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34
docs/core-concepts/Agents.md
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@@ -1,3 +1,4 @@
```markdown
---
title: crewAI Agents
description: What are crewAI Agents and how to use them.
@@ -10,26 +11,26 @@ description: What are crewAI Agents and how to use them.
<li class='leading-3'>Perform tasks</li>
<li class='leading-3'>Make decisions</li>
<li class='leading-3'>Communicate with other agents</li>
<ul>
</ul>
<br/>
Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like 'Researcher', 'Writer', or 'Customer Support', each contributing to the overall goal of the crew.
## Agent Attributes
| Attribute | Description |
| :--------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | The language model used by the agent to process and generate text. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. |
| **Tools** *(optional)* | Set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents. It's an attribute that can be set during the initialization of an agent, with a default value of an empty list. |
| **Function Calling LLM** *(optional)* | If passed, this agent will use this LLM to execute function calling for tools instead of relying on the main LLM output. |
| **Max Iter** *(optional)* | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `15`. |
| **Max RPM** *(optional)* | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. |
| **Verbose** *(optional)* | Enables detailed logging of the agent's execution for debugging or monitoring purposes when set to True. Default is `False`. |
| **Allow Delegation** *(optional)* | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`. |
| **Step Callback** *(optional)* | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. |
| **Memory** *(optional)* | Indicates whether the agent should have memory or not, with a default value of False. This impacts the agent's ability to remember past interactions. Default is `False`. |
| Attribute | Description |
| :------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. |
| **Tools** *(optional)* | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. |
| **Function Calling LLM** *(optional)* | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. |
| **Max Iter** *(optional)* | The maximum number of iterations the agent can perform before being forced to give its best answer. Default is `15`. |
| **Max RPM** *(optional)* | The maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. |
| **Verbose** *(optional)* | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. |
| **Allow Delegation** *(optional)* | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `True`. |
| **Step Callback** *(optional)* | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. |
| **Cache** *(optional)* | Indicates if the agent should use a cache for tool usage. Default is `True`. |
## Creating an Agent
@@ -58,9 +59,10 @@ agent = Agent(
verbose=True, # Optional
allow_delegation=True, # Optional
step_callback=my_intermediate_step_callback, # Optional
memory=True # Optional
cache=True # Optional
)
```
## Conclusion
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents, you can create sophisticated AI systems that leverage the power of collaborative intelligence.
```

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docs/core-concepts/Collaboration.md
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@@ -14,16 +14,17 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
## Enhanced Attributes for Improved Collaboration
The `Crew` class has been enriched with several attributes to support advanced functionalities:
- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions. It's important to note that `manager_llm` is mandatory when using a hierarchical process for ensuring proper execution flow.
- **Language Model Management (`manager_llm`, `function_calling_llm`)**: Manages language models for executing tasks and tools, facilitating sophisticated agent-tool interactions. Note that while `manager_llm` is mandatory for hierarchical processes to ensure proper execution flow, `function_calling_llm` is optional, with a default value provided for streamlined tool interaction.
- **Process Flow (`process`)**: Defines the execution logic (e.g., sequential, hierarchical) to streamline task distribution and execution.
- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level.
- **Configuration (`config`)**: Allows extensive customization to tailor the crew's behavior according to specific requirements.
- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute.
- **Internationalization Support (`language`)**: Facilitates operation in multiple languages, enhancing global usability.
- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results.
- **Callback and Telemetry (`step_callback`)**: Integrates callbacks for step-wise execution monitoring and telemetry for performance analytics.
- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models.
- **Usage Metrics (`usage_metrics`)**: Store all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement, you can check it after the crew execution.
- **Verbose Logging (`verbose`)**: Offers detailed logging capabilities for monitoring and debugging purposes. It supports both integer and boolean types to indicate the verbosity level. For example, setting `verbose` to 1 might enable basic logging, whereas setting it to True enables more detailed logs.
- **Rate Limiting (`max_rpm`)**: Ensures efficient utilization of resources by limiting requests per minute. Guidelines for setting `max_rpm` should consider the complexity of tasks and the expected load on resources.
- **Internationalization Support (`language`, `language_file`)**: Facilitates operation in multiple languages, enhancing global usability. Supported languages and the process for utilizing the `language_file` attribute for customization should be clearly documented.
- **Execution and Output Handling (`full_output`)**: Distinguishes between full and final outputs for nuanced control over task results. Examples showcasing the difference in outputs can aid in understanding the practical implications of this attribute.
- **Callback and Telemetry (`step_callback`, `task_callback`)**: Integrates callbacks for step-wise and task-level execution monitoring, alongside telemetry for performance analytics. The purpose and usage of `task_callback` alongside `step_callback` for granular monitoring should be clearly explained.
- **Crew Sharing (`share_crew`)**: Enables sharing of crew information with CrewAI for continuous improvement and training models. The privacy implications and benefits of this feature, including how it contributes to model improvement, should be outlined.
- **Usage Metrics (`usage_metrics`)**: Stores all metrics for the language model (LLM) usage during all tasks' execution, providing insights into operational efficiency and areas for improvement. Detailed information on accessing and interpreting these metrics for performance analysis should be provided.
- **Memory Usage (`memory`)**: Indicates whether the crew should use memory to store memories of its execution, enhancing task execution and agent learning.
- **Embedder Configuration (`embedder`)**: Specifies the configuration for the embedder to be used by the crew for understanding and generating language. This attribute supports customization of the language model provider.
## Delegation: Dividing to Conquer
Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.

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docs/core-concepts/Crews.md
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@@ -1,36 +1,39 @@
---
title: crewAI Crews
description: Understanding and utilizing crews in the crewAI framework.
description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
---
## What is a Crew?
!!! note "Definition of a Crew"
A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
## Crew Attributes
| Attribute | Description |
| :---------------------- | :----------------------------------------------------------- |
| **Tasks** | A list of tasks assigned to the crew. |
| **Agents** | A list of agents that are part of the crew. |
| **Process** *(optional)* | The process flow (e.g., sequential, hierarchical) the crew follows. |
| **Verbose** *(optional)* | The verbosity level for logging during execution. |
| **Manager LLM** *(optional)* | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
| Attribute | Description |
| :-------------------------- | :----------------------------------------------------------- |
| **Tasks** | A list of tasks assigned to the crew. |
| **Agents** | A list of agents that are part of the crew. |
| **Process** *(optional)* | The process flow (e.g., sequential, hierarchical) the crew follows. |
| **Verbose** *(optional)* | The verbosity level for logging during execution. |
| **Manager LLM** *(optional)*| The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
| **Function Calling LLM** *(optional)* | If passed, the crew will use this LLM to do function calling for tools for all agents in the crew. Each agent can have its own LLM, which overrides the crew's LLM for function calling. |
| **Config** *(optional)* | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
| **Max RPM** *(optional)* | Maximum requests per minute the crew adheres to during execution. |
| **Language** *(optional)* | Language used for the crew, defaults to English. |
| **Full Output** *(optional)* | Whether the crew should return the full output with all tasks outputs or just the final output. |
| **Step Callback** *(optional)* | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
| **Share Crew** *(optional)* | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
| **Config** *(optional)* | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format. |
| **Max RPM** *(optional)* | Maximum requests per minute the crew adheres to during execution. |
| **Language** *(optional)* | Language used for the crew, defaults to English. |
| **Language File** *(optional)* | Path to the language file to be used for the crew. |
| **Memory** *(optional)* | Utilized for storing execution memories (short-term, long-term, entity memory). |
| **Cache** *(optional)* | Specifies whether to use a cache for storing the results of tools' execution. |
| **Embedder** *(optional)* | Configuration for the embedder to be used by the crew. mostly used by memory for now |
| **Full Output** *(optional)*| Whether the crew should return the full output with all tasks outputs or just the final output. |
| **Step Callback** *(optional)* | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
| **Task Callback** *(optional)* | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
| **Share Crew** *(optional)* | Whether you want to share the complete crew information and execution with the crewAI team to make the library better, and allow us to train models. |
!!! note "Crew Max RPM"
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
The `max_rpm` attribute sets the maximum number of requests per minute the crew can perform to avoid rate limits and will override individual agents' `max_rpm` settings if you set it.
## Creating a Crew
!!! note "Crew Composition"
When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
When assembling a crew, you combine agents with complementary roles and tools, assign tasks, and select a process that dictates their execution order and interaction.
### Example: Assembling a Crew
@@ -71,6 +74,14 @@ my_crew = Crew(
)
```
## Memory Utilization
Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
## Cache Utilization
Caches can be employed to store the results of tools' execution, making the process more efficient by reducing the need to re-execute identical tasks.
## Crew Usage Metrics
After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.

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docs/core-concepts/Memory.md
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@@ -1,3 +1,4 @@
```markdown
---
title: crewAI Memory Systems
description: Leveraging memory systems in the crewAI framework to enhance agent capabilities.
@@ -5,7 +6,7 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
## Introduction to Memory Systems in crewAI
!!! note "Enhancing Agent Intelligence"
The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. This system comprises short-term memory, long-term memory, and entity memory, each serving a unique purpose in aiding agents to remember, reason, and learn from past interactions.
The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents. This system comprises short-term memory, long-term memory, entity memory, and newly identified contextual memory, each serving a unique purpose in aiding agents to remember, reason, and learn from past interactions.
## Memory System Components
@@ -14,10 +15,11 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
| **Short-Term Memory**| Temporarily stores recent interactions and outcomes, enabling agents to recall and utilize information relevant to their current context. |
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. |
| **Contextual Memory**| Maintains the context of interactions, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
## How Memory Systems Empower Agents
1. **Contextual Awareness**: With short-term memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.
1. **Contextual Awareness**: With short-term and contextual memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.
2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.
@@ -26,7 +28,7 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
## Implementing Memory in Your Crew
When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
By default the memory system is disabled, but you can enable it by setting `memory=True` in the crew configuration.
By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration.
The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
### Example: Configuring Memory for a Crew
@@ -40,17 +42,12 @@ my_crew = Crew(
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
# Optional: Customize the memory embedding model
# embedder={
# "provider": "huggingface",
# "config":{
# "model": 'sentence-transformers/all-mpnet-base-v2'
# }
# }
verbose=True
)
```
## Additional Embedding Providers
### Using OpenAI embeddings (already default)
```python
from crewai import Crew, Agent, Task, Process

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docs/core-concepts/Processes.md
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@@ -10,14 +10,14 @@ description: Detailed guide on workflow management through processes in CrewAI,
## Process Implementations
- **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. Note: A manager language model (`manager_llm`) must be specified in the crew to enable the hierarchical process, allowing for the creation and management of tasks by the manager.
- **Consensual Process (Planned)**: Currently under consideration for future development, this process type aims for collaborative decision-making among agents on task execution, introducing a more democratic approach to task management within CrewAI. As of now, it is not implemented in the codebase.
- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. A manager language model (`manager_llm`) must be specified in the crew to enable the hierarchical process, facilitating the creation and management of tasks by the manager.
- **Consensual Process (Planned)**: Aiming for collaborative decision-making among agents on task execution, this process type introduces a democratic approach to task management within CrewAI. It is planned for future development and is not currently implemented in the codebase.
## The Role of Processes in Teamwork
Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
## Assigning Processes to a Crew
To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. Note: For a hierarchical process, ensure to define `manager_llm` for the manager agent.
To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` for the manager agent.
```python
from crewai import Crew
@@ -48,13 +48,15 @@ This method mirrors dynamic team workflows, progressing through tasks in a thoug
To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
## Hierarchical Process
Emulates a corporate hierarchy, crewAI creates a manager automatically for you, requiring the specification of a manager language model (`manager_llm`) for the manager agent. This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
Emulates a corporate hierarchy, CrewAI automatically creates a manager for you, requiring the specification of a manager language model (`manager_llm`) for the manager agent. This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
## Process Class: Detailed Overview
The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`, and future `consensual`). This design choice guarantees that only valid processes are utilized within the CrewAI framework.
The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
## Planned Future Processes
- **Consensual Process**: This collaborative decision-making process among agents on task execution is under consideration but not currently implemented. This future enhancement aims to introduce a more democratic approach to task management within CrewAI.
## Additional Task Features
- **Asynchronous Execution**: Tasks can now be executed asynchronously, allowing for parallel processing and efficiency improvements. This feature is designed to enable tasks to be carried out concurrently, enhancing the overall productivity of the crew.
- **Human Input Review**: An optional feature that enables the review of task outputs by humans to ensure quality and accuracy before finalization. This additional step introduces a layer of oversight, providing an opportunity for human intervention and validation.
- **Output Customization**: Tasks support various output formats, including JSON (`output_json`), Pydantic models (`output_pydantic`), and file outputs (`output_file`), providing flexibility in how task results are captured and utilized. This allows for a wide range of output possibilities, catering to different needs and requirements.
## Conclusion
The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. Documentation will be regularly updated to reflect new processes and enhancements, ensuring users have access to the most current and comprehensive information.
The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents. This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.

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docs/core-concepts/Tasks.md
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@@ -1,33 +1,34 @@
---
title: crewAI Tasks
description: Overview and management of tasks within the crewAI framework.
description: Detailed guide on managing and creating tasks within the crewAI framework, reflecting the latest codebase updates.
---
## Overview of a Task
!!! note "What is a Task?"
In the CrewAI framework, tasks are individual assignments that agents complete. They encapsulate necessary information for execution, including a description, assigned agent, required tools, offering flexibility for various action complexities.
In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
Tasks in CrewAI can be designed to require collaboration between agents. For example, one agent might gather data while another analyzes it. This collaborative approach can be defined within the task properties and managed by the Crew's process.
Tasks within crewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
## Task Attributes
| Attribute | Description |
| :------------- | :----------------------------------- |
| **Description** | A clear, concise statement of what the task entails. |
| **Agent** | Optionally, you can specify which agent is responsible for the task. If not, the crew's process will determine who takes it on. |
| **Expected Output** | Clear and detailed definition of expected output for the task. |
| **Tools** *(optional)* | These are the functions or capabilities the agent can utilize to perform the task. They can be anything from simple actions like 'search' to more complex interactions with other agents or APIs. |
| **Async Execution** *(optional)* | Indicates whether the task should be executed asynchronously, allowing the crew to continue with the next task without waiting for completion. |
| **Context** *(optional)* | Other tasks that will have their output used as context for this task. If a task is asynchronous, the system will wait for that to finish before using its output as context. |
| **Output JSON** *(optional)* | Takes a pydantic model and returns the output as a JSON object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
| **Output Pydantic** *(optional)* | Takes a pydantic model and returns the output as a pydantic object. **Agent LLM needs to be using an OpenAI client, could be Ollama for example but using the OpenAI wrapper** |
| **Output File** *(optional)* | Takes a file path and saves the output of the task on it. |
| **Callback** *(optional)* | A function to be executed after the task is completed. |
| **Human Input** *(optional) - Release Candidate* | Indicates whether the agent should ask for feedback at the end of the task |
| Attribute | Description |
| :----------------------| :-------------------------------------------------------------------------------------------- |
| **Description** | A clear, concise statement of what the task entails. |
| **Agent** | The agent responsible for the task, assigned either directly or by the crew's process. |
| **Expected Output** | A detailed description of what the task's completion looks like. |
| **Tools** *(optional)* | The functions or capabilities the agent can utilize to perform the task. |
| **Async Execution** *(optional)* | If set, the task executes asynchronously, allowing progression without waiting for completion.|
| **Context** *(optional)* | Specifies tasks whose outputs are used as context for this task. |
| **Config** *(optional)* | Additional configuration details for the agent executing the task, allowing further customization. |
| **Output JSON** *(optional)* | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set. |
| **Output Pydantic** *(optional)* | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
| **Output File** *(optional)* | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
| **Callback** *(optional)* | A Python callable that is executed with the task's output upon completion. |
| **Human Input** *(optional)* | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
## Creating a Task
This is the simplest example for creating a task, it involves defining its scope and agent, but there are optional attributes that can provide a lot of flexibility:
Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility:
```python
from crewai import Task
@@ -37,12 +38,13 @@ task = Task(
agent=sales_agent
)
```
!!! note "Task Assignment"
Tasks can be assigned directly by specifying an `agent` to them, or they can be assigned in run time if you are using the `hierarchical` through CrewAI's process, considering roles, availability, or other criteria.
Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
## Integrating Tools with Tasks
Tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) enhance task performance, allowing agents to interact more effectively with their environment. Assigning specific tools to tasks can tailor agent capabilities to particular needs.
Leverage tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction.
## Creating a Task with Tools

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docs/core-concepts/Tools.md
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@@ -15,6 +15,8 @@ CrewAI tools empower agents with capabilities ranging from web searching and dat
- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
- **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
- **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
## Using crewAI Tools
@@ -91,34 +93,22 @@ crew.kickoff()
## Available crewAI Tools
Most of the tools in the crewAI toolkit offer the ability to set specific arguments or let them to be more wide open, this is the case for most of the tools, for example:
- **Error Handling**: All tools are built with error handling capabilities, allowing agents to gracefully manage exceptions and continue their tasks.
- **Caching Mechanism**: All tools support caching, enabling agents to efficiently reuse previously obtained results, reducing the load on external resources and speeding up the execution time, you can also define finner control over the caching mechanism, using `cache_function` attribute on the tool.
```python
from crewai_tools import DirectoryReadTool
# This will allow the agent with this tool to read any directory it wants during it's execution
tool = DirectoryReadTool()
# OR
# This will allow the agent with this tool to read only the directory specified during it's execution
toos = DirectoryReadTool(directory='./directory')
```
Specific per tool docs are coming soon.
Here is a list of the available tools and their descriptions:
| Tool | Description |
| :-------------------------- | :-------------------------------------------------------------------------------------------- |
| **CodeDocsSearchTool** | A RAG tool optimized for searching through code documentation and related technical documents.|
| **CodeDocsSearchTool** | A RAG tool optimized for searching through code documentation and related technical documents. |
| **CSVSearchTool** | A RAG tool designed for searching within CSV files, tailored to handle structured data. |
| **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. |
| **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. |
| **FileReadTool** | Enables reading and extracting data from files, supporting various file formats. |
| **GithubSearchTool** | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
| **SeperDevTool** | 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. |
| **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. |
| **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. |
@@ -132,9 +122,11 @@ Here is a list of the available tools and their descriptions:
| **YoutubeVideoSearchTool** | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction. |
## Creating your own Tools
!!! example "Custom Tool Creation"
Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options:
To create your own crewAI tools you will need to install our extra tools package:
```bash
@@ -142,7 +134,6 @@ pip install 'crewai[tools]'
```
Once you do that there are two main ways for one to create a crewAI tool:
### Subclassing `BaseTool`
```python
@@ -157,71 +148,37 @@ class MyCustomTool(BaseTool):
return "Result from custom tool"
```
Define a new class inheriting from `BaseTool`, specifying `name`, `description`, and the `_run` method for operational logic.
### Utilizing the `tool` Decorator
For a simpler approach, create a `Tool` object directly with the required attributes and a functional logic.
```python
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, you agent will need this information to use it."""
# Function logic here
```
```python
import json
import requests
from crewai import Agent
from crewai.tools import tool
from unstructured.partition.html import partition_html
# Annotate the function with the tool decorator from crewAI
@tool("Integration with a given API")
def integration_tool(argument: str) -> str:
"""Integration with a given API"""
# Code here
return resutls # string to be sent back to the agent
# Assign the scraping tool to an agent
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[integration_tool]
)
return "Result from your custom tool"
```
### Custom Caching Mechanism
Tools now can have an optinal attribute called `cache_function`, this cache function
can be use to fine control when to cache and when not to cache a tool retuls.
Good example my be a tool responsible for getting values from securities where
you are okay to cache treasury value but not stock values.
!!! note "Caching"
Tools can optionally implement a `cache_function` to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic.
```python
from crewai_tools import tool
@tool
def multiplcation_tool(first_number: int, second_number: int) -> str:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
@tool
def multiplication_tool(first_number: int, second_number: int) -> str:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
def cache_func(args, result):
# The cache function will receive:
# - arguments passed to the tool
# - the result of the tool
#
# In this case we only cache the resutl if it's multiple of 2
cache = result % 2 == 0
return cache
def cache_func(args, result):
# In this case, we only cache the result if it's a multiple of 2
cache = result % 2 == 0
return cache
multiplcation_tool.cache_function = cache_func
multiplication_tool.cache_function = cache_func
writer1 = Agent(
writer1 = Agent(
role="Writer",
goal="You write lesssons of math for kids.",
backstory="You're an expert in writting and you love to teach kids but you know nothing of math.",
@@ -264,4 +221,4 @@ agent = 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.
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.

72
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View File

@@ -1,78 +1,62 @@
---
title: Creating your own Tools
description: Guide on how to create and use custom tools within the crewAI framework.
title: Creating and Utilizing Tools in crewAI
description: Comprehensive guide on crafting, using, and managing custom tools within the crewAI framework, including new functionalities and error handling.
---
## Creating your own Tools
!!! example "Custom Tool Creation"
Developers can craft custom tools tailored to their agent’s needs or utilize pre-built options.
## 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, enabling agents to perform a wide range of actions.
To create your own crewAI tools, you will need to install our extra tools package:
### Prerequisites
Before creating your own tools, ensure you have the crewAI extra tools package installed:
```bash
pip install 'crewai[tools]'
```
Once installed, there are two primary methods for creating a crewAI tool:
### Subclassing `BaseTool`
To define a custom tool, create a new class that inherits from `BaseTool`. Specify the `name`, `description`, and implement the `_run` method to outline its operational logic.
To create a personalized tool, inherit from `BaseTool` and define the necessary attributes and the `_run` method.
```python
from crewai_tools import BaseTool
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
description: str = "Clear description for what this tool is useful for. Your agent will need this information to utilize it effectively."
description: str = "What this tool does. It's vital for effective utilization."
def _run(self, argument: str) -> str:
# Implementation details go here
return "Result from custom tool"
# Your tool's logic here
return "Tool's result"
```
### Utilizing the `tool` Decorator
### Using the `tool` Decorator
For a more straightforward approach, employ the `tool` decorator to create a `Tool` object directly. This method requires specifying the required attributes and functional logic within a decorated function.
Alternatively, use the `tool` decorator for a direct approach to create tools. This requires specifying attributes and the tool's logic within a function.
```python
from crewai_tools import tool
@tool("Name of my tool")
def my_tool(question: str) -> str:
"""Provide a clear description of what this tool is useful for. Your agent will need this information to use it."""
# Implement function logic here
@tool("Tool Name")
def my_simple_tool(question: str) -> str:
"""Tool description for clarity."""
# Tool logic here
return "Tool output"
```
```python
import json
import requests
from crewai import Agent
from crewai.tools import tool
# Decorate the function with the tool decorator from crewAI
@tool("Integration with a Given API")
def integration_tool(argument: str) -> str:
"""Details the integration process with a given API."""
# Implementation details
return "Results to be sent back to the agent"
```
### Defining a Cache Function for the Tool
By default, all tools have caching enabled, meaning that if a tool is called with the same arguments by any agent in the crew, it will return the same result. However, specific scenarios may require more tailored caching strategies. For these cases, use the `cache_function` attribute to assign a function that determines whether the result should be cached.
To optimize tool performance with caching, define custom caching strategies using the `cache_function` attribute.
```python
@tool("Integration with a Given API")
def integration_tool(argument: str) -> str:
"""Integration with a given API."""
# Implementation details
return "Results to be sent back to the agent"
@tool("Tool with Caching")
def cached_tool(argument: str) -> str:
"""Tool functionality description."""
return "Cachable result"
def cache_strategy(arguments: dict, result: str) -> bool:
if result == "some_value":
return True
return False
def my_cache_strategy(arguments: dict, result: str) -> bool:
# Define custom caching logic
return True if some_condition else False
integration_tool.cache_function = cache_strategy
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, you can leverage the full capabilities of the crewAI framework, enhancing both the development experience and the efficiency of your AI agents.

25
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View File

@@ -1,10 +1,11 @@
---
title: Assembling and Activating Your CrewAI Team
description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, and more.
description: A comprehensive guide to creating a dynamic CrewAI team for your projects, with updated functionalities including verbose mode, memory capabilities, asynchronous execution, output customization, language model configuration, and more.
---
## Introduction
Embark on your CrewAI journey by setting up your environment and initiating your AI crew with enhanced features. This guide ensures a seamless start, incorporating the latest updates.
Embark on your CrewAI journey by setting up your environment and initiating your AI crew with the latest features. This guide ensures a smooth start, incorporating all recent updates for an enhanced experience.
## Step 0: Installation
Install CrewAI and any necessary packages for your project. CrewAI is compatible with Python >=3.10,<=3.13.
@@ -15,11 +16,11 @@ pip install 'crewai[tools]'
```
## Step 1: Assemble Your Agents
Define your agents with distinct roles, backstories, and now, enhanced capabilities such as verbose mode and memory usage. These elements add depth and guide their task execution and interaction within the crew.
Define your agents with distinct roles, backstories, and enhanced capabilities like verbose mode and memory usage. These elements add depth and guide their task execution and interaction within the crew.
```python
import os
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
os.environ["OPENAI_API_KEY"] = "Your Key"
from crewai import Agent
@@ -68,7 +69,7 @@ research_task = Task(
description=(
"Identify the next big trend in {topic}."
"Focus on identifying pros and cons and the overall narrative."
"Your final report should clearly articulate the key points"
"Your final report should clearly articulate the key points,"
"its market opportunities, and potential risks."
),
expected_output='A comprehensive 3 paragraphs long report on the latest AI trends.',
@@ -92,21 +93,25 @@ write_task = Task(
```
## Step 3: Form the Crew
Combine your agents into a crew, setting the workflow process they'll follow to accomplish the tasks, now with the option to configure language models for enhanced interaction.
Combine your agents into a crew, setting the workflow process they'll follow to accomplish the tasks. Now with options to configure language models for enhanced interaction and additional configurations for optimizing performance.
```python
from crewai import Crew, Process
# Forming the tech-focused crew with enhanced configurations
# Forming the tech-focused crew with some enhanced configurations
crew = Crew(
agents=[researcher, writer],
tasks=[research_task, write_task],
process=Process.sequential # Optional: Sequential task execution is default
process=Process.sequential, # Optional: Sequential task execution is default
memory=True,
cache=True,
max_rpm=100,
share_crew=True
)
```
## Step 4: Kick It Off
Initiate the process with your enhanced crew ready. Observe as your agents collaborate, leveraging their new capabilities for a successful project outcome. You can also pass the inputs that will be interpolated into the agents and tasks.
Initiate the process with your enhanced crew ready. Observe as your agents collaborate, leveraging their new capabilities for a successful project outcome. Input variables will be interpolated into the agents and tasks for a personalized approach.
```python
# Starting the task execution process with enhanced feedback
@@ -115,4 +120,4 @@ print(result)
```
## Conclusion
Building and activating a crew in CrewAI has evolved with new functionalities. By incorporating verbose mode, memory capabilities, asynchronous task execution, output customization, and language model configuration, your AI team is more equipped than ever to tackle challenges efficiently. The depth of agent backstories and the precision of their objectives enrich collaboration, leading to successful project outcomes.
Building and activating a crew in CrewAI has evolved with new functionalities. By incorporating verbose mode, memory capabilities, asynchronous task execution, output customization, language model configuration, and enhanced crew configurations, your AI team is more equipped than ever to tackle challenges efficiently. The depth of agent backstories and the precision of their objectives enrich collaboration, leading to successful project outcomes. This guide aims to provide you with a clear and detailed understanding of setting up and utilizing the CrewAI framework to its full potential.

24
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View File

@@ -4,7 +4,7 @@ description: A comprehensive guide to tailoring agents for specific roles, tasks
---
## Customizable Attributes
Crafting an efficient CrewAI team hinges on the ability to tailor your AI agents dynamically to meet the unique requirements of any project. This section covers the foundational attributes you can customize.
Crafting an efficient CrewAI team hinges on the ability to dynamically tailor your AI agents to meet the unique requirements of any project. This section covers the foundational attributes you can customize.
### Key Attributes for Customization
- **Role**: Specifies the agent's job within the crew, such as 'Analyst' or 'Customer Service Rep'.
@@ -16,24 +16,20 @@ Crafting an efficient CrewAI team hinges on the ability to tailor your AI agents
Beyond the basic attributes, CrewAI allows for deeper customization to enhance an agent's behavior and capabilities significantly.
### Language Model Customization
Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities.
By default crewAI agents are ReAct agents, but by setting the `function_calling_llm` you can turn them into a function calling agents.
### Enabling Memory for Agents
CrewAI supports memory for agents, enabling them to remember past interactions. This feature is critical for tasks requiring awareness of previous contexts or decisions.
Agents can be customized with specific language models (`llm`) and function-calling language models (`function_calling_llm`), offering advanced control over their processing and decision-making abilities. It's important to note that setting the `function_calling_llm` allows for overriding the default crew function-calling language model, providing a greater degree of customization.
## Performance and Debugging Settings
Adjusting an agent's performance and monitoring its operations are crucial for efficient task execution.
### Verbose Mode and RPM Limit
- **Verbose Mode**: Enables detailed logging of an agent's actions, useful for debugging and optimization. Specifically, it provides insights into agent execution processes, aiding in the optimization of performance.
- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`), controlling the agent's query frequency to external services.
- **RPM Limit**: Sets the maximum number of requests per minute (`max_rpm`). This attribute is optional and can be set to `None` for no limit, allowing for unlimited queries to external services if needed.
### Maximum Iterations for Task Execution
The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 15, providing a balance between thoroughness and efficiency. Once the agent approaches this number it will try it's best to give a good answer.
The `max_iter` attribute allows users to define the maximum number of iterations an agent can perform for a single task, preventing infinite loops or excessively long executions. The default value is set to 15, providing a balance between thoroughness and efficiency. Once the agent approaches this number, it will try its best to give a good answer.
## Customizing Agents and Tools
Agents are customized by defining their attributes and tools during initialization. Tools are critical for an agent's functionality, enabling them to perform specialized tasks. In this example we will use the crewAI tools package to create a tool for a research analyst agent.
Agents are customized by defining their attributes and tools during initialization. Tools are critical for an agent's functionality, enabling them to perform specialized tasks. The `tools` attribute should be an array of tools the agent can utilize, and it's initialized as an empty list by default. Tools can be added or modified post-agent initialization to adapt to new requirements.
```shell
pip install 'crewai[tools]'
@@ -58,16 +54,16 @@ agent = Agent(
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[search_tool],
memory=True,
memory=True, # Enable memory
verbose=True,
max_rpm=10, # Optional: Limit requests to 10 per minute, preventing API abuse
max_iter=5, # Optional: Limit task iterations to 5 before the agent tries to give its best answer
max_rpm=None, # No limit on requests per minute
max_iter=15, # Default value for maximum iterations
allow_delegation=False
)
```
## Delegation and Autonomy
Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the crewAI framework. By default, the `allow_delegation` attribute is set to `True`, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the crewAI ecosystem.
Controlling an agent's ability to delegate tasks or ask questions is vital for tailoring its autonomy and collaborative dynamics within the CrewAI framework. By default, the `allow_delegation` attribute is set to `True`, enabling agents to seek assistance or delegate tasks as needed. This default behavior promotes collaborative problem-solving and efficiency within the CrewAI ecosystem. If needed, delegation can be disabled to suit specific operational requirements.
### Example: Disabling Delegation for an Agent
```python
@@ -75,7 +71,7 @@ agent = Agent(
role='Content Writer',
goal='Write engaging content on market trends',
backstory='A seasoned writer with expertise in market analysis.',
allow_delegation=False
allow_delegation=False # Disabling delegation
)
```

20
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@@ -21,7 +21,7 @@ By default, tasks in CrewAI are managed through a sequential process. However, a
To utilize the hierarchical process, it's essential to explicitly set the process attribute to `Process.hierarchical`, as the default behavior is `Process.sequential`. Define a crew with a designated manager and establish a clear chain of command.
!!! note "Tools and Agent Assignment"
Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance.
Assign tools at the agent level to facilitate task delegation and execution by the designated agents under the manager's guidance. Tools can also be specified at the task level for precise control over tool availability during task execution.
!!! note "Manager LLM Requirement"
Configuring the `manager_llm` parameter is crucial for the hierarchical process. The system requires a manager LLM to be set up for proper function, ensuring tailored decision-making.
@@ -30,32 +30,38 @@ To utilize the hierarchical process, it's essential to explicitly set the proces
from langchain_openai import ChatOpenAI
from crewai import Crew, Process, Agent
# Agents are defined with an optional tools parameter
# Agents are defined with attributes for backstory, cache, and verbose mode
researcher = Agent(
role='Researcher',
goal='Conduct in-depth analysis',
backstory='Experienced data analyst with a knack for uncovering hidden trends.',
cache=True,
verbose=False,
# tools=[] # This can be optionally specified; defaults to an empty list
)
writer = Agent(
role='Writer',
goal='Create engaging content',
backstory='Creative writer passionate about storytelling in technical domains.',
cache=True,
verbose=False,
# tools=[] # Optionally specify tools; defaults to an empty list
)
# Establishing the crew with a hierarchical process
# Establishing the crew with a hierarchical process and additional configurations
project_crew = Crew(
tasks=[...], # Tasks to be delegated and executed under the manager's supervision
agents=[researcher, writer],
manager_llm=ChatOpenAI(temperature=0, model="gpt-4"), # Mandatory for hierarchical process
process=Process.hierarchical # Specifies the hierarchical management approach
process=Process.hierarchical, # Specifies the hierarchical management approach
memory=True, # Enable memory usage for enhanced task execution
)
```
### Workflow in Action
1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities.
2. **Execution and Review**: Agents complete their tasks, with the manager ensuring quality standards.
1. **Task Assignment**: The manager assigns tasks strategically, considering each agent's capabilities and available tools.
2. **Execution and Review**: Agents complete their tasks with the option for asynchronous execution and callback functions for streamlined workflows.
3. **Sequential Task Progression**: Despite being a hierarchical process, tasks follow a logical order for smooth progression, facilitated by the manager's oversight.
## Conclusion
Adopting the hierarchical process in crewAI, with the correct configurations and understanding of the system's capabilities, facilitates an organized and efficient approach to project management.

20
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@@ -1,21 +1,20 @@
---
title: Human Input on Execution [Release Candidate]
description: Comprehensive guide on integrating CrewAI with human input during execution in complex decision-making processes or when needed help during complex tasks.
title: Human Input on Execution
description: Integrating CrewAI with human input during execution in complex decision-making processes and leveraging the full capabilities of the agent's attributes and tools.
---
# Human Input in Agent Execution
Human input plays a pivotal role in several agent execution scenarios, enabling agents to seek additional information or clarification when necessary. This capability is invaluable in complex decision-making processes or when agents need more details to complete a task effectively.
Human input is critical in several agent execution scenarios, allowing agents to request additional information or clarification when necessary. This feature is especially useful in complex decision-making processes or when agents require more details to complete a task effectively.
## Using Human Input with CrewAI
The easiest way to integrate human input into agent execution is by setting the `human_input` flag in the task definition. When this flag is enabled, the agent will prompt the user for input before giving it's final answer. This input can be used to provide additional context, clarify ambiguities, or validate the agent's output.
To integrate human input into agent execution, set the `human_input` flag in the task definition. When enabled, the agent prompts the user for input before delivering its final answer. This input can provide extra context, clarify ambiguities, or validate the agent's output.
### Example:
```shell
pip install crewai
pip install 'crewai[tools]'
```
```python
@@ -29,7 +28,7 @@ os.environ["OPENAI_API_KEY"] = "Your Key"
# Loading Tools
search_tool = SerperDevTool()
# Define your agents with roles, goals, and tools
# Define your agents with roles, goals, tools, and additional attributes
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
@@ -40,7 +39,8 @@ researcher = Agent(
),
verbose=True,
allow_delegation=False,
tools=[search_tool]
tools=[search_tool],
max_rpm=100
)
writer = Agent(
role='Tech Content Strategist',
@@ -50,7 +50,9 @@ writer = Agent(
"With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
),
verbose=True,
allow_delegation=True
allow_delegation=True,
tools=[search_tool],
cache=False, # Disable cache for this agent
)
# Create tasks for your agents
@@ -63,7 +65,7 @@ task1 = Task(
),
expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
agent=researcher,
human_input=True, # setting the flag on for human input in this task
human_input=True,
)
task2 = Task(

22
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@@ -5,7 +5,7 @@ description: Comprehensive guide on integrating CrewAI with various Large Langua
## Connect CrewAI to LLMs
!!! note "Default LLM"
By default, CrewAI uses OpenAI's GPT-4 model for language processing. However, you can configure your agents to use a different model or API. This guide will show you how to connect your agents to different LLMs through environment variables and direct instantiation.
By default, CrewAI uses OpenAI's GPT-4 model for language processing. You can configure your agents to use a different model or API. This guide shows how to connect your agents to various LLMs through environment variables and direct instantiation.
CrewAI offers flexibility in connecting to various LLMs, including local models via [Ollama](https://ollama.ai) and different APIs like Azure. It's compatible with all [LangChain LLM](https://python.langchain.com/docs/integrations/llms/) components, enabling diverse integrations for tailored AI solutions.
@@ -16,15 +16,16 @@ The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. He
- `role`: Defines the agent's role within the solution.
- `goal`: Specifies the agent's objective.
- `backstory`: Provides a background story to the agent.
- `llm`: Indicates the Large Language Model the agent uses.
- `function_calling_llm` *Optinal*: Will turn the ReAct crewAI agent into a function calling agent.
- `llm`: The language model that will run the agent. By default, it uses the GPT-4 model defined in the environment variable "OPENAI_MODEL_NAME".
- `function_calling_llm`: The language model that will handle the tool calling for this agent, overriding the crew function_calling_llm. Optional.
- `max_iter`: Maximum number of iterations for an agent to execute a task, default is 15.
- `memory`: Enables the agent to retain information during the execution.
- `max_rpm`: Sets the maximum number of requests per minute.
- `verbose`: Enables detailed logging of the agent's execution.
- `memory`: Enables the agent to retain information during and a across executions. Default is `False`.
- `max_rpm`: Maximum number of requests per minute the agent's execution should respect. Optional.
- `verbose`: Enables detailed logging of the agent's execution. Default is `False`.
- `allow_delegation`: Allows the agent to delegate tasks to other agents, default is `True`.
- `tools`: Specifies the tools available to the agent for task execution.
- `step_callback`: Provides a callback function to be executed after each step.
- `tools`: Specifies the tools available to the agent for task execution. Optional.
- `step_callback`: Provides a callback function to be executed after each step. Optional.
- `cache`: Determines whether the agent should use a cache for tool usage. Default is `True`.
```python
# Required
@@ -35,7 +36,8 @@ example_agent = Agent(
role='Local Expert',
goal='Provide insights about the city',
backstory="A knowledgeable local guide.",
verbose=True
verbose=True,
memory=True
)
```
@@ -51,7 +53,7 @@ OPENAI_API_KEY=''
```
## HuggingFace Integration
There are a couple different ways you can use HuggingFace to host your LLM.
There are a couple of different ways you can use HuggingFace to host your LLM.
### Your own HuggingFace endpoint
```python

6
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@@ -1,6 +1,6 @@
---
title: Using the Sequential Processes in crewAI
description: A comprehensive guide to utilizing the sequential processe for task execution in crewAI projects.
description: A comprehensive guide to utilizing the sequential processes for task execution in crewAI projects.
---
## Introduction
@@ -13,8 +13,6 @@ The sequential process ensures tasks are executed one after the other, following
- **Linear Task Flow**: Ensures orderly progression by handling tasks in a predetermined sequence.
- **Simplicity**: Best suited for projects with clear, step-by-step tasks.
- **Easy Monitoring**: Facilitates easy tracking of task completion and project progress.
## Implementing the Sequential Process
Assemble your crew and define tasks in the order they need to be executed.
@@ -57,4 +55,4 @@ report_crew = Crew(
3. **Completion**: The process concludes once the final task is executed, leading to project completion.
## Conclusion
The sequential process in CrewAI provides a clear, straightforward path for task execution. It's particularly suited for projects requiring a logical progression of tasks, ensuring each step is completed before the next begins, thereby facilitating a cohesive final product.
The sequential and hierarchical processes in CrewAI offer clear, adaptable paths for task execution. They are well-suited for projects requiring logical progression and dynamic decision-making, ensuring each step is completed effectively, thereby facilitating a cohesive final product.

2
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@@ -21,7 +21,7 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas
- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas.
### Opt-In Further Telemetry Sharing
Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed `crew` and `task` execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
Users can choose to share their complete telemetry data by enabling the `share_crew` attribute to `True` in their crew configurations. This opt-in approach respects user privacy and aligns with data protection standards by ensuring users have control over their data sharing preferences. Enabling `share_crew` results in the collection of detailed crew and task execution data, including `goal`, `backstory`, `context`, and `output` of tasks. This enables a deeper insight into usage patterns while respecting the user's choice to share.
### Updates and Revisions
We are committed to maintaining the accuracy and transparency of our documentation. Regular reviews and updates are performed to ensure our documentation accurately reflects the latest developments of our codebase and telemetry practices. Users are encouraged to review this section for the most current information on our data collection practices and how they contribute to the improvement of CrewAI.

11
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View File

@@ -1,22 +1,23 @@
```markdown
# DirectoryReadTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
## Description
The DirectoryReadTool is a highly efficient utility designed for the comprehensive listing of directory contents. It recursively navigates through the specified directory, providing users with a detailed enumeration of all files, including those nested within subdirectories. This tool is indispensable for tasks requiring a thorough inventory of directory structures or for validating the organization of files within directories.
The DirectoryReadTool is a powerful utility designed to provide a comprehensive listing of directory contents. It can recursively navigate through the specified directory, offering users a detailed enumeration of all files, including those within subdirectories. This tool is crucial for tasks that require a thorough inventory of directory structures or for validating the organization of files within directories.
## Installation
Install the `crewai_tools` package to use the DirectoryReadTool in your project. If you haven't added this package to your environment, you can easily install it with pip using the following command:
To utilize the DirectoryReadTool in your project, install the `crewai_tools` package. If this package is not yet part of your environment, you can install it using pip with the command below:
```shell
pip install 'crewai[tools]'
```
This installs the latest version of the `crewai_tools` package, allowing access to the DirectoryReadTool and other utilities.
This command installs the latest version of the `crewai_tools` package, granting access to the DirectoryReadTool among other utilities.
## Example
The DirectoryReadTool is simple to use. The code snippet below shows how to set up and use the tool to list the contents of a specified directory:
Employing the DirectoryReadTool is straightforward. The following code snippet demonstrates how to set it up and use the tool to list the contents of a specified directory:
```python
from crewai_tools import DirectoryReadTool
@@ -33,4 +34,4 @@ tool = DirectoryReadTool(directory='/path/to/your/directory')
## Arguments
The DirectoryReadTool requires minimal configuration for use. The essential argument for this tool is as follows:
- `directory`: **Optional** A argument that specifies the path to the directory whose contents you wish to list. It accepts both absolute and relative paths, guiding the tool to the desired directory for content listing.
- `directory`: **Optional**. An argument that specifies the path to the directory whose contents you wish to list. It accepts both absolute and relative paths, guiding the tool to the desired directory for content listing.

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@@ -1,48 +1,45 @@
# DirectorySearchTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
The DirectorySearchTool is under continuous development. Features and functionalities might evolve, and unexpected behavior may occur as we refine the tool.
## Description
This tool is designed to perform a semantic search for queries within the content of a specified directory. Utilizing the RAG (Retrieval-Augmented Generation) methodology, it offers a powerful means to semantically navigate through the files of a given directory. The tool can be dynamically set to search any directory specified at runtime or can be pre-configured to search within a specific directory upon initialization.
The DirectorySearchTool enables semantic search within the content of specified directories, leveraging the Retrieval-Augmented Generation (RAG) methodology for efficient navigation through files. Designed for flexibility, it allows users to dynamically specify search directories at runtime or set a fixed directory during initial setup.
## Installation
To start using the DirectorySearchTool, you need to install the crewai_tools package. Execute the following command in your terminal:
To use the DirectorySearchTool, begin by installing the crewai_tools package. Execute the following command in your terminal:
```shell
pip install 'crewai[tools]'
```
## Example
The following examples demonstrate how to initialize the DirectorySearchTool for different use cases and how to perform a search:
## Initialization and Usage
Import the DirectorySearchTool from the `crewai_tools` package to start. You can initialize the tool without specifying a directory, enabling the setting of the search directory at runtime. Alternatively, the tool can be initialized with a predefined directory.
```python
from crewai_tools import DirectorySearchTool
# To enable searching within any specified directory at runtime
# For dynamic directory specification at runtime
tool = DirectorySearchTool()
# Alternatively, to restrict searches to a specific directory
# For fixed directory searches
tool = DirectorySearchTool(directory='/path/to/directory')
```
## Arguments
- `directory` : This string argument specifies the directory within which to search. It is mandatory if the tool has not been initialized with a directory; otherwise, the tool will only search within the initialized directory.
- `directory`: A string argument that specifies the search directory. This is optional during initialization but required for searches if not set initially.
## Custom model and embeddings
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
## Custom Model and Embeddings
The DirectorySearchTool uses OpenAI for embeddings and summarization by default. Customization options for these settings include changing the model provider and configuration, enhancing flexibility for advanced users.
```python
tool = DirectorySearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
provider="ollama", # Options include ollama, google, anthropic, llama2, and more
config=dict(
model="llama2",
# temperature=0.5,
# top_p=1,
# stream=true,
# Additional configurations here
),
),
embedder=dict(

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@@ -4,16 +4,16 @@
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
## Description
The FileReadTool is a versatile component of the crewai_tools package, designed to streamline the process of reading and retrieving content from files. It is particularly useful in scenarios such as batch text file processing, runtime configuration file reading, and data importation for analytics. This tool supports various text-based file formats including `.txt`, `.csv`, `.json` and more, and adapts its functionality based on the file type, for instance, converting JSON content into a Python dictionary for easy use.
The FileReadTool conceptually represents a suite of functionalities within the crewai_tools package aimed at facilitating file reading and content retrieval. This suite includes tools for processing batch text files, reading runtime configuration files, and importing data for analytics. It supports a variety of text-based file formats such as `.txt`, `.csv`, `.json`, and more. Depending on the file type, the suite offers specialized functionality, such as converting JSON content into a Python dictionary for ease of use.
## Installation
Install the crewai_tools package to use the FileReadTool in your projects:
To utilize the functionalities previously attributed to the FileReadTool, install the crewai_tools package:
```shell
pip install 'crewai[tools]'
```
## Example
## Usage Example
To get started with the FileReadTool:
```python

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@@ -1,58 +1,60 @@
# JSONSearchTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
!!! note "Experimental Status"
The JSONSearchTool is currently in an experimental phase. This means the tool is under active development, and users might encounter unexpected behavior or changes. We highly encourage feedback on any issues or suggestions for improvements.
## Description
This tool is used to perform a RAG search within a JSON file's content. It allows users to initiate a search with a specific JSON path, focusing the search operation within that particular JSON file. If the path is provided at initialization, the tool restricts its search scope to the specified JSON file, thereby enhancing the precision of search results.
The JSONSearchTool is designed to facilitate efficient and precise searches within JSON file contents. It utilizes a RAG (Retrieve and Generate) search mechanism, allowing users to specify a JSON path for targeted searches within a particular JSON file. This capability significantly improves the accuracy and relevance of search results.
## Installation
Install the crewai_tools package by executing the following command in your terminal:
To install the JSONSearchTool, use the following pip command:
```shell
pip install 'crewai[tools]'
```
## Example
Below are examples demonstrating how to use the JSONSearchTool for searching within JSON files. You can either search any JSON content or restrict the search to a specific JSON file.
## Usage Examples
Here are updated examples on how to utilize the JSONSearchTool effectively for searching within JSON files. These examples take into account the current implementation and usage patterns identified in the codebase.
```python
from crewai_tools import JSONSearchTool
from crewai.json_tools import JSONSearchTool # Updated import path
# Example 1: Initialize the tool for a general search across any JSON content. This is useful when the path is known or can be discovered during execution.
# General JSON content search
# This approach is suitable when the JSON path is either known beforehand or can be dynamically identified.
tool = JSONSearchTool()
# Example 2: Initialize the tool with a specific JSON path, limiting the search to a particular JSON file.
# Restricting search to a specific JSON file
# Use this initialization method when you want to limit the search scope to a specific JSON file.
tool = JSONSearchTool(json_path='./path/to/your/file.json')
```
## Arguments
- `json_path` (str): An optional argument that defines the path to the JSON file to be searched. This parameter is only necessary if the tool is initialized without a specific JSON path. Providing this argument restricts the search to the specified JSON file.
- `json_path` (str, optional): Specifies the path to the JSON file to be searched. This argument is not required if the tool is initialized for a general search. When provided, it confines the search to the specified JSON file.
## Custom model and embeddings
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
## Configuration Options
The JSONSearchTool supports extensive customization through a configuration dictionary. This allows users to select different models for embeddings and summarization based on their requirements.
```python
tool = JSONSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
config=dict(
model="llama2",
config={
"llm": {
"provider": "ollama", # Other options include google, openai, anthropic, llama2, etc.
"config": {
"model": "llama2",
# Additional optional configurations can be specified here.
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
provider="google",
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# title="Embeddings",
),
),
)
},
},
"embedder": {
"provider": "google",
"config": {
"model": "models/embedding-001",
"task_type": "retrieval_document",
# Further customization options can be added here.
},
},
}
)
```

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@@ -1,47 +1,48 @@
# MDXSearchTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
The MDXSearchTool is in continuous development. Features may be added or removed, and functionality could change unpredictably as we refine the tool.
## Description
The MDX Search Tool, a key component of the `crewai_tools` package, is designed for advanced market data extraction, offering invaluable support to researchers and analysts requiring immediate market insights in the AI sector. With its ability to interface with various data sources and tools, it streamlines the process of acquiring, reading, and organizing market data efficiently.
The MDX Search Tool is a component of the `crewai_tools` package aimed at facilitating advanced market data extraction. This tool is invaluable for researchers and analysts seeking quick access to market insights, especially within the AI sector. It simplifies the task of acquiring, interpreting, and organizing market data by interfacing with various data sources.
## Installation
To utilize the MDX Search Tool, ensure the `crewai_tools` package is installed. If not already present, install it using the following command:
Before using the MDX Search Tool, ensure the `crewai_tools` package is installed. If it is not, you can install it with the following command:
```shell
pip install 'crewai[tools]'
```
## Example
Configuring and using the MDX Search Tool involves setting up environment variables and utilizing the tool within a crewAI project for market research. Here's a simple example:
## Usage Example
To use the MDX Search Tool, you must first set up the necessary environment variables. Then, integrate the tool into your crewAI project to begin your market research. Below is a basic example of how to do this:
```python
from crewai_tools import MDXSearchTool
# Initialize the tool so the agent can search any MDX content if it learns about during its execution
# Initialize the tool to search any MDX content it learns about during execution
tool = MDXSearchTool()
# OR
# Initialize the tool with a specific MDX file path for exclusive search within that document
# Initialize the tool with a specific MDX file path for an exclusive search within that document
tool = MDXSearchTool(mdx='path/to/your/document.mdx')
```
## Arguments
- mdx: **Optional** The MDX path for the search. Can be provided at initialization
## Parameters
- mdx: **Optional**. Specifies the MDX file path for the search. It can be provided during initialization.
## Custom model and embeddings
## Customization of Model and Embeddings
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
The tool defaults to using OpenAI for embeddings and summarization. For customization, utilize a configuration dictionary as shown below:
```python
tool = MDXSearchTool(
config=dict(
llm=dict(
provider="ollama", # or google, openai, anthropic, llama2, ...
provider="ollama", # Options include google, openai, anthropic, llama2, etc.
config=dict(
model="llama2",
# Optional parameters can be included here.
# temperature=0.5,
# top_p=1,
# stream=true,
@@ -52,6 +53,7 @@ tool = MDXSearchTool(
config=dict(
model="models/embedding-001",
task_type="retrieval_document",
# Optional title for the embeddings can be added here.
# title="Embeddings",
),
),

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@@ -1,37 +1,39 @@
# PGSearchTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
!!! note "Under Development"
The PGSearchTool is currently under development. This document outlines the intended functionality and interface. As development progresses, please be aware that some features may not be available or could change.
## Description
This tool is designed to facilitate semantic searches within PostgreSQL database tables. Leveraging the RAG (Retrieve and Generate) technology, the PGSearchTool provides users with an efficient means of querying database table content, specifically tailored for PostgreSQL databases. It simplifies the process of finding relevant data through semantic search queries, making it an invaluable resource for users needing to perform advanced queries on extensive datasets within a PostgreSQL database.
The PGSearchTool is envisioned as a powerful tool for facilitating semantic searches within PostgreSQL database tables. By leveraging advanced Retrieve and Generate (RAG) technology, it aims to provide an efficient means for querying database table content, specifically tailored for PostgreSQL databases. The tool's goal is to simplify the process of finding relevant data through semantic search queries, offering a valuable resource for users needing to conduct advanced queries on extensive datasets within a PostgreSQL environment.
## Installation
To install the `crewai_tools` package and utilize the PGSearchTool, execute the following command in your terminal:
The `crewai_tools` package, which will include the PGSearchTool upon its release, can be installed using the following command:
```shell
pip install 'crewai[tools]'
```
## Example
Below is an example showcasing how to use the PGSearchTool to conduct a semantic search on a table within a PostgreSQL database:
(Note: The PGSearchTool is not yet available in the current version of the `crewai_tools` package. This installation command will be updated once the tool is released.)
## Example Usage
Below is a proposed example showcasing how to use the PGSearchTool for conducting a semantic search on a table within a PostgreSQL database:
```python
from crewai_tools import PGSearchTool
rom crewai_tools import PGSearchTool
# Initialize the tool with the database URI and the target table name
tool = PGSearchTool(db_uri='postgresql://user:password@localhost:5432/mydatabase', table_name='employees')
```
## Arguments
The PGSearchTool requires the following arguments for its operation:
The PGSearchTool is designed to require the following arguments for its operation:
- `db_uri`: A string representing the URI of the PostgreSQL database to be queried. This argument is mandatory and must include the necessary authentication details and the location of the database.
- `table_name`: A string specifying the name of the table within the database on which the semantic search will be performed. This argument is mandatory.
- `db_uri`: A string representing the URI of the PostgreSQL database to be queried. This argument will be mandatory and must include the necessary authentication details and the location of the database.
- `table_name`: A string specifying the name of the table within the database on which the semantic search will be performed. This argument will also be mandatory.
## Custom model and embeddings
## Custom Model and Embeddings
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
The tool intends to use OpenAI for both embeddings and summarization by default. Users will have the option to customize the model using a config dictionary as follows:
```python
tool = PGSearchTool(

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@@ -1,36 +1,44 @@
# SeleniumScrapingTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
This tool is currently in development. As we refine its capabilities, users may encounter unexpected behavior. Your feedback is invaluable to us for making improvements.
## Description
This tool is designed for efficient web scraping, enabling users to extract content from web pages. It supports targeted scraping by allowing the specification of a CSS selector for desired elements. The flexibility of the tool enables it to be used on any website URL provided by the user, making it a versatile tool for various web scraping needs.
The SeleniumScrapingTool is crafted for high-efficiency web scraping tasks. It allows for precise extraction of content from web pages by using CSS selectors to target specific elements. Its design caters to a wide range of scraping needs, offering flexibility to work with any provided website URL.
## Installation
Install the crewai_tools package
To get started with the SeleniumScrapingTool, install the crewai_tools package using pip:
```
pip install 'crewai[tools]'
```
## Example
## Usage Examples
Below are some scenarios where the SeleniumScrapingTool can be utilized:
```python
from crewai_tools import SeleniumScrapingTool
# Example 1: Scrape any website it finds during its execution
# Example 1: Initialize the tool without any parameters to scrape the current page it navigates to
tool = SeleniumScrapingTool()
# Example 2: Scrape the entire webpage
# Example 2: Scrape the entire webpage of a given URL
tool = SeleniumScrapingTool(website_url='https://example.com')
# Example 3: Scrape a specific CSS element from the webpage
# Example 3: Target and scrape a specific CSS element from a webpage
tool = SeleniumScrapingTool(website_url='https://example.com', css_element='.main-content')
# Example 4: Scrape using optional parameters for customized scraping
tool = SeleniumScrapingTool(website_url='https://example.com', css_element='.main-content', cookie={'name': 'user', 'value': 'John Doe'})
# Example 4: Perform scraping with additional parameters for a customized experience
tool = SeleniumScrapingTool(website_url='https://example.com', css_element='.main-content', cookie={'name': 'user', 'value': 'John Doe'}, wait_time=10)
```
## Arguments
- `website_url`: Mandatory. The URL of the website to scrape.
- `css_element`: Mandatory. The CSS selector for a specific element to scrape from the website.
- `cookie`: Optional. A dictionary containing cookie information. This parameter allows the tool to simulate a session with cookie information, providing access to content that may be restricted to logged-in users.
- `wait_time`: Optional. The number of seconds the tool waits after loading the website and after setting a cookie, before scraping the content. This allows for dynamic content to load properly.
The following parameters can be used to customize the SeleniumScrapingTool's scraping process:
- `website_url`: **Mandatory**. Specifies the URL of the website from which content is to be scraped.
- `css_element`: **Mandatory**. The CSS selector for a specific element to target on the website. This enables focused scraping of a particular part of a webpage.
- `cookie`: **Optional**. A dictionary that contains cookie information. Useful for simulating a logged-in session, thereby providing access to content that might be restricted to non-logged-in users.
- `wait_time`: **Optional**. Specifies the delay (in seconds) before the content is scraped. This delay allows for the website and any dynamic content to fully load, ensuring a successful scrape.
!!! attention
Since the SeleniumScrapingTool is under active development, the parameters and functionality may evolve over time. Users are encouraged to keep the tool updated and report any issues or suggestions for enhancements.

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@@ -1,40 +1,40 @@
# WebsiteSearchTool
!!! note "Experimental"
We are still working on improving tools, so there might be unexpected behavior or changes in the future.
!!! note "Experimental Status"
The WebsiteSearchTool is currently in an experimental phase. We are actively working on incorporating this tool into our suite of offerings and will update the documentation accordingly.
## Description
This tool is specifically crafted for conducting semantic searches within the content of a particular website. Leveraging a Retrieval-Augmented Generation (RAG) model, it navigates through the information provided on a given URL. Users have the flexibility to either initiate a search across any website known or discovered during its usage or to concentrate the search on a predefined, specific website.
The WebsiteSearchTool is designed as a concept for conducting semantic searches within the content of websites. It aims to leverage advanced machine learning models like Retrieval-Augmented Generation (RAG) to navigate and extract information from specified URLs efficiently. This tool intends to offer flexibility, allowing users to perform searches across any website or focus on specific websites of interest. Please note, the current implementation details of the WebsiteSearchTool are under development, and its functionalities as described may not yet be accessible.
## Installation
Install the crewai_tools package by executing the following command in your terminal:
To prepare your environment for when the WebsiteSearchTool becomes available, you can install the foundational package with:
```shell
pip install 'crewai[tools]'
```
## Example
To utilize the WebsiteSearchTool for different use cases, follow these examples:
This command installs the necessary dependencies to ensure that once the tool is fully integrated, users can start using it immediately.
## Example Usage
Below are examples of how the WebsiteSearchTool could be utilized in different scenarios. Please note, these examples are illustrative and represent planned functionality:
```python
from crewai_tools import WebsiteSearchTool
# To enable the tool to search any website the agent comes across or learns about during its operation
# Example of initiating tool that agents can use to search across any discovered websites
tool = WebsiteSearchTool()
# OR
# To restrict the tool to only search within the content of a specific website.
# Example of limiting the search to the content of a specific website, so now agents can only search within that website
tool = WebsiteSearchTool(website='https://example.com')
```
## Arguments
- `website` : An optional argument that specifies the valid website URL to perform the search on. This becomes necessary if the tool is initialized without a specific website. In the `WebsiteSearchToolSchema`, this argument is mandatory. However, in the `FixedWebsiteSearchToolSchema`, it becomes optional if a website is provided during the tool's initialization, as it will then only search within the predefined website's content.
## Custom model and embeddings
- `website`: An optional argument intended to specify the website URL for focused searches. This argument is designed to enhance the tool's flexibility by allowing targeted searches when necessary.
## Customization Options
By default, the tool uses OpenAI for both embeddings and summarization. To customize the model, you can use a config dictionary as follows:
```python
tool = WebsiteSearchTool(
config=dict(