Merge branch 'main' into feat/add-input-to-flows

* Everything looks like its working. Waiting for lorenze review.

* Update docs as well.

* clean up for PR

.github/security.md

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

docs/concepts/flows.mdx

@@ -18,60 +18,63 @@ Flows allow you to create structured, event-driven workflows. They provide a sea
 
 4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
 
+5. **Input Flexibility**: Flows can accept inputs to initialize or update their state, with different handling for structured and unstructured state management.
+
 ## Getting Started
 
 Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
 
-```python Code
+### Passing Inputs to Flows
 
+Flows can accept inputs to initialize or update their state before execution. The way inputs are handled depends on whether the flow uses structured or unstructured state management.
+
+#### Structured State Management
+
+In structured state management, the flow's state is defined using a Pydantic `BaseModel`. Inputs must match the model's schema, and any updates will overwrite the default values.
+
+```python
 from crewai.flow.flow import Flow, listen, start
-from dotenv import load_dotenv
-from litellm import completion
+from pydantic import BaseModel
 
+class ExampleState(BaseModel):
+    counter: int = 0
+    message: str = ""
 
-class ExampleFlow(Flow):
-    model = "gpt-4o-mini"
-
+class StructuredExampleFlow(Flow[ExampleState]):
     @start()
-    def generate_city(self):
-        print("Starting flow")
+    def first_method(self):
+        # Implementation
 
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": "Return the name of a random city in the world.",
-                },
-            ],
-        )
+flow = StructuredExampleFlow()
+flow.kickoff(inputs={"counter": 10})
+```
 
-        random_city = response["choices"][0]["message"]["content"]
-        print(f"Random City: {random_city}")
+In this example, the `counter` is initialized to `10`, while `message` retains its default value.
 
-        return random_city
+#### Unstructured State Management
 
-    @listen(generate_city)
-    def generate_fun_fact(self, random_city):
-        response = completion(
-            model=self.model,
-            messages=[
-                {
-                    "role": "user",
-                    "content": f"Tell me a fun fact about {random_city}",
-                },
-            ],
-        )
+In unstructured state management, the flow's state is a dictionary. You can pass any dictionary to update the state.
 
-        fun_fact = response["choices"][0]["message"]["content"]
-        return fun_fact
+```python
+from crewai.flow.flow import Flow, listen, start
 
+class UnstructuredExampleFlow(Flow):
+    @start()
+    def first_method(self):
+        # Implementation
 
+flow = UnstructuredExampleFlow()
+flow.kickoff(inputs={"counter": 5, "message": "Initial message"})
+```
 
-flow = ExampleFlow()
-result = flow.kickoff()
+Here, both `counter` and `message` are updated based on the provided inputs.
 
-print(f"Generated fun fact: {result}")
+**Note:** Ensure that inputs for structured state management adhere to the defined schema to avoid validation errors.
+
+### Example Flow
+
+```python
+# Existing example code
 ```
 
 In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
@@ -94,14 +97,14 @@ The `@listen()` decorator can be used in several ways:
 
 1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
 
-   ```python Code
+   ```python
    @listen("generate_city")
    def generate_fun_fact(self, random_city):
        # Implementation
    ```
 
 2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
-   ```python Code
+   ```python
    @listen(generate_city)
    def generate_fun_fact(self, random_city):
        # Implementation
@@ -118,7 +121,7 @@ When you run a Flow, the final output is determined by the last method that comp
 Here's how you can access the final output:
 
 <CodeGroup>
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 
 class OutputExampleFlow(Flow):
@@ -130,18 +133,17 @@ 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 Output
+```text
 ---- Final Output ----
 Second method received: Output from first_method
-````
+```
 
 </CodeGroup>
 
@@ -156,7 +158,7 @@ Here's an example of how to update and access the state:
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
@@ -184,7 +186,7 @@ print("Final State:")
 print(flow.state)
 ```
 
-```text Output
+```text
 Final Output: Hello from first_method - updated by second_method
 Final State:
 counter=2 message='Hello from first_method - updated by second_method'
@@ -208,10 +210,10 @@ 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 Code
+```python
 from crewai.flow.flow import Flow, listen, start
 
-class UntructuredExampleFlow(Flow):
+class UnstructuredExampleFlow(Flow):
 
     @start()
     def first_method(self):
@@ -230,8 +232,7 @@ class UntructuredExampleFlow(Flow):
 
         print(f"State after third_method: {self.state}")
 
-
-flow = UntructuredExampleFlow()
+flow = UnstructuredExampleFlow()
 flow.kickoff()
 ```
 
@@ -245,16 +246,14 @@ 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 Code
+```python
 from crewai.flow.flow import Flow, listen, start
 from pydantic import BaseModel
 
-
 class ExampleState(BaseModel):
     counter: int = 0
     message: str = ""
 
-
 class StructuredExampleFlow(Flow[ExampleState]):
 
     @start()
@@ -273,7 +272,6 @@ class StructuredExampleFlow(Flow[ExampleState]):
 
         print(f"State after third_method: {self.state}")
 
-
 flow = StructuredExampleFlow()
 flow.kickoff()
 ```
@@ -307,7 +305,7 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, listen, or_, start
 
 class OrExampleFlow(Flow):
@@ -324,13 +322,11 @@ class OrExampleFlow(Flow):
     def logger(self, result):
         print(f"Logger: {result}")
 
-
-
 flow = OrExampleFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 Logger: Hello from the start method
 Logger: Hello from the second method
 ```
@@ -346,7 +342,7 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge
 
 <CodeGroup>
 
-```python Code
+```python
 from crewai.flow.flow import Flow, and_, listen, start
 
 class AndExampleFlow(Flow):
@@ -368,7 +364,7 @@ flow = AndExampleFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 ---- Logger ----
 {'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
 ```
@@ -385,7 +381,7 @@ You can specify different routes based on the output of the method, allowing you
 
 <CodeGroup>
 
-```python Code
+```python
 import random
 from crewai.flow.flow import Flow, listen, router, start
 from pydantic import BaseModel
@@ -416,12 +412,11 @@ class RouterFlow(Flow[ExampleState]):
     def fourth_method(self):
         print("Fourth method running")
 
-
 flow = RouterFlow()
 flow.kickoff()
 ```
 
-```text Output
+```text
 Starting the structured flow
 Third method running
 Fourth method running
@@ -484,7 +479,7 @@ 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 Code
+```python
 #!/usr/bin/env python
 from random import randint
 
@@ -577,18 +572,20 @@ 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:
 
-name_of_flow/
-├── crews/
-│   ├── poem_crew/
-│   │   ├── config/
-│   │   │   ├── agents.yaml
-│   │   │   └── tasks.yaml
-│   │   └── poem_crew.py
-│   └── name_of_crew/
-│       ├── config/
-│       │   ├── agents.yaml
-│       │   └── tasks.yaml
-│       └── name_of_crew.py
+| Directory/File         | Description                                                        |
+| :--------------------- | :----------------------------------------------------------------- |
+| `name_of_flow/`        | Root directory for the flow.                                       |
+| ├── `crews/`           | Contains directories for specific crews.                           |
+| │   ├── `poem_crew/`   | Directory for the "poem_crew" with its configurations and scripts. |
+| │   │   ├── `config/`  | Configuration files directory for the "poem_crew".                 |
+| │   │   │  ├── `agents.yaml` | YAML file defining the agents for "poem_crew".                     |
+| │   │   │  └── `tasks.yaml`  | YAML file defining the tasks for "poem_crew".                      |
+| │   │   └── `poem_crew.py`    | Script for "poem_crew" functionality.                              |
+| └── `name_of_crew/`       | Directory for the new crew.                                          |
+|     ├── `config/`         | Configuration files directory for the new crew.                     |
+|     │   ├── `agents.yaml` | YAML file defining the agents for the new crew.                     |
+|     │   └── `tasks.yaml`  | YAML file defining the tasks for the new crew.                      |
+|     └── `name_of_crew.py` | Script for the new crew functionality.                              |
 
 You can then customize the `agents.yaml` and `tasks.yaml` files to define the agents and tasks for your new crew. The `name_of_crew.py` file will contain the crew's logic, which you can modify to suit your needs.
 
@@ -610,7 +607,7 @@ CrewAI provides two convenient methods to generate plots of your flows:
 
 If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
 
-```python Code
+```python
 # Assuming you have a flow instance
 flow.plot("my_flow_plot")
 ```

docs/concepts/llms.mdx

@@ -25,52 +25,55 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
 - `OPENAI_API_BASE`
 - `OPENAI_API_KEY`
 
-### 2. String Identifier
-
-```python Code
-agent = Agent(llm="gpt-4o", ...)
-```
-
-### 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, ...)
-```
-
-### 4. Custom LLM Objects
+### 2. Custom LLM Objects
 
 Pass a custom LLM implementation or object from another library.
 
+See below for examples.
+
+<Tabs>
+    <Tab title="String Identifier">
+    ```python Code
+    agent = Agent(llm="gpt-4o", ...)
+    ```
+    </Tab>
+
+    <Tab title="LLM Instance">
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(model="gpt-4", temperature=0.7)
+    agent = Agent(llm=llm, ...)
+    ```
+    </Tab>
+</Tabs>
+
 ## Connecting to OpenAI-Compatible LLMs
 
 You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
 
-1. Using environment variables:
+<Tabs>
+    <Tab title="Using Environment Variables">
+    ```python Code
+    import os
 
-```python Code
-import os
+    os.environ["OPENAI_API_KEY"] = "your-api-key"
+    os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
+    ```
+    </Tab>
+    <Tab title="Using LLM Class Attributes">
+    ```python Code
+    from crewai import LLM
 
-os.environ["OPENAI_API_KEY"] = "your-api-key"
-os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
-```
-
-2. Using LLM class attributes:
-
-```python Code
-from crewai import LLM
-
-llm = LLM(
-    model="custom-model-name",
-    api_key="your-api-key",
-    base_url="https://api.your-provider.com/v1"
-)
-agent = Agent(llm=llm, ...)
-```
+    llm = LLM(
+        model="custom-model-name",
+        api_key="your-api-key",
+        base_url="https://api.your-provider.com/v1"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Tab>
+</Tabs>
 
 ## LLM Configuration Options
 
@@ -97,55 +100,157 @@ When configuring an LLM for your agent, you have access to a wide range of param
 | **api_key**          | `str`             | Your API key for authentication.                                                                 |
 
 
-## OpenAI Example Configuration
+These are examples of how to configure LLMs for your agent.
 
-```python Code
-from crewai import LLM
+<AccordionGroup>    
+    <Accordion title="OpenAI">
 
-llm = LLM(
-    model="gpt-4",
-    temperature=0.8,
-    max_tokens=150,
-    top_p=0.9,
-    frequency_penalty=0.1,
-    presence_penalty=0.1,
-    stop=["END"],
-    seed=42,
-    base_url="https://api.openai.com/v1",
-    api_key="your-api-key-here"
-)
-agent = Agent(llm=llm, ...)
-```
+    ```python Code
+    from crewai import LLM
 
-## Cerebras Example Configuration
+    llm = LLM(
+        model="gpt-4",
+        temperature=0.8,
+        max_tokens=150,
+        top_p=0.9,
+        frequency_penalty=0.1,
+        presence_penalty=0.1,
+        stop=["END"],
+        seed=42,
+        base_url="https://api.openai.com/v1",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
 
-```python Code
-from crewai import LLM
+    <Accordion title="Cerebras">
 
-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, ...) 
-```
+    ```python Code
+    from crewai import LLM
 
-## Using Ollama (Local LLMs)
+    llm = LLM(
+        model="cerebras/llama-3.1-70b",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...) 
+    ```
+    </Accordion>
+ 
+    <Accordion title="Ollama (Local LLMs)">
 
-CrewAI supports using Ollama for running open-source models locally:
+    CrewAI supports using Ollama for running open-source models locally:
 
-1. Install Ollama: [ollama.ai](https://ollama.ai/)
-2. Run a model: `ollama run llama2`
-3. Configure agent:
+    1. Install Ollama: [ollama.ai](https://ollama.ai/)
+    2. Run a model: `ollama run llama2`
+    3. Configure agent:
 
-```python Code
-from crewai import LLM
+    ```python Code
+    from crewai import LLM
 
-agent = Agent(
-    llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
-    ...
-)
-```
+    agent = Agent(
+        llm=LLM(
+            model="ollama/llama3.1", 
+            base_url="http://localhost:11434"
+        ),
+        ...
+    )
+    ```
+    </Accordion>
+
+    <Accordion title="Groq">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="groq/llama3-8b-8192", 
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Anthropic">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="anthropic/claude-3-5-sonnet-20241022",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Fireworks AI">
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",  
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Gemini">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="gemini/gemini-1.5-pro-002",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="Perplexity AI (pplx-api)">
+
+    ```python Code
+    from crewai import LLM
+
+    llm = LLM(
+        model="perplexity/mistral-7b-instruct",
+        base_url="https://api.perplexity.ai/v1",
+        api_key="your-api-key-here"
+    )
+    agent = Agent(llm=llm, ...)
+    ```
+    </Accordion>
+
+    <Accordion title="IBM watsonx.ai">
+
+    ```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

@@ -254,6 +254,31 @@ 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

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

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

@@ -6,25 +6,17 @@ icon: hammer
 
 ## Creating and Utilizing Tools in CrewAI
 
-This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools, 
-incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools, 
+This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools,
+incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
 enabling agents to perform a wide range of actions.
 
-### Prerequisites
-
-Before creating your own tools, ensure you have the crewAI extra tools package installed:
-
-```bash
-pip install 'crewai[tools]'
-```
-
 ### Subclassing `BaseTool`
 
 To create a personalized tool, inherit from `BaseTool` and define the necessary attributes, 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):
@@ -47,7 +39,7 @@ Alternatively, you can use the tool decorator `@tool`. This approach allows you
 offering a concise and efficient way to create specialized tools tailored to your needs.
 
 ```python Code
-from crewai_tools import tool
+from crewai.tools import tool
 
 @tool("Tool Name")
 def my_simple_tool(question: str) -> str:
@@ -73,5 +65,5 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
 cached_tool.cache_function = my_cache_strategy
 ```
 
-By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes, 
+By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
 you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents.

src/crewai/agent.py

@@ -10,7 +10,8 @@ from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.llm import LLM
 from crewai.memory.contextual.contextual_memory import ContextualMemory
-from crewai.tools.agent_tools import AgentTools
+from crewai.tools.agent_tools.agent_tools import AgentTools
+from crewai.tools import BaseTool
 from crewai.utilities import Converter, Prompts
 from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
 from crewai.utilities.token_counter_callback import TokenCalcHandler
@@ -192,7 +193,7 @@ class Agent(BaseAgent):
         self,
         task: Any,
         context: Optional[str] = None,
-        tools: Optional[List[Any]] = None,
+        tools: Optional[List[BaseTool]] = None,
     ) -> str:
         """Execute a task with the agent.
 
@@ -259,7 +260,9 @@ class Agent(BaseAgent):
 
         return result
 
-    def create_agent_executor(self, tools=None, task=None) -> None:
+    def create_agent_executor(
+        self, tools: Optional[List[BaseTool]] = None, task=None
+    ) -> None:
         """Create an agent executor for the agent.
 
         Returns:
@@ -332,7 +335,7 @@ 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):
@@ -391,7 +394,7 @@ class Agent(BaseAgent):
 
         return description
 
-    def _render_text_description_and_args(self, tools: List[Any]) -> str:
+    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
         """Render the tool name, description, and args in plain text.
 
             Output will be in the format of:
@@ -404,17 +407,7 @@ class Agent(BaseAgent):
         """
         tool_strings = []
         for tool in tools:
-            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}")
+            tool_strings.append(tool.description)
 
         return "\n".join(tool_strings)
 

src/crewai/agents/agent_builder/base_agent.py

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

src/crewai/agents/tools_handler.py

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

src/crewai/cli/config.py

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

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

@@ -1,7 +1,8 @@
+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/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/tools/custom_tool.py

@@ -1,7 +1,8 @@
 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/tools/custom_tool.py

@@ -1,7 +1,8 @@
 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/src/{{folder_name}}/tool.py

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

src/crewai/cli/tools/main.py

@@ -1,17 +1,15 @@
 import base64
 import os
-import platform
 import subprocess
 import tempfile
 from pathlib import Path
-from netrc import netrc
-import stat
 
 import click
 from rich.console import Console
 
 from crewai.cli import git
 from crewai.cli.command import BaseCommand, PlusAPIMixin
+from crewai.cli.config import Settings
 from crewai.cli.utils import (
     get_project_description,
     get_project_name,
@@ -153,26 +151,16 @@ 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"]
@@ -187,7 +175,11 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
             tool_handle,
         ]
         add_package_result = subprocess.run(
-            add_package_command, capture_output=False, text=True, check=True
+            add_package_command,
+            capture_output=False,
+            env=self._build_env_with_credentials(repository_handle),
+            text=True,
+            check=True
         )
 
         if add_package_result.stderr:
@@ -206,3 +198,13 @@ class ToolCommand(BaseCommand, PlusAPIMixin):
                 "[bold yellow]Tip:[/bold yellow] Navigate to a different directory and try again."
             )
             raise SystemExit
+
+    def _build_env_with_credentials(self, repository_handle: str):
+        repository_handle = repository_handle.upper().replace("-", "_")
+        settings = Settings()
+
+        env = os.environ.copy()
+        env[f"UV_INDEX_{repository_handle}_USERNAME"] = str(settings.tool_repository_username or "")
+        env[f"UV_INDEX_{repository_handle}_PASSWORD"] = str(settings.tool_repository_password or "")
+
+        return env

src/crewai/crew.py

@@ -32,7 +32,7 @@ from crewai.task import Task
 from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
-from crewai.tools.agent_tools import AgentTools
+from crewai.tools.agent_tools.agent_tools import AgentTools
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
 from crewai.utilities.constants import (

src/crewai/flow/flow.py

@@ -1,8 +1,20 @@
 import asyncio
 import inspect
-from typing import Any, Callable, Dict, Generic, List, Set, Type, TypeVar, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Generic,
+    List,
+    Optional,
+    Set,
+    Type,
+    TypeVar,
+    Union,
+    cast,
+)
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ValidationError
 
 from crewai.flow.flow_visualizer import plot_flow
 from crewai.flow.utils import get_possible_return_constants
@@ -191,10 +203,74 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """Returns the list of all outputs from executed methods."""
         return self._method_outputs
 
-    def kickoff(self) -> Any:
+    def _initialize_state(self, inputs: Dict[str, Any]) -> None:
+        """
+        Initializes or updates the state with the provided inputs.
+
+        Args:
+            inputs: Dictionary of inputs to initialize or update the state.
+
+        Raises:
+            ValueError: If inputs do not match the structured state model.
+            TypeError: If state is neither a BaseModel instance nor a dictionary.
+        """
+        if isinstance(self._state, BaseModel):
+            # Structured state management
+            try:
+                # Define a function to create the dynamic class
+                def create_model_with_extra_forbid(
+                    base_model: Type[BaseModel],
+                ) -> Type[BaseModel]:
+                    class ModelWithExtraForbid(base_model):  # type: ignore
+                        model_config = base_model.model_config.copy()
+                        model_config["extra"] = "forbid"
+
+                    return ModelWithExtraForbid
+
+                # Create the dynamic class
+                ModelWithExtraForbid = create_model_with_extra_forbid(
+                    self._state.__class__
+                )
+
+                # Create a new instance using the combined state and inputs
+                self._state = cast(
+                    T, ModelWithExtraForbid(**{**self._state.model_dump(), **inputs})
+                )
+
+            except ValidationError as e:
+                raise ValueError(f"Invalid inputs for structured state: {e}") from e
+        elif isinstance(self._state, dict):
+            # Unstructured state management
+            self._state.update(inputs)
+        else:
+            raise TypeError("State must be a BaseModel instance or a dictionary.")
+
+    def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Starts the execution of the flow synchronously.
+
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
+        if inputs is not None:
+            self._initialize_state(inputs)
         return asyncio.run(self.kickoff_async())
 
-    async def kickoff_async(self) -> Any:
+    async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
+        """
+        Starts the execution of the flow asynchronously.
+
+        Args:
+            inputs: Optional dictionary of inputs to initialize or update the state.
+
+        Returns:
+            The final output from the flow execution.
+        """
+        if inputs is not None:
+            self._initialize_state(inputs)
         if not self._start_methods:
             raise ValueError("No start method defined")
 

src/crewai/memory/contextual/contextual_memory.py

@@ -34,6 +34,7 @@ class ContextualMemory:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in stm_results]
         )
+        print("formatted_results stm", formatted_results)
         return f"Recent Insights:\n{formatted_results}" if stm_results else ""
 
     def _fetch_ltm_context(self, task) -> Optional[str]:
@@ -53,6 +54,8 @@ class ContextualMemory:
         formatted_results = list(dict.fromkeys(formatted_results))
         formatted_results = "\n".join([f"- {result}" for result in formatted_results])  # type: ignore # Incompatible types in assignment (expression has type "str", variable has type "list[str]")
 
+        print("formatted_results ltm", formatted_results)
+
         return f"Historical Data:\n{formatted_results}" if ltm_results else ""
 
     def _fetch_entity_context(self, query) -> str:
@@ -64,4 +67,5 @@ class ContextualMemory:
         formatted_results = "\n".join(
             [f"- {result['context']}" for result in em_results]  # type: ignore #  Invalid index type "str" for "str"; expected type "SupportsIndex | slice"
         )
+        print("formatted_results em", formatted_results)
         return f"Entities:\n{formatted_results}" if em_results else ""

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -70,7 +70,7 @@ class KickoffTaskOutputsSQLiteStorage:
                         task.expected_output,
                         json.dumps(output, cls=CrewJSONEncoder),
                         task_index,
-                        json.dumps(inputs),
+                        json.dumps(inputs, cls=CrewJSONEncoder),
                         was_replayed,
                     ),
                 )

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
-from crewai.memory.storage.base_rag_storage import BaseRAGStorage
-from crewai.utilities.paths import db_storage_path
+from typing import Any, Dict, List, Optional, cast
+
+from chromadb import Documents, EmbeddingFunction, Embeddings
 from chromadb.api import ClientAPI
 from chromadb.api.types import validate_embedding_function
-from chromadb import Documents, EmbeddingFunction, Embeddings
-from typing import cast
+from crewai.memory.storage.base_rag_storage import BaseRAGStorage
+from crewai.utilities.paths import db_storage_path
 
 
 @contextlib.contextmanager
@@ -21,9 +21,11 @@ def suppress_logging(
     logger = logging.getLogger(logger_name)
     original_level = logger.getEffectiveLevel()
     logger.setLevel(level)
-    with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(
-        io.StringIO()
-    ), contextlib.suppress(UserWarning):
+    with (
+        contextlib.redirect_stdout(io.StringIO()),
+        contextlib.redirect_stderr(io.StringIO()),
+        contextlib.suppress(UserWarning),
+    ):
         yield
     logger.setLevel(original_level)
 
@@ -113,12 +115,52 @@ class RAGStorage(BaseRAGStorage):
                 self.embedder_config = embedding_functions.HuggingFaceEmbeddingServer(
                     url=config.get("api_url"),
                 )
+            elif provider == "watson":
+                try:
+                    import ibm_watsonx_ai.foundation_models as watson_models
+                    from ibm_watsonx_ai import Credentials
+                    from ibm_watsonx_ai.metanames import (
+                        EmbedTextParamsMetaNames as EmbedParams,
+                    )
+                except ImportError as e:
+                    raise ImportError(
+                        "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
+                    ) from e
+
+                class WatsonEmbeddingFunction(EmbeddingFunction):
+                    def __call__(self, input: Documents) -> Embeddings:
+                        if isinstance(input, str):
+                            input = [input]
+
+                        embed_params = {
+                            EmbedParams.TRUNCATE_INPUT_TOKENS: 3,
+                            EmbedParams.RETURN_OPTIONS: {"input_text": True},
+                        }
+
+                        embedding = watson_models.Embeddings(
+                            model_id=config.get("model"),
+                            params=embed_params,
+                            credentials=Credentials(
+                                api_key=config.get("api_key"), url=config.get("api_url")
+                            ),
+                            project_id=config.get("project_id"),
+                        )
+
+                        try:
+                            embeddings = embedding.embed_documents(input)
+                            return cast(Embeddings, embeddings)
+
+                        except Exception as e:
+                            print("Error during Watson embedding:", e)
+                            raise e
+
+                self.embedder_config = WatsonEmbeddingFunction()
             else:
                 raise Exception(
-                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface]"
+                    f"Unsupported embedding provider: {provider}, supported providers: [openai, azure, ollama, vertexai, google, cohere, huggingface, watson]"
                 )
         else:
-            validate_embedding_function(self.embedder_config)  # type: ignore # used for validating embedder_config if defined a embedding function/class
+            validate_embedding_function(self.embedder_config)
             self.embedder_config = self.embedder_config
 
     def _initialize_app(self):

src/crewai/task.py

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

src/crewai/telemetry/telemetry.py

@@ -21,7 +21,7 @@ with suppress_warnings():
 
 
 from opentelemetry import trace  # noqa: E402
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter  # noqa: E402
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter # noqa: E402
 from opentelemetry.sdk.resources import SERVICE_NAME, Resource  # noqa: E402
 from opentelemetry.sdk.trace import TracerProvider  # noqa: E402
 from opentelemetry.sdk.trace.export import BatchSpanProcessor  # noqa: E402
@@ -48,6 +48,10 @@ class Telemetry:
     def __init__(self):
         self.ready = False
         self.trace_set = False
+
+        if os.getenv("OTEL_SDK_DISABLED", "false").lower() == "true":
+            return
+
         try:
             telemetry_endpoint = "https://telemetry.crewai.com:4319"
             self.resource = Resource(

src/crewai/tools/__init__.py

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

src/crewai/tools/agent_tools.py

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

src/crewai/tools/agent_tools/agent_tools.py

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

src/crewai/tools/agent_tools/ask_question_tool.py

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

src/crewai/tools/agent_tools/base_agent_tools.py

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

src/crewai/tools/agent_tools/delegate_work_tool.py

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

src/crewai/tools/base_tool.py

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

src/crewai/tools/tool_usage.py

@@ -10,6 +10,7 @@ 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
@@ -49,7 +50,7 @@ class ToolUsage:
     def __init__(
         self,
         tools_handler: ToolsHandler,
-        tools: List[Any],
+        tools: List[BaseTool],
         original_tools: List[Any],
         tools_description: str,
         tools_names: str,
@@ -298,22 +299,7 @@ class ToolUsage:
         """Render the tool name and description in plain text."""
         descriptions = []
         for tool in self.tools:
-            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}",
-                    ]
-                )
-            )
+            descriptions.append(tool.description)
         return "\n--\n".join(descriptions)
 
     def _function_calling(self, tool_string: str):

src/crewai/utilities/crew_json_encoder.py

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

tests/agent_test.py

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

tests/agents/agent_builder/base_agent_test.py

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

tests/cli/config_test.py

@@ -0,0 +1,109 @@
+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

@@ -82,6 +82,7 @@ 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:
@@ -1111,7 +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"
 
@@ -1146,7 +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:
@@ -1309,8 +1309,9 @@ def test_hierarchical_crew_creation_tasks_with_agents():
 
     assert crew.manager_agent is not None
     assert crew.manager_agent.tools is not None
-    assert crew.manager_agent.tools[0].description.startswith(
-        "Delegate a specific task to one of the following coworkers: Senior Writer"
+    assert (
+        "Delegate a specific task to one of the following coworkers: Senior Writer\n"
+        in crew.manager_agent.tools[0].description
     )
 
 
@@ -1337,8 +1338,9 @@ 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 crew.manager_agent.tools[0].description.startswith(
+    assert (
         "Delegate a specific task to one of the following coworkers: Senior Writer\n"
+        in crew.manager_agent.tools[0].description
     )
 
 
@@ -1370,8 +1372,9 @@ 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 crew.manager_agent.tools[0].description.startswith(
+    assert (
         "Delegate a specific task to one of the following coworkers: Senior Writer, Researcher, CEO\n"
+        in crew.manager_agent.tools[0].description
     )
 
 
@@ -1494,7 +1497,7 @@ 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:
@@ -1696,7 +1699,7 @@ 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:

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/agent_tools/agent_tools_test.py

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

tests/tools/test_base_tool.py

@@ -0,0 +1,109 @@
+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):
@@ -103,11 +103,7 @@ 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 (
-        "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 Name: Random Number Generator" in rendered
     assert "Tool Arguments:" in rendered
     assert (
         "'min_value': {'description': 'The minimum value of the range (inclusive)', 'type': 'int'}"
@@ -117,3 +113,11 @@ 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
+    )