                value,
+                agent_id=agent_name,
+                infer=False,
+                metadata={"type": "long_term", **metadata},
+            )
+        elif self.memory_type == "entities":
+            entity_name = None
+            self.memory.add(
+                value, user_id=entity_name, metadata={"type": "entity", **metadata}
+            )
+
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ) -> List[Any]:
+        params = {"query": query, "limit": limit}
+        if self.memory_type == "user":
+            user_id = self._get_user_id()
+            params["user_id"] = user_id
+        elif self.memory_type == "short_term":
+            agent_name = self._get_agent_name()
+            params["agent_id"] = agent_name
+            params["metadata"] = {"type": "short_term"}
+        elif self.memory_type == "long_term":
+            agent_name = self._get_agent_name()
+            params["agent_id"] = agent_name
+            params["metadata"] = {"type": "long_term"}
+        elif self.memory_type == "entities":
+            agent_name = self._get_agent_name()
+            params["agent_id"] = agent_name
+            params["metadata"] = {"type": "entity"}
+
+        # Discard the filters for now since we create the filters
+        # automatically when the crew is created.
+        results = self.memory.search(**params)
+        return [r for r in results if r["score"] >= score_threshold]
+
+    def _get_user_id(self):
+        if self.memory_type == "user":
+            if hasattr(self, "memory_config") and self.memory_config is not None:
+                return self.memory_config.get("config", {}).get("user_id")
+            else:
+                return None
+        return None
+
+    def _get_agent_name(self):
+        agents = self.crew.agents if self.crew else []
+        agents = [self._sanitize_role(agent.role) for agent in agents]
+        agents = "_".join(agents)
+        return agents

src/crewai/memory/storage/rag_storage.py

@@ -3,9 +3,13 @@ import io
 import logging
 import os
 import shutil
-from typing import Any, Dict, List, Optional
+import uuid
+from typing import Any, Dict, List, Optional, cast
 
-from crewai.memory.storage.interface import Storage
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from chromadb.api import ClientAPI
+from chromadb.api.types import validate_embedding_function
+from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities.paths import db_storage_path
 
 
@@ -17,68 +21,184 @@ def suppress_logging(
     logger = logging.getLogger(logger_name)
     original_level = logger.getEffectiveLevel()
     logger.setLevel(level)
-    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(
-        io.StringIO()
-    ), contextlib.suppress(UserWarning):
+    with (
+        contextlib.redirect_stdout(io.StringIO()),
+        contextlib.redirect_stderr(io.StringIO()),
+        contextlib.suppress(UserWarning),
+    ):
         yield
     logger.setLevel(original_level)
 
 
-class RAGStorage(Storage):
+class RAGStorage(BaseRAGStorage):
     """
     Extends Storage to handle embeddings for memory entries, improving
     search efficiency.
     """
 
-    def __init__(self, type, allow_reset=True, embedder_config=None, crew=None):
-        super().__init__()
-        if (
-            not os.getenv("OPENAI_API_KEY")
-            and not os.getenv("OPENAI_BASE_URL") == "https://api.openai.com/v1"
-        ):
-            os.environ["OPENAI_API_KEY"] = "fake"
+    app: ClientAPI | None = None
 
+    def __init__(self, type, allow_reset=True, embedder_config=None, crew=None):
+        super().__init__(type, allow_reset, embedder_config, crew)
         agents = crew.agents if crew else []
         agents = [self._sanitize_role(agent.role) for agent in agents]
         agents = "_".join(agents)
+        self.agents = agents
 
-        config = {
-            "app": {
-                "config": {"name": type, "collect_metrics": False, "log_level": "ERROR"}
-            },
-            "chunker": {
-                "chunk_size": 5000,
-                "chunk_overlap": 100,
-                "length_function": "len",
-                "min_chunk_size": 150,
-            },
-            "vectordb": {
-                "provider": "chroma",
-                "config": {
-                    "collection_name": type,
-                    "dir": f"{db_storage_path()}/{type}/{agents}",
-                    "allow_reset": allow_reset,
-                },
-            },
-        }
-
-        if embedder_config:
-            config["embedder"] = embedder_config
         self.type = type
-        self.config = config
+
         self.allow_reset = allow_reset
+        self._initialize_app()
+
+    def _set_embedder_config(self):
+        if self.embedder_config is None:
+            self.embedder_config = self._create_default_embedding_function()
+
+        if isinstance(self.embedder_config, dict):
+            provider = self.embedder_config.get("provider")
+            config = self.embedder_config.get("config", {})
+            model_name = config.get("model")
+            if provider == "openai":
+                from chromadb.utils.embedding_functions.openai_embedding_function import (
+                    OpenAIEmbeddingFunction,
+                )
+
+                self.embedder_config = OpenAIEmbeddingFunction(
+                    api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+                    model_name=model_name,
+                )
+            elif provider == "azure":
+                from chromadb.utils.embedding_functions.openai_embedding_function import (
+                    OpenAIEmbeddingFunction,
+                )
+
+                self.embedder_config = OpenAIEmbeddingFunction(
+                    api_key=config.get("api_key"),
+                    api_base=config.get("api_base"),
+                    api_type=config.get("api_type", "azure"),
+                    api_version=config.get("api_version"),
+                    model_name=model_name,
+                )
+            elif provider == "ollama":
+                from chromadb.utils.embedding_functions.ollama_embedding_function import (
+                    OllamaEmbeddingFunction,
+                )
+
+                self.embedder_config = OllamaEmbeddingFunction(
+                    url=config.get("url", "http://localhost:11434/api/embeddings"),
+                    model_name=model_name,
+                )
+            elif provider == "vertexai":
+                from chromadb.utils.embedding_functions.google_embedding_function import (
+                    GoogleVertexEmbeddingFunction,
+                )
+
+                self.embedder_config = GoogleVertexEmbeddingFunction(
+                    model_name=model_name,
+                    api_key=config.get("api_key"),
+                )
+            elif provider == "google":
+                from chromadb.utils.embedding_functions.google_embedding_function import (
+                    GoogleGenerativeAiEmbeddingFunction,
+                )
+
+                self.embedder_config = GoogleGenerativeAiEmbeddingFunction(
+                    model_name=model_name,
+                    api_key=config.get("api_key"),
+                )
+            elif provider == "cohere":
+                from chromadb.utils.embedding_functions.cohere_embedding_function import (
+                    CohereEmbeddingFunction,
+                )
+
+                self.embedder_config = CohereEmbeddingFunction(
+                    model_name=model_name,
+                    api_key=config.get("api_key"),
+                )
+            elif provider == "bedrock":
+                from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+                    AmazonBedrockEmbeddingFunction,
+                )
+
+                self.embedder_config = AmazonBedrockEmbeddingFunction(
+                    session=config.get("session"),
+                )
+            elif provider == "huggingface":
+                from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+                    HuggingFaceEmbeddingServer,
+                )
+
+                self.embedder_config = HuggingFaceEmbeddingServer(
+                    url=config.get("api_url"),
+                )
+            elif provider == "watson":
+                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
+
+                class WatsonEmbeddingFunction(EmbeddingFunction):
+                    def __call__(self, input: Documents) -> Embeddings:
+                        if isinstance(input, str):
+                            input = [input]
+
+                        embed_params = {
+                            EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
+                            EmbedParams.RETURN_OPTIONS: {"input_text": True},
+                        }
+
+                        embedding = watson_models.Embeddings(
+                            model_id=config.get("model"),
+                            params=embed_params,
+                            credentials=Credentials(
+                                api_key=config.get("api_key"), url=config.get("api_url")
+                            ),
+                            project_id=config.get("project_id"),
+                        )
+
+                        try:
+                            embeddings = embedding.embed_documents(input)
+                            return cast(Embeddings, embeddings)
+
+                        except Exception as e:
+                            print("Error during Watson embedding:", e)
+                            raise e
+
+                self.embedder_config = WatsonEmbeddingFunction()
+            else:
+                raise Exception(
+                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
+                )
+        else:
+            validate_embedding_function(self.embedder_config)
+            self.embedder_config = self.embedder_config
 
     def _initialize_app(self):
-        from embedchain import App
-        from embedchain.llm.base import BaseLlm
+        import chromadb
+        from chromadb.config import Settings
 
-        class FakeLLM(BaseLlm):
-            pass
+        self._set_embedder_config()
+        chroma_client = chromadb.PersistentClient(
+            path=f"{db_storage_path()}/{self.type}/{self.agents}",
+            settings=Settings(allow_reset=self.allow_reset),
+        )
 
-        self.app = App.from_config(config=self.config)
-        self.app.llm = FakeLLM()
-        if self.allow_reset:
-            self.app.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
+            )
 
     def _sanitize_role(self, role: str) -> str:
         """
@@ -87,11 +207,14 @@ class RAGStorage(Storage):
         return role.replace("\n", "").replace(" ", "_").replace("/", "_")
 
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
-        if not hasattr(self, "app"):
+        if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
-        self._generate_embedding(value, metadata)
+        try:
+            self._generate_embedding(value, metadata)
+        except Exception as e:
+            logging.error(f"Error during {self.type} save: {str(e)}")
 
-    def search(  # type: ignore # BUG?: Signature of "search" incompatible with supertype "Storage"
+    def search(
         self,
         query: str,
         limit: int = 3,
@@ -100,31 +223,56 @@ class RAGStorage(Storage):
     ) -> List[Any]:
         if not hasattr(self, "app"):
             self._initialize_app()
-        from embedchain.vectordb.chroma import InvalidDimensionException
 
-        with suppress_logging():
-            try:
-                results = (
-                    self.app.search(query, limit, where=filter)
-                    if filter
-                    else self.app.search(query, limit)
-                )
-            except InvalidDimensionException:
-                self.app.reset()
-                return []
-        return [r for r in results if r["metadata"]["score"] >= score_threshold]
+        try:
+            with suppress_logging():
+                response = self.collection.query(query_texts=query, n_results=limit)
 
-    def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> Any:
-        if not hasattr(self, "app"):
+            results = []
+            for i in range(len(response["ids"][0])):
+                result = {
+                    "id": response["ids"][0][i],
+                    "metadata": response["metadatas"][0][i],
+                    "context": response["documents"][0][i],
+                    "score": response["distances"][0][i],
+                }
+                if result["score"] >= score_threshold:
+                    results.append(result)
+
+            return results
+        except Exception as e:
+            logging.error(f"Error during {self.type} search: {str(e)}")
+            return []
+
+    def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> None:  # type: ignore
+        if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
-        from embedchain.models.data_type import DataType
 
-        self.app.add(text, data_type=DataType.TEXT, metadata=metadata)
+        self.collection.add(
+            documents=[text],
+            metadatas=[metadata or {}],
+            ids=[str(uuid.uuid4())],
+        )
 
     def reset(self) -> None:
         try:
             shutil.rmtree(f"{db_storage_path()}/{self.type}")
+            if self.app:
+                self.app.reset()
         except Exception as e:
-            raise Exception(
-                f"An error occurred while resetting the {self.type} memory: {e}"
-            )
+            if "attempt to write a readonly database" in str(e):
+                # Ignore this specific error
+                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"
+        )

src/crewai/memory/user/user_memory.py

@@ -0,0 +1,45 @@
+from typing import Any, Dict, Optional
+
+from crewai.memory.memory import Memory
+
+
+class UserMemory(Memory):
+    """
+    UserMemory class for handling user memory storage and retrieval.
+    Inherits from the Memory class and utilizes an instance of a class that
+    adheres to the Storage for data storage, specifically working with
+    MemoryItem instances.
+    """
+
+    def __init__(self, crew=None):
+        try:
+            from crewai.memory.storage.mem0_storage import Mem0Storage
+        except ImportError:
+            raise ImportError(
+                "Mem0 is not installed. Please install it with `pip install mem0ai`."
+            )
+        storage = Mem0Storage(type="user", crew=crew)
+        super().__init__(storage)
+
+    def save(
+        self,
+        value,
+        metadata: Optional[Dict[str, Any]] = None,
+        agent: Optional[str] = None,
+    ) -> None:
+        # TODO: Change this function since we want to take care of the case where we save memories for the usr
+        data = f"Remember the details about the user: {value}"
+        super().save(data, metadata)
+
+    def search(
+        self,
+        query: str,
+        limit: int = 3,
+        score_threshold: float = 0.35,
+    ):
+        results = super().search(
+            query=query,
+            limit=limit,
+            score_threshold=score_threshold,
+        )
+        return results

src/crewai/memory/user/user_memory_item.py

@@ -0,0 +1,8 @@
+from typing import Any, Dict, Optional
+
+
+class UserMemoryItem:
+    def __init__(self, data: Any, user: str, metadata: Optional[Dict[str, Any]] = None):
+        self.data = data
+        self.user = user
+        self.metadata = metadata if metadata is not None else {}

src/crewai/project/__init__.py

@@ -1,5 +1,7 @@
 from .annotations import (
+    after_kickoff,
     agent,
+    before_kickoff,
     cache_handler,
     callback,
     crew,
@@ -26,4 +28,6 @@ __all__ = [
     "llm",
     "cache_handler",
     "pipeline",
+    "before_kickoff",
+    "after_kickoff",
 ]

src/crewai/project/annotations.py

@@ -5,6 +5,16 @@ from crewai import Crew
 from crewai.project.utils import memoize
 
 
+def before_kickoff(func):
+    func.is_before_kickoff = True
+    return func
+
+
+def after_kickoff(func):
+    func.is_after_kickoff = True
+    return func
+
+
 def task(func):
     func.is_task = True
 
@@ -76,27 +86,13 @@ def crew(func) -> Callable[..., Crew]:
         instantiated_agents = []
         agent_roles = set()
 
-        # Collect methods from crew in order
-        all_functions = [
-            (name, getattr(self, name))
-            for name, attr in self.__class__.__dict__.items()
-            if callable(attr)
-        ]
-        tasks = [
-            (name, method)
-            for name, method in all_functions
-            if hasattr(method, "is_task")
-        ]
-
-        agents = [
-            (name, method)
-            for name, method in all_functions
-            if hasattr(method, "is_agent")
-        ]
+        # Use the preserved task and agent information
+        tasks = self._original_tasks.items()
+        agents = self._original_agents.items()
 
         # Instantiate tasks in order
         for task_name, task_method in tasks:
-            task_instance = task_method()
+            task_instance = task_method(self)
             instantiated_tasks.append(task_instance)
             agent_instance = getattr(task_instance, "agent", None)
             if agent_instance and agent_instance.role not in agent_roles:
@@ -105,7 +101,7 @@ def crew(func) -> Callable[..., Crew]:
 
         # Instantiate agents not included by tasks
         for agent_name, agent_method in agents:
-            agent_instance = agent_method()
+            agent_instance = agent_method(self)
             if agent_instance.role not in agent_roles:
                 instantiated_agents.append(agent_instance)
                 agent_roles.add(agent_instance.role)
@@ -113,6 +109,19 @@ def crew(func) -> Callable[..., Crew]:
         self.agents = instantiated_agents
         self.tasks = instantiated_tasks
 
-        return func(self, *args, **kwargs)
+        crew = func(self, *args, **kwargs)
 
-    return wrapper
+        def callback_wrapper(callback, instance):
+            def wrapper(*args, **kwargs):
+                return callback(instance, *args, **kwargs)
+
+            return wrapper
+
+        for _, callback in self._before_kickoff.items():
+            crew.before_kickoff_callbacks.append(callback_wrapper(callback, self))
+        for _, callback in self._after_kickoff.items():
+            crew.after_kickoff_callbacks.append(callback_wrapper(callback, self))
+
+        return crew
+
+    return memoize(wrapper)

src/crewai/project/crew_base.py

@@ -34,6 +34,39 @@ def CrewBase(cls: T) -> T:
             self.map_all_agent_variables()
             self.map_all_task_variables()
 
+            # Preserve all decorated functions
+            self._original_functions = {
+                name: method
+                for name, method in cls.__dict__.items()
+                if any(
+                    hasattr(method, attr)
+                    for attr in [
+                        "is_task",
+                        "is_agent",
+                        "is_before_kickoff",
+                        "is_after_kickoff",
+                        "is_kickoff",
+                    ]
+                )
+            }
+
+            # Store specific function types
+            self._original_tasks = self._filter_functions(
+                self._original_functions, "is_task"
+            )
+            self._original_agents = self._filter_functions(
+                self._original_functions, "is_agent"
+            )
+            self._before_kickoff = self._filter_functions(
+                self._original_functions, "is_before_kickoff"
+            )
+            self._after_kickoff = self._filter_functions(
+                self._original_functions, "is_after_kickoff"
+            )
+            self._kickoff = self._filter_functions(
+                self._original_functions, "is_kickoff"
+            )
+
         @staticmethod
         def load_yaml(config_path: Path):
             try:

src/crewai/task.py

@@ -20,6 +20,7 @@ from pydantic import (
 from pydantic_core import PydanticCustomError
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.tools.base_tool import BaseTool
 from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry.telemetry import Telemetry
@@ -91,7 +92,7 @@ class Task(BaseModel):
     output: Optional[TaskOutput] = Field(
         description="Task output, it's final result after being executed", default=None
     )
-    tools: Optional[List[Any]] = Field(
+    tools: Optional[List[BaseTool]] = Field(
         default_factory=list,
         description="Tools the agent is limited to use for this task.",
     )
@@ -185,7 +186,7 @@ class Task(BaseModel):
         self,
         agent: Optional[BaseAgent] = None,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> TaskOutput:
         """Execute the task synchronously."""
         return self._execute_core(agent, context, tools)
@@ -202,7 +203,7 @@ class Task(BaseModel):
         self,
         agent: BaseAgent | None = None,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> Future[TaskOutput]:
         """Execute the task asynchronously."""
         future: Future[TaskOutput] = Future()

src/crewai/telemetry/telemetry.py

@@ -21,7 +21,7 @@ with suppress_warnings():
 
 
 from opentelemetry import trace  # noqa: E402
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter  # noqa: E402
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
 from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
@@ -48,6 +48,10 @@ class Telemetry:
     def __init__(self):
         self.ready = False
         self.trace_set = False
+
+        if os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true":
+            return
+
         try:
             telemetry_endpoint = "https://telemetry.crewai.com:4319"
             self.resource = Resource(
@@ -65,7 +69,7 @@ class Telemetry:
 
             self.provider.add_span_processor(processor)
             self.ready = True
-        except BaseException as e:
+        except Exception as e:
             if isinstance(
                 e,
                 (SystemExit, KeyboardInterrupt, GeneratorExit, asyncio.CancelledError),
@@ -83,404 +87,33 @@ class Telemetry:
                 self.ready = False
                 self.trace_set = False
 
+    def _safe_telemetry_operation(self, operation):
+        if not self.ready:
+            return
+        try:
+            operation()
+        except Exception:
+            pass
+
     def crew_creation(self, crew: Crew, inputs: dict[str, Any] | None):
         """Records the creation of a crew."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Crew Created")
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                self._add_attribute(span, "python_version", platform.python_version())
-                self._add_attribute(span, "crew_key", crew.key)
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(span, "crew_process", crew.process)
-                self._add_attribute(span, "crew_memory", crew.memory)
-                self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
-                self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
-                if crew.share_crew:
-                    self._add_attribute(
-                        span,
-                        "crew_agents",
-                        json.dumps(
-                            [
-                                {
-                                    "key": agent.key,
-                                    "id": str(agent.id),
-                                    "role": agent.role,
-                                    "goal": agent.goal,
-                                    "backstory": agent.backstory,
-                                    "verbose?": agent.verbose,
-                                    "max_iter": agent.max_iter,
-                                    "max_rpm": agent.max_rpm,
-                                    "i18n": agent.i18n.prompt_file,
-                                    "function_calling_llm": (
-                                        agent.function_calling_llm.model
-                                        if agent.function_calling_llm
-                                        else ""
-                                    ),
-                                    "llm": agent.llm.model,
-                                    "delegation_enabled?": agent.allow_delegation,
-                                    "allow_code_execution?": agent.allow_code_execution,
-                                    "max_retry_limit": agent.max_retry_limit,
-                                    "tools_names": [
-                                        tool.name.casefold()
-                                        for tool in agent.tools or []
-                                    ],
-                                }
-                                for agent in crew.agents
-                            ]
-                        ),
-                    )
-                    self._add_attribute(
-                        span,
-                        "crew_tasks",
-                        json.dumps(
-                            [
-                                {
-                                    "key": task.key,
-                                    "id": str(task.id),
-                                    "description": task.description,
-                                    "expected_output": task.expected_output,
-                                    "async_execution?": task.async_execution,
-                                    "human_input?": task.human_input,
-                                    "agent_role": (
-                                        task.agent.role if task.agent else "None"
-                                    ),
-                                    "agent_key": task.agent.key if task.agent else None,
-                                    "context": (
-                                        [task.description for task in task.context]
-                                        if task.context
-                                        else None
-                                    ),
-                                    "tools_names": [
-                                        tool.name.casefold()
-                                        for tool in task.tools or []
-                                    ],
-                                }
-                                for task in crew.tasks
-                            ]
-                        ),
-                    )
-                    self._add_attribute(span, "platform", platform.platform())
-                    self._add_attribute(span, "platform_release", platform.release())
-                    self._add_attribute(span, "platform_system", platform.system())
-                    self._add_attribute(span, "platform_version", platform.version())
-                    self._add_attribute(span, "cpus", os.cpu_count())
-                    self._add_attribute(
-                        span, "crew_inputs", json.dumps(inputs) if inputs else None
-                    )
-                else:
-                    self._add_attribute(
-                        span,
-                        "crew_agents",
-                        json.dumps(
-                            [
-                                {
-                                    "key": agent.key,
-                                    "id": str(agent.id),
-                                    "role": agent.role,
-                                    "verbose?": agent.verbose,
-                                    "max_iter": agent.max_iter,
-                                    "max_rpm": agent.max_rpm,
-                                    "function_calling_llm": (
-                                        agent.function_calling_llm.model
-                                        if agent.function_calling_llm
-                                        else ""
-                                    ),
-                                    "llm": agent.llm.model,
-                                    "delegation_enabled?": agent.allow_delegation,
-                                    "allow_code_execution?": agent.allow_code_execution,
-                                    "max_retry_limit": agent.max_retry_limit,
-                                    "tools_names": [
-                                        tool.name.casefold()
-                                        for tool in agent.tools or []
-                                    ],
-                                }
-                                for agent in crew.agents
-                            ]
-                        ),
-                    )
-                    self._add_attribute(
-                        span,
-                        "crew_tasks",
-                        json.dumps(
-                            [
-                                {
-                                    "key": task.key,
-                                    "id": str(task.id),
-                                    "async_execution?": task.async_execution,
-                                    "human_input?": task.human_input,
-                                    "agent_role": (
-                                        task.agent.role if task.agent else "None"
-                                    ),
-                                    "agent_key": task.agent.key if task.agent else None,
-                                    "tools_names": [
-                                        tool.name.casefold()
-                                        for tool in task.tools or []
-                                    ],
-                                }
-                                for task in crew.tasks
-                            ]
-                        ),
-                    )
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
 
-    def task_started(self, crew: Crew, task: Task) -> Span | None:
-        """Records task started in a crew."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-
-                created_span = tracer.start_span("Task Created")
-
-                self._add_attribute(created_span, "crew_key", crew.key)
-                self._add_attribute(created_span, "crew_id", str(crew.id))
-                self._add_attribute(created_span, "task_key", task.key)
-                self._add_attribute(created_span, "task_id", str(task.id))
-
-                if crew.share_crew:
-                    self._add_attribute(
-                        created_span, "formatted_description", task.description
-                    )
-                    self._add_attribute(
-                        created_span, "formatted_expected_output", task.expected_output
-                    )
-
-                created_span.set_status(Status(StatusCode.OK))
-                created_span.end()
-
-                span = tracer.start_span("Task Execution")
-
-                self._add_attribute(span, "crew_key", crew.key)
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(span, "task_key", task.key)
-                self._add_attribute(span, "task_id", str(task.id))
-
-                if crew.share_crew:
-                    self._add_attribute(span, "formatted_description", task.description)
-                    self._add_attribute(
-                        span, "formatted_expected_output", task.expected_output
-                    )
-
-                return span
-            except Exception:
-                pass
-
-        return None
-
-    def task_ended(self, span: Span, task: Task, crew: Crew):
-        """Records task execution in a crew."""
-        if self.ready:
-            try:
-                if crew.share_crew:
-                    self._add_attribute(
-                        span,
-                        "task_output",
-                        task.output.raw if task.output else "",
-                    )
-
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def tool_repeated_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the repeated usage 'error' of a tool by an agent."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Tool Repeated Usage")
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                self._add_attribute(span, "tool_name", tool_name)
-                self._add_attribute(span, "attempts", attempts)
-                if llm:
-                    self._add_attribute(span, "llm", llm.model)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def tool_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the usage of a tool by an agent."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Tool Usage")
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                self._add_attribute(span, "tool_name", tool_name)
-                self._add_attribute(span, "attempts", attempts)
-                if llm:
-                    self._add_attribute(span, "llm", llm.model)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def tool_usage_error(self, llm: Any):
-        """Records the usage of a tool by an agent."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Tool Usage Error")
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                if llm:
-                    self._add_attribute(span, "llm", llm.model)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def individual_test_result_span(
-        self, crew: Crew, quality: float, exec_time: int, model_name: str
-    ):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Crew Individual Test Result")
-
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                self._add_attribute(span, "crew_key", crew.key)
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(span, "quality", str(quality))
-                self._add_attribute(span, "exec_time", str(exec_time))
-                self._add_attribute(span, "model_name", model_name)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def test_execution_span(
-        self,
-        crew: Crew,
-        iterations: int,
-        inputs: dict[str, Any] | None,
-        model_name: str,
-    ):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Crew Test Execution")
-
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                self._add_attribute(span, "crew_key", crew.key)
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(span, "iterations", str(iterations))
-                self._add_attribute(span, "model_name", model_name)
-
-                if crew.share_crew:
-                    self._add_attribute(
-                        span, "inputs", json.dumps(inputs) if inputs else None
-                    )
-
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def deploy_signup_error_span(self):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Deploy Signup Error")
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def start_deployment_span(self, uuid: Optional[str] = None):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Start Deployment")
-                if uuid:
-                    self._add_attribute(span, "uuid", uuid)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def create_crew_deployment_span(self):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Create Crew Deployment")
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def get_crew_logs_span(self, uuid: Optional[str], log_type: str = "deployment"):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Get Crew Logs")
-                self._add_attribute(span, "log_type", log_type)
-                if uuid:
-                    self._add_attribute(span, "uuid", uuid)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def remove_crew_span(self, uuid: Optional[str] = None):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Remove Crew")
-                if uuid:
-                    self._add_attribute(span, "uuid", uuid)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def crew_execution_span(self, crew: Crew, inputs: dict[str, Any] | None):
-        """Records the complete execution of a crew.
-        This is only collected if the user has opted-in to share the crew.
-        """
-        self.crew_creation(crew, inputs)
-
-        if (self.ready) and (crew.share_crew):
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Crew Execution")
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                self._add_attribute(span, "crew_key", crew.key)
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(
-                    span, "crew_inputs", json.dumps(inputs) if inputs else None
-                )
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Crew Created")
+            self._add_attribute(
+                span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            self._add_attribute(span, "python_version", platform.python_version())
+            self._add_attribute(span, "crew_key", crew.key)
+            self._add_attribute(span, "crew_id", str(crew.id))
+            self._add_attribute(span, "crew_process", crew.process)
+            self._add_attribute(span, "crew_memory", crew.memory)
+            self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
+            self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
+            if crew.share_crew:
                 self._add_attribute(
                     span,
                     "crew_agents",
@@ -496,8 +129,15 @@ class Telemetry:
                                 "max_iter": agent.max_iter,
                                 "max_rpm": agent.max_rpm,
                                 "i18n": agent.i18n.prompt_file,
+                                "function_calling_llm": (
+                                    agent.function_calling_llm.model
+                                    if agent.function_calling_llm
+                                    else ""
+                                ),
                                 "llm": agent.llm.model,
                                 "delegation_enabled?": agent.allow_delegation,
+                                "allow_code_execution?": agent.allow_code_execution,
+                                "max_retry_limit": agent.max_retry_limit,
                                 "tools_names": [
                                     tool.name.casefold() for tool in agent.tools or []
                                 ],
@@ -512,12 +152,15 @@ class Telemetry:
                     json.dumps(
                         [
                             {
+                                "key": task.key,
                                 "id": str(task.id),
                                 "description": task.description,
                                 "expected_output": task.expected_output,
                                 "async_execution?": task.async_execution,
                                 "human_input?": task.human_input,
-                                "agent_role": task.agent.role if task.agent else "None",
+                                "agent_role": (
+                                    task.agent.role if task.agent else "None"
+                                ),
                                 "agent_key": task.agent.key if task.agent else None,
                                 "context": (
                                     [task.description for task in task.context]
@@ -532,78 +175,433 @@ class Telemetry:
                         ]
                     ),
                 )
-                return span
-            except Exception:
-                pass
-
-    def end_crew(self, crew, final_string_output):
-        if (self.ready) and (crew.share_crew):
-            try:
+                self._add_attribute(span, "platform", platform.platform())
+                self._add_attribute(span, "platform_release", platform.release())
+                self._add_attribute(span, "platform_system", platform.system())
+                self._add_attribute(span, "platform_version", platform.version())
+                self._add_attribute(span, "cpus", os.cpu_count())
                 self._add_attribute(
-                    crew._execution_span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
+                    span, "crew_inputs", json.dumps(inputs) if inputs else None
                 )
+            else:
                 self._add_attribute(
-                    crew._execution_span, "crew_output", final_string_output
-                )
-                self._add_attribute(
-                    crew._execution_span,
-                    "crew_tasks_output",
+                    span,
+                    "crew_agents",
                     json.dumps(
                         [
                             {
+                                "key": agent.key,
+                                "id": str(agent.id),
+                                "role": agent.role,
+                                "verbose?": agent.verbose,
+                                "max_iter": agent.max_iter,
+                                "max_rpm": agent.max_rpm,
+                                "function_calling_llm": (
+                                    agent.function_calling_llm.model
+                                    if agent.function_calling_llm
+                                    else ""
+                                ),
+                                "llm": agent.llm.model,
+                                "delegation_enabled?": agent.allow_delegation,
+                                "allow_code_execution?": agent.allow_code_execution,
+                                "max_retry_limit": agent.max_retry_limit,
+                                "tools_names": [
+                                    tool.name.casefold() for tool in agent.tools or []
+                                ],
+                            }
+                            for agent in crew.agents
+                        ]
+                    ),
+                )
+                self._add_attribute(
+                    span,
+                    "crew_tasks",
+                    json.dumps(
+                        [
+                            {
+                                "key": task.key,
                                 "id": str(task.id),
-                                "description": task.description,
-                                "output": task.output.raw_output,
+                                "async_execution?": task.async_execution,
+                                "human_input?": task.human_input,
+                                "agent_role": (
+                                    task.agent.role if task.agent else "None"
+                                ),
+                                "agent_key": task.agent.key if task.agent else None,
+                                "tools_names": [
+                                    tool.name.casefold() for tool in task.tools or []
+                                ],
                             }
                             for task in crew.tasks
                         ]
                     ),
                 )
-                crew._execution_span.set_status(Status(StatusCode.OK))
-                crew._execution_span.end()
-            except Exception:
-                pass
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def task_started(self, crew: Crew, task: Task) -> Span | None:
+        """Records task started in a crew."""
+
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+
+            created_span = tracer.start_span("Task Created")
+
+            self._add_attribute(created_span, "crew_key", crew.key)
+            self._add_attribute(created_span, "crew_id", str(crew.id))
+            self._add_attribute(created_span, "task_key", task.key)
+            self._add_attribute(created_span, "task_id", str(task.id))
+
+            if crew.share_crew:
+                self._add_attribute(
+                    created_span, "formatted_description", task.description
+                )
+                self._add_attribute(
+                    created_span, "formatted_expected_output", task.expected_output
+                )
+
+            created_span.set_status(Status(StatusCode.OK))
+            created_span.end()
+
+            span = tracer.start_span("Task Execution")
+
+            self._add_attribute(span, "crew_key", crew.key)
+            self._add_attribute(span, "crew_id", str(crew.id))
+            self._add_attribute(span, "task_key", task.key)
+            self._add_attribute(span, "task_id", str(task.id))
+
+            if crew.share_crew:
+                self._add_attribute(span, "formatted_description", task.description)
+                self._add_attribute(
+                    span, "formatted_expected_output", task.expected_output
+                )
+
+            return span
+
+        return self._safe_telemetry_operation(operation)
+
+    def task_ended(self, span: Span, task: Task, crew: Crew):
+        """Records task execution in a crew."""
+
+        def operation():
+            if crew.share_crew:
+                self._add_attribute(
+                    span,
+                    "task_output",
+                    task.output.raw if task.output else "",
+                )
+
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def tool_repeated_usage(self, llm: Any, tool_name: str, attempts: int):
+        """Records the repeated usage 'error' of a tool by an agent."""
+
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Tool Repeated Usage")
+            self._add_attribute(
+                span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            self._add_attribute(span, "tool_name", tool_name)
+            self._add_attribute(span, "attempts", attempts)
+            if llm:
+                self._add_attribute(span, "llm", llm.model)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def tool_usage(self, llm: Any, tool_name: str, attempts: int):
+        """Records the usage of a tool by an agent."""
+
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Tool Usage")
+            self._add_attribute(
+                span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            self._add_attribute(span, "tool_name", tool_name)
+            self._add_attribute(span, "attempts", attempts)
+            if llm:
+                self._add_attribute(span, "llm", llm.model)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def tool_usage_error(self, llm: Any):
+        """Records the usage of a tool by an agent."""
+
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Tool Usage Error")
+            self._add_attribute(
+                span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            if llm:
+                self._add_attribute(span, "llm", llm.model)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def individual_test_result_span(
+        self, crew: Crew, quality: float, exec_time: int, model_name: str
+    ):
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Crew Individual Test Result")
+
+            self._add_attribute(
+                span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            self._add_attribute(span, "crew_key", crew.key)
+            self._add_attribute(span, "crew_id", str(crew.id))
+            self._add_attribute(span, "quality", str(quality))
+            self._add_attribute(span, "exec_time", str(exec_time))
+            self._add_attribute(span, "model_name", model_name)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def test_execution_span(
+        self,
+        crew: Crew,
+        iterations: int,
+        inputs: dict[str, Any] | None,
+        model_name: str,
+    ):
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Crew Test Execution")
+
+            self._add_attribute(
+                span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            self._add_attribute(span, "crew_key", crew.key)
+            self._add_attribute(span, "crew_id", str(crew.id))
+            self._add_attribute(span, "iterations", str(iterations))
+            self._add_attribute(span, "model_name", model_name)
+
+            if crew.share_crew:
+                self._add_attribute(
+                    span, "inputs", json.dumps(inputs) if inputs else None
+                )
+
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def deploy_signup_error_span(self):
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Deploy Signup Error")
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def start_deployment_span(self, uuid: Optional[str] = None):
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Start Deployment")
+            if uuid:
+                self._add_attribute(span, "uuid", uuid)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def create_crew_deployment_span(self):
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Create Crew Deployment")
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def get_crew_logs_span(self, uuid: Optional[str], log_type: str = "deployment"):
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Get Crew Logs")
+            self._add_attribute(span, "log_type", log_type)
+            if uuid:
+                self._add_attribute(span, "uuid", uuid)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def remove_crew_span(self, uuid: Optional[str] = None):
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Remove Crew")
+            if uuid:
+                self._add_attribute(span, "uuid", uuid)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
+
+    def crew_execution_span(self, crew: Crew, inputs: dict[str, Any] | None):
+        """Records the complete execution of a crew.
+        This is only collected if the user has opted-in to share the crew.
+        """
+        self.crew_creation(crew, inputs)
+
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Crew Execution")
+            self._add_attribute(
+                span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            self._add_attribute(span, "crew_key", crew.key)
+            self._add_attribute(span, "crew_id", str(crew.id))
+            self._add_attribute(
+                span, "crew_inputs", json.dumps(inputs) if inputs else None
+            )
+            self._add_attribute(
+                span,
+                "crew_agents",
+                json.dumps(
+                    [
+                        {
+                            "key": agent.key,
+                            "id": str(agent.id),
+                            "role": agent.role,
+                            "goal": agent.goal,
+                            "backstory": agent.backstory,
+                            "verbose?": agent.verbose,
+                            "max_iter": agent.max_iter,
+                            "max_rpm": agent.max_rpm,
+                            "i18n": agent.i18n.prompt_file,
+                            "llm": agent.llm.model,
+                            "delegation_enabled?": agent.allow_delegation,
+                            "tools_names": [
+                                tool.name.casefold() for tool in agent.tools or []
+                            ],
+                        }
+                        for agent in crew.agents
+                    ]
+                ),
+            )
+            self._add_attribute(
+                span,
+                "crew_tasks",
+                json.dumps(
+                    [
+                        {
+                            "id": str(task.id),
+                            "description": task.description,
+                            "expected_output": task.expected_output,
+                            "async_execution?": task.async_execution,
+                            "human_input?": task.human_input,
+                            "agent_role": task.agent.role if task.agent else "None",
+                            "agent_key": task.agent.key if task.agent else None,
+                            "context": (
+                                [task.description for task in task.context]
+                                if task.context
+                                else None
+                            ),
+                            "tools_names": [
+                                tool.name.casefold() for tool in task.tools or []
+                            ],
+                        }
+                        for task in crew.tasks
+                    ]
+                ),
+            )
+            return span
+
+        if crew.share_crew:
+            return self._safe_telemetry_operation(operation)
+        return None
+
+    def end_crew(self, crew, final_string_output):
+        def operation():
+            self._add_attribute(
+                crew._execution_span,
+                "crewai_version",
+                pkg_resources.get_distribution("crewai").version,
+            )
+            self._add_attribute(
+                crew._execution_span, "crew_output", final_string_output
+            )
+            self._add_attribute(
+                crew._execution_span,
+                "crew_tasks_output",
+                json.dumps(
+                    [
+                        {
+                            "id": str(task.id),
+                            "description": task.description,
+                            "output": task.output.raw_output,
+                        }
+                        for task in crew.tasks
+                    ]
+                ),
+            )
+            crew._execution_span.set_status(Status(StatusCode.OK))
+            crew._execution_span.end()
+
+        if crew.share_crew:
+            self._safe_telemetry_operation(operation)
 
     def _add_attribute(self, span, key, value):
         """Add an attribute to a span."""
-        try:
+
+        def operation():
             return span.set_attribute(key, value)
-        except Exception:
-            pass
+
+        self._safe_telemetry_operation(operation)
 
     def flow_creation_span(self, flow_name: str):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Flow Creation")
-                self._add_attribute(span, "flow_name", flow_name)
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Flow Creation")
+            self._add_attribute(span, "flow_name", flow_name)
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
 
     def flow_plotting_span(self, flow_name: str, node_names: list[str]):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Flow Plotting")
-                self._add_attribute(span, "flow_name", flow_name)
-                self._add_attribute(span, "node_names", json.dumps(node_names))
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Flow Plotting")
+            self._add_attribute(span, "flow_name", flow_name)
+            self._add_attribute(span, "node_names", json.dumps(node_names))
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)
 
     def flow_execution_span(self, flow_name: str, node_names: list[str]):
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Flow Execution")
-                self._add_attribute(span, "flow_name", flow_name)
-                self._add_attribute(span, "node_names", json.dumps(node_names))
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
+        def operation():
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Flow Execution")
+            self._add_attribute(span, "flow_name", flow_name)
+            self._add_attribute(span, "node_names", json.dumps(node_names))
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+
+        self._safe_telemetry_operation(operation)

src/crewai/tools/__init__.py

@@ -0,0 +1 @@
+from .base_tool import BaseTool, tool

src/crewai/tools/agent_tools.py

@@ -1,25 +0,0 @@
-from crewai.agents.agent_builder.utilities.base_agent_tool import BaseAgentTools
-
-
-class AgentTools(BaseAgentTools):
-    """Default tools around agent delegation"""
-
-    def tools(self):
-        from langchain.tools import StructuredTool
-
-        coworkers = ", ".join([f"{agent.role}" for agent in self.agents])
-        tools = [
-            StructuredTool.from_function(
-                func=self.delegate_work,
-                name="Delegate work to coworker",
-                description=self.i18n.tools("delegate_work").format(
-                    coworkers=coworkers
-                ),
-            ),
-            StructuredTool.from_function(
-                func=self.ask_question,
-                name="Ask question to coworker",
-                description=self.i18n.tools("ask_question").format(coworkers=coworkers),
-            ),
-        ]
-        return tools

src/crewai/tools/agent_tools/agent_tools.py

@@ -0,0 +1,32 @@
+from crewai.tools.base_tool import BaseTool
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.utilities import I18N
+
+from .delegate_work_tool import DelegateWorkTool
+from .ask_question_tool import AskQuestionTool
+
+
+class AgentTools:
+    """Manager class for agent-related tools"""
+
+    def __init__(self, agents: list[BaseAgent], i18n: I18N = I18N()):
+        self.agents = agents
+        self.i18n = i18n
+
+    def tools(self) -> list[BaseTool]:
+        """Get all available agent tools"""
+        coworkers = ", ".join([f"{agent.role}" for agent in self.agents])
+
+        delegate_tool = DelegateWorkTool(
+            agents=self.agents,
+            i18n=self.i18n,
+            description=self.i18n.tools("delegate_work").format(coworkers=coworkers),
+        )
+
+        ask_tool = AskQuestionTool(
+            agents=self.agents,
+            i18n=self.i18n,
+            description=self.i18n.tools("ask_question").format(coworkers=coworkers),
+        )
+
+        return [delegate_tool, ask_tool]

src/crewai/tools/agent_tools/ask_question_tool.py

@@ -0,0 +1,26 @@
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+from typing import Optional
+from pydantic import BaseModel, Field
+
+
+class AskQuestionToolSchema(BaseModel):
+    question: str = Field(..., description="The question to ask")
+    context: str = Field(..., description="The context for the question")
+    coworker: str = Field(..., description="The role/name of the coworker to ask")
+
+
+class AskQuestionTool(BaseAgentTool):
+    """Tool for asking questions to coworkers"""
+
+    name: str = "Ask question to coworker"
+    args_schema: type[BaseModel] = AskQuestionToolSchema
+
+    def _run(
+        self,
+        question: str,
+        context: str,
+        coworker: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        coworker = self._get_coworker(coworker, **kwargs)
+        return self._execute(coworker, question, context)

src/crewai/tools/agent_tools/base_agent_tools.py

@@ -1,22 +1,19 @@
-from abc import ABC, abstractmethod
-from typing import List, Optional, Union
-
-from pydantic import BaseModel, Field
+from typing import Optional, Union
+from pydantic import Field
 
+from crewai.tools.base_tool import BaseTool
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.task import Task
 from crewai.utilities import I18N
 
 
-class BaseAgentTools(BaseModel, ABC):
-    """Default tools around agent delegation"""
+class BaseAgentTool(BaseTool):
+    """Base class for agent-related tools"""
 
-    agents: List[BaseAgent] = Field(description="List of agents in this crew.")
-    i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
-
-    @abstractmethod
-    def tools(self):
-        pass
+    agents: list[BaseAgent] = Field(description="List of available agents")
+    i18n: I18N = Field(
+        default_factory=I18N, description="Internationalization settings"
+    )
 
     def _get_coworker(self, coworker: Optional[str], **kwargs) -> Optional[str]:
         coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker")
@@ -24,27 +21,11 @@ class BaseAgentTools(BaseModel, ABC):
             is_list = coworker.startswith("[") and coworker.endswith("]")
             if is_list:
                 coworker = coworker[1:-1].split(",")[0]
-
         return coworker
 
-    def delegate_work(
-        self, task: str, context: str, coworker: Optional[str] = None, **kwargs
-    ):
-        """Useful to delegate a specific task to a coworker passing all necessary context and names."""
-        coworker = self._get_coworker(coworker, **kwargs)
-        return self._execute(coworker, task, context)
-
-    def ask_question(
-        self, question: str, context: str, coworker: Optional[str] = None, **kwargs
-    ):
-        """Useful to ask a question, opinion or take from a coworker passing all necessary context and names."""
-        coworker = self._get_coworker(coworker, **kwargs)
-        return self._execute(coworker, question, context)
-
     def _execute(
         self, agent_name: Union[str, None], task: str, context: Union[str, None]
-    ):
-        """Execute the command."""
+    ) -> str:
         try:
             if agent_name is None:
                 agent_name = ""
@@ -57,7 +38,6 @@ class BaseAgentTools(BaseModel, ABC):
             # when it should look like this:
             # {"task": "....", "coworker": "...."}
             agent_name = agent_name.casefold().replace('"', "").replace("\n", "")
-
             agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
                 available_agent
                 for available_agent in self.agents

src/crewai/tools/agent_tools/delegate_work_tool.py

@@ -0,0 +1,29 @@
+from crewai.tools.agent_tools.base_agent_tools import BaseAgentTool
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class DelegateWorkToolSchema(BaseModel):
+    task: str = Field(..., description="The task to delegate")
+    context: str = Field(..., description="The context for the task")
+    coworker: str = Field(
+        ..., description="The role/name of the coworker to delegate to"
+    )
+
+
+class DelegateWorkTool(BaseAgentTool):
+    """Tool for delegating work to coworkers"""
+
+    name: str = "Delegate work to coworker"
+    args_schema: type[BaseModel] = DelegateWorkToolSchema
+
+    def _run(
+        self,
+        task: str,
+        context: str,
+        coworker: Optional[str] = None,
+        **kwargs,
+    ) -> str:
+        coworker = self._get_coworker(coworker, **kwargs)
+        return self._execute(coworker, task, context)

src/crewai/tools/base_tool.py

@@ -0,0 +1,186 @@
+from abc import ABC, abstractmethod
+from typing import Any, Callable, Type, get_args, get_origin
+
+from langchain_core.tools import StructuredTool
+from pydantic import BaseModel, ConfigDict, Field, validator
+from pydantic import BaseModel as PydanticBaseModel
+
+
+class BaseTool(BaseModel, ABC):
+    class _ArgsSchemaPlaceholder(PydanticBaseModel):
+        pass
+
+    model_config = ConfigDict()
+
+    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)
+    """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."""
+    result_as_answer: bool = False
+    """Flag to check if the tool should be the final agent answer."""
+
+    @validator("args_schema", always=True, pre=True)
+    def _default_args_schema(
+        cls, v: Type[PydanticBaseModel]
+    ) -> Type[PydanticBaseModel]:
+        if not isinstance(v, cls._ArgsSchemaPlaceholder):
+            return v
+
+        return type(
+            f"{cls.__name__}Schema",
+            (PydanticBaseModel,),
+            {
+                "__annotations__": {
+                    k: v for k, v in cls._run.__annotations__.items() if k != "return"
+                },
+            },
+        )
+
+    def model_post_init(self, __context: Any) -> None:
+        self._generate_description()
+
+        super().model_post_init(__context)
+
+    def run(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        print(f"Using Tool: {self.name}")
+        return self._run(*args, **kwargs)
+
+    @abstractmethod
+    def _run(
+        self,
+        *args: Any,
+        **kwargs: Any,
+    ) -> Any:
+        """Here goes the actual implementation of the tool."""
+
+    def to_langchain(self) -> StructuredTool:
+        self._set_args_schema()
+        return StructuredTool(
+            name=self.name,
+            description=self.description,
+            args_schema=self.args_schema,
+            func=self._run,
+        )
+
+    @classmethod
+    def from_langchain(cls, tool: StructuredTool) -> "BaseTool":
+        if cls == Tool:
+            if tool.func is None:
+                raise ValueError("StructuredTool must have a callable 'func'")
+            return Tool(
+                name=tool.name,
+                description=tool.description,
+                args_schema=tool.args_schema,
+                func=tool.func,
+            )
+        raise NotImplementedError(f"from_langchain not implemented for {cls.__name__}")
+
+    def _set_args_schema(self):
+        if self.args_schema is None:
+            class_name = f"{self.__class__.__name__}Schema"
+            self.args_schema = type(
+                class_name,
+                (PydanticBaseModel,),
+                {
+                    "__annotations__": {
+                        k: v
+                        for k, v in self._run.__annotations__.items()
+                        if k != "return"
+                    },
+                },
+            )
+
+    def _generate_description(self):
+        args_schema = {
+            name: {
+                "description": field.description,
+                "type": BaseTool._get_arg_annotations(field.annotation),
+            }
+            for name, field in self.args_schema.model_fields.items()
+        }
+
+        self.description = f"Tool Name: {self.name}\nTool Arguments: {args_schema}\nTool Description: {self.description}"
+
+    @staticmethod
+    def _get_arg_annotations(annotation: type[Any] | None) -> str:
+        if annotation is None:
+            return "None"
+
+        origin = get_origin(annotation)
+        args = get_args(annotation)
+
+        if origin is None:
+            return (
+                annotation.__name__
+                if hasattr(annotation, "__name__")
+                else str(annotation)
+            )
+
+        if args:
+            args_str = ", ".join(BaseTool._get_arg_annotations(arg) for arg in args)
+            return f"{origin.__name__}[{args_str}]"
+
+        return origin.__name__
+
+
+class Tool(BaseTool):
+    func: Callable
+    """The function that will be executed when the tool is called."""
+
+    def _run(self, *args: Any, **kwargs: Any) -> Any:
+        return self.func(*args, **kwargs)
+
+
+def to_langchain(
+    tools: list[BaseTool | StructuredTool],
+) -> list[StructuredTool]:
+    return [t.to_langchain() if isinstance(t, BaseTool) else t for t in tools]
+
+
+def tool(*args):
+    """
+    Decorator to create a tool from a function.
+    """
+
+    def _make_with_name(tool_name: str) -> Callable:
+        def _make_tool(f: Callable) -> BaseTool:
+            if f.__doc__ is None:
+                raise ValueError("Function must have a docstring")
+            if f.__annotations__ is None:
+                raise ValueError("Function must have type annotations")
+
+            class_name = "".join(tool_name.split()).title()
+            args_schema = type(
+                class_name,
+                (PydanticBaseModel,),
+                {
+                    "__annotations__": {
+                        k: v for k, v in f.__annotations__.items() if k != "return"
+                    },
+                },
+            )
+
+            return Tool(
+                name=tool_name,
+                description=f.__doc__,
+                func=f,
+                args_schema=args_schema,
+            )
+
+        return _make_tool
+
+    if len(args) == 1 and callable(args[0]):
+        return _make_with_name(args[0].__name__)(args[0])
+    if len(args) == 1 and isinstance(args[0], str):
+        return _make_with_name(args[0])
+    raise ValueError("Invalid arguments")

src/crewai/tools/tool_usage.py

@@ -6,14 +6,14 @@ from difflib import SequenceMatcher
 from textwrap import dedent
 from typing import Any, List, Union
 
+import crewai.utilities.events as events
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.task import Task
 from crewai.telemetry import Telemetry
+from crewai.tools import BaseTool
 from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
 from crewai.tools.tool_usage_events import ToolUsageError, ToolUsageFinished
 from crewai.utilities import I18N, Converter, ConverterError, Printer
-import crewai.utilities.events as events
-
 
 agentops = None
 if os.environ.get("AGENTOPS_API_KEY"):
@@ -50,7 +50,7 @@ class ToolUsage:
     def __init__(
         self,
         tools_handler: ToolsHandler,
-        tools: List[Any],
+        tools: List[BaseTool],
         original_tools: List[Any],
         tools_description: str,
         tools_names: str,
@@ -299,19 +299,7 @@ class ToolUsage:
         """Render the tool name and description in plain text."""
         descriptions = []
         for tool in self.tools:
-            args = {
-                k: {k2: v2 for k2, v2 in v.items() if k2 in ["description", "type"]}
-                for k, v in tool.args.items()
-            }
-            descriptions.append(
-                "\n".join(
-                    [
-                        f"Tool Name: {tool.name.lower()}",
-                        f"Tool Description: {tool.description}",
-                        f"Tool Arguments: {args}",
-                    ]
-                )
-            )
+            descriptions.append(tool.description)
         return "\n--\n".join(descriptions)
 
     def _function_calling(self, tool_string: str):

src/crewai/types/usage_metrics.py

@@ -8,6 +8,7 @@ class UsageMetrics(BaseModel):
     Attributes:
         total_tokens: Total number of tokens used.
         prompt_tokens: Number of tokens used in prompts.
+        cached_prompt_tokens: Number of cached prompt tokens used.
         completion_tokens: Number of tokens used in completions.
         successful_requests: Number of successful requests made.
     """
@@ -16,6 +17,9 @@ class UsageMetrics(BaseModel):
     prompt_tokens: int = Field(
         default=0, description="Number of tokens used in prompts."
     )
+    cached_prompt_tokens: int = Field(
+        default=0, description="Number of cached prompt tokens used."
+    )
     completion_tokens: int = Field(
         default=0, description="Number of tokens used in completions."
     )
@@ -32,5 +36,6 @@ class UsageMetrics(BaseModel):
         """
         self.total_tokens += usage_metrics.total_tokens
         self.prompt_tokens += usage_metrics.prompt_tokens
+        self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens
         self.completion_tokens += usage_metrics.completion_tokens
         self.successful_requests += usage_metrics.successful_requests

src/crewai/utilities/crew_json_encoder.py

@@ -2,13 +2,14 @@ from datetime import datetime, date
 import json
 from uuid import UUID
 from pydantic import BaseModel
+from decimal import Decimal
 
 
 class CrewJSONEncoder(json.JSONEncoder):
     def default(self, obj):
         if isinstance(obj, BaseModel):
             return self._handle_pydantic_model(obj)
-        elif isinstance(obj, UUID):
+        elif isinstance(obj, UUID) or isinstance(obj, Decimal):
             return str(obj)
 
         elif isinstance(obj, datetime) or isinstance(obj, date):

src/crewai/utilities/file_handler.py

@@ -16,7 +16,11 @@ class FileHandler:
 
     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"
+        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")
 
@@ -63,7 +67,7 @@ class PickleHandler:
 
         with open(self.file_path, "rb") as file:
             try:
-                return pickle.load(file)
+                return pickle.load(file)  # nosec
             except EOFError:
                 return {}  # Return an empty dictionary if the file is empty or corrupted
             except Exception:

src/crewai/utilities/token_counter_callback.py

@@ -1,5 +1,5 @@
 from litellm.integrations.custom_logger import CustomLogger
-
+from litellm.types.utils import Usage
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 
 
@@ -11,8 +11,11 @@ class TokenCalcHandler(CustomLogger):
         if self.token_cost_process is None:
             return
 
+        usage : Usage = response_obj["usage"]
         self.token_cost_process.sum_successful_requests(1)
-        self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens)
-        self.token_cost_process.sum_completion_tokens(
-            response_obj["usage"].completion_tokens
-        )
+        self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
+        self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
+        if usage.prompt_tokens_details:
+            self.token_cost_process.sum_cached_prompt_tokens(
+                usage.prompt_tokens_details.cached_tokens
+            )

tests/agent_test.py

@@ -5,7 +5,6 @@ from unittest import mock
 from unittest.mock import patch
 
 import pytest
-from crewai_tools import tool
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
@@ -14,6 +13,7 @@ from crewai.agents.parser import AgentAction, CrewAgentParser, OutputParserExcep
 from crewai.llm import LLM
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
+from crewai.tools import tool
 from crewai.tools.tool_usage_events import ToolUsageFinished
 from crewai.utilities import RPMController
 from crewai.utilities.events import Emitter
@@ -277,9 +277,10 @@ def test_cache_hitting():
         "multiplier-{'first_number': 12, 'second_number': 3}": 36,
     }
 
-    with patch.object(CacheHandler, "read") as read, patch.object(
-        Emitter, "emit"
-    ) as emit:
+    with (
+        patch.object(CacheHandler, "read") as read,
+        patch.object(Emitter, "emit") as emit,
+    ):
         read.return_value = "0"
         task = Task(
             description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
@@ -604,7 +605,7 @@ def test_agent_respect_the_max_rpm_set(capsys):
 def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
     from unittest.mock import patch
 
-    from crewai_tools import tool
+    from crewai.tools import tool
 
     @tool
     def get_final_answer() -> float:
@@ -642,7 +643,7 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
 def test_agent_without_max_rpm_respet_crew_rpm(capsys):
     from unittest.mock import patch
 
-    from crewai_tools import tool
+    from crewai.tools import tool
 
     @tool
     def get_final_answer() -> float:
@@ -696,7 +697,7 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
 def test_agent_error_on_parsing_tool(capsys):
     from unittest.mock import patch
 
-    from crewai_tools import tool
+    from crewai.tools import tool
 
     @tool
     def get_final_answer() -> float:
@@ -739,7 +740,7 @@ def test_agent_error_on_parsing_tool(capsys):
 def test_agent_remembers_output_format_after_using_tools_too_many_times():
     from unittest.mock import patch
 
-    from crewai_tools import tool
+    from crewai.tools import tool
 
     @tool
     def get_final_answer() -> float:
@@ -863,11 +864,16 @@ def test_agent_function_calling_llm():
 
     from crewai.tools.tool_usage import ToolUsage
 
-    with patch.object(
-        instructor, "from_litellm", wraps=instructor.from_litellm
-    ) as mock_from_litellm, patch.object(
-        ToolUsage, "_original_tool_calling", side_effect=Exception("Forced exception")
-    ) as mock_original_tool_calling:
+    with (
+        patch.object(
+            instructor, "from_litellm", wraps=instructor.from_litellm
+        ) as mock_from_litellm,
+        patch.object(
+            ToolUsage,
+            "_original_tool_calling",
+            side_effect=Exception("Forced exception"),
+        ) as mock_original_tool_calling,
+    ):
         crew.kickoff()
         mock_from_litellm.assert_called()
         mock_original_tool_calling.assert_called()
@@ -894,7 +900,7 @@ def test_agent_count_formatting_error():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
-    from crewai_tools import BaseTool
+    from crewai.tools import BaseTool
 
     class MyCustomTool(BaseTool):
         name: str = "Get Greetings"
@@ -924,7 +930,7 @@ def test_tool_result_as_answer_is_the_final_answer_for_the_agent():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_tool_usage_information_is_appended_to_agent():
-    from crewai_tools import BaseTool
+    from crewai.tools import BaseTool
 
     class MyCustomTool(BaseTool):
         name: str = "Decide Greetings"

tests/agents/agent_builder/base_agent_test.py

@@ -2,6 +2,7 @@ import hashlib
 from typing import Any, List, Optional
 
 from crewai.agents.agent_builder.base_agent import BaseAgent
+from crewai.tools.base_tool import BaseTool
 from pydantic import BaseModel
 
 
@@ -10,13 +11,13 @@ class TestAgent(BaseAgent):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         return ""
 
     def create_agent_executor(self, tools=None) -> None: ...
 
-    def _parse_tools(self, tools: List[Any]) -> List[Any]:
+    def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
         return []
 
     def get_delegation_tools(self, agents: List["BaseAgent"]): ...

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+      adopting these innovative systems to meet the growing food demand while reducing
+      transportation emissions.\n\n4. **Climate-Resilient Plants**: Scientists are
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+      conditions such as droughts, floods, and high temperatures, helping farmers
+      adapt to climate change and ensuring crop yields remain stable.\n\n5. **Edible
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+      with trials showing that certain plants can be engineered to express antigens
+      that could serve as edible vaccines, offering a low-cost and easy delivery method
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tests/cli/cli_test.py

@@ -1,7 +1,9 @@
+from pathlib import Path
 from unittest import mock
 
 import pytest
 from click.testing import CliRunner
+
 from crewai.cli.cli import (
     deploy_create,
     deploy_list,
@@ -9,6 +11,7 @@ from crewai.cli.cli import (
     deploy_push,
     deploy_remove,
     deply_status,
+    flow_add_crew,
     reset_memories,
     signup,
     test,
@@ -277,3 +280,42 @@ def test_deploy_remove_no_uuid(command, runner):
 
     assert result.exit_code == 0
     mock_deploy.remove_crew.assert_called_once_with(uuid=None)
+
+
+@mock.patch("crewai.cli.add_crew_to_flow.create_embedded_crew")
+@mock.patch("pathlib.Path.exists", return_value=True)  # Mock the existence check
+def test_flow_add_crew(mock_path_exists, mock_create_embedded_crew, runner):
+    crew_name = "new_crew"
+    result = runner.invoke(flow_add_crew, [crew_name])
+
+    # Log the output for debugging
+    print(result.output)
+
+    assert result.exit_code == 0, f"Command failed with output: {result.output}"
+    assert f"Adding crew {crew_name} to the flow" in result.output
+
+    # Verify that create_embedded_crew was called with the correct arguments
+    mock_create_embedded_crew.assert_called_once()
+    call_args, call_kwargs = mock_create_embedded_crew.call_args
+    assert call_args[0] == crew_name
+    assert "parent_folder" in call_kwargs
+    assert isinstance(call_kwargs["parent_folder"], Path)
+
+
+def test_add_crew_to_flow_not_in_root(runner):
+    # Simulate not being in the root of a flow project
+    with mock.patch("pathlib.Path.exists", autospec=True) as mock_exists:
+        # Mock Path.exists to return False when checking for pyproject.toml
+        def exists_side_effect(self):
+            if self.name == "pyproject.toml":
+                return False  # Simulate that pyproject.toml does not exist
+            return True  # All other paths exist
+
+        mock_exists.side_effect = exists_side_effect
+
+        result = runner.invoke(flow_add_crew, ["new_crew"])
+
+        assert result.exit_code != 0
+        assert "This command must be run from the root of a flow project." in str(
+            result.output
+        )