Added security.md file

.github/workflows/security-checker.yml

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

docs/concepts/crews.mdx

@@ -22,8 +22,7 @@ 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).                                                                                                                                                                           |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory). Defaults to `False`.                                                                                                                                                       |
-| **Memory Config** _(optional)_        | `memory_config`        | Configuration for the memory provider to be used by the crew.                                                                                                                                                                                          |
 | **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`.                                                                                                                                                          |
 | **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`.                                                                                                                                                                     |
 | **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`.                                                                                                                                       |

docs/concepts/flows.mdx

@@ -18,63 +18,60 @@ 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.
 
-### Passing Inputs to Flows
+```python Code
 
-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 pydantic import BaseModel
+from dotenv import load_dotenv
+from litellm import completion
 
-class ExampleState(BaseModel):
-    counter: int = 0
-    message: str = ""
 
-class StructuredExampleFlow(Flow[ExampleState]):
+class ExampleFlow(Flow):
+    model = "gpt-4o-mini"
+
     @start()
-    def first_method(self):
+    def generate_city(self):
-        # Implementation
+        print("Starting flow")
 
-flow = StructuredExampleFlow()
+        response = completion(
-flow.kickoff(inputs={"counter": 10})
+            model=self.model,
-```
+            messages=[
+                {
+                    "role": "user",
+                    "content": "Return the name of a random city in the world.",
+                },
+            ],
+        )
 
-In this example, the `counter` is initialized to `10`, while `message` retains its default value.
+        random_city = response["choices"][0]["message"]["content"]
+        print(f"Random City: {random_city}")
 
-#### Unstructured State Management
+        return random_city
 
-In unstructured state management, the flow's state is a dictionary. You can pass any dictionary to update the state.
+    @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}",
+                },
+            ],
+        )
 
-```python
+        fun_fact = response["choices"][0]["message"]["content"]
-from crewai.flow.flow import Flow, listen, start
+        return fun_fact
 
-class UnstructuredExampleFlow(Flow):
-    @start()
-    def first_method(self):
-        # Implementation
 
-flow = UnstructuredExampleFlow()
-flow.kickoff(inputs={"counter": 5, "message": "Initial message"})
-```
 
-Here, both `counter` and `message` are updated based on the provided inputs.
+flow = ExampleFlow()
+result = flow.kickoff()
 
-**Note:** Ensure that inputs for structured state management adhere to the defined schema to avoid validation errors.
+print(f"Generated fun fact: {result}")
-
-### 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.
@@ -97,14 +94,14 @@ 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
+   ```python Code
    @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
+   ```python Code
    @listen(generate_city)
    def generate_fun_fact(self, random_city):
        # Implementation
@@ -121,7 +118,7 @@ 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
+```python Code
 from crewai.flow.flow import Flow, listen, start
 
 class OutputExampleFlow(Flow):
@@ -133,17 +130,18 @@ class OutputExampleFlow(Flow):
     def second_method(self, first_output):
         return f"Second method received: {first_output}"
 
+
 flow = OutputExampleFlow()
 final_output = flow.kickoff()
 
 print("---- Final Output ----")
 print(final_output)
-```
+````
 
-```text
+``` text Output
 ---- Final Output ----
 Second method received: Output from first_method
-```
+````
 
 </CodeGroup>
 
@@ -158,7 +156,7 @@ Here's an example of how to update and access the state:
 
 <CodeGroup>
 
-```python
+```python Code
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
@@ -186,7 +184,7 @@ print("Final State:")
 print(flow.state)
 ```
 
-```text
+```text Output
 Final Output: Hello from first_method - updated by second_method
 Final State:
 counter=2 message='Hello from first_method - updated by second_method'
@@ -210,10 +208,10 @@ allowing developers to choose the approach that best fits their application's ne
 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
+```python Code
 from crewai.flow.flow import Flow, listen, start
 
-class UnstructuredExampleFlow(Flow):
+class UntructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
@@ -232,7 +230,8 @@ class UnstructuredExampleFlow(Flow):
 
         print(f"State after third_method: {self.state}")
 
-flow = UnstructuredExampleFlow()
+
+flow = UntructuredExampleFlow()
 flow.kickoff()
 ```
 
@@ -246,14 +245,16 @@ flow.kickoff()
 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
+```python Code
 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()
@@ -272,6 +273,7 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
         print(f"State after third_method: {self.state}")
 
+
 flow = StructuredExampleFlow()
 flow.kickoff()
 ```
@@ -305,7 +307,7 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger
 
 <CodeGroup>
 
-```python
+```python Code
 from crewai.flow.flow import Flow, listen, or_, start
 
 class OrExampleFlow(Flow):
@@ -322,11 +324,13 @@ class OrExampleFlow(Flow):
     def logger(self, result):
         print(f"Logger: {result}")
 
+
+
 flow = OrExampleFlow()
 flow.kickoff()
 ```
 
-```text
+```text Output
 Logger: Hello from the start method
 Logger: Hello from the second method
 ```
@@ -342,7 +346,7 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge
 
 <CodeGroup>
 
-```python
+```python Code
 from crewai.flow.flow import Flow, and_, listen, start
 
 class AndExampleFlow(Flow):
@@ -364,7 +368,7 @@ flow = AndExampleFlow()
 flow.kickoff()
 ```
 
-```text
+```text Output
 ---- Logger ----
 {'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
 ```
@@ -381,7 +385,7 @@ You can specify different routes based on the output of the method, allowing you
 
 <CodeGroup>
 
-```python
+```python Code
 import random
 from crewai.flow.flow import Flow, listen, router, start
 from pydantic import BaseModel
@@ -412,11 +416,12 @@ class RouterFlow(Flow[ExampleState]):
     def fourth_method(self):
         print("Fourth method running")
 
+
 flow = RouterFlow()
 flow.kickoff()
 ```
 
-```text
+```text Output
 Starting the structured flow
 Third method running
 Fourth method running
@@ -479,7 +484,7 @@ 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
+```python Code
 #!/usr/bin/env python
 from random import randint
 
@@ -572,20 +577,18 @@ This command will create a new directory for your crew within the `crews` folder
 
 After adding a new crew, your folder structure will look like this:
 
-| Directory/File         | Description                                                        |
+name_of_flow/
-| :--------------------- | :----------------------------------------------------------------- |
+├── crews/
-| `name_of_flow/`        | Root directory for the flow.                                       |
+│   ├── poem_crew/
-| ├── `crews/`           | Contains directories for specific crews.                           |
+│   │   ├── config/
-| │   ├── `poem_crew/`   | Directory for the "poem_crew" with its configurations and scripts. |
+│   │   │   ├── agents.yaml
-| │   │   ├── `config/`  | Configuration files directory for the "poem_crew".                 |
+│   │   │   └── tasks.yaml
-| │   │   │  ├── `agents.yaml` | YAML file defining the agents for "poem_crew".                     |
+│   │   └── poem_crew.py
-| │   │   │  └── `tasks.yaml`  | YAML file defining the tasks for "poem_crew".                      |
+│   └── name_of_crew/
-| │   │   └── `poem_crew.py`    | Script for "poem_crew" functionality.                              |
+│       ├── config/
-| └── `name_of_crew/`       | Directory for the new crew.                                          |
+│       │   ├── agents.yaml
-|     ├── `config/`         | Configuration files directory for the new crew.                     |
+│       │   └── tasks.yaml
-|     │   ├── `agents.yaml` | YAML file defining the agents for the new crew.                     |
+│       └── name_of_crew.py
-|     │   └── `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.
 
@@ -607,7 +610,7 @@ 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
+```python Code
 # Assuming you have a flow instance
 flow.plot("my_flow_plot")
 ```

docs/concepts/llms.mdx

@@ -25,136 +25,42 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
 - `OPENAI_API_BASE`
 - `OPENAI_API_KEY`
 
-### 2. Updating YAML files
+### 2. String Identifier
 
-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
-    # ...
-```
-
-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:
-
-<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>
-
-    <Accordion title="Anthropic">
-    ```python Code
-    ANTHROPIC_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <Accordion title="Google">
-    ```python Code
-    GEMINI_API_KEY=<your-api-key>
-    ```
-    </Accordion>
-
-    <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">
+### 3. LLM Instance
+
+List of [more providers](https://docs.litellm.ai/docs/providers).
+
 ```python Code
 from crewai import LLM
 
 llm = LLM(model="gpt-4", temperature=0.7)
 agent = Agent(llm=llm, ...)
 ```
-    </Tab>
+
-</Tabs>
+### 4. Custom LLM Objects
+
+Pass a custom LLM implementation or object from another library.
 
 ## 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:
 
-<Tabs>
+1. Using environment variables:
-    <Tab title="Using Environment Variables">
+
 ```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">
+2. Using LLM class attributes:
+
 ```python Code
 from crewai import LLM
 
@@ -165,8 +71,6 @@ You can connect to OpenAI-compatible LLMs using either environment variables or
 )
 agent = Agent(llm=llm, ...)
 ```
-    </Tab>
-</Tabs>
 
 ## LLM Configuration Options
 
@@ -193,10 +97,7 @@ When configuring an LLM for your agent, you have access to a wide range of param
 | **api_key**          | `str`             | Your API key for authentication.                                                                 |
 
 
-These are examples of how to configure LLMs for your agent.
+## OpenAI Example Configuration
-
-<AccordionGroup>
-    <Accordion title="OpenAI">
 
 ```python Code
 from crewai import LLM
@@ -215,22 +116,21 @@ These are examples of how to configure LLMs for your agent.
 )
 agent = Agent(llm=llm, ...)
 ```
-    </Accordion>
 
-    <Accordion title="Cerebras">
+## Cerebras Example Configuration
 
 ```python Code
 from crewai import LLM
 
 llm = LLM(
     model="cerebras/llama-3.1-70b",
+    base_url="https://api.cerebras.ai/v1",
     api_key="your-api-key-here"
 )
 agent = Agent(llm=llm, ...) 
 ```
-    </Accordion>
 
-    <Accordion title="Ollama (Local LLMs)">
+## Using Ollama (Local LLMs)
 
 CrewAI supports using Ollama for running open-source models locally:
 
@@ -242,131 +142,10 @@ These are examples of how to configure LLMs for your agent.
 from crewai import LLM
 
 agent = Agent(
-        llm=LLM(
+    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
-            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
 

docs/concepts/memory.mdx

@@ -18,7 +18,6 @@ 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
 
@@ -93,47 +92,6 @@ 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
 
@@ -296,31 +254,6 @@ my_crew = Crew(
 )
 ```
 
-### 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>",
-        }
-    }
-)
-```
-
 ### Resetting Memory
 
 ```shell

docs/concepts/tools.mdx

@@ -5,7 +5,6 @@ icon: screwdriver-wrench
 ---
 
 ## Introduction
-
 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.
 
@@ -105,7 +104,7 @@ 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.                                                          |
@@ -140,17 +139,21 @@ Here is a list of the available tools and their descriptions:
 ## Creating your own Tools
 
 <Tip>
-  Developers can craft `custom tools` tailored for their agent’s needs or
+  Developers can craft `custom tools` tailored for their agent’s needs or utilize pre-built options.
-  utilize pre-built options.
 </Tip>
 
-There are two main ways for one to create a CrewAI tool:
+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:
 
 ### Subclassing `BaseTool`
 
 ```python Code
-from crewai.tools import BaseTool
+from crewai_tools import BaseTool
-
 
 class MyCustomTool(BaseTool):
     name: str = "Name of my tool"
@@ -164,7 +167,7 @@ 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."""
@@ -175,13 +178,11 @@ def my_tool(question: str) -> str:
 ### Custom Caching Mechanism
 
 <Tip>
-  Tools can optionally implement a `cache_function` to fine-tune caching
+  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.
-  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:

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

@@ -10,13 +10,21 @@ This guide provides detailed instructions on creating custom tools for the CrewA
 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, including the `args_schema` for input validation, and the `_run` method.
 
 ```python Code
 from typing import Type
-from crewai.tools import BaseTool
+from crewai_tools import BaseTool
 from pydantic import BaseModel, Field
 
 class MyToolInput(BaseModel):
@@ -39,7 +47,7 @@ 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:

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](http://app.crewai.com/), where you can deploy your crew in a few clicks.
+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.

docs/tools/githubsearchtool.mdx

@@ -34,7 +34,6 @@ from crewai_tools import GithubSearchTool
 # Initialize the tool for semantic searches within a specific GitHub repository
 tool = GithubSearchTool(
 	github_repo='https://github.com/example/repo',
-	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 
@@ -42,7 +41,6 @@ tool = GithubSearchTool(
 
 # Initialize the tool for semantic searches within a specific GitHub repository, so the agent can search any repository if it learns about during its execution
 tool = GithubSearchTool(
-	gh_token='your_github_personal_access_token',
 	content_types=['code', 'issue'] # Options: code, repo, pr, issue
 )
 ```
@@ -50,7 +48,6 @@ tool = GithubSearchTool(
 ## Arguments
 
 - `github_repo` : The URL of the GitHub repository where the search will be conducted. This is a mandatory field and specifies the target repository for your search.
-- `gh_token` : Your GitHub Personal Access Token (PAT) required for authentication. You can create one in your GitHub account settings under Developer Settings > Personal Access Tokens.
 - `content_types` : Specifies the types of content to include in your search. You must provide a list of content types from the following options: `code` for searching within the code, 
 `repo` for searching within the repository's general information, `pr` for searching within pull requests, and `issue` for searching within issues. 
 This field is mandatory and allows tailoring the search to specific content types within the GitHub repository.
@@ -81,3 +78,4 @@ tool = GithubSearchTool(
         ),
     )
 )
+```

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 = ">=1.33.2,<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\""},
+    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 grpcio-status = [
-    {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\""},
+    {version = ">=1.33.2,<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 = ">=1.22.4", markers = "python_version < \"3.11\""},
     {version = ">=1.23.2", markers = "python_version == \"3.11\""},
+    {version = ">=1.22.4", 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 = "0.79.4"
+version = "0.76.9"
 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"
@@ -16,7 +16,7 @@ dependencies = [
     "opentelemetry-exporter-otlp-proto-http>=1.22.0",
     "instructor>=1.3.3",
     "regex>=2024.9.11",
-    "crewai-tools>=0.14.0",
+    "crewai-tools>=0.13.4",
     "click>=8.1.7",
     "python-dotenv>=1.0.0",
     "appdirs>=1.4.4",
@@ -27,8 +27,8 @@ dependencies = [
     "pyvis>=0.3.2",
     "uv>=0.4.25",
     "tomli-w>=1.1.0",
+    "chromadb>=0.4.24",
     "tomli>=2.0.2",
-    "chromadb>=0.5.18",
 ]
 
 [project.urls]
@@ -37,9 +37,8 @@ Documentation = "https://docs.crewai.com"
 Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
-tools = ["crewai-tools>=0.14.0"]
+tools = ["crewai-tools>=0.13.4"]
 agentops = ["agentops>=0.3.0"]
-mem0 = ["mem0ai>=0.1.29"]
 
 [tool.uv]
 dev-dependencies = [
@@ -53,7 +52,7 @@ dev-dependencies = [
     "mkdocs-material-extensions>=1.3.1",
     "pillow>=10.2.0",
     "cairosvg>=2.7.1",
-    "crewai-tools>=0.14.0",
+    "crewai-tools>=0.13.4",
     "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__ = "0.79.4"
+__version__ = "0.76.9"
 __all__ = ["Agent", "Crew", "Process", "Task", "Pipeline", "Router", "LLM", "Flow"]

src/crewai/agent.py

@@ -8,11 +8,9 @@ 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.agent_tools import AgentTools
+from crewai.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
@@ -123,11 +121,6 @@ class Agent(BaseAgent):
     @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):
@@ -137,12 +130,8 @@ class Agent(BaseAgent):
             # If it's already an LLM instance, keep it as is
             pass
         elif self.llm is None:
-            # Determine the model name from environment variables or use default
+            # If it's None, use environment variables or default
-            model_name = (
+            model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-4o-mini")
-                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(
@@ -151,44 +140,9 @@ class Agent(BaseAgent):
             if api_base:
                 llm_params["base_url"] = api_base
 
-            set_provider = model_name.split("/")[0] if "/" in model_name else "openai"
+            api_key = os.environ.get("OPENAI_API_KEY")
-
+            if api_key:
-            # Iterate over all environment variables to find matching API keys or use defaults
+                llm_params["api_key"] = api_key
-            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:
@@ -238,7 +192,7 @@ class Agent(BaseAgent):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[List[Any]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -262,11 +216,9 @@ 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() != "":
@@ -307,9 +259,7 @@ class Agent(BaseAgent):
 
         return result
 
-    def create_agent_executor(
+    def create_agent_executor(self, tools=None, task=None) -> None:
-        self, tools: Optional[List[BaseTool]] = None, task=None
-    ) -> None:
         """Create an agent executor for the agent.
 
         Returns:
@@ -382,7 +332,7 @@ 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):
@@ -441,7 +391,7 @@ class Agent(BaseAgent):
 
         return description
 
-    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
+    def _render_text_description_and_args(self, tools: List[Any]) -> str:
         """Render the tool name, description, and args in plain text.
 
             Output will be in the format of:
@@ -454,7 +404,17 @@ class Agent(BaseAgent):
         """
         tool_strings = []
         for tool in tools:
-            tool_strings.append(tool.description)
+            args_schema = {
+                name: {
+                    "description": field.description,
+                    "type": field.annotation.__name__,
+                }
+                for name, field in tool.args_schema.model_fields.items()
+            }
+            description = (
+                f"Tool Name: {tool.name}\nTool Description: {tool.description}"
+            )
+            tool_strings.append(f"{description}\nTool Arguments: {args_schema}")
 
         return "\n".join(tool_strings)
 

src/crewai/agents/agent_builder/base_agent.py

@@ -18,7 +18,6 @@ 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
 
@@ -50,11 +49,11 @@ class BaseAgent(ABC, BaseModel):
 
 
     Methods:
-        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[BaseTool]] = None) -> str:
+        execute_task(task: Any, context: Optional[str] = None, tools: Optional[List[Any]] = 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[BaseTool]) -> List[Any]:
+        _parse_tools(tools: List[Any]) -> 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.
@@ -106,7 +105,7 @@ class BaseAgent(ABC, BaseModel):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: Optional[List[BaseTool]] = Field(
+    tools: Optional[List[Any]] = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: Optional[int] = Field(
@@ -189,7 +188,7 @@ class BaseAgent(ABC, BaseModel):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[List[Any]] = None,
     ) -> str:
         pass
 
@@ -198,11 +197,11 @@ class BaseAgent(ABC, BaseModel):
         pass
 
     @abstractmethod
-    def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
+    def _parse_tools(self, tools: List[Any]) -> List[Any]:
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[BaseTool]:
+    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[Any]:
         """Set the task tools that init BaseAgenTools class."""
         pass
 

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

@@ -1,19 +1,22 @@
-from typing import Optional, Union
+from abc import ABC, abstractmethod
-from pydantic import Field
+from typing import List, Optional, Union
+
+from pydantic import BaseModel, 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 BaseAgentTool(BaseTool):
+class BaseAgentTools(BaseModel, ABC):
-    """Base class for agent-related tools"""
+    """Default tools around agent delegation"""
 
-    agents: list[BaseAgent] = Field(description="List of available agents")
+    agents: List[BaseAgent] = Field(description="List of agents in this crew.")
-    i18n: I18N = Field(
+    i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
-        default_factory=I18N, description="Internationalization settings"
+
-    )
+    @abstractmethod
+    def tools(self):
+        pass
 
     def _get_coworker(self, coworker: Optional[str], **kwargs) -> Optional[str]:
         coworker = coworker or kwargs.get("co_worker") or kwargs.get("coworker")
@@ -21,11 +24,27 @@ class BaseAgentTool(BaseTool):
             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]
-    ) -> str:
+    ):
+        """Execute the command."""
         try:
             if agent_name is None:
                 agent_name = ""
@@ -38,6 +57,7 @@ class BaseAgentTool(BaseTool):
             # 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/agents/agent_builder/utilities/base_token_process.py

@@ -4,7 +4,6 @@ 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
 
@@ -16,9 +15,6 @@ 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
 
@@ -26,7 +22,6 @@ 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

@@ -117,15 +117,6 @@ 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)
@@ -151,7 +142,6 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         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),
@@ -333,9 +323,9 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             if self.crew is not None and hasattr(self.crew, "_train_iteration"):
                 train_iteration = self.crew._train_iteration
                 if agent_id in training_data and isinstance(train_iteration, int):
-                    training_data[agent_id][train_iteration]["improved_output"] = (
+                    training_data[agent_id][train_iteration][
-                        result.output
+                        "improved_output"
-                    )
+                    ] = result.output
                     training_handler.save(training_data)
                 else:
                     self._logger.log(
@@ -386,5 +376,4 @@ 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.cache_tools import CacheTools
+from ..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

@@ -54,7 +54,7 @@ def create_embedded_crew(crew_name: str, parent_folder: Path) -> None:
 
     templates_dir = Path(__file__).parent / "templates" / "crew"
     config_template_files = ["agents.yaml", "tasks.yaml"]
-    crew_template_file = f"{folder_name}.py"  # Updated file name
+    crew_template_file = f"{folder_name}_crew.py"  # Updated file name
 
     for file_name in config_template_files:
         src_file = templates_dir / "config" / file_name

src/crewai/cli/authentication/main.py

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

src/crewai/cli/config.py

@@ -1,38 +0,0 @@
-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

@@ -1,168 +1,19 @@
 ENV_VARS = {
-    "openai": [
+    'openai': ['OPENAI_API_KEY'],
-        {
+    'anthropic': ['ANTHROPIC_API_KEY'],
-            "prompt": "Enter your OPENAI API key (press Enter to skip)",
+    'gemini': ['GEMINI_API_KEY'],
-            "key_name": "OPENAI_API_KEY",
+    'groq': ['GROQ_API_KEY'],
-        }
+    'ollama': ['FAKE_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']
-PROVIDERS = [
-    "openai",
-    "anthropic",
-    "gemini",
-    "groq",
-    "ollama",
-    "watson",
-    "bedrock",
-    "azure",
-    "cerebras",
-]
 
 MODELS = {
-    "openai": ["gpt-4", "gpt-4o", "gpt-4o-mini", "o1-mini", "o1-preview"],
+    'openai': ['gpt-4', 'gpt-4o', 'gpt-4o-mini', 'o1-mini', 'o1-preview'],
-    "anthropic": [
+    'anthropic': ['claude-3-5-sonnet-20240620', 'claude-3-sonnet-20240229', 'claude-3-opus-20240229', 'claude-3-haiku-20240307'],
-        "claude-3-5-sonnet-20240620",
+    'gemini': ['gemini-1.5-flash', 'gemini-1.5-pro', 'gemini-gemma-2-9b-it', 'gemini-gemma-2-27b-it'],
-        "claude-3-sonnet-20240229",
+    'groq': ['llama-3.1-8b-instant', 'llama-3.1-70b-versatile', 'llama-3.1-405b-reasoning', 'gemma2-9b-it', 'gemma-7b-it'],
-        "claude-3-opus-20240229",
+    'ollama': ['llama3.1', 'mixtral'],
-        "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

@@ -1,11 +1,11 @@
-import shutil
 import sys
 from pathlib import Path
 
 import click
 
-from crewai.cli.constants import ENV_VARS, MODELS
+from crewai.cli.constants import ENV_VARS
 from crewai.cli.provider import (
+    PROVIDERS,
     get_provider_data,
     select_model,
     select_provider,
@@ -29,14 +29,14 @@ def create_folder_structure(name, parent_folder=None):
             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
+    else:
-
         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:
@@ -92,10 +92,7 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
 
         existing_provider = None
         for provider, env_keys in ENV_VARS.items():
-            if any(
+            if any(key in env_vars for key in env_keys):
-                "key_name" in details and details["key_name"] in env_vars
-                for details in env_keys
-            ):
                 existing_provider = provider
                 break
 
@@ -121,8 +118,6 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
                 "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'
@@ -131,38 +126,39 @@ def create_crew(name, provider=None, skip_provider=False, parent_folder=None):
             if selected_model:  # Valid selection
                 break
             click.secho(
-                    "No model selected. Please try again or press 'q' to exit.",
+                "No model selected. Please try again or press 'q' to exit.", fg="red"
-                    fg="red",
             )
-            env_vars["MODEL"] = selected_model
 
-        # Check if the selected provider requires API keys
+        if selected_provider in PROVIDERS:
-        if selected_provider in ENV_VARS:
+            api_key_var = ENV_VARS[selected_provider][0]
-            provider_env_vars = ENV_VARS[selected_provider]
+        else:
-            for details in provider_env_vars:
+            api_key_var = click.prompt(
-                if details.get("default", False):
+                f"Enter the environment variable name for your {selected_provider.capitalize()} API key",
-                    # Automatically add default key-value pairs
+                type=str,
-                    for key, value in details.items():
+                default="",
-                        if key not in ["prompt", "key_name", "default"]:
+            )
-                            env_vars[key] = value
+
-                elif "key_name" in details:
+        api_key_value = ""
-                    # Prompt for non-default key-value pairs
+        click.echo(
-                    prompt = details["prompt"]
+            f"Enter your {selected_provider.capitalize()} API key (press Enter to skip): ",
-                    key_name = details["key_name"]
+            nl=False,
-                    api_key_value = click.prompt(prompt, default="", show_default=False)
+        )
+        try:
+            api_key_value = input()
+        except (KeyboardInterrupt, EOFError):
+            api_key_value = ""
 
         if api_key_value.strip():
-                        env_vars[key_name] = api_key_value
+            env_vars = {api_key_var: 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")
+            click.secho("API key saved to .env file", fg="green")
         else:
             click.secho(
-                "No API keys provided. Skipping .env file creation.", fg="yellow"
+                "No API key provided. Skipping .env file creation.", fg="yellow"
             )
 
-        click.secho(f"Selected model: {env_vars.get('MODEL', 'N/A')}", fg="green")
+        env_vars["MODEL"] = selected_model
+        click.secho(f"Selected model: {selected_model}", fg="green")
 
     package_dir = Path(__file__).parent
     templates_dir = package_dir / "templates" / "crew"

src/crewai/cli/provider.py

@@ -164,7 +164,7 @@ def fetch_provider_data(cache_file):
     - dict or None: The fetched provider data or None if the operation fails.
     """
     try:
-        response = requests.get(JSON_URL, stream=True, timeout=60)
+        response = requests.get(JSON_URL, stream=True, timeout=10)
         response.raise_for_status()
         data = download_data(response)
         with open(cache_file, "w") as f:

src/crewai/cli/run_crew.py

@@ -24,6 +24,7 @@ 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)

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

@@ -8,12 +8,9 @@ from crewai.project import CrewBase, agent, crew, task
 # from crewai_tools import SerperDevTool
 
 @CrewBase
-class {{crew_name}}():
+class {{crew_name}}Crew():
 	"""{{crew_name}} crew"""
 
-	agents_config = 'config/agents.yaml'
-	tasks_config = 'config/tasks.yaml'
-
 	@agent
 	def researcher(self) -> Agent:
 		return Agent(

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

@@ -1,10 +1,6 @@
 #!/usr/bin/env python
 import sys
-import warnings
+from {{folder_name}}.crew import {{crew_name}}Crew
-
-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 unnecessary logic into this file.
@@ -18,7 +14,7 @@ def run():
     inputs = {
         'topic': 'AI LLMs'
     }
-    {{crew_name}}().crew().kickoff(inputs=inputs)
+    {{crew_name}}Crew().crew().kickoff(inputs=inputs)
 
 
 def train():
@@ -29,7 +25,7 @@ def train():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}().crew().train(n_iterations=int(sys.argv[1]), filename=sys.argv[2], inputs=inputs)
+        {{crew_name}}Crew().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}")
@@ -39,7 +35,7 @@ def replay():
     Replay the crew execution from a specific task.
     """
     try:
-        {{crew_name}}().crew().replay(task_id=sys.argv[1])
+        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
 
     except Exception as e:
         raise Exception(f"An error occurred while replaying the crew: {e}")
@@ -52,7 +48,7 @@ def test():
         "topic": "AI LLMs"
     }
     try:
-        {{crew_name}}().crew().test(n_iterations=int(sys.argv[1]), openai_model_name=sys.argv[2], inputs=inputs)
+        {{crew_name}}Crew().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]>=0.79.4,<1.0.0"
+    "crewai[tools]>=0.76.9,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -1,8 +1,7 @@
-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.")

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

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<=3.13"
 dependencies = [
-    "crewai[tools]>=0.79.4,<1.0.0",
+    "crewai[tools]>=0.76.9,<1.0.0",
 ]
 
 [project.scripts]

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

@@ -1,6 +1,6 @@
 from typing import Type
 
-from crewai.tools import BaseTool
+from crewai_tools import BaseTool
 from pydantic import BaseModel, Field
 
 

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 = ">=0.79.4,<1.0.0" }
+crewai = { extras = ["tools"], version = ">=0.76.9,<1.0.0" }
 asyncio = "*"
 
 [tool.poetry.scripts]

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

@@ -1,8 +1,7 @@
 from typing import Type
-from crewai.tools import BaseTool
+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.")

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]>=0.79.4,<1.0.0"
+    "crewai[tools]>=0.76.9,<1.0.0"
 ]
 
 [project.scripts]

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

@@ -1,8 +1,7 @@
 from typing import Type
-from crewai.tools import BaseTool
+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.")

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]>=0.79.4"
+    "crewai[tools]>=0.76.9"
 ]
 

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

@@ -1,5 +1,4 @@
-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

@@ -1,15 +1,17 @@
 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,
@@ -151,16 +153,26 @@ 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"]
@@ -175,11 +187,7 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             tool_handle,
         ]
         add_package_result = subprocess.run(
-            add_package_command,
+            add_package_command, capture_output=False, text=True, check=True
-            capture_output=False,
-            env=self._build_env_with_credentials(repository_handle),
-            text=True,
-            check=True
         )
 
         if add_package_result.stderr:
@@ -198,13 +206,3 @@ 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/crew.py

@@ -27,13 +27,12 @@ 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.agent_tools import AgentTools
+from crewai.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 (
@@ -72,7 +71,6 @@ 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).
@@ -96,7 +94,6 @@ 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)
@@ -117,10 +114,6 @@ 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",
@@ -133,11 +126,7 @@ class Crew(BaseModel):
         default=None,
         description="An Instance of the EntityMemory to be used by the Crew",
     )
-    user_memory: Optional[InstanceOf[UserMemory]] = Field(
+    embedder: Optional[Any] = 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=None,
         description="Configuration for the embedder to be used for the crew.",
     )
@@ -249,22 +238,13 @@ class Crew(BaseModel):
             self._short_term_memory = (
                 self.short_term_memory
                 if self.short_term_memory
-                else ShortTermMemory(
+                else ShortTermMemory(crew=self, embedder_config=self.embedder)
-                    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")
@@ -465,7 +445,6 @@ class Crew(BaseModel):
         training_data = CrewTrainingHandler(TRAINING_DATA_FILE).load()
 
         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)
             )

src/crewai/flow/flow.py

@@ -1,20 +1,8 @@
 import asyncio
 import inspect
-from typing import (
+from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
-    Any,
-    Callable,
-    Dict,
-    Generic,
-    List,
-    Optional,
-    Set,
-    Type,
-    TypeVar,
-    Union,
-    cast,
-)
 
-from pydantic import BaseModel, ValidationError
+from pydantic import BaseModel
 
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
@@ -131,6 +119,7 @@ class FlowMeta(type):
                 condition_type = getattr(attr_value, "__condition_type__", "OR")
                 listeners[attr_name] = (condition_type, methods)
 
+            # TODO: should we add a check for __condition_type__ 'AND'?
             elif hasattr(attr_value, "__is_router__"):
                 routers[attr_value.__router_for__] = attr_name
                 possible_returns = get_possible_return_constants(attr_value)
@@ -170,7 +159,8 @@ class Flow(Generic[T], metaclass=FlowMeta):
     def __init__(self) -> None:
         self._methods: Dict[str, Callable] = {}
         self._state: T = self._create_initial_state()
-        self._method_execution_counts: Dict[str, int] = {}
+        self._executed_methods: Set[str] = set()
+        self._scheduled_tasks: Set[str] = set()
         self._pending_and_listeners: Dict[str, Set[str]] = {}
         self._method_outputs: List[Any] = []  # List to store all method outputs
 
@@ -201,74 +191,10 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """Returns the list of all outputs from executed methods."""
         return self._method_outputs
 
-    def _initialize_state(self, inputs: Dict[str, Any]) -> None:
+    def kickoff(self) -> Any:
-        """
-        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:
+    async def kickoff_async(self) -> 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")
 
@@ -307,10 +233,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         )
         self._method_outputs.append(result)  # Store the output
 
-        # Track method execution counts
+        self._executed_methods.add(method_name)
-        self._method_execution_counts[method_name] = (
-            self._method_execution_counts.get(method_name, 0) + 1
-        )
 
         return result
 
@@ -320,33 +243,34 @@ 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(
-                self._routers[trigger_method], router_method
+                trigger_method, router_method
-            )
+            )  # TODO: Change or not?
+            # Use the path as the new trigger method
             trigger_method = path
 
         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
+                    if (
+                        listener_name not in self._executed_methods
+                        and listener_name not in self._scheduled_tasks
+                    ):
+                        self._scheduled_tasks.add(listener_name)
                         listener_tasks.append(
                             self._execute_single_listener(listener_name, result)
                         )
             elif condition_type == "AND":
-                # Initialize pending methods for this listener if not already done
+                if all(method in self._executed_methods for method in methods):
-                if listener_name not in self._pending_and_listeners:
+                    if (
-                    self._pending_and_listeners[listener_name] = set(methods)
+                        listener_name not in self._executed_methods
-                # Remove the trigger method from pending methods
+                        and listener_name not in self._scheduled_tasks
-                self._pending_and_listeners[listener_name].discard(trigger_method)
+                    ):
-                if not self._pending_and_listeners[listener_name]:
+                        self._scheduled_tasks.add(listener_name)
-                    # All required methods have been executed
                         listener_tasks.append(
                             self._execute_single_listener(listener_name, result)
                         )
-                    # 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
-        if listener_tasks:
         await asyncio.gather(*listener_tasks)
 
     async def _execute_single_listener(self, listener_name: str, result: Any) -> None:
@@ -367,6 +291,9 @@ class Flow(Generic[T], metaclass=FlowMeta):
                 # If listener does not expect parameters, call without arguments
                 listener_result = await self._execute_method(listener_name, method)
 
+            # Remove from scheduled tasks after execution
+            self._scheduled_tasks.discard(listener_name)
+
             # Execute listeners of this listener
             await self._execute_listeners(listener_name, listener_result)
         except Exception as e:

src/crewai/llm.py

@@ -1,10 +1,7 @@
-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
 
@@ -12,6 +9,9 @@ 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
-        self.set_callbacks(callbacks)
+        litellm.callbacks = callbacks
 
     def call(self, messages: List[Dict[str, str]], callbacks: List[Any] = []) -> str:
         with suppress_warnings():
             if callbacks and len(callbacks) > 0:
-                self.set_callbacks(callbacks)
+                litellm.callbacks = callbacks
 
             try:
                 params = {
@@ -181,15 +181,3 @@ 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

@@ -1,6 +1,5 @@
 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__ = ["UserMemory", "EntityMemory", "LongTermMemory", "ShortTermMemory"]
+__all__ = ["EntityMemory", "LongTermMemory", "ShortTermMemory"]

src/crewai/memory/contextual/contextual_memory.py

@@ -1,25 +1,13 @@
-from typing import Optional, Dict, Any
+from typing import Optional
 
-from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory, UserMemory
+from crewai.memory import EntityMemory, LongTermMemory, ShortTermMemory
 
 
 class ContextualMemory:
-    def __init__(
+    def __init__(self, stm: ShortTermMemory, ltm: LongTermMemory, em: EntityMemory):
-        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:
         """
@@ -35,8 +23,6 @@ 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:
@@ -46,10 +32,7 @@ class ContextualMemory:
         """
         stm_results = self.stm.search(query)
         formatted_results = "\n".join(
-            [
+            [f"- {result['context']}" for result in stm_results]
-                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 ""
 
@@ -79,26 +62,6 @@ 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

@@ -11,20 +11,6 @@ class EntityMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
-            self.memory_provider = crew.memory_config.get("provider")
-        else:
-            self.memory_provider = None
-
-        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
@@ -39,14 +25,6 @@ class EntityMemory(Memory):
 
     def save(self, item: EntityMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
         """Saves an entity item into the SQLite storage."""
-        if self.memory_provider == "mem0":
-            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)
 

src/crewai/memory/memory.py

@@ -23,12 +23,5 @@ class Memory:
 
         self.storage.save(value, metadata)
 
-    def search(
+    def search(self, query: str) -> List[Dict[str, Any]]:
-        self,
+        return self.storage.search(query)
-        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

@@ -14,20 +14,6 @@ class ShortTermMemory(Memory):
     """
 
     def __init__(self, crew=None, embedder_config=None, storage=None):
-        if hasattr(crew, "memory_config") and crew.memory_config is not None:
-            self.memory_provider = crew.memory_config.get("provider")
-        else:
-            self.memory_provider = None
-
-        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
@@ -44,20 +30,11 @@ 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(
+    def search(self, query: str, score_threshold: float = 0.35):
-        self,
+        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
-        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/interface.py

@@ -7,10 +7,8 @@ class Storage:
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         pass
 
-    def search(
+    def search(self, key: str) -> List[Dict[str, Any]]:  # type: ignore
-        self, query: str, limit: int, score_threshold: float
+        pass
-    ) -> 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, cls=CrewJSONEncoder),
+                        json.dumps(inputs),
                         was_replayed,
                     ),
                 )
@@ -103,7 +103,7 @@ class KickoffTaskOutputsSQLiteStorage:
                         else value
                     )
 
-                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"  # nosec
+                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"
                 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

@@ -1,104 +0,0 @@
-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

@@ -4,13 +4,13 @@ import logging
 import os
 import shutil
 import uuid
-from typing import Any, Dict, List, Optional, cast
+from typing import Any, Dict, List, Optional
-
-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
+from chromadb.api import ClientAPI
+from chromadb.api.types import validate_embedding_function
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from typing import cast
 
 
 @contextlib.contextmanager
@@ -21,11 +21,9 @@ def suppress_logging(
     logger = logging.getLogger(logger_name)
     original_level = logger.getEffectiveLevel()
     logger.setLevel(level)
-    with (
+    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(
-        contextlib.redirect_stdout(io.StringIO()),
+        io.StringIO()
-        contextlib.redirect_stderr(io.StringIO()),
+    ), contextlib.suppress(UserWarning):
-        contextlib.suppress(UserWarning),
-    ):
         yield
     logger.setLevel(original_level)
 
@@ -51,6 +49,8 @@ class RAGStorage(BaseRAGStorage):
         self._initialize_app()
 
     def _set_embedder_config(self):
+        import chromadb.utils.embedding_functions as embedding_functions
+
         if self.embedder_config is None:
             self.embedder_config = self._create_default_embedding_function()
 
@@ -59,20 +59,12 @@ class RAGStorage(BaseRAGStorage):
             config = self.embedder_config.get("config", {})
             model_name = config.get("model")
             if provider == "openai":
-                from chromadb.utils.embedding_functions.openai_embedding_function import (
+                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
-                    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 (
+                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
-                    OpenAIEmbeddingFunction,
-                )
-
-                self.embedder_config = OpenAIEmbeddingFunction(
                     api_key=config.get("api_key"),
                     api_base=config.get("api_base"),
                     api_type=config.get("api_type", "azure"),
@@ -80,103 +72,53 @@ class RAGStorage(BaseRAGStorage):
                     model_name=model_name,
                 )
             elif provider == "ollama":
-                from chromadb.utils.embedding_functions.ollama_embedding_function import (
+                from openai import OpenAI
-                    OllamaEmbeddingFunction,
-                )
 
-                self.embedder_config = OllamaEmbeddingFunction(
+                class OllamaEmbeddingFunction(EmbeddingFunction):
-                    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):
+                        client = OpenAI(
-                            input = [input]
+                            base_url="http://localhost:11434/v1",
-
+                            api_key=config.get("api_key", "ollama"),
-                        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)
+                            response = client.embeddings.create(
+                                input=input, model=model_name
+                            )
+                            embeddings = [item.embedding for item in response.data]
                             return cast(Embeddings, embeddings)
-
                         except Exception as e:
-                            print("Error during Watson embedding:", e)
                             raise e
 
-                self.embedder_config = WatsonEmbeddingFunction()
+                self.embedder_config = OllamaEmbeddingFunction()
-            else:
+            elif provider == "vertexai":
-                raise Exception(
+                self.embedder_config = (
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
+                    embedding_functions.GoogleVertexEmbeddingFunction(
+                        model_name=model_name,
+                        api_key=config.get("api_key"),
+                    )
+                )
+            elif provider == "google":
+                self.embedder_config = (
+                    embedding_functions.GoogleGenerativeAiEmbeddingFunction(
+                        model_name=model_name,
+                        api_key=config.get("api_key"),
+                    )
+                )
+            elif provider == "cohere":
+                self.embedder_config = embedding_functions.CohereEmbeddingFunction(
+                    model_name=model_name,
+                    api_key=config.get("api_key"),
+                )
+            elif provider == "huggingface":
+                self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
+                    url=config.get("api_url"),
                 )
             else:
-            validate_embedding_function(self.embedder_config)
+                raise Exception(
+                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface]"
+                )
+        else:
+            validate_embedding_function(self.embedder_config)  # type: ignore # used for validating embedder_config if defined a embedding function/class
             self.embedder_config = self.embedder_config
 
     def _initialize_app(self):
@@ -269,10 +211,8 @@ class RAGStorage(BaseRAGStorage):
                 )
 
     def _create_default_embedding_function(self):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
+        import chromadb.utils.embedding_functions as embedding_functions
-            OpenAIEmbeddingFunction,
-        )
 
-        return OpenAIEmbeddingFunction(
+        return embedding_functions.OpenAIEmbeddingFunction(
             api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
         )

src/crewai/memory/user/user_memory.py

@@ -1,45 +0,0 @@
-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

@@ -1,8 +0,0 @@
-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/task.py

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

src/crewai/telemetry/telemetry.py

@@ -48,10 +48,6 @@ 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(

src/crewai/tools/__init__.py

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

src/crewai/tools/agent_tools.py

@@ -0,0 +1,25 @@
+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

@@ -1,32 +0,0 @@
-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

@@ -1,26 +0,0 @@
-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/delegate_work_tool.py

@@ -1,29 +0,0 @@
-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

@@ -1,186 +0,0 @@
-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

@@ -10,7 +10,6 @@ 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
@@ -50,7 +49,7 @@ class ToolUsage:
     def __init__(
         self,
         tools_handler: ToolsHandler,
-        tools: List[BaseTool],
+        tools: List[Any],
         original_tools: List[Any],
         tools_description: str,
         tools_names: str,
@@ -299,7 +298,22 @@ class ToolUsage:
         """Render the tool name and description in plain text."""
         descriptions = []
         for tool in self.tools:
-            descriptions.append(tool.description)
+            args = {
+                name: {
+                    "description": field.description,
+                    "type": field.annotation.__name__,
+                }
+                for name, field in tool.args_schema.model_fields.items()
+            }
+            descriptions.append(
+                "\n".join(
+                    [
+                        f"Tool Name: {tool.name.lower()}",
+                        f"Tool Description: {tool.description}",
+                        f"Tool Arguments: {args}",
+                    ]
+                )
+            )
         return "\n--\n".join(descriptions)
 
     def _function_calling(self, tool_string: str):

src/crewai/types/usage_metrics.py

@@ -8,7 +8,6 @@ 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.
     """
@@ -17,9 +16,6 @@ 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."
     )
@@ -36,6 +32,5 @@ 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

@@ -2,14 +2,13 @@ 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) or isinstance(obj, Decimal):
+        elif isinstance(obj, UUID):
             return str(obj)
 
         elif isinstance(obj, datetime) or isinstance(obj, date):

src/crewai/utilities/file_handler.py

@@ -16,11 +16,7 @@ class FileHandler:
 
     def log(self, **kwargs):
         now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        message = (
+        message = f"{now}: " + ", ".join([f"{key}=\"{value}\"" for key, value in kwargs.items()]) + "\n"
-            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")
 
@@ -67,7 +63,7 @@ class PickleHandler:
 
         with open(self.file_path, "rb") as file:
             try:
-                return pickle.load(file)  # nosec
+                return pickle.load(file)
             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
 
 
@@ -11,11 +11,8 @@ 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(usage.prompt_tokens)
+        self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens)
-        self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
+        self.token_cost_process.sum_completion_tokens(
-        if usage.prompt_tokens_details:
+            response_obj["usage"].completion_tokens
-            self.token_cost_process.sum_cached_prompt_tokens(
-                usage.prompt_tokens_details.cached_tokens
         )

tests/agent_test.py

@@ -5,6 +5,7 @@ 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
@@ -13,7 +14,6 @@ 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
@@ -277,10 +277,9 @@ def test_cache_hitting():
         "multiplier-{'first_number': 12, 'second_number': 3}": 36,
     }
 
-    with (
+    with patch.object(CacheHandler, "read") as read, patch.object(
-        patch.object(CacheHandler, "read") as read,
+        Emitter, "emit"
-        patch.object(Emitter, "emit") as 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.",
@@ -605,7 +604,7 @@ 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:
@@ -643,7 +642,7 @@ 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:
@@ -697,7 +696,7 @@ 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:
@@ -740,7 +739,7 @@ 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:
@@ -864,16 +863,11 @@ def test_agent_function_calling_llm():
 
     from crewai.tools.tool_usage import ToolUsage
 
-    with (
+    with patch.object(
-        patch.object(
         instructor, "from_litellm", wraps=instructor.from_litellm
-        ) as mock_from_litellm,
+    ) as mock_from_litellm, patch.object(
-        patch.object(
+        ToolUsage, "_original_tool_calling", side_effect=Exception("Forced exception")
-            ToolUsage,
+    ) as mock_original_tool_calling:
-            "_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()
@@ -900,7 +894,7 @@ 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"
@@ -930,7 +924,7 @@ 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/agent_tools/agent_tools_test.py

@@ -3,7 +3,7 @@
 import pytest
 
 from crewai.agent import Agent
-from crewai.tools.agent_tools.agent_tools import AgentTools
+from crewai.tools.agent_tools import AgentTools
 
 researcher = Agent(
     role="researcher",
@@ -11,14 +11,12 @@ researcher = Agent(
     backstory="You're an expert researcher, specialized in technology",
     allow_delegation=False,
 )
-tools = AgentTools(agents=[researcher]).tools()
+tools = AgentTools(agents=[researcher])
-delegate_tool = tools[0]
-ask_tool = tools[1]
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegate_work():
-    result = delegate_tool.run(
+    result = tools.delegate_work(
         coworker="researcher",
         task="share your take on AI Agents",
         context="I heard you hate them",
@@ -32,8 +30,8 @@ def test_delegate_work():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegate_work_with_wrong_co_worker_variable():
-    result = delegate_tool.run(
+    result = tools.delegate_work(
-        coworker="researcher",
+        co_worker="researcher",
         task="share your take on AI Agents",
         context="I heard you hate them",
     )
@@ -46,7 +44,7 @@ def test_delegate_work_with_wrong_co_worker_variable():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_ask_question():
-    result = ask_tool.run(
+    result = tools.ask_question(
         coworker="researcher",
         question="do you hate AI Agents?",
         context="I heard you LOVE them",
@@ -60,8 +58,8 @@ def test_ask_question():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_ask_question_with_wrong_co_worker_variable():
-    result = ask_tool.run(
+    result = tools.ask_question(
-        coworker="researcher",
+        co_worker="researcher",
         question="do you hate AI Agents?",
         context="I heard you LOVE them",
     )
@@ -74,8 +72,8 @@ def test_ask_question_with_wrong_co_worker_variable():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegate_work_withwith_coworker_as_array():
-    result = delegate_tool.run(
+    result = tools.delegate_work(
-        coworker="[researcher]",
+        co_worker="[researcher]",
         task="share your take on AI Agents",
         context="I heard you hate them",
     )
@@ -88,8 +86,8 @@ def test_delegate_work_withwith_coworker_as_array():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_ask_question_with_coworker_as_array():
-    result = ask_tool.run(
+    result = tools.ask_question(
-        coworker="[researcher]",
+        co_worker="[researcher]",
         question="do you hate AI Agents?",
         context="I heard you LOVE them",
     )
@@ -101,7 +99,7 @@ def test_ask_question_with_coworker_as_array():
 
 
 def test_delegate_work_to_wrong_agent():
-    result = ask_tool.run(
+    result = tools.ask_question(
         coworker="writer",
         question="share your take on AI Agents",
         context="I heard you hate them",
@@ -114,7 +112,7 @@ def test_delegate_work_to_wrong_agent():
 
 
 def test_ask_question_to_wrong_agent():
-    result = ask_tool.run(
+    result = tools.ask_question(
         coworker="writer",
         question="do you hate AI Agents?",
         context="I heard you LOVE them",

tests/agents/agent_builder/base_agent_test.py

@@ -2,7 +2,6 @@ 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
 
 
@@ -11,13 +10,13 @@ class TestAgent(BaseAgent):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[BaseTool]] = None,
+        tools: Optional[List[Any]] = None,
     ) -> str:
         return ""
 
     def create_agent_executor(self, tools=None) -> None: ...
 
-    def _parse_tools(self, tools: List[BaseTool]) -> List[BaseTool]:
+    def _parse_tools(self, tools: List[Any]) -> List[Any]:
         return []
 
     def get_delegation_tools(self, agents: List["BaseAgent"]): ...

tests/cassettes/test_crew_kickoff_usage_metrics.yaml

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-      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop":
+      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o"}'
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tests/cli/config_test.py

@@ -1,109 +0,0 @@
-import unittest
-import json
-import tempfile
-import shutil
-from pathlib import Path
-from crewai.cli.config import Settings
-
-class TestSettings(unittest.TestCase):
-    def setUp(self):
-        self.test_dir = Path(tempfile.mkdtemp())
-        self.config_path = self.test_dir / "settings.json"
-
-    def tearDown(self):
-        shutil.rmtree(self.test_dir)
-
-    def test_empty_initialization(self):
-        settings = Settings(config_path=self.config_path)
-        self.assertIsNone(settings.tool_repository_username)
-        self.assertIsNone(settings.tool_repository_password)
-
-    def test_initialization_with_data(self):
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
-        )
-        self.assertEqual(settings.tool_repository_username, "user1")
-        self.assertIsNone(settings.tool_repository_password)
-
-    def test_initialization_with_existing_file(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({"tool_repository_username": "file_user"}, f)
-
-        settings = Settings(config_path=self.config_path)
-        self.assertEqual(settings.tool_repository_username, "file_user")
-
-    def test_merge_file_and_input_data(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({
-                "tool_repository_username": "file_user",
-                "tool_repository_password": "file_pass"
-            }, f)
-
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="new_user"
-        )
-        self.assertEqual(settings.tool_repository_username, "new_user")
-        self.assertEqual(settings.tool_repository_password, "file_pass")
-
-    def test_dump_new_settings(self):
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertEqual(saved_data["tool_repository_username"], "user1")
-
-    def test_update_existing_settings(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            json.dump({"existing_setting": "value"}, f)
-
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username="user1"
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertEqual(saved_data["existing_setting"], "value")
-        self.assertEqual(saved_data["tool_repository_username"], "user1")
-
-    def test_none_values(self):
-        settings = Settings(
-            config_path=self.config_path,
-            tool_repository_username=None
-        )
-        settings.dump()
-
-        with self.config_path.open("r") as f:
-            saved_data = json.load(f)
-
-        self.assertIsNone(saved_data.get("tool_repository_username"))
-
-    def test_invalid_json_in_config(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        with self.config_path.open("w") as f:
-            f.write("invalid json")
-
-        try:
-            settings = Settings(config_path=self.config_path)
-            self.assertIsNone(settings.tool_repository_username)
-        except json.JSONDecodeError:
-            self.fail("Settings initialization should handle invalid JSON")
-
-    def test_empty_config_file(self):
-        self.config_path.parent.mkdir(parents=True, exist_ok=True)
-        self.config_path.touch()
-
-        settings = Settings(config_path=self.config_path)
-        self.assertIsNone(settings.tool_repository_username)

tests/cli/tools/test_main.py

@@ -82,7 +82,6 @@ def test_install_success(mock_get, mock_subprocess_run):
         capture_output=False,
         text=True,
         check=True,
-        env=unittest.mock.ANY
     )
 
     assert "Succesfully installed sample-tool" in output

tests/crew_test.py

@@ -456,7 +456,7 @@ def test_crew_verbose_output(capsys):
 def test_cache_hitting_between_agents():
     from unittest.mock import call, patch
 
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def multiplier(first_number: int, second_number: int) -> float:
@@ -499,7 +499,7 @@ def test_cache_hitting_between_agents():
 def test_api_calls_throttling(capsys):
     from unittest.mock import patch
 
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def get_final_answer() -> float:
@@ -564,7 +564,6 @@ def test_crew_kickoff_usage_metrics():
         assert result.token_usage.prompt_tokens > 0
         assert result.token_usage.completion_tokens > 0
         assert result.token_usage.successful_requests > 0
-        assert result.token_usage.cached_prompt_tokens == 0
 
 
 def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
@@ -1112,7 +1111,7 @@ def test_dont_set_agents_step_callback_if_already_set():
 def test_crew_function_calling_llm():
     from unittest.mock import patch
 
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     llm = "gpt-4o"
 
@@ -1147,7 +1146,7 @@ def test_crew_function_calling_llm():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_with_no_arguments():
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def return_data() -> str:
@@ -1281,11 +1280,10 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
     assert result.raw == "Howdy!"
 
     assert result.token_usage == UsageMetrics(
-        total_tokens=1673,
+        total_tokens=2626,
-        prompt_tokens=1562,
+        prompt_tokens=2482,
-        completion_tokens=111,
+        completion_tokens=144,
-        successful_requests=3,
+        successful_requests=5,
-        cached_prompt_tokens=0
     )
 
 
@@ -1311,9 +1309,8 @@ def test_hierarchical_crew_creation_tasks_with_agents():
 
     assert crew.manager_agent is not None
     assert crew.manager_agent.tools is not None
-    assert (
+    assert crew.manager_agent.tools[0].description.startswith(
-        "Delegate a specific task to one of the following coworkers: Senior Writer\n"
+        "Delegate a specific task to one of the following coworkers: Senior Writer"
-        in crew.manager_agent.tools[0].description
     )
 
 
@@ -1340,9 +1337,8 @@ def test_hierarchical_crew_creation_tasks_with_async_execution():
     crew.kickoff()
     assert crew.manager_agent is not None
     assert crew.manager_agent.tools is not None
-    assert (
+    assert crew.manager_agent.tools[0].description.startswith(
         "Delegate a specific task to one of the following coworkers: Senior Writer\n"
-        in crew.manager_agent.tools[0].description
     )
 
 
@@ -1374,9 +1370,8 @@ def test_hierarchical_crew_creation_tasks_with_sync_last():
     crew.kickoff()
     assert crew.manager_agent is not None
     assert crew.manager_agent.tools is not None
-    assert (
+    assert crew.manager_agent.tools[0].description.startswith(
         "Delegate a specific task to one of the following coworkers: Senior Writer, Researcher, CEO\n"
-        in crew.manager_agent.tools[0].description
     )
 
 
@@ -1499,7 +1494,7 @@ def test_task_callback_on_crew():
 def test_tools_with_custom_caching():
     from unittest.mock import patch
 
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def multiplcation_tool(first_number: int, second_number: int) -> int:
@@ -1701,7 +1696,7 @@ def test_manager_agent_in_agents_raises_exception():
 
 
 def test_manager_agent_with_tools_raises_exception():
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def testing_tool(first_number: int, second_number: int) -> int:
@@ -1779,22 +1774,26 @@ def test_crew_train_success(
         ]
     )
 
-    crew_training_handler.assert_any_call("training_data.pkl")
+    crew_training_handler.assert_has_calls(
-    crew_training_handler().load.assert_called()
+        [
-
+            mock.call("training_data.pkl"),
-    crew_training_handler.assert_any_call("trained_agents_data.pkl")
+            mock.call().load(),
-    crew_training_handler().load.assert_called()
+            mock.call("trained_agents_data.pkl"),
-
+            mock.call().save_trained_data(
-    crew_training_handler().save_trained_data.assert_has_calls([
-        mock.call(
                 agent_id="Researcher",
                 trained_data=task_evaluator().evaluate_training_data().model_dump(),
             ),
-        mock.call(
+            mock.call("trained_agents_data.pkl"),
+            mock.call().save_trained_data(
                 agent_id="Senior Writer",
                 trained_data=task_evaluator().evaluate_training_data().model_dump(),
+            ),
+            mock.call(),
+            mock.call().load(),
+            mock.call(),
+            mock.call().load(),
+        ]
     )
-    ])
 
 
 def test_crew_train_error():

tests/flow_test.py

@@ -1,264 +0,0 @@
-"""Test Flow creation and execution basic functionality."""
-
-import asyncio
-
-import pytest
-from crewai.flow.flow import Flow, and_, listen, or_, router, start
-
-
-def test_simple_sequential_flow():
-    """Test a simple flow with two steps called sequentially."""
-    execution_order = []
-
-    class SimpleFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append("step_2")
-
-    flow = SimpleFlow()
-    flow.kickoff()
-
-    assert execution_order == ["step_1", "step_2"]
-
-
-def test_flow_with_multiple_starts():
-    """Test a flow with multiple start methods."""
-    execution_order = []
-
-    class MultiStartFlow(Flow):
-        @start()
-        def step_a(self):
-            execution_order.append("step_a")
-
-        @start()
-        def step_b(self):
-            execution_order.append("step_b")
-
-        @listen(step_a)
-        def step_c(self):
-            execution_order.append("step_c")
-
-        @listen(step_b)
-        def step_d(self):
-            execution_order.append("step_d")
-
-    flow = MultiStartFlow()
-    flow.kickoff()
-
-    assert "step_a" in execution_order
-    assert "step_b" in execution_order
-    assert "step_c" in execution_order
-    assert "step_d" in execution_order
-    assert execution_order.index("step_c") > execution_order.index("step_a")
-    assert execution_order.index("step_d") > execution_order.index("step_b")
-
-
-def test_cyclic_flow():
-    """Test a cyclic flow that runs a finite number of iterations."""
-    execution_order = []
-
-    class CyclicFlow(Flow):
-        iteration = 0
-        max_iterations = 3
-
-        @start("loop")
-        def step_1(self):
-            if self.iteration >= self.max_iterations:
-                return  # Do not proceed further
-            execution_order.append(f"step_1_{self.iteration}")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append(f"step_2_{self.iteration}")
-
-        @router(step_2)
-        def step_3(self):
-            execution_order.append(f"step_3_{self.iteration}")
-            self.iteration += 1
-            if self.iteration < self.max_iterations:
-                return "loop"
-
-            return "exit"
-
-    flow = CyclicFlow()
-    flow.kickoff()
-
-    expected_order = []
-    for i in range(flow.max_iterations):
-        expected_order.extend([f"step_1_{i}", f"step_2_{i}", f"step_3_{i}"])
-
-    assert execution_order == expected_order
-
-
-def test_flow_with_and_condition():
-    """Test a flow where a step waits for multiple other steps to complete."""
-    execution_order = []
-
-    class AndConditionFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-
-        @start()
-        def step_2(self):
-            execution_order.append("step_2")
-
-        @listen(and_(step_1, step_2))
-        def step_3(self):
-            execution_order.append("step_3")
-
-    flow = AndConditionFlow()
-    flow.kickoff()
-
-    assert "step_1" in execution_order
-    assert "step_2" in execution_order
-    assert execution_order[-1] == "step_3"
-    assert execution_order.index("step_3") > execution_order.index("step_1")
-    assert execution_order.index("step_3") > execution_order.index("step_2")
-
-
-def test_flow_with_or_condition():
-    """Test a flow where a step is triggered when any of multiple steps complete."""
-    execution_order = []
-
-    class OrConditionFlow(Flow):
-        @start()
-        def step_a(self):
-            execution_order.append("step_a")
-
-        @start()
-        def step_b(self):
-            execution_order.append("step_b")
-
-        @listen(or_(step_a, step_b))
-        def step_c(self):
-            execution_order.append("step_c")
-
-    flow = OrConditionFlow()
-    flow.kickoff()
-
-    assert "step_a" in execution_order or "step_b" in execution_order
-    assert "step_c" in execution_order
-    assert execution_order.index("step_c") > min(
-        execution_order.index("step_a"), execution_order.index("step_b")
-    )
-
-
-def test_flow_with_router():
-    """Test a flow that uses a router method to determine the next step."""
-    execution_order = []
-
-    class RouterFlow(Flow):
-        @start()
-        def start_method(self):
-            execution_order.append("start_method")
-
-        @router(start_method)
-        def router(self):
-            execution_order.append("router")
-            # Ensure the condition is set to True to follow the "step_if_true" path
-            condition = True
-            return "step_if_true" if condition else "step_if_false"
-
-        @listen("step_if_true")
-        def truthy(self):
-            execution_order.append("step_if_true")
-
-        @listen("step_if_false")
-        def falsy(self):
-            execution_order.append("step_if_false")
-
-    flow = RouterFlow()
-    flow.kickoff()
-
-    assert execution_order == ["start_method", "router", "step_if_true"]
-
-
-def test_async_flow():
-    """Test an asynchronous flow."""
-    execution_order = []
-
-    class AsyncFlow(Flow):
-        @start()
-        async def step_1(self):
-            execution_order.append("step_1")
-            await asyncio.sleep(0.1)
-
-        @listen(step_1)
-        async def step_2(self):
-            execution_order.append("step_2")
-            await asyncio.sleep(0.1)
-
-    flow = AsyncFlow()
-    asyncio.run(flow.kickoff_async())
-
-    assert execution_order == ["step_1", "step_2"]
-
-
-def test_flow_with_exceptions():
-    """Test flow behavior when exceptions occur in steps."""
-    execution_order = []
-
-    class ExceptionFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-            raise ValueError("An error occurred in step_1")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append("step_2")
-
-    flow = ExceptionFlow()
-
-    with pytest.raises(ValueError):
-        flow.kickoff()
-
-    # Ensure step_2 did not execute
-    assert execution_order == ["step_1"]
-
-
-def test_flow_restart():
-    """Test restarting a flow after it has completed."""
-    execution_order = []
-
-    class RestartableFlow(Flow):
-        @start()
-        def step_1(self):
-            execution_order.append("step_1")
-
-        @listen(step_1)
-        def step_2(self):
-            execution_order.append("step_2")
-
-    flow = RestartableFlow()
-    flow.kickoff()
-    flow.kickoff()  # Restart the flow
-
-    assert execution_order == ["step_1", "step_2", "step_1", "step_2"]
-
-
-def test_flow_with_custom_state():
-    """Test a flow that maintains and modifies internal state."""
-
-    class StateFlow(Flow):
-        def __init__(self):
-            super().__init__()
-            self.counter = 0
-
-        @start()
-        def step_1(self):
-            self.counter += 1
-
-        @listen(step_1)
-        def step_2(self):
-            self.counter *= 2
-            assert self.counter == 2
-
-    flow = StateFlow()
-    flow.kickoff()
-    assert flow.counter == 2

tests/llm_test.py

@@ -1,30 +0,0 @@
-import pytest
-
-from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
-from crewai.llm import LLM
-from crewai.utilities.token_counter_callback import TokenCalcHandler
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_llm_callback_replacement():
-    llm = LLM(model="gpt-4o-mini")
-
-    calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
-    calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
-
-    llm.call(
-        messages=[{"role": "user", "content": "Hello, world!"}],
-        callbacks=[calc_handler_1],
-    )
-    usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
-
-    llm.call(
-        messages=[{"role": "user", "content": "Hello, world from another agent!"}],
-        callbacks=[calc_handler_2],
-    )
-    usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
-
-    # The first handler should not have been updated
-    assert usage_metrics_1.successful_requests == 1
-    assert usage_metrics_2.successful_requests == 1
-    assert usage_metrics_1 == calc_handler_1.token_cost_process.get_summary()

tests/memory/cassettes/test_save_and_search_with_provider.yaml

@@ -1,270 +0,0 @@
-interactions:
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-    headers:
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-      accept-encoding:
-      - gzip, deflate
-      connection:
-      - keep-alive
-      host:
-      - api.mem0.ai
-      user-agent:
-      - python-httpx/0.27.0
-    method: GET
-    uri: https://api.mem0.ai/v1/memories/?user_id=test
-  response:
-    body:
-      string: '[]'
-    headers:
-      CF-Cache-Status:
-      - DYNAMIC
-      CF-RAY:
-      - 8b477138bad847b9-BOM
-      Connection:
-      - keep-alive
-      Content-Length:
-      - '2'
-      Content-Type:
-      - application/json
-      Date:
-      - Sat, 17 Aug 2024 06:00:11 GMT
-      NEL:
-      - '{"success_fraction":0,"report_to":"cf-nel","max_age":604800}'
-      Report-To:
-      - '{"endpoints":[{"url":"https:\/\/a.nel.cloudflare.com\/report\/v4?s=uuyH2foMJVDpV%2FH52g1q%2FnvXKe3dBKVzvsK0mqmSNezkiszNR9OgrEJfVqmkX%2FlPFRP2sH4zrOuzGo6k%2FjzsjYJczqSWJUZHN2pPujiwnr1E9W%2BdLGKmG6%2FqPrGYAy2SBRWkkJVWsTO3OQ%3D%3D"}],"group":"cf-nel","max_age":604800}'
-      Server:
-      - cloudflare
-      allow:
-      - GET, POST, DELETE, OPTIONS
-      alt-svc:
-      - h3=":443"; ma=86400
-      cross-origin-opener-policy:
-      - same-origin
-      referrer-policy:
-      - same-origin
-      vary:
-      - Accept, origin, Cookie
-      x-content-type-options:
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-      x-frame-options:
-      - DENY
-    status:
-      code: 200
-      message: OK
-- request:
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-    uri: https://us.i.posthog.com/batch/
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-    headers:
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-      - origin, access-control-request-method, access-control-request-headers
-      x-envoy-upstream-service-time:
-      - '69'
-    status:
-      code: 200
-      message: OK
-- request:
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-      from Agent run: test value with provider"}], "metadata": {"task": "test_task_provider",
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tests/task_test.py

@@ -15,7 +15,7 @@ from pydantic_core import ValidationError
 
 
 def test_task_tool_reflect_agent_tools():
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def fake_tool() -> None:
@@ -39,7 +39,7 @@ def test_task_tool_reflect_agent_tools():
 
 
 def test_task_tool_takes_precedence_over_agent_tools():
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def fake_tool() -> None:
@@ -656,7 +656,7 @@ def test_increment_delegations_for_sequential_process():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_increment_tool_errors():
-    from crewai.tools import tool
+    from crewai_tools import tool
 
     @tool
     def scoring_examples() -> None:

tests/tools/test_base_tool.py

@@ -1,109 +0,0 @@
-from typing import Callable
-from crewai.tools import BaseTool, tool
-
-
-def test_creating_a_tool_using_annotation():
-    @tool("Name of my tool")
-    def my_tool(question: str) -> str:
-        """Clear description for what this tool is useful for, you agent will need this information to use it."""
-        return question
-
-    # Assert all the right attributes were defined
-    assert my_tool.name == "Name of my tool"
-    assert (
-        my_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
-    )
-    assert my_tool.args_schema.schema()["properties"] == {
-        "question": {"title": "Question", "type": "string"}
-    }
-    assert (
-        my_tool.func("What is the meaning of life?") == "What is the meaning of life?"
-    )
-
-    # Assert the langchain tool conversion worked as expected
-    converted_tool = my_tool.to_langchain()
-    assert converted_tool.name == "Name of my tool"
-
-    assert (
-        converted_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
-    )
-    assert converted_tool.args_schema.schema()["properties"] == {
-        "question": {"title": "Question", "type": "string"}
-    }
-    assert (
-        converted_tool.func("What is the meaning of life?")
-        == "What is the meaning of life?"
-    )
-
-
-def test_creating_a_tool_using_baseclass():
-    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."
-        )
-
-        def _run(self, question: str) -> str:
-            return question
-
-    my_tool = MyCustomTool()
-    # Assert all the right attributes were defined
-    assert my_tool.name == "Name of my tool"
-
-    assert (
-        my_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
-    )
-    assert my_tool.args_schema.schema()["properties"] == {
-        "question": {"title": "Question", "type": "string"}
-    }
-    assert my_tool.run("What is the meaning of life?") == "What is the meaning of life?"
-
-    # Assert the langchain tool conversion worked as expected
-    converted_tool = my_tool.to_langchain()
-    assert converted_tool.name == "Name of my tool"
-
-    assert (
-        converted_tool.description
-        == "Tool Name: Name of my tool\nTool Arguments: {'question': {'description': None, 'type': 'str'}}\nTool Description: Clear description for what this tool is useful for, you agent will need this information to use it."
-    )
-    assert converted_tool.args_schema.schema()["properties"] == {
-        "question": {"title": "Question", "type": "string"}
-    }
-    assert (
-        converted_tool.run("What is the meaning of life?")
-        == "What is the meaning of life?"
-    )
-
-
-def test_setting_cache_function():
-    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."
-        )
-        cache_function: Callable = lambda: False
-
-        def _run(self, question: str) -> str:
-            return question
-
-    my_tool = MyCustomTool()
-    # Assert all the right attributes were defined
-    assert not my_tool.cache_function()
-
-
-def test_default_cache_function_is_true():
-    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."
-        )
-
-        def _run(self, question: str) -> str:
-            return question
-
-    my_tool = MyCustomTool()
-    # Assert all the right attributes were defined
-    assert my_tool.cache_function()

tests/tools/test_tool_usage.py

@@ -3,11 +3,11 @@ import random
 from unittest.mock import MagicMock
 
 import pytest
+from crewai_tools import BaseTool
 from pydantic import BaseModel, Field
 
 from crewai import Agent, Task
 from crewai.tools.tool_usage import ToolUsage
-from crewai.tools import BaseTool
 
 
 class RandomNumberToolInput(BaseModel):
@@ -103,7 +103,11 @@ def test_tool_usage_render():
     rendered = tool_usage._render()
 
     # Updated checks to match the actual output
-    assert "Tool Name: Random Number Generator" in rendered
+    assert "Tool Name: random number generator" in rendered
+    assert (
+        "Random Number Generator(min_value: 'integer', max_value: 'integer') - Generates a random number within a specified range min_value: 'The minimum value of the range (inclusive)', max_value: 'The maximum value of the range (inclusive)'"
+        in rendered
+    )
     assert "Tool Arguments:" in rendered
     assert (
         "'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}"
@@ -113,11 +117,3 @@ def test_tool_usage_render():
         "'max_value': {'description': 'The maximum value of the range (inclusive)', 'type': 'int'}"
         in rendered
     )
-    assert (
-        "Tool Description: Generates a random number within a specified range"
-        in rendered
-    )
-    assert (
-        "Tool Name: Random Number Generator\nTool Arguments: {'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}, 'max_value': {'description': 'The maximum value of the range (inclusive)', 'type': 'int'}}\nTool Description: Generates a random number within a specified range"
-        in rendered
-    )

uv.lock

@@ -490,32 +490,28 @@ wheels = [
 
 [[package]]
 name = "chroma-hnswlib"
-version = "0.7.6"
+version = "0.7.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "numpy" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/73/09/10d57569e399ce9cbc5eee2134996581c957f63a9addfa6ca657daf006b8/chroma_hnswlib-0.7.6.tar.gz", hash = "sha256:4dce282543039681160259d29fcde6151cc9106c6461e0485f57cdccd83059b7", size = 32256 }
+sdist = { url = "https://files.pythonhosted.org/packages/c0/59/1224cbae62c7b84c84088cdf6c106b9b2b893783c000d22c442a1672bc75/chroma-hnswlib-0.7.3.tar.gz", hash = "sha256:b6137bedde49fffda6af93b0297fe00429fc61e5a072b1ed9377f909ed95a932", size = 31876 }
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 ]
 
 [[package]]
 name = "chromadb"
-version = "0.5.18"
+version = "0.4.24"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "bcrypt" },
@@ -523,7 +519,6 @@ dependencies = [
     { name = "chroma-hnswlib" },
     { name = "fastapi" },
     { name = "grpcio" },
-    { name = "httpx" },
     { name = "importlib-resources" },
     { name = "kubernetes" },
     { name = "mmh3" },
@@ -536,10 +531,11 @@ dependencies = [
     { name = "orjson" },
     { name = "overrides" },
     { name = "posthog" },
+    { name = "pulsar-client" },
     { name = "pydantic" },
     { name = "pypika" },
     { name = "pyyaml" },
-    { name = "rich" },
+    { name = "requests" },
     { name = "tenacity" },
     { name = "tokenizers" },
     { name = "tqdm" },
@@ -547,9 +543,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uvicorn", extra = ["standard"] },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/15/95/d1a3f14c864e37d009606b82bd837090902b5e5a8e892fcab07eeaec0438/chromadb-0.5.18.tar.gz", hash = "sha256:cfbb3e5aeeb1dd532b47d80ed9185e8a9886c09af41c8e6123edf94395d76aec", size = 33620708 }
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 [[package]]
@@ -608,7 +604,7 @@ wheels = [
 
 [[package]]
 name = "crewai"
-version = "0.79.4"
+version = "0.76.9"
 source = { editable = "." }
 dependencies = [
     { name = "appdirs" },
@@ -638,9 +634,6 @@ dependencies = [
 agentops = [
     { name = "agentops" },
 ]
-mem0 = [
-    { name = "mem0ai" },
-]
 tools = [
     { name = "crewai-tools" },
 ]
@@ -670,16 +663,15 @@ requires-dist = [
     { name = "agentops", marker = "extra == 'agentops'", specifier = ">=0.3.0" },
     { name = "appdirs", specifier = ">=1.4.4" },
     { name = "auth0-python", specifier = ">=4.7.1" },
-    { name = "chromadb", specifier = ">=0.5.18" },
+    { name = "chromadb", specifier = ">=0.4.24" },
     { name = "click", specifier = ">=8.1.7" },
-    { name = "crewai-tools", specifier = ">=0.14.0" },
+    { name = "crewai-tools", specifier = ">=0.13.4" },
-    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.14.0" },
+    { name = "crewai-tools", marker = "extra == 'tools'", specifier = ">=0.13.4" },
     { name = "instructor", specifier = ">=1.3.3" },
     { name = "json-repair", specifier = ">=0.25.2" },
     { name = "jsonref", specifier = ">=1.1.0" },
     { name = "langchain", specifier = ">=0.2.16" },
     { name = "litellm", specifier = ">=1.44.22" },
-    { name = "mem0ai", marker = "extra == 'mem0'", specifier = ">=0.1.29" },
     { name = "openai", specifier = ">=1.13.3" },
     { name = "opentelemetry-api", specifier = ">=1.22.0" },
     { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.22.0" },
@@ -696,7 +688,7 @@ requires-dist = [
 [package.metadata.requires-dev]
 dev = [
     { name = "cairosvg", specifier = ">=2.7.1" },
-    { name = "crewai-tools", specifier = ">=0.14.0" },
+    { name = "crewai-tools", specifier = ">=0.13.4" },
     { name = "mkdocs", specifier = ">=1.4.3" },
     { name = "mkdocs-material", specifier = ">=9.5.7" },
     { name = "mkdocs-material-extensions", specifier = ">=1.3.1" },
@@ -715,7 +707,7 @@ dev = [
 
 [[package]]
 name = "crewai-tools"
-version = "0.14.0"
+version = "0.13.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "beautifulsoup4" },
@@ -733,9 +725,9 @@ dependencies = [
     { name = "requests" },
     { name = "selenium" },
 ]
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