Fix types

.gitignore

@@ -5,6 +5,4 @@ dist/
 .env
 assets/*
 .idea
-test/
-docs_crew/
-chroma.sqlite3
+test.py

README.md

@@ -24,7 +24,6 @@
 - [Key Features](#key-features)
 - [Examples](#examples)
   - [Quick Tutorial](#quick-tutorial)
-  - [Write Job Descriptions](#write-job-descriptions)
   - [Trip Planner](#trip-planner)
   - [Stock Analysis](#stock-analysis)
 - [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
@@ -49,12 +48,6 @@ To get started with CrewAI, follow these simple steps:
 pip install crewai
 ```
 
-If you want to also install crewai-tools, which is a package with tools that can be used by the agents, but more dependencies, you can install it with:
-
-```shell
-pip install 'crewai[tools]'
-```
-
 The example below also uses DuckDuckGo's Search. You can install it with `pip` too:
 
 ```shell
@@ -69,10 +62,9 @@ from crewai import Agent, Task, Crew, Process
 
 os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
 
-# You can choose to use a local model through Ollama for example. See https://docs.crewai.com/how-to/LLM-Connections/ for more information.
-# osOPENAI_API_BASE='http://localhost:11434/v1'
-# OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
-# OPENAI_API_KEY='sk-111111111111111111111111111111111111111111111111'
+# You can choose to use a local model through Ollama for example. See ./docs/how-to/llm-connections.md for more information.
+# from langchain_community.llms import Ollama
+# ollama_llm = Ollama(model="openhermes")
 
 # Install duckduckgo-search for this example:
 # !pip install -U duckduckgo-search
@@ -92,12 +84,12 @@ researcher = Agent(
   tools=[search_tool]
   # You can pass an optional llm attribute specifying what mode you wanna use.
   # It can be a local model through Ollama / LM Studio or a remote
-  # model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)
+  # model like OpenAI, Mistral, Antrophic or others (https://python.langchain.com/docs/integrations/llms/)
   #
-  # import os
-  # os.environ['OPENAI_MODEL_NAME'] = 'gpt-3.5-turbo'
+  # Examples:
   #
-  # OR
+  # from langchain_community.llms import Ollama
+  # llm=ollama_llm # was defined above in the file
   #
   # from langchain_openai import ChatOpenAI
   # llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
@@ -108,14 +100,15 @@ writer = Agent(
   backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
   You transform complex concepts into compelling narratives.""",
   verbose=True,
-  allow_delegation=True
+  allow_delegation=True,
+  # (optional) llm=ollama_llm
 )
 
 # Create tasks for your agents
 task1 = Task(
   description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
-  Identify key trends, breakthrough technologies, and potential industry impacts.""",
-  expected_output="Full analysis report in bullet points",
+  Identify key trends, breakthrough technologies, and potential industry impacts.
+  Your final answer MUST be a full analysis report""",
   agent=researcher
 )
 
@@ -123,8 +116,8 @@ task2 = Task(
   description="""Using the insights provided, develop an engaging blog
   post that highlights the most significant AI advancements.
   Your post should be informative yet accessible, catering to a tech-savvy audience.
-  Make it sound cool, avoid complex words so it doesn't sound like AI.""",
-  expected_output="Full blog post of at least 4 paragraphs",
+  Make it sound cool, avoid complex words so it doesn't sound like AI.
+  Your final answer MUST be the full blog post of at least 4 paragraphs.""",
   agent=writer
 )
 
@@ -150,9 +143,7 @@ In addition to the sequential process, you can use the hierarchical process, whi
 - **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
 - **Flexible Task Management**: Define tasks with customizable tools and assign them to agents dynamically.
 - **Processes Driven**: Currently only supports `sequential` task execution and `hierarchical` processes, but more complex processes like consensual and autonomous are being worked on.
-- **Save output as file**: Save the output of individual tasks as a file, so you can use it later.
-- **Parse output as Pydantic or Json**: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
-- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
+- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models, even ones running locally!
 
 ![CrewAI Mind Map](./docs/crewAI-mindmap.png "CrewAI Mind Map")
 
@@ -169,12 +160,6 @@ You can test different real life examples of AI crews in the [crewAI-examples re
 
 [![CrewAI Tutorial](https://img.youtube.com/vi/tnejrr-0a94/maxresdefault.jpg)](https://www.youtube.com/watch?v=tnejrr-0a94 "CrewAI Tutorial")
 
-### Write Job Descriptions
-
-[Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/job-posting) or watch a video below:
-
-[![Jobs postings](https://img.youtube.com/vi/u98wEMz-9to/maxresdefault.jpg)](https://www.youtube.com/watch?v=u98wEMz-9to "Jobs postings")
-
 ### Trip Planner
 
 [Check out code for this example](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner) or watch a video below:
@@ -195,7 +180,7 @@ Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-
 
 ## How CrewAI Compares
 
-- **Autogen**: While Autogen does good in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
+- **Autogen**: While Autogen excels in creating conversational agents capable of working together, it lacks an inherent concept of process. In Autogen, orchestrating agents' interactions requires additional programming, which can become complex and cumbersome as the scale of tasks grows.
 
 - **ChatDev**: ChatDev introduced the idea of processes into the realm of AI agents, but its implementation is quite rigid. Customizations in ChatDev are limited and not geared towards production environments, which can hinder scalability and flexibility in real-world applications.
 
@@ -267,27 +252,15 @@ There is NO data being collected on the prompts, tasks descriptions agents backs
 
 Data collected includes:
 - Version of crewAI
-  - So we can understand how many users are using the latest version
 - Version of Python
-  - So we can decide on what versions to better support
 - General OS (e.g. number of CPUs, macOS/Windows/Linux)
-  - So we know what OS we should focus on and if we could build specific OS related features
 - Number of agents and tasks in a crew
-  - So we make sure we are testing internally with similar use cases and educate people on the best practices
 - Crew Process being used
-  - Understand where we should focus our efforts
 - If Agents are using memory or allowing delegation
-  - Understand if we improved the features or maybe even drop them
 - If Tasks are being executed in parallel or sequentially
-  - Understand if we should focus more on parallel execution
 - Language model being used
-  - Improved support on most used languages
 - Roles of agents in a crew
-  - Understand high level use cases so we can build better tools, integrations and examples about it
 - Tools names available
-  - Understand out of the publically available tools, which ones are being used the most so we can improve them
-
-Users can opt-in sharing the complete telemetry data by setting the `share_crew` attribute to `True` on their Crews.
 
 ## License
 

docs/core-concepts/Agents.md

@@ -10,33 +10,29 @@ 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>
       <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. |
+| **Tools**    | Set of capabilities or functions that the agent can use to perform tasks. Tools can be shared or exclusive to specific agents. |
+| **Max Iter**    | The maximum number of iterations the agent can perform before forced to give its best answer |
+| **Max RPM**    | The maximum number of requests per minute the agent can perform to avoid rate limits |
+| **Verbose**    | This allow you to actually see what is going on during the Crew execution. |
+| **Allow Delegation**    | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. |
+
 
 ## Creating an Agent
 
 !!! note "Agent Interaction"
-    Agents can interact with each other using crewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
+    Agents can interact with each other using the CrewAI's built-in delegation and communication mechanisms.<br/>This allows for dynamic task management and problem-solving within the crew.
 
-To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example including all attributes:
+To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example:
 
 ```python
 # Example: Creating an agent with all attributes
@@ -50,15 +46,11 @@ agent = Agent(
   to the business.
   You're currently working on a project to analyze the
   performance of our marketing campaigns.""",
-  tools=[my_tool1, my_tool2],  # Optional, defaults to an empty list
-  llm=my_llm,  # Optional
-  function_calling_llm=my_llm,  # Optional
-  max_iter=15,  # Optional
-  max_rpm=None, # Optional
-  verbose=True,  # Optional
-  allow_delegation=True,  # Optional
-  step_callback=my_intermediate_step_callback,  # Optional
-  memory=True  # Optional
+  tools=[my_tool1, my_tool2],
+  max_iter=10,
+  max_rpm=10,
+  verbose=True,
+  allow_delegation=True
 )
 ```
 

docs/core-concepts/Collaboration.md

@@ -1,7 +1,6 @@
-```markdown
 ---
 title: How Agents Collaborate in CrewAI
-description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
+description: Exploring the dynamics of agent collaboration within the CrewAI framework.
 ---
 
 ## Collaboration Fundamentals
@@ -12,28 +11,14 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
 - **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
 - **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.
 
-## 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.
-- **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.
-
 ## Delegation: Dividing to Conquer
 Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
 
 ## Implementing Collaboration and Delegation
-Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation, with enhanced customization and monitoring features to adapt to various operational needs.
+Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation.
 
 ## Example Scenario
-Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.
+Imagine a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The writer can delegate research tasks or ask questions to the researcher, facilitating a seamless workflow.
 
 ## Conclusion
-The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.
+Collaboration and delegation are pivotal, transforming individual AI agents into a coherent, intelligent crew capable of tackling complex tasks. CrewAI's framework not only simplifies these interactions but enhances their effectiveness, paving the way for sophisticated AI-driven solutions.

docs/core-concepts/Crews.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Crews
 description: Understanding and utilizing crews in the crewAI framework.
@@ -10,23 +9,19 @@ description: Understanding and utilizing crews in the crewAI framework.
 
 ## 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.** |
-| **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. |
+| Attribute            | Description                                                  |
+| :------------------- | :----------------------------------------------------------- |
+| **Tasks**            | A list of tasks assigned to the crew.                        |
+| **Agents**           | A list of agents that are part of the crew.                  |
+| **Process**          | The process flow (e.g., sequential, hierarchical) the crew follows. |
+| **Verbose**          | The verbosity level for logging during execution.            |
+| **Manager LLM**      | The language model used by the manager agent in a hierarchical process. |
+| **Config**           | Configuration settings for the crew.                         |
+| **Max RPM**          | Maximum requests per minute the crew adheres to during execution. |
+| **Language**         | Language setting for the crew's operation.                   |
 
 !!! 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
 
@@ -48,45 +43,26 @@ researcher = Agent(
 
 writer = Agent(
     role='Content Writer',
-    goal='Write engaging articles on AI discoveries',
-    verbose=True
+    goal='Write engaging articles on AI discoveries'
 )
 
 # Create tasks for the agents
-research_task = Task(
-    description='Identify breakthrough AI technologies',
-    agent=researcher
-)
-write_article_task = Task(
-    description='Draft an article on the latest AI technologies',
-    agent=writer
-)
+research_task = Task(description='Identify breakthrough AI technologies', agent=researcher)
+write_article_task = Task(description='Draft an article on the latest AI technologies', agent=writer)
 
 # Assemble the crew with a sequential process
 my_crew = Crew(
     agents=[researcher, writer],
     tasks=[research_task, write_article_task],
     process=Process.sequential,
-    full_output=True,
-    verbose=True,
+    verbose=True
 )
 ```
 
-## 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.
-
-```python
-# Access the crew's usage metrics
-crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
-crew.kickoff()
-print(crew.usage_metrics)
-```
-
 ## Crew Execution Process
 
 - **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
-- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` is required for this process and it's essential for validating the process flow.
+- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding.
 
 ### Kicking Off a Crew
 
@@ -96,3 +72,4 @@ Once your crew is assembled, initiate the workflow with the `kickoff()` method.
 # Start the crew's task execution
 result = my_crew.kickoff()
 print(result)
+```

docs/core-concepts/Processes.md

@@ -1,60 +1,48 @@
 ---
 title: Managing Processes in CrewAI
-description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
+description: An overview of workflow management through processes in CrewAI.
 ---
 
 ## Understanding Processes
 !!! note "Core Concept"
-    In CrewAI, processes orchestrate the execution of tasks by agents, akin to project management in human teams. These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
+    Processes in CrewAI orchestrate how tasks are executed by agents, akin to project management in human teams. They ensure tasks are distributed and completed efficiently, according to a predefined game plan.
 
 ## 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.
+- **Sequential**: Executes tasks one after another, ensuring a linear and orderly progression.
+- **Hierarchical**: Implements a chain of command, where tasks are delegated and executed based on a managerial structure.
+- **Consensual (WIP)**: Future process type aiming for collaborative decision-making among agents on task execution.
 
 ## 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.
+Processes transform individual agents into a unified team, coordinating their efforts to achieve common goals with efficiency and harmony.
 
 ## 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.
+Specify the process during crew creation to determine the execution strategy:
 
 ```python
 from crewai import Crew
 from crewai.process import Process
-from langchain_openai import ChatOpenAI
 
 # Example: Creating a crew with a sequential process
-crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
-    process=Process.sequential
-)
+crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.sequential)
 
 # Example: Creating a crew with a hierarchical process
-# Ensure to provide a manager_llm
-crew = Crew(
-    agents=my_agents,
-    tasks=my_tasks,
-    process=Process.hierarchical,
-    manager_llm=ChatOpenAI(model="gpt-4")
-)
+crew = Crew(agents=my_agents, tasks=my_tasks, process=Process.hierarchical)
 ```
-**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, `manager_llm` is also required.
 
 ## Sequential Process
-This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
+Ensures a natural flow of work, mirroring human team dynamics by progressing through tasks thoughtfully and systematically.
 
-To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
+Tasks need to be pre-assigned to agents, and the order of execution is determined by the order of the tasks in the list.
+
+Tasks are executed one after another, ensuring a linear and orderly progression and the output of one task is automatically used as context into the next task.
+
+You can also define specific task's outputs that should be used as context for another task by using the `context` parameter in the `Task` class.
 
 ## 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.
+Mimics a corporate hierarchy, where a manager oversees task execution, planning, delegation, and validation, enhancing task coordination.
 
-## 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.
-
-## 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.
+In this process tasks don't need to be pre-assigned to agents, the manager will decide which agent will perform each task, review the output and decide if the task is completed or not.
 
 ## 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.
+Processes are vital for structured collaboration within CrewAI, enabling agents to work together systematically. Future updates will introduce new processes, further mimicking the adaptability and complexity of human teamwork.

docs/core-concepts/Tasks.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 title: crewAI Tasks
 description: Overview and management of tasks within the crewAI framework.
@@ -6,39 +5,36 @@ description: Overview and management of tasks within the crewAI framework.
 
 ## 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 individual assignments that agents complete. They encapsulate necessary information for execution, including a description, assigned agent, and required tools, offering flexibility for various 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.
 
 ## 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.  |
+| **Expected Output** *(optional)*      | 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. |
+| **Async Execution** *(optional)*      | If the task should be executed asynchronously.  |
+| **Context**  *(optional)*     | Other tasks that will have their output used as context for this task, if one is an asynchronous task it will wait for that to finish |
 | **Callback**  *(optional)*  | A function to be executed after the task is completed. |
 
 ## 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:
+This is the simpliest example for creating a task, it involves defining its scope and agent, but there are optional attributes that can provide a lot of flexibility:
 
 ```python
 from crewai import Task
 
 task = Task(
-    description='Find and summarize the latest and most relevant news on AI',
-    agent=sales_agent
+	description='Find and summarize the latest and most relevant news on AI',
+	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.
+	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.
 
 ## Integrating Tools with Tasks
 
@@ -49,33 +45,35 @@ Tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and
 ```python
 import os
 os.environ["OPENAI_API_KEY"] = "Your Key"
-os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
 
 from crewai import Agent, Task, Crew
-from crewai_tools import SerperDevTool
+from langchain.agents import Tool
+from langchain_community.tools import DuckDuckGoSearchRun
 
 research_agent = Agent(
-    role='Researcher',
-    goal='Find and summarize the latest AI news',
-    backstory="""You're a researcher at a large company.
-    You're responsible for analyzing data and providing insights
-    to the business."""
-    verbose=True
+	role='Researcher',
+	goal='Find and summarize the latest AI news',
+	backstory="""You're a researcher at a large company.
+	You're responsible for analyzing data and providing insights
+	to the business."""
+	verbose=True
 )
 
-search_tool = SerperDevTool()
+# Install duckduckgo-search for this example:
+# !pip install -U duckduckgo-search
+search_tool = DuckDuckGoSearchRun()
 
 task = Task(
   description='Find and summarize the latest AI news',
-  expected_output='A bullet list summary of the top 5 most important AI news',
-  agent=research_agent,
+	expected_output='A bullet list summary of the top 5 most important AI news',
+	agent=research_agent,
   tools=[search_tool]
 )
 
 crew = Crew(
-    agents=[research_agent],
-    tasks=[task],
-    verbose=2
+	agents=[research_agent],
+	tasks=[task],
+	verbose=2
 )
 
 result = crew.kickoff()
@@ -84,36 +82,27 @@ print(result)
 
 This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
 
-## Referring to Other Tasks
+## Refering other Tasks
 
-In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple should be used as context for another task.
+In crewAI the output of one task is automatically relayed into the next one, but you can specifically define what tasks output should be used as context for another task.
 
 This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
 
 ```python
 # ...
 
-research_ai_task = Task(
+research_task = Task(
   description='Find and summarize the latest AI news',
-  expected_output='A bullet list summary of the top 5 most important AI news',
-  async_execution=True,
-  agent=research_agent,
-  tools=[search_tool]
-)
-
-research_ops_task = Task(
-  description='Find and summarize the latest AI Ops news',
-  expected_output='A bullet list summary of the top 5 most important AI Ops news',
-  async_execution=True,
-  agent=research_agent,
+	expected_output='A bullet list summary of the top 5 most important AI news',
+	agent=research_agent,
   tools=[search_tool]
 )
 
 write_blog_task = Task(
-  description="Write a full blog post about the importance of AI and its latest news",
-  expected_output='Full blog post that is 4 paragraphs long',
-  agent=writer_agent,
-  context=[research_ai_task, research_ops_task]
+  description="Write a full blog post about the importante of AI and it's latest news",
+	expected_output='Full blog post that is 4 paragraphs long',
+	agent=writer_agent,
+  context=[research_task]
 )
 
 #...
@@ -121,7 +110,7 @@ write_blog_task = Task(
 
 ## Asynchronous Execution
 
-You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
+You can define a task to be executed asynchronously, this means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
 
 You can then use the `context` attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.
 
@@ -129,7 +118,7 @@ You can then use the `context` attribute to define in a future task that it shou
 #...
 
 list_ideas = Task(
-	description="List of 5 interesting ideas to explore for an article about AI.",
+	description="List of 5 interesting ideas to explore for na article about AI.",
 	expected_output="Bullet point list of 5 ideas for an article.",
 	agent=researcher,
 	async_execution=True # Will be executed asynchronously
@@ -143,7 +132,7 @@ list_important_history = Task(
 )
 
 write_article = Task(
-	description="Write an article about AI, its history, and interesting ideas.",
+	description="Write an article about AI, it's history and interesting ideas.",
 	expected_output="A 4 paragraph article about AI.",
 	agent=writer,
 	context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
@@ -154,7 +143,7 @@ write_article = Task(
 
 ## Callback Mechanism
 
-The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
+You can define a callback function that will be executed after the task is completed. This is useful for tasks that need to trigger some side effect after they are completed, while the crew is still running.
 
 ```python
 # ...
@@ -165,7 +154,7 @@ def callback_function(output: TaskOutput):
 	print(f"""
 		Task completed!
 		Task: {output.description}
-		Output: {output.raw_output}
+		Output: {output.result}
 	""")
 
 research_task = Task(
@@ -179,7 +168,7 @@ research_task = Task(
 #...
 ```
 
-## Accessing a Specific Task Output
+## Accessing a specific Task Output
 
 Once a crew finishes running, you can access the output of a specific task by using the `output` attribute of the task object:
 
@@ -206,23 +195,18 @@ result = crew.kickoff()
 print(f"""
 	Task completed!
 	Task: {task1.output.description}
-	Output: {task1.output.raw_output}
+	Output: {task1.output.result}
 """)
 ```
 
+
+
+
 ## Tool Override Mechanism
 
 Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
 
-## Error Handling and Validation Mechanisms
-
-While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:
-
-- Ensuring only one output type is set per task to maintain clear output expectations.
-- Preventing the manual assignment of the `id` attribute to uphold the integrity of the unique identifier system.
-
-These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
-
 ## Conclusion
 
-Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit. Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.
+Tasks are the driving force behind the actions of agents in crewAI. By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit.
+Equipping tasks with appropriate tools is crucial for maximizing CrewAI's potential, ensuring agents are effectively prepared for their assignments.

docs/core-concepts/Tools.md

@@ -1,212 +1,65 @@
-```
 ---
 title: crewAI Tools
-description: Understanding and leveraging tools within the crewAI framework for agent collaboration and task execution.
+description: Understanding and leveraging tools within the crewAI framework.
 ---
 
-## Introduction
-CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers. This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
-
 ## What is a Tool?
 !!! note "Definition"
-    A tool in CrewAI is a skill or function that agents can utilize to perform various actions. This includes tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), enabling everything from simple searches to complex interactions and effective teamwork among agents.
+    A tool in CrewAI, is a skill, something Agents can use perform tasks, right now those can be tools from the [crewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools), those are basically functions that an agent can utilize for various actions, from simple searches to complex interactions with external systems.
 
 ## Key Characteristics of Tools
 
-- **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.
-
-## Using crewAI Tools
-
-To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
-
-```bash
-pip install 'crewai[tools]'
-```
-
-Here's an example demonstrating their use:
-
-```python
-import os
-from crewai import Agent, Task, Crew
-# Importing crewAI tools
-from crewai_tools import (
-    DirectoryReadTool,
-    FileReadTool,
-    SerperDevTool,
-    WebsiteSearchTool
-)
-
-# Set up API keys
-os.environ["SERPER_API_KEY"] = "Your Key"
-os.environ["OPENAI_API_KEY"] = "Your Key"
-
-# Instantiate tools
-docs_tool = DirectoryReadTool(directory='./blog-posts')
-file_tool = FileReadTool()
-search_tool = SeperDevTool()
-web_rag_tool = WebsiteSearchTool()
-
-# Create agents
-researcher = Agent(
-    role='Market Research Analyst',
-    goal='Provide up-to-date market analysis of the AI industry',
-    backstory='An expert analyst with a keen eye for market trends.',
-    tools=[search_tool, website_rag],
-    verbose=True
-)
-
-writer = Agent(
-    role='Content Writer',
-    goal='Craft engaging blog posts about the AI industry',
-    backstory='A skilled writer with a passion for technology.',
-    tools=[docs_tool, file_tool],
-    verbose=True
-)
-
-# Define tasks
-research = Task(
-    description='Research the latest trends in the AI industry and provide a summary.',
-    expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
-    agent=researcher
-)
-
-write = Task(
-    description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
-    expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
-    agent=writer,
-    output_file='blog-posts/new_post.md'  # The final blog post will be saved here
-)
-
-# Assemble a crew
-crew = Crew(
-    agents=[researcher, writer],
-    tasks=[research, write],
-    verbose=2
-)
-
-# Execute tasks
-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:
-
-```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.|
-| **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.          |
-| **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.      |
-| **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.    |
-| **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
-| **RagTool**                 | A general-purpose RAG tool capable of handling various data sources and types.                 |
-| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction.     |
-| **ScrapeWebsiteTool**       | Facilitates scraping entire websites, ideal for comprehensive data collection.                 |
-| **WebsiteSearchTool**       | A RAG tool for searching website content, optimized for web data extraction.                   |
-| **XMLSearchTool**           | A RAG tool designed for searching within XML files, suitable for structured data formats.      |
-| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis.           |
-| **YoutubeVideoSearchTool**  | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction.          |
+- **Utility**: Designed for specific tasks such as web searching, data analysis, or content generation.
+- **Integration**: Enhance agent capabilities by integrating tools directly into their workflow.
+- **Customizability**: Offers the flexibility to develop custom tools or use existing ones from LangChain's ecosystem.
 
 ## 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
-pip install 'crewai[tools]'
-```
-
-Once you do that there are two main ways for one to create a crewAI tool:
-
-### Subclassing `BaseTool`
-
-```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, you agent will need this information to use it."
-
-    def _run(self, argument: str) -> str:
-        # Implementation goes here
-        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
-```
+    Developers can craft custom tools tailored for their agent’s needs or utilize pre-built options. Here’s how to create one:
 
 ```python
 import json
 import requests
 from crewai import Agent
-from crewai.tools import tool
+from langchain.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 integtation_tool(argument: str) -> str:
-    """Integration with a given API"""
-    # Code here
-    return resutls # string to be sent back to the agent
+class BrowserTools():
+
+	# Anotate the fuction with the tool decorator from LangChain
+	@tool("Scrape website content")
+	def scrape_website(website):
+		# Write logic for the tool.
+		# In this case a function to scrape website content
+		url = f"https://chrome.browserless.io/content?token={config('BROWSERLESS_API_KEY')}"
+		payload = json.dumps({"url": website})
+		headers = {'cache-control': 'no-cache', 'content-type': 'application/json'}
+		response = requests.request("POST", url, headers=headers, data=payload)
+		elements = partition_html(text=response.text)
+		content = "\n\n".join([str(el) for el in elements])
+		return content[:5000]
 
 # 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=[integtation_tool]
+	role='Research Analyst',
+	goal='Provide up-to-date market analysis',
+	backstory='An expert analyst with a keen eye for market trends.',
+	tools=[BrowserTools().scrape_website]
 )
 ```
 
 ## Using LangChain Tools
 !!! info "LangChain Integration"
-    CrewAI seamlessly integrates with LangChain’s comprehensive toolkit for search-based queries and more, here are the available built-in tools that are offered by Langchain [LangChain Toolkit](https://python.langchain.com/docs/integrations/tools/)
- :
+    CrewAI seamlessly integrates with LangChain’s comprehensive toolkit. Assigning an existing tool to an agent is straightforward:
 
 ```python
 from crewai import Agent
 from langchain.agents import Tool
 from langchain.utilities import GoogleSerperAPIWrapper
+import os
 
 # Setup API keys
+os.environ["OPENAI_API_KEY"] = "Your Key"
 os.environ["SERPER_API_KEY"] = "Your Key"
 
 search = GoogleSerperAPIWrapper()
@@ -224,9 +77,7 @@ agent = Agent(
   backstory='An expert analyst with a keen eye for market trends.',
   tools=[serper_tool]
 )
-
-# rest of the code ...
 ```
 
 ## 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 crucial for extending the capabilities of CrewAI agents, allowing them to undertake a diverse array of tasks and collaborate efficiently. When building your AI solutions with CrewAI, consider both custom and existing tools to empower your agents and foster a dynamic AI ecosystem.

docs/how-to/Create-Custom-Tools.md

@@ -1,68 +0,0 @@
----
-title: Creating your own Tools
-description: Guide on how to create and use custom tools within the crewAI framework.
----
-
-## 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
-pip install 'crewai[tools]'
-```
-
-Once you do that there are two main ways for one to create a crewAI tool:
-
-### Subclassing `BaseTool`
-
-```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, you agent will need this information to use it."
-
-    def _run(self, argument: str) -> str:
-        # Implementation goes here
-        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 integtation_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=[integtation_tool]
-)
-```

docs/how-to/Creating-a-Crew-and-kick-it-off.md

@@ -1,118 +1,112 @@
 ---
 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 step-by-step guide to creating a cohesive CrewAI team for your projects.
 ---
 
 ## 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.
+Embarking on your CrewAI journey involves a few straightforward steps to set up your environment and initiate your AI crew. This guide ensures a seamless start.
 
 ## Step 0: Installation
-Install CrewAI and any necessary packages for your project.
+Begin by installing CrewAI and any additional packages required for your project. For instance, the `duckduckgo-search` package is used in this example for enhanced search capabilities.
 
 ```shell
 pip install crewai
-pip install 'crewai[tools]'
+pip install duckduckgo-search
 ```
 
 ## 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.
+Begin by defining your agents with distinct roles and backstories. These elements not only add depth but also 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["OPENAI_API_KEY"] = "Your Key"
 
 from crewai import Agent
-from crewai_tools import SerperDevTool
-search_tool = SerperDevTool()
 
-# Creating a senior researcher agent with memory and verbose mode
+# Topic that will be used in the crew run
+topic = 'AI in healthcare'
+
+# Creating a senior researcher agent
 researcher = Agent(
   role='Senior Researcher',
-  goal='Uncover groundbreaking technologies in {topic}',
+  goal=f'Uncover groundbreaking technologies around {topic}',
   verbose=True,
-  memory=True,
-  backstory=(
-    "Driven by curiosity, you're at the forefront of"
-    "innovation, eager to explore and share knowledge that could change"
-    "the world."
-  ),
-  tools=[search_tool],
-  allow_delegation=True
+  backstory="""Driven by curiosity, you're at the forefront of
+  innovation, eager to explore and share knowledge that could change
+  the world."""
 )
 
-# Creating a writer agent with custom tools and delegation capability
+# Creating a writer agent
 writer = Agent(
   role='Writer',
-  goal='Narrate compelling tech stories about {topic}',
+  goal=f'Narrate compelling tech stories around {topic}',
   verbose=True,
-  memory=True,
-  backstory=(
-    "With a flair for simplifying complex topics, you craft"
-    "engaging narratives that captivate and educate, bringing new"
-    "discoveries to light in an accessible manner."
-  ),
-  tools=[search_tool],
-  allow_delegation=False
+  backstory="""With a flair for simplifying complex topics, you craft
+  engaging narratives that captivate and educate, bringing new
+  discoveries to light in an accessible manner."""
 )
 ```
 
 ## Step 2: Define the Tasks
-Detail the specific objectives for your agents, including new features for asynchronous execution and output customization. These tasks ensure a targeted approach to their roles.
+Detail the specific objectives for your agents. These tasks guide their focus and ensure a targeted approach to their roles.
 
 ```python
 from crewai import Task
 
-# Research task
+# Install duckduckgo-search for this example:
+# !pip install -U duckduckgo-search
+
+from langchain_community.tools import DuckDuckGoSearchRun
+search_tool = DuckDuckGoSearchRun()
+
+# Research task for identifying AI trends
 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"
-    "its market opportunities, and potential risks."
-  ),
+  description=f"""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,
+  its market opportunities, and potential risks.
+  """,
   expected_output='A comprehensive 3 paragraphs long report on the latest AI trends.',
+  max_inter=3,
   tools=[search_tool],
-  agent=researcher,
+  agent=researcher
 )
 
-# Writing task with language model configuration
+# Writing task based on research findings
 write_task = Task(
-  description=(
-    "Compose an insightful article on {topic}."
-    "Focus on the latest trends and how it's impacting the industry."
-    "This article should be easy to understand, engaging, and positive."
-  ),
-  expected_output='A 4 paragraph article on {topic} advancements formatted as markdown.',
+  description=f"""Compose an insightful article on {topic}.
+  Focus on the latest trends and how it's impacting the industry.
+  This article should be easy to understand, engaging and positive.
+  """,
+  expected_output=f'A 4 paragraph article on {topic} advancements.',
   tools=[search_tool],
-  agent=writer,
-  async_execution=False,
-  output_file='new-blog-post.md'  # Example of output customization
+  agent=writer
 )
 ```
 
 ## 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.
 
 ```python
 from crewai import Crew, Process
 
-# Forming the tech-focused crew with enhanced configurations
+# Forming the tech-focused crew
 crew = Crew(
   agents=[researcher, writer],
   tasks=[research_task, write_task],
-  process=Process.sequential  # Optional: Sequential task execution is default
+  process=Process.sequential  # Sequential task execution
 )
 ```
 
 ## 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.
+With your crew ready and the stage set, initiate the process. Watch as your agents collaborate, each contributing their expertise to achieve the collective goal.
 
 ```python
-# Starting the task execution process with enhanced feedback
-result = crew.kickoff(inputs={'topic': 'AI in healthcare'})
+# Starting the task execution process
+result = crew.kickoff()
 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 is a seamless process. By carefully assigning roles, tasks, and a clear process, your AI team is equipped to tackle challenges efficiently. The depth of agent backstories and the precision of their objectives enrich the collaboration, leading to successful project outcomes.

docs/how-to/Customizing-Agents.md

@@ -1,73 +1,55 @@
 ---
 title: Customizing Agents in CrewAI
-description: A comprehensive guide to tailoring agents for specific roles, tasks, and advanced customizations within the CrewAI framework.
+description: A guide to tailoring agents for specific roles and tasks within the CrewAI framework.
 ---
 
 ## 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.
+Tailoring your AI agents is pivotal in crafting an efficient CrewAI team. Customization allows agents to be dynamically adapted to the unique requirements of any project.
 
 ### Key Attributes for Customization
-- **Role**: Specifies the agent's job within the crew, such as 'Analyst' or 'Customer Service Rep'.
-- **Goal**: Defines what the agent aims to achieve, in alignment with its role and the overarching objectives of the crew.
-- **Backstory**: Provides depth to the agent's persona, enriching its motivations and engagements within the crew.
-- **Tools**: Represents the capabilities or methods the agent uses to perform tasks, from simple functions to intricate integrations.
+- **Role**: Defines the agent's job within the crew, such as 'Analyst' or 'Customer Service Rep'.
+- **Goal**: The agent's objective, aligned with its role and the crew's overall goals.
+- **Backstory**: Adds depth to the agent's character, enhancing its role and motivations within the crew.
+- **Tools**: The capabilities or methods the agent employs to accomplish tasks, ranging from simple functions to complex integrations.
 
-## Advanced Customization Options
-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.
-
-## 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.
-
-### 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.
+## Understanding Tools in CrewAI
+Tools empower agents with functionalities to interact and manipulate their environment, from generic utilities to specialized functions. Integrating with LangChain offers access to a broad range of tools for diverse tasks.
 
 ## 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.
-
-```shell
-pip install 'crewai[tools]'
-```
+Agents are customized by defining their attributes during initialization, with tools being a critical aspect of their functionality.
 
 ### Example: Assigning Tools to an Agent
 ```python
-import os
 from crewai import Agent
-from crewai_tools import SerperDevTool
+from langchain.agents import Tool
+from langchain.utilities import GoogleSerperAPIWrapper
+import os
 
 # Set API keys for tool initialization
 os.environ["OPENAI_API_KEY"] = "Your Key"
 os.environ["SERPER_API_KEY"] = "Your Key"
 
 # Initialize a search tool
-search_tool = SerperDevTool()
+search_tool = GoogleSerperAPIWrapper()
 
-# Initialize the agent with advanced options
+# Define and assign the tool to an agent
+serper_tool = Tool(
+  name="Intermediate Answer",
+  func=search_tool.run,
+  description="Useful for search-based queries"
+)
+
+# Initialize the agent with the tool
 agent = Agent(
   role='Research Analyst',
   goal='Provide up-to-date market analysis',
   backstory='An expert analyst with a keen eye for market trends.',
-  tools=[search_tool],
-  memory=True,
-  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
-  allow_delegation=False
+  tools=[serper_tool]
 )
 ```
 
 ## 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.
+Agents in CrewAI can delegate tasks or ask questions, enhancing the crew's collaborative dynamics. This feature can be disabled to ensure straightforward task execution.
 
 ### Example: Disabling Delegation for an Agent
 ```python
@@ -80,4 +62,4 @@ agent = Agent(
 ```
 
 ## Conclusion
-Customizing agents in CrewAI by setting their roles, goals, backstories, and tools, alongside advanced options like language model customization, memory, performance settings, and delegation preferences, equips a nuanced and capable AI team ready for complex challenges.
+Customizing agents is key to leveraging the full potential of CrewAI. By thoughtfully setting agents' roles, goals, backstories, and tools, you craft a nuanced and capable AI team ready to tackle complex challenges.

docs/how-to/Hierarchical.md

@@ -1,61 +1,60 @@
 ---
 title: Implementing the Hierarchical Process in CrewAI
-description: A comprehensive guide to understanding and applying the hierarchical process within your CrewAI projects, updated to reflect the latest coding practices and functionalities.
+description: Understanding and applying the hierarchical process within your CrewAI projects.
 ---
 
 ## Introduction
-The hierarchical process in CrewAI introduces a structured approach to task management, simulating traditional organizational hierarchies for efficient task delegation and execution. This systematic workflow enhances project outcomes by ensuring tasks are handled with optimal efficiency and accuracy.
+The hierarchical process in CrewAI introduces a structured approach to task management, mimicking traditional organizational hierarchies for efficient task delegation and execution.
 
-!!! note "Complexity and Efficiency"
-    The hierarchical process is designed to leverage advanced models like GPT-4, optimizing token usage while handling complex tasks with greater efficiency.
+!!! note "Complexity"
+	The current implementation of the hierarchical process relies on tools usage that usually require more complex models like GPT-4 and usually imply of a higher token usage.
 
 ## Hierarchical Process Overview
-By default, tasks in CrewAI are managed through a sequential process. However, adopting a hierarchical approach allows for a clear hierarchy in task management, where a 'manager' agent coordinates the workflow, delegates tasks, and validates outcomes for streamlined and effective execution. This manager agent is automatically created by crewAI so you don't need to worry about it.
+In this process, tasks are assigned and executed based on a defined hierarchy, where a 'manager' agent coordinates the workflow, delegating tasks to other agents and validating their outcomes before proceeding.
 
 ### Key Features
-- **Task Delegation**: A manager agent allocates tasks among crew members based on their roles and capabilities.
-- **Result Validation**: The manager evaluates outcomes to ensure they meet the required standards.
-- **Efficient Workflow**: Emulates corporate structures, providing an organized approach to task management.
+- **Task Delegation**: A manager agent oversees task distribution among crew members.
+- **Result Validation**: The manager reviews outcomes before passing tasks along, ensuring quality and relevance.
+- **Efficient Workflow**: Mimics corporate structures for a familiar and organized task management approach.
 
 ## Implementing the Hierarchical Process
-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.
+To utilize the hierarchical process, you must define a crew with a designated manager and a clear chain of command for task execution.
 
-!!! 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.
+!!! note "Tools on the hierarchical process"
+	For tools when using the hierarchical process, you want to make sure to assign them to the agents instead of the tasks, as the manager will be the one delegating the tasks and the agents will be the ones executing them.
 
-!!! 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.
+!!! note "Manager LLM"
+	A manager will be automatically set for the crew, you don't need to define it. You do need to set the `manager_llm` parameter in the crew though.
 
 ```python
 from langchain_openai import ChatOpenAI
 from crewai import Crew, Process, Agent
 
-# Agents are defined with an optional tools parameter
+# Define your agents, no need to define a manager
 researcher = Agent(
-    role='Researcher',
-    goal='Conduct in-depth analysis',
-    # tools=[]  # This can be optionally specified; defaults to an empty list
+	role='Researcher',
+	goal='Conduct in-depth analysis',
+	# tools = [...]
 )
 writer = Agent(
-    role='Writer',
-    goal='Create engaging content',
-    # tools=[]  # Optionally specify tools; defaults to an empty list
+	role='Writer',
+	goal='Create engaging content',
+	# tools = [...]
 )
 
-# Establishing the crew with a hierarchical process
+# Form the crew with a hierarchical process
 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
+	tasks=[...], # Tasks that that manager will figure out how to complete
+	agents=[researcher, writer],
+	manager_llm=ChatOpenAI(temperature=0, model="gpt-4"), # The manager's LLM that will be used internally
+	process=Process.hierarchical  # Designating the hierarchical approach
 )
 ```
 
 ### 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.
-3. **Sequential Task Progression**: Despite being a hierarchical process, tasks follow a logical order for smooth progression, facilitated by the manager's oversight.
-
+1. **Task Assignment**: The manager assigns tasks based on agent roles and capabilities.
+2. **Execution and Review**: Agents perform their tasks, with the manager reviewing outcomes for approval.
+3. **Sequential Task Progression**: Tasks are completed in a sequence dictated by the manager, ensuring orderly progression.
 
 ## 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.
+The hierarchical process in CrewAI offers a familiar, structured way to manage tasks within a project. By leveraging a chain of command, it enhances efficiency and quality control, making it ideal for complex projects requiring meticulous oversight.

docs/how-to/Human-Input-on-Execution.md

@@ -1,77 +1,63 @@
-```markdown
-# Human Input in Agent Execution
+# Human Input on 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 inputs is important in many agent execution use cases, humans are AGI so they can can be prompted to step in and provide extra details ins necessary.
+Using it with crewAI is pretty straightforward and you can do it through a LangChain Tool.
+Check [LangChain Integration](https://python.langchain.com/docs/integrations/tools/human_tools) for more details:
 
-## Using Human Input with CrewAI
-
-Incorporating human input with CrewAI is straightforward, enhancing the agent's ability to make informed decisions. While the documentation previously mentioned using a "LangChain Tool" and a specific "DuckDuckGoSearchRun" tool from `langchain_community.tools`, it's important to clarify that the integration of such tools should align with the actual capabilities and configurations defined within your `Agent` class setup.
-
-### Example:
-
-```shell
-pip install crewai
-pip install 'crewai[tools]'
-```
+Example:
 
 ```python
 import os
-from crewai import Agent, Task, Crew
-from crewai_tools import SerperDevTool
-
+from crewai import Agent, Task, Crew, Process
+from langchain_community.tools import DuckDuckGoSearchRun
 from langchain.agents import load_tools
 
-os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
-os.environ["OPENAI_API_KEY"] = "Your Key"
-
+search_tool = DuckDuckGoSearchRun()
 
 # Loading Human Tools
 human_tools = load_tools(["human"])
-search_tool = SerperDevTool()
 
-# Define your agents with roles, goals, and tools
+# Define your agents with roles and goals
 researcher = Agent(
   role='Senior Research Analyst',
-  goal='Uncover cutting-edge developments in AI and data science',
-  backstory=(
-    "You are a Senior Research Analyst at a leading tech think tank."
-    "Your expertise lies in identifying emerging trends and technologies in AI and data science."
-    "You have a knack for dissecting complex data and presenting actionable insights."
-  ),
+  goal='Uncover cutting-edge developments in AI and data science in',
+  backstory="""You are a Senior Research Analyst at a leading tech think tank.
+  Your expertise lies in identifying emerging trends and technologies in AI and
+  data science. You have a knack for dissecting complex data and presenting
+  actionable insights.""",
   verbose=True,
   allow_delegation=False,
-  tools=[search_tool]+human_tools # Passing human tools to the agent
+  # Passing human tools to the agent
+  tools=[search_tool]+human_tools
 )
 writer = Agent(
   role='Tech Content Strategist',
   goal='Craft compelling content on tech advancements',
-  backstory=(
-    "You are a renowned Tech Content Strategist, known for your insightful and engaging articles on technology and innovation."
-    "With a deep understanding of the tech industry, you transform complex concepts into compelling narratives."
-  ),
+  backstory="""You are a renowned Tech Content Strategist, known for your insightful
+  and engaging articles on technology and innovation. With a deep understanding of
+  the tech industry, you transform complex concepts into compelling narratives.""",
   verbose=True,
   allow_delegation=True
 )
 
 # Create tasks for your agents
+# Being explicit on the task to ask for human feedback.
 task1 = Task(
-  description=(
-    "Conduct a comprehensive analysis of the latest advancements in AI in 2024."
-    "Identify key trends, breakthrough technologies, and potential industry impacts."
-    "Compile your findings in a detailed report."
-    "Make sure to check with a human if the draft is good before finalizing your answer."
-  ),
-  expected_output='A comprehensive full report on the latest AI advancements in 2024, leave nothing out',
-  agent=researcher,
+  description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
+  Identify key trends, breakthrough technologies, and potential industry impacts.
+  Compile your findings in a detailed report.
+  Make sure to check with the human if the draft is good before returning your Final Answer.
+  Your final answer MUST be a full analysis report""",
+  agent=researcher
 )
 
 task2 = Task(
-  description=(
-    "Using the insights from the researcher's report, develop an engaging blog post that highlights the most significant AI advancements."
-    "Your post should be informative yet accessible, catering to a tech-savvy audience."
-    "Aim for a narrative that captures the essence of these breakthroughs and their implications for the future."
-  ),
-  expected_output='A compelling 3 paragraphs blog post formatted as markdown about the latest AI advancements in 2024',
+  description="""Using the insights from the researcher's report, develop an engaging blog
+  post that highlights the most significant AI advancements.
+  Your post should be informative yet accessible, catering to a tech-savvy audience.
+  Aim for a narrative that captures the essence of these breakthroughs and their
+  implications for the future.
+  Your final answer MUST be the full blog post of at least 3 paragraphs.""",
   agent=writer
 )
 

docs/how-to/LLM-Connections.md

@@ -1,82 +1,80 @@
 ---
 title: Connect CrewAI to LLMs
-description: Comprehensive guide on integrating CrewAI with various Large Language Models (LLMs), including detailed class attributes and methods.
+description: Guide on integrating CrewAI with various Large Language Models (LLMs).
 ---
 
 ## 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. 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.
 
 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.
 
-## CrewAI Agent Overview
-The `Agent` class is the cornerstone for implementing AI solutions in CrewAI. Here's an updated overview reflecting the latest codebase changes:
+## Ollama Integration
+Ollama is preferred for local LLM integration, offering customization and privacy benefits. It requires installation and configuration, including model adjustments via a Modelfile to optimize performance.
 
-- **Attributes**:
-    - `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.
-    - `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.
-    - `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.
+### Setting Up Ollama
+- **Installation**: Follow Ollama's guide for setup.
+- **Configuration**: [Adjust your local model with a Modelfile](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md), considering adding `Observation` as a stop word and playing with parameters like `top_p` and `temperature`.
+
+### Integrating Ollama with CrewAI
+Instantiate Ollama and pass it to your agents within CrewAI, enhancing them with the local model's capabilities.
 
 ```python
-# Required
-os.environ["OPENAI_MODEL_NAME"]="gpt-4-0125-preview"
+from langchain_community.llms import Ollama
 
-# Agent will automatically use the model defined in the environment variable
-example_agent = Agent(
+# Assuming you have Ollama installed and downloaded the openhermes model
+ollama_openhermes = Ollama(model="openhermes")
+
+local_expert = Agent(
   role='Local Expert',
   goal='Provide insights about the city',
   backstory="A knowledgeable local guide.",
+  tools=[SearchTools.search_internet, BrowserTools.scrape_and_summarize_website],
+  llm=ollama_openhermes,
   verbose=True
 )
 ```
 
-## Ollama Integration
-Ollama is preferred for local LLM integration, offering customization and privacy benefits. To integrate Ollama with CrewAI, set the appropriate environment variables as shown below. Note: Detailed Ollama setup is beyond this document's scope, but general guidance is provided.
-
-### Setting Up Ollama
-- **Environment Variables Configuration**: To integrate Ollama, set the following environment variables:
-```sh
-OPENAI_API_BASE='http://localhost:11434/v1'
-OPENAI_MODEL_NAME='openhermes'  # Adjust based on available model
-OPENAI_API_KEY=''
-```
-
 ## OpenAI Compatible API Endpoints
-Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, and Mistral AI.
+You can use environment variables for easy switch between APIs and models, supporting diverse platforms like FastChat, LM Studio, and Mistral AI.
 
 ### Configuration Examples
-#### FastChat
+
+### FastChat
 ```sh
+# Required
 OPENAI_API_BASE="http://localhost:8001/v1"
-OPENAI_MODEL_NAME='oh-2.5m7b-q51'
 OPENAI_API_KEY=NA
+MODEL_NAME='oh-2.5m7b-q51' # Depending on the model you have available
 ```
 
-#### LM Studio
+### LM Studio
 ```sh
+# Required
 OPENAI_API_BASE="http://localhost:8000/v1"
-OPENAI_MODEL_NAME=NA
 OPENAI_API_KEY=NA
+MODEL_NAME=NA
 ```
 
-#### Mistral API
+### Mistral API
 ```sh
 OPENAI_API_KEY=your-mistral-api-key
 OPENAI_API_BASE=https://api.mistral.ai/v1
-OPENAI_MODEL_NAME="mistral-small"
+MODEL_NAME="mistral-small" # Check documentation for available models
 ```
 
-### Azure Open AI Configuration
-For Azure OpenAI API integration, set the following environment variables:
+### text-gen-web-ui
+```sh
+# Required
+API_BASE_URL=http://localhost:5000
+OPENAI_API_KEY=NA
+MODEL_NAME=NA
+```
+
+### Azure Open AI
+Azure's OpenAI API needs a distinct setup, utilizing the `langchain_openai` component for Azure-specific configurations.
+
+Configuration settings:
 ```sh
 AZURE_OPENAI_VERSION="2022-12-01"
 AZURE_OPENAI_DEPLOYMENT=""
@@ -84,24 +82,22 @@ AZURE_OPENAI_ENDPOINT=""
 AZURE_OPENAI_KEY=""
 ```
 
-### Example Agent with Azure LLM
 ```python
 from dotenv import load_dotenv
-from crewai import Agent
 from langchain_openai import AzureChatOpenAI
 
 load_dotenv()
 
-azure_llm = AzureChatOpenAI(
+default_llm = AzureChatOpenAI(
     azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
     api_key=os.environ.get("AZURE_OPENAI_KEY")
 )
 
-azure_agent = Agent(
+example_agent = Agent(
   role='Example Agent',
   goal='Demonstrate custom LLM configuration',
   backstory='A diligent explorer of GitHub docs.',
-  llm=azure_llm
+  llm=default_llm
 )
 ```
 

docs/how-to/Sequential.md

@@ -1,47 +1,37 @@
 ---
-title: Using the Sequential Processes in crewAI
-description: A comprehensive guide to utilizing the sequential processe for task execution in crewAI projects.
+title: Implementing the Sequential Process in CrewAI
+description: A guide to utilizing the sequential process for task execution in CrewAI projects.
 ---
 
 ## Introduction
-CrewAI offers a flexible framework for executing tasks in a structured manner, supporting both sequential and hierarchical processes. This guide outlines how to effectively implement these processes to ensure efficient task execution and project completion.
+The sequential process in CrewAI ensures tasks are executed one after the other, following a linear progression. This approach is akin to a relay race, where each agent completes their task before passing the baton to the next.
 
 ## Sequential Process Overview
-The sequential process ensures tasks are executed one after the other, following a linear progression. This approach is ideal for projects requiring tasks to be completed in a specific order.
+This process is straightforward and effective, particularly for projects where tasks must be completed in a specific order to achieve the desired outcome.
 
 ### Key Features
-- **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.
-
+- **Linear Task Flow**: Tasks are handled in a predetermined sequence, ensuring orderly progression.
+- **Simplicity**: Ideal for projects with clearly defined, step-by-step tasks.
+- **Easy Monitoring**: Task completion can be easily tracked, offering clear insights into project progress.
 
 ## Implementing the Sequential Process
-Assemble your crew and define tasks in the order they need to be executed.
+To apply the sequential process, assemble your crew and define the tasks in the order they need to be executed.
+
+!!! note "Task assignment"
+	In the sequential process you need to make sure all tasks are assigned to the agents, as the agents will be the ones executing them.
 
 ```python
 from crewai import Crew, Process, Agent, Task
 
 # Define your agents
-researcher = Agent(
-  role='Researcher',
-  goal='Conduct foundational research',
-  backstory='An experienced researcher with a passion for uncovering insights'
-)
-analyst = Agent(
-  role='Data Analyst',
-  goal='Analyze research findings',
-  backstory='A meticulous analyst with a knack for uncovering patterns'
-)
-writer = Agent(
-  role='Writer',
-  goal='Draft the final report',
-  backstory='A skilled writer with a talent for crafting compelling narratives'
-)
+researcher = Agent(role='Researcher', goal='Conduct foundational research')
+analyst = Agent(role='Data Analyst', goal='Analyze research findings')
+writer = Agent(role='Writer', goal='Draft the final report')
 
 # Define the tasks in sequence
-research_task = Task(description='Gather relevant data...', agent=researcher)
-analysis_task = Task(description='Analyze the data...', agent=analyst)
-writing_task = Task(description='Compose the report...', agent=writer)
+research_task = Task(description='Gather relevant data', agent=researcher)
+analysis_task = Task(description='Analyze the data', agent=analyst)
+writing_task = Task(description='Compose the report', agent=writer)
 
 # Form the crew with a sequential process
 report_crew = Crew(
@@ -52,9 +42,9 @@ report_crew = Crew(
 ```
 
 ### Workflow in Action
-1. **Initial Task**: In a sequential process, the first agent completes their task and signals completion.
-2. **Subsequent Tasks**: Agents pick up their tasks based on the process type, with outcomes of preceding tasks or manager directives guiding their execution.
-3. **Completion**: The process concludes once the final task is executed, leading to project completion.
+1. **Initial Task**: The first agent completes their task and signals completion.
+2. **Subsequent Tasks**: Following agents pick up their tasks in the order defined, using the outcomes of preceding tasks as inputs.
+3. **Completion**: The process concludes once the final task is executed, culminating in the project's 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.

docs/index.md

@@ -44,12 +44,7 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                 </a>
             </li>
             <li>
-                <a href="./how-to/Create Custom Tools">
-                    Create Custom Tools
-                </a>
-            </li>
-            <li>
-                <a href="./how-to/Sequential">
+                <a href="./how-to/how-to/Sequential">
                     Using Sequential Process
                 </a>
             </li>

docs/telemetry/Telemetry.md

@@ -1,32 +1,17 @@
-```markdown
 ## Telemetry
 
-CrewAI utilizes anonymous telemetry to gather usage statistics with the primary goal of enhancing the library. Our focus is on improving and developing the features, integrations, and tools most utilized by our users.
+CrewAI uses anonymous telemetry to collect usage data with the main purpose of helping us improve the library by focusing our efforts on the most used features, integrations and tools.
 
-It's pivotal to understand that **NO data is collected** concerning prompts, task descriptions, agents' backstories or goals, usage of tools, API calls, responses, any data processed by the agents, or secrets and environment variables, with the exception of the conditions mentioned. When the `share_crew` feature is enabled, detailed data including task descriptions, agents' backstories or goals, and other specific attributes are collected to provide deeper insights while respecting user privacy.
+There is NO data being collected on the prompts, tasks descriptions agents backstories or goals nor tools usage, no API calls, nor responses nor any data that is being processed by the agents, nor any secrets and env vars.
 
-### Data Collected Includes:
-- **Version of CrewAI**: Assessing the adoption rate of our latest version helps us understand user needs and guide our updates.
-- **Python Version**: Identifying the Python versions our users operate with assists in prioritizing our support efforts for these versions.
-- **General OS Information**: Details like the number of CPUs and the operating system type (macOS, Windows, Linux) enable us to focus our development on the most used operating systems and explore the potential for OS-specific features.
-- **Platform-Specific Information**: Collection of `platform`, `platform_release`, `platform_system`, and `platform_version` provides a comprehensive overview of the users' operating systems.
-- **Number of Agents and Tasks in a Crew**: Ensures our internal testing mirrors real-world scenarios, helping us guide users towards best practices.
-- **Crew Process Utilization**: Understanding how crews are utilized aids in directing our development focus. Attributes such as `crew_process` including execution details are collected for this purpose.
-- **Memory and Delegation Use by Agents**: Insights into how these features are used help evaluate their effectiveness and future.
-- **Task Execution Mode**: Knowing whether tasks are executed in parallel or sequentially influences our emphasis on enhancing parallel execution capabilities.
-- **Language Model Utilization**: Supports our goal to improve support for the most popular languages among our users. This includes the collection of `crew_language` to indicate the default language model being used.
-- **Roles of Agents within a Crew**: Understanding the various roles agents play aids in crafting better tools, integrations, and examples.
-- **Tool Usage**: Identifying which tools are most frequently used allows us to prioritize improvements in those areas. This includes the collection of tool names used by agents and tasks.
-- **Crew and Agent Specific Attributes**: Information such as `crew_id`, `verbose?`, `max_iter`, `max_rpm`, `i18n.language`, and `llm` attributes (like `name`, `model_name`, `base_url`, `model`, `top_k`, `temperature`) are collected to enhance our understanding and support of the ecosystem.
-
-### 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.
-
-### 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.
-
-### Transparency and Review
-To foster greater transparency, users can inquire about the telemetry data being collected by contacting our support team. This initiative aims to provide users with the opportunity to understand the scope of our data collection practices better and aligns with our commitment to transparency and trust.
-
-By ensuring the documentation is up-to-date with these changes, we aim to maintain transparency with our users, fostering trust, and promoting an understanding of how telemetry data is used to enhance CrewAI.
-```
+Data collected includes:
+- Version of crewAI
+- Version of Python
+- General OS (e.g. number of CPUs, macOS/Windows/Linux)
+- Number of agents and tasks in a crew
+- Crew Process being used
+- If Agents are using memory or allowing delegation
+- If Tasks are being executed in parallel or sequentially
+- Language model being used
+- Roles of agents in a crew
+- Tools names available

pyproject.toml

@@ -1,7 +1,7 @@
 
 [tool.poetry]
 name = "crewai"
-version = "0.19.0"
+version = "0.5.5"
 description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
 authors = ["Joao Moura <joao@crewai.com>"]
 readme = "README.md"
@@ -16,25 +16,18 @@ Documentation = "https://github.com/joaomdmoura/CrewAI/wiki/Index"
 Repository = "https://github.com/joaomdmoura/crewai"
 
 [tool.poetry.dependencies]
-python = ">=3.10,<=3.13"
+python = ">=3.10,<4.0"
 pydantic = "^2.4.2"
-langchain = "^0.1.10"
-openai = "^1.13.3"
-langchain-openai = "^0.0.5"
+langchain = "0.1.0"
+openai = "^1.7.1"
+langchain-openai = "^0.0.2"
 opentelemetry-api = "^1.22.0"
 opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
-instructor = "^0.5.2"
-regex = "^2023.12.25"
-crewai-tools = { version = "^0.0.15", optional = true }
-click = "^8.1.7"
-
-[tool.poetry.extras]
-tools = ["crewai-tools"]
 
 [tool.poetry.group.dev.dependencies]
 isort = "^5.13.2"
-pyright = ">=1.1.350,<2.0.0"
+pyright = "1.1.333"
 black = {git = "https://github.com/psf/black.git", rev = "stable"}
 autoflake = "^2.2.1"
 pre-commit = "^3.6.0"
@@ -45,14 +38,13 @@ mkdocs-material = {extras = ["imaging"], version = "^9.5.7"}
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai_tools = "^0.0.15"
 
 [tool.isort]
 profile = "black"
 known_first_party = ["crewai"]
 
 [tool.poetry.group.test.dependencies]
-pytest = "^8.0.0"
+pytest = "^7.4"
 pytest-vcr = "^1.0.2"
 python-dotenv = "1.0.0"
 

src/crewai/agent.py

@@ -1,12 +1,11 @@
-import os
 import uuid
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, List, Optional
 
 from langchain.agents.agent import RunnableAgent
-from langchain.agents.tools import tool as LangChainTool
+from langchain.agents.format_scratchpad import format_log_to_str
 from langchain.memory import ConversationSummaryMemory
 from langchain.tools.render import render_text_description
-from langchain_core.agents import AgentAction
+from langchain_core.runnables.config import RunnableConfig
 from langchain_openai import ChatOpenAI
 from pydantic import (
     UUID4,
@@ -20,9 +19,13 @@ from pydantic import (
 )
 from pydantic_core import PydanticCustomError
 
-from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser, ToolsHandler
+from crewai.agents import (
+    CacheHandler,
+    CrewAgentExecutor,
+    CrewAgentOutputParser,
+    ToolsHandler,
+)
 from crewai.utilities import I18N, Logger, Prompts, RPMController
-from crewai.utilities.token_counter_callback import TokenCalcHandler, TokenProcess
 
 
 class Agent(BaseModel):
@@ -37,23 +40,19 @@ class Agent(BaseModel):
             goal: The objective of the agent.
             backstory: The backstory of the agent.
             llm: The language model that will run the agent.
-            function_calling_llm: The language model that will the tool calling for this agent, it overrides the crew function_calling_llm.
             max_iter: Maximum number of iterations for an agent to execute a task.
             memory: Whether the agent should have memory or not.
             max_rpm: Maximum number of requests per minute for the agent execution to be respected.
             verbose: Whether the agent execution should be in verbose mode.
             allow_delegation: Whether the agent is allowed to delegate tasks to other agents.
             tools: Tools at agents disposal
-            step_callback: Callback to be executed after each step of the agent execution.
     """
 
     __hash__ = object.__hash__  # type: ignore
     _logger: Logger = PrivateAttr()
     _rpm_controller: RPMController = PrivateAttr(default=None)
     _request_within_rpm_limit: Any = PrivateAttr(default=None)
-    _token_process: TokenProcess = TokenProcess()
 
-    formatting_errors: int = 0
     model_config = ConfigDict(arbitrary_types_allowed=True)
     id: UUID4 = Field(
         default_factory=uuid.uuid4,
@@ -68,7 +67,7 @@ class Agent(BaseModel):
         description="Maximum number of requests per minute for the agent execution to be respected.",
     )
     memory: bool = Field(
-        default=False, description="Whether the agent should have memory or not"
+        default=True, description="Whether the agent should have memory or not"
     )
     verbose: bool = Field(
         default=False, description="Verbose mode for the Agent Execution"
@@ -76,7 +75,7 @@ class Agent(BaseModel):
     allow_delegation: bool = Field(
         default=True, description="Allow delegation of tasks to agents"
     )
-    tools: Optional[List[Any]] = Field(
+    tools: List[Any] = Field(
         default_factory=list, description="Tools at agents disposal"
     )
     max_iter: Optional[int] = Field(
@@ -91,20 +90,13 @@ class Agent(BaseModel):
     cache_handler: InstanceOf[CacheHandler] = Field(
         default=CacheHandler(), description="An instance of the CacheHandler class."
     )
-    step_callback: Optional[Any] = Field(
-        default=None,
-        description="Callback to be executed after each step of the agent execution.",
-    )
     i18n: I18N = Field(default=I18N(), description="Internationalization settings.")
     llm: Any = Field(
         default_factory=lambda: ChatOpenAI(
-            model=os.environ.get("OPENAI_MODEL_NAME", "gpt-4")
+            model="gpt-4",
         ),
         description="Language model that will run the agent.",
     )
-    function_calling_llm: Optional[Any] = Field(
-        description="Language model that will run the agent.", default=None
-    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -125,19 +117,15 @@ class Agent(BaseModel):
         return self
 
     @model_validator(mode="after")
-    def set_agent_executor(self) -> "Agent":
-        """set agent executor is set."""
-        if hasattr(self.llm, "model_name"):
-            self.llm.callbacks = [
-                TokenCalcHandler(self.llm.model_name, self._token_process)
-            ]
+    def check_agent_executor(self) -> "Agent":
+        """Check if the agent executor is set."""
         if not self.agent_executor:
             self.set_cache_handler(self.cache_handler)
         return self
 
     def execute_task(
         self,
-        task: Any,
+        task: str,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
     ) -> str:
@@ -151,29 +139,22 @@ class Agent(BaseModel):
         Returns:
             Output of the agent
         """
-        self.tools_handler.last_used_tool = {}
-
-        task_prompt = task.prompt()
 
         if context:
-            task_prompt = self.i18n.slice("task_with_context").format(
-                task=task_prompt, context=context
+            task = self.i18n.slice("task_with_context").format(
+                task=task, context=context
             )
 
-        tools = self._parse_tools(tools or self.tools)
-        self.create_agent_executor(tools=tools)
+        tools = tools or self.tools
         self.agent_executor.tools = tools
-        self.agent_executor.task = task
-
-        self.agent_executor.tools_description = render_text_description(tools)
-        self.agent_executor.tools_names = self.__tools_names(tools)
 
         result = self.agent_executor.invoke(
             {
-                "input": task_prompt,
-                "tool_names": self.agent_executor.tools_names,
-                "tools": self.agent_executor.tools_description,
-            }
+                "input": task,
+                "tool_names": self.__tools_names(tools),
+                "tools": render_text_description(tools),
+            },
+            RunnableConfig(callbacks=[self.tools_handler]),
         )["output"]
 
         if self.max_rpm:
@@ -189,7 +170,7 @@ class Agent(BaseModel):
         """
         self.cache_handler = cache_handler
         self.tools_handler = ToolsHandler(cache=self.cache_handler)
-        self.create_agent_executor()
+        self._create_agent_executor()
 
     def set_rpm_controller(self, rpm_controller: RPMController) -> None:
         """Set the rpm controller for the agent.
@@ -199,41 +180,32 @@ class Agent(BaseModel):
         """
         if not self._rpm_controller:
             self._rpm_controller = rpm_controller
-            self.create_agent_executor()
+            self._create_agent_executor()
 
-    def create_agent_executor(self, tools=None) -> None:
+    def _create_agent_executor(self) -> None:
         """Create an agent executor for the agent.
 
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        tools = tools or self.tools
-
         agent_args = {
             "input": lambda x: x["input"],
             "tools": lambda x: x["tools"],
             "tool_names": lambda x: x["tool_names"],
-            "agent_scratchpad": lambda x: self.format_log_to_str(
-                x["intermediate_steps"]
-            ),
+            "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
         }
-
         executor_args = {
-            "llm": self.llm,
             "i18n": self.i18n,
-            "tools": self._parse_tools(tools),
+            "tools": self.tools,
             "verbose": self.verbose,
             "handle_parsing_errors": True,
             "max_iterations": self.max_iter,
-            "step_callback": self.step_callback,
-            "tools_handler": self.tools_handler,
-            "function_calling_llm": self.function_calling_llm,
         }
 
         if self._rpm_controller:
-            executor_args[
-                "request_within_rpm_limit"
-            ] = self._rpm_controller.check_or_wait
+            executor_args["request_within_rpm_limit"] = (
+                self._rpm_controller.check_or_wait
+            )
 
         if self.memory:
             summary_memory = ConversationSummaryMemory(
@@ -241,9 +213,9 @@ class Agent(BaseModel):
             )
             executor_args["memory"] = summary_memory
             agent_args["chat_history"] = lambda x: x["chat_history"]
-            prompt = Prompts(i18n=self.i18n, tools=tools).task_execution_with_memory()
+            prompt = Prompts(i18n=self.i18n).task_execution_with_memory()
         else:
-            prompt = Prompts(i18n=self.i18n, tools=tools).task_execution()
+            prompt = Prompts(i18n=self.i18n).task_execution()
 
         execution_prompt = prompt.partial(
             goal=self.goal,
@@ -252,52 +224,20 @@ class Agent(BaseModel):
         )
 
         bind = self.llm.bind(stop=[self.i18n.slice("observation")])
-        inner_agent = agent_args | execution_prompt | bind | CrewAgentParser(agent=self)
+        inner_agent = (
+            agent_args
+            | execution_prompt
+            | bind
+            | CrewAgentOutputParser(
+                tools_handler=self.tools_handler,
+                cache=self.cache_handler,
+                i18n=self.i18n,
+            )
+        )
         self.agent_executor = CrewAgentExecutor(
             agent=RunnableAgent(runnable=inner_agent), **executor_args
         )
 
-    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
-        """Interpolate inputs into the agent description and backstory."""
-        if inputs:
-            self.role = self.role.format(**inputs)
-            self.goal = self.goal.format(**inputs)
-            self.backstory = self.backstory.format(**inputs)
-
-    def increment_formatting_errors(self) -> None:
-        """Count the formatting errors of the agent."""
-        self.formatting_errors += 1
-
-    def format_log_to_str(
-        self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
-        observation_prefix: str = "Observation: ",
-        llm_prefix: str = "",
-    ) -> str:
-        """Construct the scratchpad that lets the agent continue its thought process."""
-        thoughts = ""
-        for action, observation in intermediate_steps:
-            thoughts += action.log
-            thoughts += f"\n{observation_prefix}{observation}\n{llm_prefix}"
-        return thoughts
-
-    def _parse_tools(self, tools: List[Any]) -> List[LangChainTool]:
-        """Parse tools to be used for the task."""
-        # tentatively try to import from crewai_tools import BaseTool as CrewAITool
-        tools_list = []
-        try:
-            from crewai_tools import BaseTool as CrewAITool
-
-            for tool in tools:
-                if isinstance(tool, CrewAITool):
-                    tools_list.append(tool.to_langchain())
-                else:
-                    tools_list.append(tool)
-        except ModuleNotFoundError:
-            for tool in tools:
-                tools_list.append(tool)
-        return tools_list
-
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/agents/__init__.py

@@ -1,4 +1,4 @@
 from .cache.cache_handler import CacheHandler
 from .executor import CrewAgentExecutor
-from .parser import CrewAgentParser
+from .output_parser import CrewAgentOutputParser
 from .tools_handler import ToolsHandler

src/crewai/agents/cache/__init__.py

@@ -1 +1,2 @@
 from .cache_handler import CacheHandler
+from .cache_hit import CacheHit

src/crewai/agents/cache/cache_handler.py

@@ -10,7 +10,9 @@ class CacheHandler:
         self._cache = {}
 
     def add(self, tool, input, output):
+        input = input.strip()
         self._cache[f"{tool}-{input}"] = output
 
     def read(self, tool, input) -> Optional[str]:
+        input = input.strip()
         return self._cache.get(f"{tool}-{input}")

src/crewai/agents/cache/cache_hit.py

@@ -0,0 +1,14 @@
+from langchain_core.agents import AgentAction
+from pydantic.v1 import BaseModel, Field
+
+from .cache_handler import CacheHandler
+
+
+class CacheHit(BaseModel):
+    """Cache Hit Object."""
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    action: AgentAction = Field(description="Action taken")
+    cache: CacheHandler = Field(description="Cache Handler for the tool")

src/crewai/agents/exceptions.py

@@ -0,0 +1,30 @@
+from langchain_core.exceptions import OutputParserException
+
+from crewai.utilities import I18N
+
+
+class TaskRepeatedUsageException(OutputParserException):
+    """Exception raised when a task is used twice in a roll."""
+
+    i18n: I18N = I18N()
+    error: str = "TaskRepeatedUsageException"
+    message: str
+
+    def __init__(self, i18n: I18N, tool: str, tool_input: str, text: str):
+        self.i18n = i18n
+        self.text = text
+        self.tool = tool
+        self.tool_input = tool_input
+        self.message = self.i18n.errors("task_repeated_usage").format(
+            tool=tool, tool_input=tool_input
+        )
+
+        super().__init__(
+            error=self.error,
+            observation=self.message,
+            send_to_llm=True,
+            llm_output=self.text,
+        )
+
+    def __str__(self):
+        return self.message

src/crewai/agents/executor.py

@@ -3,33 +3,25 @@ from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
+from langchain.agents.tools import InvalidTool
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain_core.agents import AgentAction, AgentFinish, AgentStep
 from langchain_core.exceptions import OutputParserException
 from langchain_core.pydantic_v1 import root_validator
 from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
-from pydantic import InstanceOf
 
-from crewai.agents.tools_handler import ToolsHandler
-from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
+from crewai.agents.cache.cache_hit import CacheHit
+from crewai.tools.cache_tools import CacheTools
 from crewai.utilities import I18N
 
 
 class CrewAgentExecutor(AgentExecutor):
-    _i18n: I18N = I18N()
-    llm: Any = None
+    i18n: I18N = I18N()
     iterations: int = 0
-    task: Any = None
-    tools_description: str = ""
-    tools_names: str = ""
-    function_calling_llm: Any = None
     request_within_rpm_limit: Any = None
-    tools_handler: InstanceOf[ToolsHandler] = None
     max_iterations: Optional[int] = 15
-    have_forced_answer: bool = False
     force_answer_max_iterations: Optional[int] = None
-    step_callback: Optional[Any] = None
 
     @root_validator()
     def set_force_answer_max_iterations(cls, values: Dict) -> Dict:
@@ -37,9 +29,12 @@ class CrewAgentExecutor(AgentExecutor):
         return values
 
     def _should_force_answer(self) -> bool:
-        return (
-            self.iterations == self.force_answer_max_iterations
-        ) and not self.have_forced_answer
+        return True if self.iterations == self.force_answer_max_iterations else False
+
+    def _force_answer(self, output: AgentAction):
+        return AgentStep(
+            action=output, observation=self.i18n.errors("force_final_answer")
+        )
 
     def _call(
         self,
@@ -68,10 +63,6 @@ class CrewAgentExecutor(AgentExecutor):
                     intermediate_steps,
                     run_manager=run_manager,
                 )
-
-                if self.step_callback:
-                    self.step_callback(next_step_output)
-
                 if isinstance(next_step_output, AgentFinish):
                     return self._return(
                         next_step_output, intermediate_steps, run_manager=run_manager
@@ -106,20 +97,26 @@ class CrewAgentExecutor(AgentExecutor):
         Override this to take control of how the agent makes and acts on choices.
         """
         try:
-            if self._should_force_answer():
-                error = self._i18n.errors("force_final_answer")
-                output = AgentAction("_Exception", error, error)
-                self.have_forced_answer = True
-                yield AgentStep(action=output, observation=error)
-                return
-
             intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
+
             # Call the LLM to see what to do.
             output = self.agent.plan(
                 intermediate_steps,
                 callbacks=run_manager.get_child() if run_manager else None,
                 **inputs,
             )
+            if self._should_force_answer():
+                if isinstance(output, CacheHit):
+                    output = output.action
+                if isinstance(output, AgentAction):
+                    yield self._force_answer(output)
+                    return
+                if isinstance(output, list):
+                    yield from [self._force_answer(action) for action in output]
+                    return
+
+                yield output
+                return
 
         except OutputParserException as e:
             if isinstance(self.handle_parsing_errors, bool):
@@ -133,35 +130,33 @@ class CrewAgentExecutor(AgentExecutor):
                     "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
                     f"This is the error: {str(e)}"
                 )
-            str(e)
+            text = str(e)
             if isinstance(self.handle_parsing_errors, bool):
                 if e.send_to_llm:
-                    observation = f"\n{str(e.observation)}"
-                    str(e.llm_output)
+                    observation = str(e.observation)
+                    text = str(e.llm_output)
                 else:
-                    observation = ""
+                    observation = "Invalid or incomplete response"
             elif isinstance(self.handle_parsing_errors, str):
-                observation = f"\n{self.handle_parsing_errors}"
+                observation = self.handle_parsing_errors
             elif callable(self.handle_parsing_errors):
-                observation = f"\n{self.handle_parsing_errors(e)}"
+                observation = self.handle_parsing_errors(e)
             else:
                 raise ValueError("Got unexpected type of `handle_parsing_errors`")
-            output = AgentAction("_Exception", observation, "")
+            output = AgentAction("_Exception", observation, text)
             if run_manager:
                 run_manager.on_agent_action(output, color="green")
             tool_run_kwargs = self.agent.tool_run_logging_kwargs()
             observation = ExceptionTool().run(
                 output.tool_input,
-                verbose=False,
+                verbose=self.verbose,
                 color=None,
                 callbacks=run_manager.get_child() if run_manager else None,
                 **tool_run_kwargs,
             )
 
             if self._should_force_answer():
-                error = self._i18n.errors("force_final_answer")
-                output = AgentAction("_Exception", error, error)
-                yield AgentStep(action=output, observation=error)
+                yield self._force_answer(output)
                 return
 
             yield AgentStep(action=output, observation=observation)
@@ -172,6 +167,17 @@ class CrewAgentExecutor(AgentExecutor):
             yield output
             return
 
+        # Override tool usage to use CacheTools
+        if isinstance(output, CacheHit):
+            cache = output.cache
+            action = output.action
+            tool = CacheTools(cache_handler=cache).tool()
+            output = action.copy()
+            output.tool_input = f"tool:{action.tool}|input:{action.tool_input}"
+            output.tool = tool.name
+            name_to_tool_map[tool.name] = tool
+            color_mapping[tool.name] = color_mapping[action.tool]
+
         actions: List[AgentAction]
         actions = [output] if isinstance(output, AgentAction) else output
         yield from actions
@@ -179,27 +185,31 @@ class CrewAgentExecutor(AgentExecutor):
             if run_manager:
                 run_manager.on_agent_action(agent_action, color="green")
             # Otherwise we lookup the tool
-            tool_usage = ToolUsage(
-                tools_handler=self.tools_handler,
-                tools=self.tools,
-                tools_description=self.tools_description,
-                tools_names=self.tools_names,
-                function_calling_llm=self.function_calling_llm,
-                task=self.task,
-                action=agent_action,
-            )
-            tool_calling = tool_usage.parse(agent_action.log)
-
-            if isinstance(tool_calling, ToolUsageErrorException):
-                observation = tool_calling.message
+            if agent_action.tool in name_to_tool_map:
+                tool = name_to_tool_map[agent_action.tool]
+                return_direct = tool.return_direct
+                color = color_mapping[agent_action.tool]
+                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
+                if return_direct:
+                    tool_run_kwargs["llm_prefix"] = ""
+                # We then call the tool on the tool input to get an observation
+                observation = tool.run(
+                    agent_action.tool_input,
+                    verbose=self.verbose,
+                    color=color,
+                    callbacks=run_manager.get_child() if run_manager else None,
+                    **tool_run_kwargs,
+                )
             else:
-                if tool_calling.tool_name.lower().strip() in [
-                    name.lower().strip() for name in name_to_tool_map
-                ]:
-                    observation = tool_usage.use(tool_calling, agent_action.log)
-                else:
-                    observation = self._i18n.errors("wrong_tool_name").format(
-                        tool=tool_calling.tool_name,
-                        tools=", ".join([tool.name for tool in self.tools]),
-                    )
+                tool_run_kwargs = self.agent.tool_run_logging_kwargs()
+                observation = InvalidTool().run(
+                    {
+                        "requested_tool_name": agent_action.tool,
+                        "available_tool_names": list(name_to_tool_map.keys()),
+                    },
+                    verbose=self.verbose,
+                    color=None,
+                    callbacks=run_manager.get_child() if run_manager else None,
+                    **tool_run_kwargs,
+                )
             yield AgentStep(action=agent_action, observation=observation)

src/crewai/agents/output_parser.py

@@ -0,0 +1,79 @@
+import re
+from typing import Union
+
+from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain_core.agents import AgentAction, AgentFinish
+
+from crewai.agents.cache import CacheHandler, CacheHit
+from crewai.agents.exceptions import TaskRepeatedUsageException
+from crewai.agents.tools_handler import ToolsHandler
+from crewai.utilities import I18N
+
+FINAL_ANSWER_ACTION = "Final Answer:"
+FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
+    "Parsing LLM output produced both a final answer and a parse-able action:"
+)
+
+
+class CrewAgentOutputParser(ReActSingleInputOutputParser):
+    """Parses ReAct-style LLM calls that have a single tool input.
+
+    Expects output to be in one of two formats.
+
+    If the output signals that an action should be taken,
+    should be in the below format. This will result in an AgentAction
+    being returned.
+
+    ```
+    Thought: agent thought here
+    Action: search
+    Action Input: what is the temperature in SF?
+    ```
+
+    If the output signals that a final answer should be given,
+    should be in the below format. This will result in an AgentFinish
+    being returned.
+
+    ```
+    Thought: agent thought here
+    Final Answer: The temperature is 100 degrees
+    ```
+
+    It also prevents tools from being reused in a roll.
+    """
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    tools_handler: ToolsHandler
+    cache: CacheHandler
+    i18n: I18N
+
+    def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
+        regex = (
+            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
+        )
+        if action_match := re.search(regex, text, re.DOTALL):
+            action = action_match.group(1).strip()
+            action_input = action_match.group(2)
+            tool_input = action_input.strip(" ")
+            tool_input = tool_input.strip('"')
+
+            if last_tool_usage := self.tools_handler.last_used_tool:
+                usage = {
+                    "tool": action,
+                    "input": tool_input,
+                }
+                if usage == last_tool_usage:
+                    raise TaskRepeatedUsageException(
+                        text=text,
+                        tool=action,
+                        tool_input=tool_input,
+                        i18n=self.i18n,
+                    )
+
+            if self.cache.read(action, tool_input):
+                agent_action = AgentAction(action, tool_input, text)
+                return CacheHit(action=agent_action, cache=self.cache)
+
+        return super().parse(text)

src/crewai/agents/parser.py

@@ -1,90 +0,0 @@
-import re
-from typing import Any, Union
-
-from langchain.agents.output_parsers import ReActSingleInputOutputParser
-from langchain_core.agents import AgentAction, AgentFinish
-from langchain_core.exceptions import OutputParserException
-
-from crewai.utilities import I18N
-
-FINAL_ANSWER_ACTION = "Final Answer:"
-MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE = "I did it wrong. Invalid Format: I missed the 'Action:' after 'Thought:'. I will do right next, and don't use a tool I have already used.\n"
-MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE = "I did it wrong. Invalid Format: I missed the 'Action Input:' after 'Action:'. I will do right next, and don't use a tool I have already used.\n"
-FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"
-
-
-class CrewAgentParser(ReActSingleInputOutputParser):
-    """Parses ReAct-style LLM calls that have a single tool input.
-
-    Expects output to be in one of two formats.
-
-    If the output signals that an action should be taken,
-    should be in the below format. This will result in an AgentAction
-    being returned.
-
-    Thought: agent thought here
-    Action: search
-    Action Input: what is the temperature in SF?
-
-    If the output signals that a final answer should be given,
-    should be in the below format. This will result in an AgentFinish
-    being returned.
-
-    Thought: agent thought here
-    Final Answer: The temperature is 100 degrees
-    """
-
-    _i18n: I18N = I18N()
-    agent: Any = None
-
-    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
-        includes_answer = FINAL_ANSWER_ACTION in text
-        regex = (
-            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
-        )
-        action_match = re.search(regex, text, re.DOTALL)
-        if action_match:
-            if includes_answer:
-                raise OutputParserException(
-                    f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
-                )
-            action = action_match.group(1).strip()
-            action_input = action_match.group(2)
-            tool_input = action_input.strip(" ")
-            tool_input = tool_input.strip('"')
-
-            return AgentAction(action, tool_input, text)
-
-        elif includes_answer:
-            return AgentFinish(
-                {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
-            )
-
-        if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
-            self.agent.increment_formatting_errors()
-            raise OutputParserException(
-                f"Could not parse LLM output: `{text}`",
-                observation=f"{MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE}\n{self._i18n.slice('final_answer_format')}",
-                llm_output=text,
-                send_to_llm=True,
-            )
-        elif not re.search(
-            r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
-        ):
-            self.agent.increment_formatting_errors()
-            raise OutputParserException(
-                f"Could not parse LLM output: `{text}`",
-                observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
-                llm_output=text,
-                send_to_llm=True,
-            )
-        else:
-            format = self._i18n.slice("format_without_tools")
-            error = f"{format}"
-            self.agent.increment_formatting_errors()
-            raise OutputParserException(
-                error,
-                observation=error,
-                llm_output=text,
-                send_to_llm=True,
-            )

src/crewai/agents/tools_handler.py

@@ -1,27 +1,44 @@
-from typing import Any
+from typing import Any, Dict
+
+from langchain.callbacks.base import BaseCallbackHandler
 
 from ..tools.cache_tools import CacheTools
-from ..tools.tool_calling import ToolCalling
 from .cache.cache_handler import CacheHandler
 
 
-class ToolsHandler:
+class ToolsHandler(BaseCallbackHandler):
     """Callback handler for tool usage."""
 
-    last_used_tool: ToolCalling = {}
+    last_used_tool: Dict[str, Any] = {}
     cache: CacheHandler
 
-    def __init__(self, cache: CacheHandler):
+    def __init__(self, cache: CacheHandler, **kwargs: Any):
         """Initialize the callback handler."""
         self.cache = cache
-        self.last_used_tool = {}
+        super().__init__(**kwargs)
 
-    def on_tool_use(self, calling: ToolCalling, output: str) -> Any:
+    def on_tool_start(
+        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
+    ) -> Any:
+        """Run when tool starts running."""
+        name = serialized.get("name")
+        if name not in ["invalid_tool", "_Exception"]:
+            tools_usage = {
+                "tool": name,
+                "input": input_str,
+            }
+            self.last_used_tool = tools_usage
+
+    def on_tool_end(self, output: str, **kwargs: Any) -> Any:
         """Run when tool ends running."""
-        self.last_used_tool = calling
-        if calling.tool_name != CacheTools().name:
-            self.cache.add(
-                tool=calling.tool_name,
-                input=calling.arguments,
-                output=output,
-            )
+        if (
+            "is not a valid tool" not in output
+            and "Invalid or incomplete response" not in output
+            and "Invalid Format" not in output
+        ):
+            if self.last_used_tool["tool"] != CacheTools().name:
+                self.cache.add(
+                    tool=self.last_used_tool["tool"],
+                    input=self.last_used_tool["input"],
+                    output=output,
+                )

src/crewai/crew.py

@@ -19,7 +19,7 @@ from crewai.agent import Agent
 from crewai.agents.cache import CacheHandler
 from crewai.process import Process
 from crewai.task import Task
-from crewai.telemetry import Telemetry
+from crewai.telemtry import Telemetry
 from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, Logger, RPMController
 
@@ -32,19 +32,15 @@ class Crew(BaseModel):
         tasks: List of tasks assigned to the crew.
         agents: List of agents part of this crew.
         manager_llm: The language model that will run manager agent.
-        function_calling_llm: The language model that will run the tool calling for all the agents.
         process: The process flow that the crew will follow (e.g., sequential).
         verbose: Indicates the verbosity level for logging during execution.
         config: Configuration settings for the crew.
+        _cache_handler: Handles caching for the crew's operations.
         max_rpm: Maximum number of requests per minute for the crew execution to be respected.
         id: A unique identifier for the crew instance.
-        full_output: Whether the crew should return the full output with all tasks outputs or just the final output.
-        step_callback: Callback to be executed after each step for every agents execution.
-        share_crew: Whether you want to share the complete crew infromation and execution with crewAI to make the library better, and allow us to train models.
     """
 
     __hash__ = object.__hash__  # type: ignore
-    _execution_span: Any = PrivateAttr()
     _rpm_controller: RPMController = PrivateAttr()
     _logger: Logger = PrivateAttr()
     _cache_handler: InstanceOf[CacheHandler] = PrivateAttr(default=CacheHandler())
@@ -53,27 +49,11 @@ class Crew(BaseModel):
     agents: List[Agent] = Field(default_factory=list)
     process: Process = Field(default=Process.sequential)
     verbose: Union[int, bool] = Field(default=0)
-    usage_metrics: Optional[dict] = Field(
-        default=None,
-        description="Metrics for the LLM usage during all tasks execution.",
-    )
-    full_output: Optional[bool] = Field(
-        default=False,
-        description="Whether the crew should return the full output with all tasks outputs or just the final output.",
-    )
     manager_llm: Optional[Any] = Field(
         description="Language model that will run the agent.", default=None
     )
-    function_calling_llm: Optional[Any] = Field(
-        description="Language model that will run the agent.", default=None
-    )
     config: Optional[Union[Json, Dict[str, Any]]] = Field(default=None)
     id: UUID4 = Field(default_factory=uuid.uuid4, frozen=True)
-    share_crew: Optional[bool] = Field(default=False)
-    step_callback: Optional[Any] = Field(
-        default=None,
-        description="Callback to be executed after each step for all agents execution.",
-    )
     max_rpm: Optional[int] = Field(
         default=None,
         description="Maximum number of requests per minute for the crew execution to be respected.",
@@ -114,7 +94,6 @@ class Crew(BaseModel):
         self._logger = Logger(self.verbose)
         self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
         self._telemetry = Telemetry()
-        self._telemetry.set_tracer()
         self._telemetry.crew_creation(self)
         return self
 
@@ -158,7 +137,6 @@ class Crew(BaseModel):
                 "missing_keys_in_config", "Config should have 'agents' and 'tasks'.", {}
             )
 
-        self.process = self.config.get("process", self.process)
         self.agents = [Agent(**agent) for agent in self.config["agents"]]
         self.tasks = [self._create_task(task) for task in self.config["tasks"]]
 
@@ -177,53 +155,29 @@ class Crew(BaseModel):
         del task_config["agent"]
         return Task(**task_config, agent=task_agent)
 
-    def kickoff(self, inputs: Optional[Dict[str, Any]] = {}) -> str:
+    def kickoff(self) -> str:
         """Starts the crew to work on its assigned tasks."""
-        self._execution_span = self._telemetry.crew_execution_span(self)
-        self._interpolate_inputs(inputs)
-
         for agent in self.agents:
             agent.i18n = I18N(language=self.language)
 
-            if not agent.function_calling_llm:
-                agent.function_calling_llm = self.function_calling_llm
-                agent.create_agent_executor()
-            if not agent.step_callback:
-                agent.step_callback = self.step_callback
-                agent.create_agent_executor()
-
-        metrics = []
-
         if self.process == Process.sequential:
-            result = self._run_sequential_process()
-        elif self.process == Process.hierarchical:
-            result, manager_metrics = self._run_hierarchical_process()
-            metrics.append(manager_metrics)
+            return self._run_sequential_process()
+        if self.process == Process.hierarchical:
+            return self._run_hierarchical_process()
 
-        else:
-            raise NotImplementedError(
-                f"The process '{self.process}' is not implemented yet."
-            )
-
-        metrics = metrics + [
-            agent._token_process.get_summary() for agent in self.agents
-        ]
-        self.usage_metrics = {
-            key: sum([m[key] for m in metrics if m is not None]) for key in metrics[0]
-        }
-
-        return result
+        raise NotImplementedError(
+            f"The process '{self.process}' is not implemented yet."
+        )
 
     def _run_sequential_process(self) -> str:
         """Executes tasks sequentially and returns the final output."""
-        task_output = ""
+        task_output: str = ""
         for task in self.tasks:
-            if task.agent.allow_delegation:
+            if task.agent is not None and task.agent.allow_delegation:
                 agents_for_delegation = [
                     agent for agent in self.agents if agent != task.agent
                 ]
-                if len(self.agents) > 1 and len(agents_for_delegation) > 0:
-                    task.tools += AgentTools(agents=agents_for_delegation).tools()
+                task.tools += AgentTools(agents=agents_for_delegation).tools()
 
             role = task.agent.role if task.agent is not None else "None"
             self._logger.log("debug", f"Working Agent: {role}")
@@ -231,13 +185,16 @@ class Crew(BaseModel):
 
             output = task.execute(context=task_output)
             if not task.async_execution:
+                assert output is not None
                 task_output = output
 
             role = task.agent.role if task.agent is not None else "None"
             self._logger.log("debug", f"[{role}] Task output: {task_output}\n\n")
 
-        self._finish_execution(task_output)
-        return self._format_output(task_output)
+        if self.max_rpm:
+            self._rpm_controller.stop_rpm_counter()
+
+        return task_output
 
     def _run_hierarchical_process(self) -> str:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
@@ -252,38 +209,23 @@ class Crew(BaseModel):
             verbose=True,
         )
 
-        task_output = ""
+        task_output: str = ""
         for task in self.tasks:
             self._logger.log("debug", f"Working Agent: {manager.role}")
             self._logger.log("info", f"Starting Task: {task.description}")
 
-            task_output = task.execute(
+            output = task.execute(
                 agent=manager, context=task_output, tools=manager.tools
             )
+            if not task.async_execution:
+                assert output is not None
+                task_output = output
 
             self._logger.log(
                 "debug", f"[{manager.role}] Task output: {task_output}\n\n"
             )
 
-        self._finish_execution(task_output)
-        return self._format_output(task_output), manager._token_process.get_summary()
-
-    def _interpolate_inputs(self, inputs: Dict[str, Any]) -> str:
-        """Interpolates the inputs in the tasks and agents."""
-        [task.interpolate_inputs(inputs) for task in self.tasks]
-        [agent.interpolate_inputs(inputs) for agent in self.agents]
-
-    def _format_output(self, output: str) -> str:
-        """Formats the output of the crew execution."""
-        if self.full_output:
-            return {
-                "final_output": output,
-                "tasks_outputs": [task.output for task in self.tasks if task],
-            }
-        else:
-            return output
-
-    def _finish_execution(self, output) -> None:
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
-        self._telemetry.end_crew(self, output)
+
+        return task_output

src/crewai/task.py

@@ -1,15 +1,13 @@
 import threading
 import uuid
-from typing import Any, Dict, List, Optional, Type
+from typing import Any, List, Optional
 
-from langchain_openai import ChatOpenAI
 from pydantic import UUID4, BaseModel, Field, field_validator, model_validator
 from pydantic_core import PydanticCustomError
 
 from crewai.agent import Agent
 from crewai.tasks.task_output import TaskOutput
-from crewai.utilities import I18N, Converter, ConverterError, Printer
-from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+from crewai.utilities import I18N
 
 
 class Task(BaseModel):
@@ -19,21 +17,19 @@ class Task(BaseModel):
         arbitrary_types_allowed = True
 
     __hash__ = object.__hash__  # type: ignore
-    used_tools: int = 0
-    tools_errors: int = 0
-    delegations: int = 0
     i18n: I18N = I18N()
-    thread: threading.Thread = None
+    thread: threading.Thread | None = None
     description: str = Field(description="Description of the actual task.")
-    expected_output: str = Field(
-        description="Clear definition of expected output for the task."
-    )
     callback: Optional[Any] = Field(
         description="Callback to be executed after the task is completed.", default=None
     )
     agent: Optional[Agent] = Field(
         description="Agent responsible for execution the task.", default=None
     )
+    expected_output: Optional[str] = Field(
+        description="Clear definition of expected output for the task.",
+        default=None,
+    )
     context: Optional[List["Task"]] = Field(
         description="Other tasks that will have their output used as context for this task.",
         default=None,
@@ -42,22 +38,10 @@ class Task(BaseModel):
         description="Whether the task should be executed asynchronously or not.",
         default=False,
     )
-    output_json: Optional[Type[BaseModel]] = Field(
-        description="A Pydantic model to be used to create a JSON output.",
-        default=None,
-    )
-    output_pydantic: Optional[Type[BaseModel]] = Field(
-        description="A Pydantic model to be used to create a Pydantic output.",
-        default=None,
-    )
-    output_file: Optional[str] = Field(
-        description="A file path to be used to create a file output.",
-        default=None,
-    )
     output: Optional[TaskOutput] = Field(
         description="Task output, it's final result after being executed", default=None
     )
-    tools: Optional[List[Any]] = Field(
+    tools: List[Any] = Field(
         default_factory=list,
         description="Tools the agent is limited to use for this task.",
     )
@@ -82,24 +66,12 @@ class Task(BaseModel):
             self.tools.extend(self.agent.tools)
         return self
 
-    @model_validator(mode="after")
-    def check_output(self):
-        """Check if an output type is set."""
-        output_types = [self.output_json, self.output_pydantic]
-        if len([type for type in output_types if type]) > 1:
-            raise PydanticCustomError(
-                "output_type",
-                "Only one output type can be set, either output_pydantic or output_json.",
-                {},
-            )
-        return self
-
     def execute(
         self,
         agent: Agent | None = None,
         context: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-    ) -> str:
+    ) -> str | None:
         """Execute the task.
 
         Returns:
@@ -113,51 +85,38 @@ class Task(BaseModel):
             )
 
         if self.context:
-            context = []
+            results = []
             for task in self.context:
                 if task.async_execution:
+                    assert task.thread is not None
                     task.thread.join()
-                if task and task.output:
-                    context.append(task.output.raw_output)
-            context = "\n".join(context)
+                if task.output is not None:
+                    results.append(task.output.result)
+            context = "\n".join(results)
 
         tools = tools or self.tools
 
         if self.async_execution:
             self.thread = threading.Thread(
-                target=self._execute, args=(agent, self, context, tools)
+                target=self._execute, args=(agent, self._prompt(), context, tools)
             )
             self.thread.start()
         else:
             result = self._execute(
-                task=self,
                 agent=agent,
+                task_prompt=self._prompt(),
                 context=context,
                 tools=tools,
             )
             return result
 
-    def _execute(self, agent, task, context, tools):
-        result = agent.execute_task(
-            task=task,
-            context=context,
-            tools=tools,
-        )
+    def _execute(self, agent, task_prompt, context, tools):
+        result = agent.execute_task(task=task_prompt, context=context, tools=tools)
+        self.output = TaskOutput(description=self.description, result=result)
+        self.callback(self.output) if self.callback else None
+        return result
 
-        exported_output = self._export_output(result)
-
-        self.output = TaskOutput(
-            description=self.description,
-            exported_output=exported_output,
-            raw_output=result,
-        )
-
-        if self.callback:
-            self.callback(self.output)
-
-        return exported_output
-
-    def prompt(self) -> str:
+    def _prompt(self) -> str:
         """Prompt the task.
 
         Returns:
@@ -165,66 +124,9 @@ class Task(BaseModel):
         """
         tasks_slices = [self.description]
 
-        output = self.i18n.slice("expected_output").format(
-            expected_output=self.expected_output
-        )
-        tasks_slices = [self.description, output]
+        if self.expected_output:
+            output = self.i18n.slice("expected_output").format(
+                expected_output=self.expected_output
+            )
+            tasks_slices = [self.description, output]
         return "\n".join(tasks_slices)
-
-    def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
-        """Interpolate inputs into the task description and expected output."""
-        if inputs:
-            self.description = self.description.format(**inputs)
-            self.expected_output = self.expected_output.format(**inputs)
-
-    def increment_tools_errors(self) -> None:
-        """Increment the tools errors counter."""
-        self.tools_errors += 1
-
-    def increment_delegations(self) -> None:
-        """Increment the delegations counter."""
-        self.delegations += 1
-
-    def _export_output(self, result: str) -> Any:
-        exported_result = result
-        instructions = "I'm gonna convert this raw text into valid JSON."
-
-        if self.output_pydantic or self.output_json:
-            model = self.output_pydantic or self.output_json
-            llm = self.agent.function_calling_llm or self.agent.llm
-
-            if not self._is_gpt(llm):
-                model_schema = PydanticSchemaParser(model=model).get_schema()
-                instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
-
-            converter = Converter(
-                llm=llm, text=result, model=model, instructions=instructions
-            )
-
-            if self.output_pydantic:
-                exported_result = converter.to_pydantic()
-            elif self.output_json:
-                exported_result = converter.to_json()
-
-            if isinstance(exported_result, ConverterError):
-                Printer().print(
-                    content=f"{exported_result.message} Using raw output instead.",
-                    color="red",
-                )
-                exported_result = result
-
-        if self.output_file:
-            content = (
-                exported_result if not self.output_pydantic else exported_result.json()
-            )
-            self._save_file(content)
-
-        return exported_result
-
-    def _is_gpt(self, llm) -> bool:
-        return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None
-
-    def _save_file(self, result: Any) -> None:
-        with open(self.output_file, "w") as file:
-            file.write(result)
-        return None

src/crewai/tasks/task_output.py

@@ -1,4 +1,4 @@
-from typing import Optional, Union
+from typing import Optional
 
 from pydantic import BaseModel, Field, model_validator
 
@@ -8,16 +8,10 @@ class TaskOutput(BaseModel):
 
     description: str = Field(description="Description of the task")
     summary: Optional[str] = Field(description="Summary of the task", default=None)
-    exported_output: Union[str, BaseModel] = Field(
-        description="Output of the task", default=None
-    )
-    raw_output: str = Field(description="Result of the task")
+    result: str = Field(description="Result of the task")
 
     @model_validator(mode="after")
     def set_summary(self):
         excerpt = " ".join(self.description.split(" ")[:10])
         self.summary = f"{excerpt}..."
         return self
-
-    def result(self):
-        return self.exported_output

src/crewai/telemetry/telemetry.py

@@ -1,262 +0,0 @@
-import json
-import os
-import platform
-from typing import Any
-
-import pkg_resources
-from opentelemetry import trace
-from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
-from opentelemetry.sdk.resources import SERVICE_NAME, Resource
-from opentelemetry.sdk.trace import TracerProvider
-from opentelemetry.sdk.trace.export import BatchSpanProcessor
-from opentelemetry.trace import Status, StatusCode
-
-
-class Telemetry:
-    """A class to handle anonymous telemetry for the crewai package.
-
-    The data being collected is for development purpose, all data is anonymous.
-
-    There is NO data being collected on the prompts, tasks descriptions
-    agents backstories or goals nor responses or any data that is being
-    processed by the agents, nor any secrets and env vars.
-
-    Data collected includes:
-    - Version of crewAI
-    - Version of Python
-    - General OS (e.g. number of CPUs, macOS/Windows/Linux)
-    - Number of agents and tasks in a crew
-    - Crew Process being used
-    - If Agents are using memory or allowing delegation
-    - If Tasks are being executed in parallel or sequentially
-    - Language model being used
-    - Roles of agents in a crew
-    - Tools names available
-
-    Users can opt-in to sharing more complete data suing the `share_crew`
-    attribute in the Crew class.
-    """
-
-    def __init__(self):
-        self.ready = False
-        try:
-            telemetry_endpoint = "http://telemetry.crewai.com:4318"
-            self.resource = Resource(
-                attributes={SERVICE_NAME: "crewAI-telemetry"},
-            )
-            self.provider = TracerProvider(resource=self.resource)
-            processor = BatchSpanProcessor(
-                OTLPSpanExporter(endpoint=f"{telemetry_endpoint}/v1/traces", timeout=15)
-            )
-            self.provider.add_span_processor(processor)
-            self.ready = True
-        except Exception:
-            pass
-
-    def set_tracer(self):
-        if self.ready:
-            try:
-                trace.set_tracer_provider(self.provider)
-            except Exception:
-                pass
-
-    def crew_creation(self, crew):
-        """Records the creation of a crew."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Crew Created")
-                self._add_attribute(
-                    span,
-                    "crewai_version",
-                    pkg_resources.get_distribution("crewai").version,
-                )
-                self._add_attribute(span, "python_version", platform.python_version())
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(span, "crew_process", crew.process)
-                self._add_attribute(span, "crew_language", crew.language)
-                self._add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
-                self._add_attribute(span, "crew_number_of_agents", len(crew.agents))
-                self._add_attribute(
-                    span,
-                    "crew_agents",
-                    json.dumps(
-                        [
-                            {
-                                "id": str(agent.id),
-                                "role": agent.role,
-                                "memory_enabled?": agent.memory,
-                                "verbose?": agent.verbose,
-                                "max_iter": agent.max_iter,
-                                "max_rpm": agent.max_rpm,
-                                "i18n": agent.i18n.language,
-                                "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
-                                "delegation_enabled?": agent.allow_delegation,
-                                "tools_names": [tool.name for tool in agent.tools],
-                            }
-                            for agent in crew.agents
-                        ]
-                    ),
-                )
-                self._add_attribute(
-                    span,
-                    "crew_tasks",
-                    json.dumps(
-                        [
-                            {
-                                "id": str(task.id),
-                                "async_execution?": task.async_execution,
-                                "agent_role": task.agent.role if task.agent else "None",
-                                "tools_names": [tool.name for tool in task.tools],
-                            }
-                            for task in crew.tasks
-                        ]
-                    ),
-                )
-                self._add_attribute(span, "platform", platform.platform())
-                self._add_attribute(span, "platform_release", platform.release())
-                self._add_attribute(span, "platform_system", platform.system())
-                self._add_attribute(span, "platform_version", platform.version())
-                self._add_attribute(span, "cpus", os.cpu_count())
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def tool_repeated_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the repeated usage 'error' of a tool by an agent."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Tool Repeated Usage")
-                self._add_attribute(span, "tool_name", tool_name)
-                self._add_attribute(span, "attempts", attempts)
-                self._add_attribute(
-                    span, "llm", json.dumps(self._safe_llm_attributes(llm))
-                )
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def tool_usage(self, llm: Any, tool_name: str, attempts: int):
-        """Records the usage of a tool by an agent."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Tool Usage")
-                self._add_attribute(span, "tool_name", tool_name)
-                self._add_attribute(span, "attempts", attempts)
-                self._add_attribute(
-                    span, "llm", json.dumps(self._safe_llm_attributes(llm))
-                )
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def tool_usage_error(self, llm: Any):
-        """Records the usage of a tool by an agent."""
-        if self.ready:
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Tool Usage Error")
-                self._add_attribute(
-                    span, "llm", json.dumps(self._safe_llm_attributes(llm))
-                )
-                span.set_status(Status(StatusCode.OK))
-                span.end()
-            except Exception:
-                pass
-
-    def crew_execution_span(self, crew):
-        """Records the complete execution of a crew.
-        This is only collected if the user has opted-in to share the crew.
-        """
-        if (self.ready) and (crew.share_crew):
-            try:
-                tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Crew Execution")
-                self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(
-                    span,
-                    "crew_agents",
-                    json.dumps(
-                        [
-                            {
-                                "id": str(agent.id),
-                                "role": agent.role,
-                                "goal": agent.goal,
-                                "backstory": agent.backstory,
-                                "memory_enabled?": agent.memory,
-                                "verbose?": agent.verbose,
-                                "max_iter": agent.max_iter,
-                                "max_rpm": agent.max_rpm,
-                                "i18n": agent.i18n.language,
-                                "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
-                                "delegation_enabled?": agent.allow_delegation,
-                                "tools_names": [tool.name for tool in agent.tools],
-                            }
-                            for agent in crew.agents
-                        ]
-                    ),
-                )
-                self._add_attribute(
-                    span,
-                    "crew_tasks",
-                    json.dumps(
-                        [
-                            {
-                                "id": str(task.id),
-                                "description": task.description,
-                                "async_execution?": task.async_execution,
-                                "output": task.expected_output,
-                                "agent_role": task.agent.role if task.agent else "None",
-                                "context": [task.description for task in task.context]
-                                if task.context
-                                else "None",
-                                "tools_names": [tool.name for tool in task.tools],
-                            }
-                            for task in crew.tasks
-                        ]
-                    ),
-                )
-                return span
-            except Exception:
-                pass
-
-    def end_crew(self, crew, output):
-        if (self.ready) and (crew.share_crew):
-            try:
-                self._add_attribute(crew._execution_span, "crew_output", output)
-                self._add_attribute(
-                    crew._execution_span,
-                    "crew_tasks_output",
-                    json.dumps(
-                        [
-                            {
-                                "id": str(task.id),
-                                "description": task.description,
-                                "output": task.output.raw_output,
-                            }
-                            for task in crew.tasks
-                        ]
-                    ),
-                )
-                crew._execution_span.set_status(Status(StatusCode.OK))
-                crew._execution_span.end()
-            except Exception:
-                pass
-
-    def _add_attribute(self, span, key, value):
-        """Add an attribute to a span."""
-        try:
-            return span.set_attribute(key, value)
-        except Exception:
-            pass
-
-    def _safe_llm_attributes(self, llm):
-        attributes = ["name", "model_name", "base_url", "model", "top_k", "temperature"]
-        safe_attributes = {k: v for k, v in vars(llm).items() if k in attributes}
-        safe_attributes["class"] = llm.__class__.__name__
-        return safe_attributes

src/crewai/telemtry/telemetry.py

@@ -0,0 +1,114 @@
+import json
+import os
+import platform
+import socket
+
+import pkg_resources
+from opentelemetry import trace
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+from opentelemetry.sdk.resources import SERVICE_NAME, Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from opentelemetry.trace import Status, StatusCode
+
+
+class Telemetry:
+    """A class to handle anonymous telemetry for the crewai package.
+
+    The data being collected is for development purpose, all data is anonymous.
+
+    There is NO data being collected on the prompts, tasks descriptions
+    agents backstories or goals nor responses or any data that is being
+    processed by the agents, nor any secrets and env vars.
+
+    Data collected includes:
+    - Version of crewAI
+    - Version of Python
+    - General OS (e.g. number of CPUs, macOS/Windows/Linux)
+    - Number of agents and tasks in a crew
+    - Crew Process being used
+    - If Agents are using memory or allowing delegation
+    - If Tasks are being executed in parallel or sequentially
+    - Language model being used
+    - Roles of agents in a crew
+    - Tools names available
+    """
+
+    def __init__(self):
+        telemetry_endpoint = "http://telemetry.crewai.com:4318"
+        self.resource = Resource(attributes={SERVICE_NAME: "crewAI-telemetry"})
+        provider = TracerProvider(resource=self.resource)
+        processor = BatchSpanProcessor(
+            OTLPSpanExporter(endpoint=f"{telemetry_endpoint}/v1/traces")
+        )
+        provider.add_span_processor(processor)
+        trace.set_tracer_provider(provider)
+
+    def crew_creation(self, crew):
+        """Records the creation of a crew."""
+        try:
+            tracer = trace.get_tracer("crewai.telemetry")
+            span = tracer.start_span("Crew Created")
+            self.add_attribute(
+                span, "crewai_version", pkg_resources.get_distribution("crewai").version
+            )
+            self.add_attribute(span, "python_version", platform.python_version())
+            self.add_attribute(span, "hostname", socket.gethostname())
+            self.add_attribute(span, "crewid", str(crew.id))
+            self.add_attribute(span, "crew_process", crew.process)
+            self.add_attribute(span, "crew_language", crew.language)
+            self.add_attribute(span, "crew_number_of_tasks", len(crew.tasks))
+            self.add_attribute(span, "crew_number_of_agents", len(crew.agents))
+            self.add_attribute(
+                span,
+                "crew_agents",
+                json.dumps(
+                    [
+                        {
+                            "id": str(agent.id),
+                            "role": agent.role,
+                            "memory_enabled?": agent.memory,
+                            "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
+                            "delegation_enabled?": agent.allow_delegation,
+                            "tools_names": [tool.name for tool in agent.tools],
+                        }
+                        for agent in crew.agents
+                    ]
+                ),
+            )
+            self.add_attribute(
+                span,
+                "crew_tasks",
+                json.dumps(
+                    [
+                        {
+                            "id": str(task.id),
+                            "async_execution?": task.async_execution,
+                            "tools_names": [tool.name for tool in task.tools],
+                        }
+                        for task in crew.tasks
+                    ]
+                ),
+            )
+            self.add_attribute(span, "platform", platform.platform())
+            self.add_attribute(span, "platform_release", platform.release())
+            self.add_attribute(span, "platform_system", platform.system())
+            self.add_attribute(span, "platform_version", platform.version())
+            self.add_attribute(span, "cpus", os.cpu_count())
+            span.set_status(Status(StatusCode.OK))
+            span.end()
+        except Exception:
+            pass
+
+    def add_attribute(self, span, key, value):
+        """Add an attribute to a span."""
+        try:
+            return span.set_attribute(key, value)
+        except Exception:
+            pass
+
+    def _safe_llm_attributes(self, llm):
+        attributes = ["name", "model_name", "base_url", "model", "top_k", "temperature"]
+        safe_attributes = {k: v for k, v in vars(llm).items() if k in attributes}
+        safe_attributes["class"] = llm.__class__.__name__
+        return safe_attributes

src/crewai/tools/agent_tools.py

@@ -1,10 +1,9 @@
 from typing import List
 
-from langchain.tools import StructuredTool
+from langchain.tools import Tool
 from pydantic import BaseModel, Field
 
 from crewai.agent import Agent
-from crewai.task import Task
 from crewai.utilities import I18N
 
 
@@ -16,52 +15,50 @@ class AgentTools(BaseModel):
 
     def tools(self):
         return [
-            StructuredTool.from_function(
+            Tool.from_function(
                 func=self.delegate_work,
                 name="Delegate work to co-worker",
                 description=self.i18n.tools("delegate_work").format(
-                    coworkers=[f"{agent.role}" for agent in self.agents]
+                    coworkers=", ".join([agent.role for agent in self.agents])
                 ),
             ),
-            StructuredTool.from_function(
+            Tool.from_function(
                 func=self.ask_question,
                 name="Ask question to co-worker",
                 description=self.i18n.tools("ask_question").format(
-                    coworkers=[f"{agent.role}" for agent in self.agents]
+                    coworkers=", ".join([agent.role for agent in self.agents])
                 ),
             ),
         ]
 
-    def delegate_work(self, coworker: str, task: str, context: str):
-        """Useful to delegate a specific task to a coworker passing all necessary context and names."""
-        return self._execute(coworker, task, context)
+    def delegate_work(self, command):
+        """Useful to delegate a specific task to a coworker."""
+        return self._execute(command)
 
-    def ask_question(self, coworker: str, question: str, context: str):
-        """Useful to ask a question, opinion or take from a coworker passing all necessary context and names."""
-        return self._execute(coworker, question, context)
+    def ask_question(self, command):
+        """Useful to ask a question, opinion or take from a coworker."""
+        return self._execute(command)
 
-    def _execute(self, agent, task, context):
+    def _execute(self, command):
         """Execute the command."""
         try:
-            agent = [
-                available_agent
-                for available_agent in self.agents
-                if available_agent.role.strip().lower() == agent.strip().lower()
-            ]
-        except:
-            return self.i18n.errors("agent_tool_unexsiting_coworker").format(
-                coworkers="\n".join([f"- {agent.role}" for agent in self.agents])
-            )
+            agent, task, context = command.split("|")
+        except ValueError:
+            return self.i18n.errors("agent_tool_missing_param")
+
+        if not agent or not task or not context:
+            return self.i18n.errors("agent_tool_missing_param")
+
+        agent = [
+            available_agent
+            for available_agent in self.agents
+            if available_agent.role == agent
+        ]
 
         if not agent:
             return self.i18n.errors("agent_tool_unexsiting_coworker").format(
-                coworkers="\n".join([f"- {agent.role}" for agent in self.agents])
+                coworkers=", ".join([agent.role for agent in self.agents])
             )
 
         agent = agent[0]
-        task = Task(
-            description=task,
-            agent=agent,
-            expected_output="Your best answer to your coworker asking you this, accounting for the context shared.",
-        )
         return agent.execute_task(task, context)

src/crewai/tools/cache_tools.py

@@ -1,4 +1,4 @@
-from langchain.tools import StructuredTool
+from langchain.tools import Tool
 from pydantic import BaseModel, ConfigDict, Field
 
 from crewai.agents.cache import CacheHandler
@@ -15,7 +15,7 @@ class CacheTools(BaseModel):
     )
 
     def tool(self):
-        return StructuredTool.from_function(
+        return Tool.from_function(
             func=self.hit_cache,
             name=self.name,
             description="Reads directly from the cache",

src/crewai/tools/tool_calling.py

@@ -1,21 +0,0 @@
-from typing import Any, Dict, Optional
-
-from pydantic import BaseModel as PydanticBaseModel
-from pydantic import Field as PydanticField
-from pydantic.v1 import BaseModel, Field
-
-
-class ToolCalling(BaseModel):
-    tool_name: str = Field(..., description="The name of the tool to be called.")
-    arguments: Optional[Dict[str, Any]] = Field(
-        ..., description="A dictinary of arguments to be passed to the tool."
-    )
-
-
-class InstructorToolCalling(PydanticBaseModel):
-    tool_name: str = PydanticField(
-        ..., description="The name of the tool to be called."
-    )
-    arguments: Optional[Dict[str, Any]] = PydanticField(
-        ..., description="A dictinary of arguments to be passed to the tool."
-    )

src/crewai/tools/tool_output_parser.py

@@ -1,39 +0,0 @@
-import json
-from typing import Any, List
-
-import regex
-from langchain.output_parsers import PydanticOutputParser
-from langchain_core.exceptions import OutputParserException
-from langchain_core.outputs import Generation
-from langchain_core.pydantic_v1 import ValidationError
-
-
-class ToolOutputParser(PydanticOutputParser):
-    """Parses the function calling of a tool usage and it's arguments."""
-
-    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
-        result[0].text = self._transform_in_valid_json(result[0].text)
-        json_object = super().parse_result(result)
-        try:
-            return self.pydantic_object.parse_obj(json_object)
-        except ValidationError as e:
-            name = self.pydantic_object.__name__
-            msg = f"Failed to parse {name} from completion {json_object}. Got: {e}"
-            raise OutputParserException(msg, llm_output=json_object)
-
-    def _transform_in_valid_json(self, text) -> str:
-        text = text.replace("```", "").replace("json", "")
-        json_pattern = r"\{(?:[^{}]|(?R))*\}"
-        matches = regex.finditer(json_pattern, text)
-
-        for match in matches:
-            try:
-                # Attempt to parse the matched string as JSON
-                json_obj = json.loads(match.group())
-                # Return the first successfully parsed JSON object
-                json_obj = json.dumps(json_obj)
-                return str(json_obj)
-            except json.JSONDecodeError:
-                # If parsing fails, skip to the next match
-                continue
-        return text

src/crewai/tools/tool_usage.py

@@ -1,287 +0,0 @@
-import ast
-from textwrap import dedent
-from typing import Any, List, Union
-
-from langchain_core.tools import BaseTool
-from langchain_openai import ChatOpenAI
-
-from crewai.agents.tools_handler import ToolsHandler
-from crewai.telemetry import Telemetry
-from crewai.tools.tool_calling import InstructorToolCalling, ToolCalling
-from crewai.utilities import I18N, Converter, ConverterError, Printer
-
-OPENAI_BIGGER_MODELS = ["gpt-4"]
-
-
-class ToolUsageErrorException(Exception):
-    """Exception raised for errors in the tool usage."""
-
-    def __init__(self, message: str) -> None:
-        self.message = message
-        super().__init__(self.message)
-
-
-class ToolUsage:
-    """
-    Class that represents the usage of a tool by an agent.
-
-    Attributes:
-        task: Task being executed.
-        tools_handler: Tools handler that will manage the tool usage.
-        tools: List of tools available for the agent.
-        tools_description: Description of the tools available for the agent.
-        tools_names: Names of the tools available for the agent.
-        function_calling_llm: Language model to be used for the tool usage.
-    """
-
-    def __init__(
-        self,
-        tools_handler: ToolsHandler,
-        tools: List[BaseTool],
-        tools_description: str,
-        tools_names: str,
-        task: Any,
-        function_calling_llm: Any,
-        action: Any,
-    ) -> None:
-        self._i18n: I18N = I18N()
-        self._printer: Printer = Printer()
-        self._telemetry: Telemetry = Telemetry()
-        self._run_attempts: int = 1
-        self._max_parsing_attempts: int = 3
-        self._remember_format_after_usages: int = 3
-        self.tools_description = tools_description
-        self.tools_names = tools_names
-        self.tools_handler = tools_handler
-        self.tools = tools
-        self.task = task
-        self.action = action
-        self.function_calling_llm = function_calling_llm
-
-        # Set the maximum parsing attempts for bigger models
-        if (isinstance(self.function_calling_llm, ChatOpenAI)) and (
-            self.function_calling_llm.openai_api_base == None
-        ):
-            if self.function_calling_llm.model_name in OPENAI_BIGGER_MODELS:
-                self._max_parsing_attempts = 2
-                self._remember_format_after_usages = 4
-
-    def parse(self, tool_string: str):
-        """Parse the tool string and return the tool calling."""
-        return self._tool_calling(tool_string)
-
-    def use(
-        self, calling: Union[ToolCalling, InstructorToolCalling], tool_string: str
-    ) -> str:
-        if isinstance(calling, ToolUsageErrorException):
-            error = calling.message
-            self._printer.print(content=f"\n\n{error}\n", color="red")
-            self.task.increment_tools_errors()
-            return error
-        try:
-            tool = self._select_tool(calling.tool_name)
-        except Exception as e:
-            error = getattr(e, "message", str(e))
-            self.task.increment_tools_errors()
-            self._printer.print(content=f"\n\n{error}\n", color="red")
-            return error
-        return f"{self._use(tool_string=tool_string, tool=tool, calling=calling)}"
-
-    def _use(
-        self,
-        tool_string: str,
-        tool: BaseTool,
-        calling: Union[ToolCalling, InstructorToolCalling],
-    ) -> None:
-        if self._check_tool_repeated_usage(calling=calling):
-            try:
-                result = self._i18n.errors("task_repeated_usage").format(
-                    tool_names=self.tools_names
-                )
-                self._printer.print(content=f"\n\n{result}\n", color="yellow")
-                self._telemetry.tool_repeated_usage(
-                    llm=self.function_calling_llm,
-                    tool_name=tool.name,
-                    attempts=self._run_attempts,
-                )
-                result = self._format_result(result=result)
-                return result
-            except Exception:
-                self.task.increment_tools_errors()
-
-        result = self.tools_handler.cache.read(
-            tool=calling.tool_name, input=calling.arguments
-        )
-
-        if not result:
-            try:
-                if calling.tool_name in [
-                    "Delegate work to co-worker",
-                    "Ask question to co-worker",
-                ]:
-                    self.task.increment_delegations()
-
-                if calling.arguments:
-                    try:
-                        acceptable_args = tool.args_schema.schema()["properties"].keys()
-                        arguments = {
-                            k: v
-                            for k, v in calling.arguments.items()
-                            if k in acceptable_args
-                        }
-                        result = tool._run(**arguments)
-                    except Exception:
-                        if tool.args_schema:
-                            arguments = calling.arguments
-                            result = tool._run(**arguments)
-                        else:
-                            arguments = calling.arguments.values()
-                            result = tool._run(*arguments)
-                else:
-                    result = tool._run()
-            except Exception as e:
-                self._run_attempts += 1
-                if self._run_attempts > self._max_parsing_attempts:
-                    self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                    error_message = self._i18n.errors("tool_usage_exception").format(
-                        error=e, tool=tool.name, tool_inputs=tool.description
-                    )
-                    error = ToolUsageErrorException(
-                        f'\n{error_message}.\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
-                    ).message
-                    self.task.increment_tools_errors()
-                    self._printer.print(content=f"\n\n{error_message}\n", color="red")
-                    return error
-                self.task.increment_tools_errors()
-                return self.use(calling=calling, tool_string=tool_string)
-
-            self.tools_handler.on_tool_use(calling=calling, output=result)
-
-        self._printer.print(content=f"\n\n{result}\n", color="yellow")
-        self._telemetry.tool_usage(
-            llm=self.function_calling_llm,
-            tool_name=tool.name,
-            attempts=self._run_attempts,
-        )
-        result = self._format_result(result=result)
-        return result
-
-    def _format_result(self, result: Any) -> None:
-        self.task.used_tools += 1
-        if self._should_remember_format():
-            result = self._remember_format(result=result)
-        return result
-
-    def _should_remember_format(self) -> None:
-        return self.task.used_tools % self._remember_format_after_usages == 0
-
-    def _remember_format(self, result: str) -> None:
-        result = str(result)
-        result += "\n\n" + self._i18n.slice("tools").format(
-            tools=self.tools_description, tool_names=self.tools_names
-        )
-        return result
-
-    def _check_tool_repeated_usage(
-        self, calling: Union[ToolCalling, InstructorToolCalling]
-    ) -> None:
-        if last_tool_usage := self.tools_handler.last_used_tool:
-            return (calling.tool_name == last_tool_usage.tool_name) and (
-                calling.arguments == last_tool_usage.arguments
-            )
-
-    def _select_tool(self, tool_name: str) -> BaseTool:
-        for tool in self.tools:
-            if tool.name.lower().strip() == tool_name.lower().strip():
-                return tool
-        self.task.increment_tools_errors()
-        if tool_name and tool_name != "":
-            raise Exception(
-                f"Action '{tool_name}' don't exist, these are the only available Actions: {self.tools_description}"
-            )
-        else:
-            raise Exception(
-                f"I forgot the Action name, these are the only available Actions: {self.tools_description}"
-            )
-
-    def _render(self) -> str:
-        """Render the tool name and description in plain text."""
-        descriptions = []
-        for tool in self.tools:
-            args = {
-                k: {k2: v2 for k2, v2 in v.items() if k2 in ["description", "type"]}
-                for k, v in tool.args.items()
-            }
-            descriptions.append(
-                "\n".join(
-                    [
-                        f"Tool Name: {tool.name.lower()}",
-                        f"Tool Description: {tool.description}",
-                        f"Tool Arguments: {args}",
-                    ]
-                )
-            )
-        return "\n--\n".join(descriptions)
-
-    def _is_gpt(self, llm) -> bool:
-        return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None
-
-    def _tool_calling(
-        self, tool_string: str
-    ) -> Union[ToolCalling, InstructorToolCalling]:
-        try:
-            if self.function_calling_llm:
-                model = (
-                    InstructorToolCalling
-                    if self._is_gpt(self.function_calling_llm)
-                    else ToolCalling
-                )
-                converter = Converter(
-                    text=f"Only tools available:\n###\n{self._render()}\n\nReturn a valid schema for the tool, the tool name must be exactly equal one of the options, use this text to inform the valid ouput schema:\n\n{tool_string}```",
-                    llm=self.function_calling_llm,
-                    model=model,
-                    instructions=dedent(
-                        """\
-                                            The schema should have the following structure, only two keys:
-                                            - tool_name: str
-                                            - arguments: dict (with all arguments being passed)
-
-                                            Example:
-                                            {"tool_name": "tool name", "arguments": {"arg_name1": "value", "arg_name2": 2}}""",
-                    ),
-                    max_attemps=1,
-                )
-                calling = converter.to_pydantic()
-
-                if isinstance(calling, ConverterError):
-                    raise calling
-            else:
-                tool_name = self.action.tool
-                tool = self._select_tool(tool_name)
-                try:
-                    arguments = ast.literal_eval(self.action.tool_input)
-                except Exception:
-                    return ToolUsageErrorException(
-                        f'{self._i18n.errors("tool_arguments_error")}'
-                    )
-                if not isinstance(arguments, dict):
-                    return ToolUsageErrorException(
-                        f'{self._i18n.errors("tool_arguments_error")}'
-                    )
-                calling = ToolCalling(
-                    tool_name=tool.name,
-                    arguments=arguments,
-                    log=tool_string,
-                )
-        except Exception as e:
-            self._run_attempts += 1
-            if self._run_attempts > self._max_parsing_attempts:
-                self._telemetry.tool_usage_error(llm=self.function_calling_llm)
-                self.task.increment_tools_errors()
-                self._printer.print(content=f"\n\n{e}\n", color="red")
-                return ToolUsageErrorException(
-                    f'{self._i18n.errors("tool_usage_error").format(error=e)}\nMoving on then. {self._i18n.slice("format").format(tool_names=self.tools_names)}'
-                )
-            return self._tool_calling(tool_string)
-
-        return calling

src/crewai/translations/el.json

@@ -9,19 +9,18 @@
     "task": "Αρχή! Αυτό είναι ΠΟΛΥ σημαντικό για εσάς, η δουλειά σας εξαρτάται από αυτό!\n\nΤρέχουσα εργασία: {input}",
     "memory": "Αυτή είναι η περίληψη της μέχρι τώρα δουλειάς σας:\n{chat_history}",
     "role_playing": "Είσαι {role}.\n{backstory}\n\nΟ προσωπικός σας στόχος είναι: {goal}",
-    "tools": "ΕΡΓΑΛΕΙΑ:\n------\nΈχετε πρόσβαση μόνο στα ακόλουθα εργαλεία:\n\n{tools}\n\nΓια να χρησιμοποιήσετε ένα εργαλείο, χρησιμοποιήστε την ακόλουθη ακριβώς μορφή:\n\n```\nThought: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Ναι\nΕνέργεια: το εργαλείο που θέλετε να χρησιμοποιήσετε, θα πρέπει να είναι ένα από τα [{tool_names}], μόνο το όνομα.\nΕισαγωγή ενέργειας: Οποιαδήποτε και όλες οι σχετικές πληροφορίες και το πλαίσιο χρήσης του εργαλείου\nΠαρατήρηση: το αποτέλεσμα της χρήσης του εργαλείου\n```\n\nΌταν έχετε μια απάντηση για την εργασία σας ή εάν δεν χρειάζεται να χρησιμοποιήσετε ένα εργαλείο, ΠΡΕΠΕΙ να χρησιμοποιήσετε τη μορφή:\n\n```\nΣκέψη: Πρέπει να χρησιμοποιήσω ένα εργαλείο ? Όχι\nΤελική απάντηση: [η απάντησή σας εδώ]```",
+    "tools": "ΕΡΓΑΛΕΙΑ:\n------\nΈχετε πρόσβαση μόνο στα ακόλουθα εργαλεία:\n\n{tools}\n\nΓια να χρησιμοποιήσετε ένα εργαλείο, χρησιμοποιήστε την ακόλουθη ακριβώς μορφή:\n\n```\nΣκέψη: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Ναί\nΔράση: η ενέργεια που πρέπει να γίνει, πρέπει να είναι μία από τις[{tool_names}], μόνο το όνομα.\nΕνέργεια προς εισαγωγή: η είσοδος στη δράση\nΠαρατήρηση: το αποτέλεσμα της δράσης\n```\n\nΌταν έχετε μια απάντηση για την εργασία σας ή εάν δεν χρειάζεται να χρησιμοποιήσετε ένα εργαλείο, ΠΡΕΠΕΙ να χρησιμοποιήσετε τη μορφή:\n\n```\nΣκέψη: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Οχι\nΤελική απάντηση: [η απάντησή σας εδώ]```",
     "task_with_context": "{task}\nΑυτό είναι το πλαίσιο με το οποίο εργάζεστε:\n{context}",
     "expected_output": "Η τελική σας απάντηση πρέπει να είναι: {expected_output}"
   },
   "errors": {
     "force_final_answer": "Στην πραγματικότητα, χρησιμοποίησα πάρα πολλά εργαλεία, οπότε θα σταματήσω τώρα και θα σας δώσω την απόλυτη ΚΑΛΥΤΕΡΗ τελική μου απάντηση ΤΩΡΑ, χρησιμοποιώντας την αναμενόμενη μορφή: ```\nΣκέφτηκα: Χρειάζεται να χρησιμοποιήσω ένα εργαλείο; Όχι\nΤελική απάντηση: [η απάντησή σας εδώ]```",
-    "agent_tool_unexsiting_coworker": "\nΣφάλμα κατά την εκτέλεση του εργαλείου. Ο συνάδελφος που αναφέρεται στο Action Input δεν βρέθηκε, πρέπει να είναι μία από τις ακόλουθες επιλογές:\n{coworkers}..\n",
-    "task_repeated_usage": "Μόλις χρησιμοποίησα το εργαλείο {tool} με είσοδο {tool_input}. Άρα το ξέρω ήδη και πρέπει να σταματήσω να το χρησιμοποιώ στη σειρά με την ίδια είσοδο. \nΘα μπορούσα να δώσω την τελική μου απάντηση εάν είμαι έτοιμος, χρησιμοποιώντας ακριβώς την αναμενόμενη μορφή παρακάτω: \n\nΣκέφτηκα: Χρειάζεται να χρησιμοποιήσω κάποιο εργαλείο; Όχι\nΤελική απάντηση: [η απάντησή σας εδώ]\n",
-    "tool_usage_error": "Φαίνεται ότι αντιμετωπίσαμε ένα απροσδόκητο σφάλμα κατά την προσπάθεια χρήσης του εργαλείου.",
-    "tool_usage_exception": "Φαίνεται ότι αντιμετωπίσαμε ένα απροσδόκητο σφάλμα κατά την προσπάθεια χρήσης του εργαλείου. Αυτό ήταν το σφάλμα: {error}"
+    "agent_tool_missing_param": "\nΣφάλμα κατά την εκτέλεση του εργαλείου. Λείπουν ακριβώς 3 διαχωρισμένες τιμές σωλήνων (|). Για παράδειγμα, `coworker|task|context`. Πρέπει να φροντίσω να περάσω το πλαίσιο ως πλαίσιο.\n",
+    "agent_tool_unexsiting_coworker": "\nΣφάλμα κατά την εκτέλεση του εργαλείου. Ο συνάδελφος που αναφέρεται στο Ενέργεια προς εισαγωγή δεν βρέθηκε, πρέπει να είναι μία από τις ακόλουθες επιλογές: {coworkers}.\n",
+    "task_repeated_usage": "Μόλις χρησιμοποίησα το {tool} εργαλείο με είσοδο {tool_input}. Άρα ξέρω ήδη το αποτέλεσμα αυτού και δεν χρειάζεται να το χρησιμοποιήσω τώρα.\n"
   },
   "tools": {
-    "delegate_work": "Αναθέστε μια συγκεκριμένη εργασία σε έναν από τους παρακάτω συναδέλφους:\n{coworkers}.\nΗ εισαγωγή σε αυτό το εργαλείο θα πρέπει να είναι ο ρόλος του συναδέλφου, η εργασία που θέλετε να κάνει και ΟΛΟ το απαραίτητο πλαίσιο για την εκτέλεση της εργασίας, δεν γνωρίζουν τίποτα για την εργασία, γι' αυτό μοιραστείτε απολύτως όλα όσα γνωρίζετε, μην αναφέρετε πράγματα, αλλά εξηγήστε τα.",
-    "ask_question": "Κάντε μια συγκεκριμένη ερώτηση σε έναν από τους παρακάτω συναδέλφους:\n{coworkers}.\nΗ είσοδος σε αυτό το εργαλείο θα πρέπει να είναι ο ρόλος του συναδέλφου, η ερώτηση που έχετε για αυτόν και ΟΛΟ το απαραίτητο πλαίσιο για να κάνετε σωστά την ερώτηση, δεν γνωρίζουν τίποτα για την ερώτηση, γι' αυτό μοιραστείτε απολύτως όλα όσα γνωρίζετε, μην αναφέρετε πράγματα, αλλά εξηγήστε τα."
+    "delegate_work": "Χρήσιμο για την ανάθεση μιας συγκεκριμένης εργασίας σε έναν από τους παρακάτω συναδέλφους: {coworkers}.\nΗ είσοδος σε αυτό το εργαλείο θα πρέπει να είναι ένα κείμενο χωρισμένο σε σωλήνα (|) μήκους 3 (τρία), που αντιπροσωπεύει τον συνάδελφο στον οποίο θέλετε να του ζητήσετε (μία από τις επιλογές), την εργασία και όλο το πραγματικό πλαίσιο που έχετε για την εργασία .\nΓια παράδειγμα, `coworker|task|context`.",
+    "ask_question": "Χρήσιμο για να κάνετε μια ερώτηση, γνώμη ή αποδοχή από τους παρακάτω συναδέλφους: {coworkers}.\nΗ είσοδος σε αυτό το εργαλείο θα πρέπει να είναι ένα κείμενο χωρισμένο σε σωλήνα (|) μήκους 3 (τρία), που αντιπροσωπεύει τον συνάδελφο στον οποίο θέλετε να το ρωτήσετε (μία από τις επιλογές), την ερώτηση και όλο το πραγματικό πλαίσιο που έχετε για την ερώτηση.\nΓια παράδειγμα, `coworker|question|context`."
   }
 }

src/crewai/translations/en.json

@@ -6,29 +6,21 @@
   },
   "slices": {
     "observation": "\nObservation",
-    "task": "\n\nCurrent Task: {input}\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought: ",
+    "task": "Begin! This is VERY important to you, your job depends on it!\n\nCurrent Task: {input}",
     "memory": "This is the summary of your work so far:\n{chat_history}",
-    "role_playing": "You are {role}. {backstory}\nYour personal goal is: {goal}",
-    "tools": "\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [{tool_names}], just the name, exactly as it's written.\nAction Input: the input to the action, just a simple a python dictionary using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n",
-    "no_tools": "To give my best complete final answer to the task use the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\nYour final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!\n\nThought: ",
-    "format": "I MUST either use a tool (use one at time) OR give my best final answer. To Use the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action, dictionary\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\nYour final answer must be the great and the most complete as possible, it must be outcome described\n\n ",
-    "final_answer_format": "If you don't need to use any more tools, you must give your best complete final answer, make sure it satisfy the expect criteria, use the EXACT format below:\n\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task.\n\n",
-    "format_without_tools": "\nSorry, I didn't use the right format. I MUST either use a tool (among the available ones), OR give my best final answer.\nI just remembered the expected format I must follow:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now can give a great answer\nFinal Answer: my best complete final answer to the task\nYour final answer must be the great and the most complete as possible, it must be outcome described\n\n",
-    "task_with_context": "{task}\n\nThis is the context you're working with:\n{context}",
-    "expected_output": "\nThis is the expect criteria for your final answer: {expected_output} \n you MUST return the actual complete content as the final answer, not a summary."
+    "role_playing": "You are {role}.\n{backstory}\n\nYour personal goal is: {goal}",
+    "tools": "TOOLS:\n------\nYou have access to only the following tools:\n\n{tools}\n\nTo use a tool, please use the exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the action to take, should be one of [{tool_names}], just the name.\nAction Input: the input to the action\nObservation: the result of the action\n```\n\nWhen you have a response for your task, or if you do not need to use a tool, you MUST use the format:\n\n```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]```",
+    "task_with_context": "{task}\nThis is the context you're working with:\n{context}",
+    "expected_output": "Your final answer must be: {expected_output}"
   },
   "errors": {
-    "unexpected_format": "\nSorry, I didn't use the expected format, I MUST either use a tool (use one at time) OR give my best final answer.\n",
-    "force_final_answer": "Tool won't be use because it's time to give your final answer. Don't use tools and just your absolute BEST Final answer.",
-    "agent_tool_unexsiting_coworker": "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n{coworkers}\n",
-    "task_repeated_usage": "I tried reusing the same input, I must stop using this action input. I'll try something else instead.\n\n",
-    "tool_usage_error": "I encountered an error: {error}",
-    "tool_arguments_error": "Error: the Action Input is not a valid key, value dictionary.",
-    "wrong_tool_name": "You tried to use the tool {tool}, but it doesn't exist. You must use one of the following tools, use one at time: {tools}.",
-    "tool_usage_exception": "I encountered an error while trying to use the tool. This was the error: {error}.\n Tool {tool} accepts there inputs: {tool_inputs}"
+    "force_final_answer": "Actually, I used too many tools, so I'll stop now and give you my absolute BEST Final answer NOW, using the expected format: ```\nThought: Do I need to use a tool? No\nFinal Answer: [your response here]```",
+    "agent_tool_missing_param": "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n",
+    "agent_tool_unexsiting_coworker": "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: {coworkers}.\n",
+    "task_repeated_usage": "I just used the {tool} tool with input {tool_input}. So I already know the result of that and don't need to use it now.\n"
   },
   "tools": {
-    "delegate_work": "Delegate a specific task to one of the following co-workers: {coworkers}\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to exectue the task, they know nothing about the task, so share absolute everything you know, don't reference things but instead explain them.",
-    "ask_question": "Ask a specific question to one of the following co-workers: {coworkers}\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don't reference things but instead explain them."
+    "delegate_work": "Useful to delegate a specific task to one of the following co-workers: {coworkers}.\nThe input to this tool should be a pipe (|) separated text of length 3 (three), representing the co-worker you want to ask it to (one of the options), the task and all actual context you have for the task.\nFor example, `coworker|task|context`.",
+    "ask_question": "Useful to ask a question, opinion or take from on of the following co-workers: {coworkers}.\nThe input to this tool should be a pipe (|) separated text of length 3 (three), representing the co-worker you want to ask it to (one of the options), the question and all actual context you have for the question.\n For example, `coworker|question|context`."
   }
 }

src/crewai/utilities/__init__.py

@@ -1,7 +1,4 @@
-from .converter import Converter, ConverterError
 from .i18n import I18N
-from .instructor import Instructor
 from .logger import Logger
-from .printer import Printer
 from .prompts import Prompts
 from .rpm_controller import RPMController

src/crewai/utilities/converter.py

@@ -1,87 +0,0 @@
-import json
-from typing import Any, Optional
-
-from langchain.schema import HumanMessage, SystemMessage
-from langchain_openai import ChatOpenAI
-from pydantic import BaseModel, Field, PrivateAttr, model_validator
-
-
-class ConverterError(Exception):
-    """Error raised when Converter fails to parse the input."""
-
-    def __init__(self, message: str, *args: object) -> None:
-        super().__init__(message, *args)
-        self.message = message
-
-
-class Converter(BaseModel):
-    """Class that converts text into either pydantic or json."""
-
-    _is_gpt: bool = PrivateAttr(default=True)
-    text: str = Field(description="Text to be converted.")
-    llm: Any = Field(description="The language model to be used to convert the text.")
-    model: Any = Field(description="The model to be used to convert the text.")
-    instructions: str = Field(description="Conversion instructions to the LLM.")
-    max_attemps: Optional[int] = Field(
-        description="Max number of attemps to try to get the output formated.",
-        default=3,
-    )
-
-    @model_validator(mode="after")
-    def check_llm_provider(self):
-        if not self._is_gpt(self.llm):
-            self._is_gpt = False
-
-    def to_pydantic(self, current_attempt=1):
-        """Convert text to pydantic."""
-        try:
-            if self._is_gpt:
-                return self._create_instructor().to_pydantic()
-            else:
-                return self._create_chain().invoke({})
-        except Exception as e:
-            if current_attempt < self.max_attemps:
-                return self.to_pydantic(current_attempt + 1)
-            return ConverterError(
-                f"Failed to convert text into a pydantic model due to the following error: {e}"
-            )
-
-    def to_json(self, current_attempt=1):
-        """Convert text to json."""
-        try:
-            if self._is_gpt:
-                return self._create_instructor().to_json()
-            else:
-                return json.dumps(self._create_chain().invoke({}).model_dump())
-        except Exception:
-            if current_attempt < self.max_attemps:
-                return self.to_json(current_attempt + 1)
-            return ConverterError("Failed to convert text into JSON.")
-
-    def _create_instructor(self):
-        """Create an instructor."""
-        from crewai.utilities import Instructor
-
-        inst = Instructor(
-            llm=self.llm,
-            max_attemps=self.max_attemps,
-            model=self.model,
-            content=self.text,
-            instructions=self.instructions,
-        )
-        return inst
-
-    def _create_chain(self):
-        """Create a chain."""
-        from crewai.utilities.crew_pydantic_output_parser import (
-            CrewPydanticOutputParser,
-        )
-
-        parser = CrewPydanticOutputParser(pydantic_object=self.model)
-        new_prompt = HumanMessage(content=self.text) + SystemMessage(
-            content=self.instructions
-        )
-        return new_prompt | self.llm | parser
-
-    def _is_gpt(self, llm) -> bool:
-        return isinstance(llm, ChatOpenAI) and llm.openai_api_base == None

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -1,43 +0,0 @@
-import json
-from typing import Any, List, Type, Union
-
-import regex
-from langchain.output_parsers import PydanticOutputParser
-from langchain_core.exceptions import OutputParserException
-from langchain_core.outputs import Generation
-from langchain_core.pydantic_v1 import ValidationError
-from pydantic import BaseModel
-from pydantic.v1 import BaseModel as V1BaseModel
-
-
-class CrewPydanticOutputParser(PydanticOutputParser):
-    """Parses the text into pydantic models"""
-
-    pydantic_object: Union[Type[BaseModel], Type[V1BaseModel]]
-
-    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
-        result[0].text = self._transform_in_valid_json(result[0].text)
-        json_object = super().parse_result(result)
-        try:
-            return self.pydantic_object.parse_obj(json_object)
-        except ValidationError as e:
-            name = self.pydantic_object.__name__
-            msg = f"Failed to parse {name} from completion {json_object}. Got: {e}"
-            raise OutputParserException(msg, llm_output=json_object)
-
-    def _transform_in_valid_json(self, text) -> str:
-        text = text.replace("```", "").replace("json", "")
-        json_pattern = r"\{(?:[^{}]|(?R))*\}"
-        matches = regex.finditer(json_pattern, text)
-
-        for match in matches:
-            try:
-                # Attempt to parse the matched string as JSON
-                json_obj = json.loads(match.group())
-                # Return the first successfully parsed JSON object
-                json_obj = json.dumps(json_obj)
-                return str(json_obj)
-            except json.JSONDecodeError:
-                # If parsing fails, skip to the next match
-                continue
-        return text

src/crewai/utilities/instructor.py

@@ -1,50 +0,0 @@
-from typing import Any, Optional, Type
-
-import instructor
-from pydantic import BaseModel, Field, PrivateAttr, model_validator
-
-
-class Instructor(BaseModel):
-    """Class that wraps an agent llm with instructor."""
-
-    _client: Any = PrivateAttr()
-    content: str = Field(description="Content to be sent to the instructor.")
-    agent: Optional[Any] = Field(
-        description="The agent that needs to use instructor.", default=None
-    )
-    llm: Optional[Any] = Field(
-        description="The agent that needs to use instructor.", default=None
-    )
-    instructions: Optional[str] = Field(
-        description="Instructions to be sent to the instructor.",
-        default=None,
-    )
-    model: Type[BaseModel] = Field(
-        description="Pydantic model to be used to create an output."
-    )
-
-    @model_validator(mode="after")
-    def set_instructor(self):
-        """Set instructor."""
-        if self.agent and not self.llm:
-            self.llm = self.agent.function_calling_llm or self.agent.llm
-
-        self._client = instructor.patch(
-            self.llm.client._client,
-            mode=instructor.Mode.TOOLS,
-        )
-        return self
-
-    def to_json(self):
-        model = self.to_pydantic()
-        return model.model_dump_json(indent=2)
-
-    def to_pydantic(self):
-        messages = [{"role": "user", "content": self.content}]
-        if self.instructions:
-            messages.append({"role": "system", "content": self.instructions})
-
-        model = self._client.chat.completions.create(
-            model=self.llm.model_name, response_model=self.model, messages=messages
-        )
-        return model

src/crewai/utilities/printer.py

@@ -1,14 +0,0 @@
-class Printer:
-    def print(self, content: str, color: str):
-        if color == "yellow":
-            self._print_yellow(content)
-        elif color == "red":
-            self._print_red(content)
-        else:
-            print(content)
-
-    def _print_yellow(self, content):
-        print("\033[93m {}\033[00m".format(content))
-
-    def _print_red(self, content):
-        print("\033[91m {}\033[00m".format(content))

src/crewai/utilities/prompts.py

@@ -1,6 +1,6 @@
-from typing import Any, ClassVar
+from typing import ClassVar
 
-from langchain.prompts import BasePromptTemplate, PromptTemplate
+from langchain.prompts import PromptTemplate, BasePromptTemplate
 from pydantic import BaseModel, Field
 
 from crewai.utilities import I18N
@@ -10,18 +10,12 @@ class Prompts(BaseModel):
     """Manages and generates prompts for a generic agent with support for different languages."""
 
     i18n: I18N = Field(default=I18N())
-    tools: list[Any] = Field(default=[])
+
     SCRATCHPAD_SLICE: ClassVar[str] = "\n{agent_scratchpad}"
 
     def task_execution_with_memory(self) -> BasePromptTemplate:
         """Generate a prompt for task execution with memory components."""
-        slices = ["role_playing"]
-        if len(self.tools) > 0:
-            slices.append("tools")
-        else:
-            slices.append("no_tools")
-        slices.extend(["memory", "task"])
-        return self._build_prompt(slices)
+        return self._build_prompt(["role_playing", "tools", "memory", "task"])
 
     def task_execution_without_tools(self) -> BasePromptTemplate:
         """Generate a prompt for task execution without tools components."""
@@ -29,17 +23,10 @@ class Prompts(BaseModel):
 
     def task_execution(self) -> BasePromptTemplate:
         """Generate a standard prompt for task execution."""
-        slices = ["role_playing"]
-        if len(self.tools) > 0:
-            slices.append("tools")
-        else:
-            slices.append("no_tools")
-        slices.append("task")
-        return self._build_prompt(slices)
+        return self._build_prompt(["role_playing", "tools", "task"])
 
     def _build_prompt(self, components: list[str]) -> BasePromptTemplate:
         """Constructs a prompt string from specified components."""
         prompt_parts = [self.i18n.slice(component) for component in components]
         prompt_parts.append(self.SCRATCHPAD_SLICE)
-        prompt = PromptTemplate.from_template("".join(prompt_parts))
-        return prompt
+        return PromptTemplate.from_template("".join(prompt_parts))

src/crewai/utilities/pydantic_schema_parser.py

@@ -1,40 +0,0 @@
-from typing import Type, get_args, get_origin
-
-from pydantic import BaseModel
-
-
-class PydanticSchemaParser(BaseModel):
-    model: Type[BaseModel]
-
-    def get_schema(self) -> str:
-        """
-        Public method to get the schema of a Pydantic model.
-
-        :param model: The Pydantic model class to generate schema for.
-        :return: String representation of the model schema.
-        """
-        return self._get_model_schema(self.model)
-
-    def _get_model_schema(self, model, depth=0) -> str:
-        lines = []
-        for field_name, field in model.model_fields.items():
-            field_type_str = self._get_field_type(field, depth + 1)
-            lines.append(f"{' ' * 4 * depth}- {field_name}: {field_type_str}")
-
-        return "\n".join(lines)
-
-    def _get_field_type(self, field, depth) -> str:
-        field_type = field.annotation
-        if get_origin(field_type) is list:
-            list_item_type = get_args(field_type)[0]
-            if isinstance(list_item_type, type) and issubclass(
-                list_item_type, BaseModel
-            ):
-                nested_schema = self._get_model_schema(list_item_type, depth + 1)
-                return f"List[\n{nested_schema}\n{' ' * 4 * depth}]"
-            else:
-                return f"List[{list_item_type.__name__}]"
-        elif issubclass(field_type, BaseModel):
-            return f"\n{self._get_model_schema(field_type, depth)}"
-        else:
-            return field_type.__name__

src/crewai/utilities/token_counter_callback.py

@@ -1,60 +0,0 @@
-from typing import Any, Dict, List
-
-import tiktoken
-from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import LLMResult
-
-
-class TokenProcess:
-    total_tokens: int = 0
-    prompt_tokens: int = 0
-    completion_tokens: int = 0
-    successful_requests: int = 0
-
-    def sum_prompt_tokens(self, tokens: int):
-        self.prompt_tokens = self.prompt_tokens + tokens
-        self.total_tokens = self.total_tokens + tokens
-
-    def sum_completion_tokens(self, tokens: int):
-        self.completion_tokens = self.completion_tokens + tokens
-        self.total_tokens = self.total_tokens + tokens
-
-    def sum_successful_requests(self, requests: int):
-        self.successful_requests = self.successful_requests + requests
-
-    def get_summary(self) -> str:
-        return {
-            "total_tokens": self.total_tokens,
-            "prompt_tokens": self.prompt_tokens,
-            "completion_tokens": self.completion_tokens,
-            "successful_requests": self.successful_requests,
-        }
-
-
-class TokenCalcHandler(BaseCallbackHandler):
-    model: str = ""
-    token_cost_process: TokenProcess
-
-    def __init__(self, model, token_cost_process):
-        self.model = model
-        self.token_cost_process = token_cost_process
-
-    def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> None:
-        if "gpt" in self.model:
-            encoding = tiktoken.encoding_for_model(self.model)
-        else:
-            encoding = tiktoken.get_encoding("cl100k_base")
-
-        if self.token_cost_process == None:
-            return
-
-        for prompt in prompts:
-            self.token_cost_process.sum_prompt_tokens(len(encoding.encode(prompt)))
-
-    async def on_llm_new_token(self, token: str, **kwargs) -> None:
-        self.token_cost_process.sum_completion_tokens(1)
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        self.token_cost_process.sum_successful_requests(1)

tests/agent_test.py

@@ -4,15 +4,11 @@ from unittest.mock import patch
 
 import pytest
 from langchain.tools import tool
-from langchain_core.exceptions import OutputParserException
 from langchain_openai import ChatOpenAI
 
 from crewai import Agent, Crew, Task
 from crewai.agents.cache import CacheHandler
 from crewai.agents.executor import CrewAgentExecutor
-from crewai.agents.parser import CrewAgentParser
-from crewai.tools.tool_calling import InstructorToolCalling
-from crewai.tools.tool_usage import ToolUsage
 from crewai.utilities import RPMController
 
 
@@ -66,14 +62,9 @@ def test_agent_without_memory():
         llm=ChatOpenAI(temperature=0, model="gpt-4"),
     )
 
-    task = Task(
-        description="How much is 1 + 1?",
-        agent=no_memory_agent,
-        expected_output="the result of the math operation.",
-    )
-    result = no_memory_agent.execute_task(task)
+    result = no_memory_agent.execute_task("How much is 1 + 1?")
 
-    assert result == "The result of the math operation 1 + 1 is 2."
+    assert result == "1 + 1 equals 2."
     assert no_memory_agent.agent_executor.memory is None
     assert memory_agent.agent_executor.memory is not None
 
@@ -87,22 +78,20 @@ def test_agent_execution():
         allow_delegation=False,
     )
 
-    task = Task(
-        description="How much is 1 + 1?",
-        agent=agent,
-        expected_output="the result of the math operation.",
-    )
-
-    output = agent.execute_task(task)
-    assert output == "The result of the math operation 1 + 1 is 2."
+    output = agent.execute_task("How much is 1 + 1?")
+    assert output == "2"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_execution_with_tools():
     @tool
-    def multiplier(first_number: int, second_number: int) -> float:
-        """Useful for when you need to multiply two numbers together."""
-        return first_number * second_number
+    def multiplier(numbers) -> float:
+        """Useful for when you need to multiply two numbers together.
+        The input to this tool should be a comma separated list of numbers of
+        length two, representing the two numbers you want to multiply together.
+        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
+        a, b = numbers.split(",")
+        return int(a) * int(b)
 
     agent = Agent(
         role="test role",
@@ -112,21 +101,20 @@ def test_agent_execution_with_tools():
         allow_delegation=False,
     )
 
-    task = Task(
-        description="What is 3 times 4?",
-        agent=agent,
-        expected_output="The result of the multiplication.",
-    )
-    output = agent.execute_task(task)
-    assert output == "The result of 3 times 4 is 12."
+    output = agent.execute_task("What is 3 times 4")
+    assert output == "12"
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_logging_tool_usage():
     @tool
-    def multiplier(first_number: int, second_number: int) -> float:
-        """Useful for when you need to multiply two numbers together."""
-        return first_number * second_number
+    def multiplier(numbers) -> float:
+        """Useful for when you need to multiply two numbers together.
+        The input to this tool should be a comma separated list of numbers of
+        length two, representing the two numbers you want to multiply together.
+        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
+        a, b = numbers.split(",")
+        return int(a) * int(b)
 
     agent = Agent(
         role="test role",
@@ -138,28 +126,26 @@ def test_logging_tool_usage():
     )
 
     assert agent.tools_handler.last_used_tool == {}
-    task = Task(
-        description="What is 3 times 4?",
-        agent=agent,
-        expected_output="The result of the multiplication.",
-    )
-    # force cleaning cache
-    agent.tools_handler.cache = CacheHandler()
-    output = agent.execute_task(task)
-    tool_usage = InstructorToolCalling(
-        tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
-    )
-    assert output == "12"
-    assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
-    assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments
+    output = agent.execute_task("What is 3 times 5?")
+    tool_usage = {
+        "tool": "multiplier",
+        "input": "3,5",
+    }
+
+    assert output == "3 times 5 is 15."
+    assert agent.tools_handler.last_used_tool == tool_usage
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_cache_hitting():
     @tool
-    def multiplier(first_number: int, second_number: int) -> float:
-        """Useful for when you need to multiply two numbers together."""
-        return first_number * second_number
+    def multiplier(numbers) -> float:
+        """Useful for when you need to multiply two numbers together.
+        The input to this tool should be a comma separated list of numbers of
+        length two and ONLY TWO, representing the two numbers you want to multiply together.
+        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
+        a, b = numbers.split(",")
+        return int(a) * int(b)
 
     cache_handler = CacheHandler()
 
@@ -173,58 +159,34 @@ def test_cache_hitting():
         verbose=True,
     )
 
-    task1 = Task(
-        description="What is 2 times 6?",
-        agent=agent,
-        expected_output="The result of the multiplication.",
-    )
-    task2 = Task(
-        description="What is 3 times 3?",
-        agent=agent,
-        expected_output="The result of the multiplication.",
-    )
-
-    output = agent.execute_task(task1)
-    output = agent.execute_task(task2)
+    output = agent.execute_task("What is 2 times 6 times 3?")
+    output = agent.execute_task("What is 3 times 3?")
     assert cache_handler._cache == {
-        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
-        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
+        "multiplier-12,3": "36",
+        "multiplier-2,6": "12",
+        "multiplier-3,3": "9",
     }
 
-    task = Task(
-        description="What is 2 times 6 times 3? Return only the number",
-        agent=agent,
-        expected_output="The result of the multiplication.",
-    )
-    output = agent.execute_task(task)
+    output = agent.execute_task("What is 2 times 6 times 3? Return only the number")
     assert output == "36"
 
-    assert cache_handler._cache == {
-        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
-        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
-        "multiplier-{'first_number': 12, 'second_number': 3}": 36,
-    }
-
     with patch.object(CacheHandler, "read") as read:
         read.return_value = "0"
-        task = Task(
-            description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool.",
-            agent=agent,
-            expected_output="The result of the multiplication.",
-        )
-        output = agent.execute_task(task)
+        output = agent.execute_task("What is 2 times 6?")
         assert output == "0"
-        read.assert_called_with(
-            tool="multiplier", input={"first_number": 2, "second_number": 6}
-        )
+        read.assert_called_with("multiplier", "2,6")
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_execution_with_specific_tools():
     @tool
-    def multiplier(first_number: int, second_number: int) -> float:
-        """Useful for when you need to multiply two numbers together."""
-        return first_number * second_number
+    def multiplier(numbers) -> float:
+        """Useful for when you need to multiply two numbers together.
+        The input to this tool should be a comma separated list of numbers of
+        length two, representing the two numbers you want to multiply together.
+        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
+        a, b = numbers.split(",")
+        return int(a) * int(b)
 
     agent = Agent(
         role="test role",
@@ -233,13 +195,8 @@ def test_agent_execution_with_specific_tools():
         allow_delegation=False,
     )
 
-    task = Task(
-        description="What is 3 times 4",
-        agent=agent,
-        expected_output="The result of the multiplication.",
-    )
-    output = agent.execute_task(task=task, tools=[multiplier])
-    assert output == "The result of the multiplication is 12."
+    output = agent.execute_task(task="What is 3 times 4", tools=[multiplier])
+    assert output == "3 times 4 is 12."
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -261,51 +218,13 @@ def test_agent_custom_max_iterations():
     with patch.object(
         CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
     ) as private_mock:
-        task = Task(
-            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
-            expected_output="The final answer",
-        )
         agent.execute_task(
-            task=task,
+            task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
             tools=[get_final_answer],
         )
         private_mock.assert_called_once()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_repeated_tool_usage(capsys):
-    @tool
-    def get_final_answer(anything: str) -> float:
-        """Get the final answer but don't give it yet, just re-use this
-        tool non-stop."""
-        return 42
-
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        max_iter=4,
-        llm=ChatOpenAI(model="gpt-4-0125-preview"),
-        allow_delegation=False,
-        verbose=True,
-    )
-
-    task = Task(
-        description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
-        expected_output="The final answer",
-    )
-    # force cleaning cache
-    agent.tools_handler.cache = CacheHandler()
-    agent.execute_task(
-        task=task,
-        tools=[get_final_answer],
-    )
-
-    captured = capsys.readouterr()
-
-    assert "The final answer is 42." in captured.out
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_moved_on_after_max_iterations():
     @tool
@@ -322,21 +241,24 @@ def test_agent_moved_on_after_max_iterations():
         allow_delegation=False,
     )
 
-    task = Task(
-        description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool over and over until you're told you can give yout final answer.",
-        expected_output="The final answer",
-    )
-    output = agent.execute_task(
-        task=task,
-        tools=[get_final_answer],
-    )
-    assert output == "42"
+    with patch.object(
+        CrewAgentExecutor, "_force_answer", wraps=agent.agent_executor._force_answer
+    ) as private_mock:
+        output = agent.execute_task(
+            task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
+            tools=[get_final_answer],
+        )
+        assert (
+            output
+            == "I have used the tool multiple times and the final answer remains 42."
+        )
+        private_mock.assert_called_once()
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_respect_the_max_rpm_set(capsys):
     @tool
-    def get_final_answer(anything: str) -> float:
+    def get_final_answer(numbers) -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
         return 42
@@ -353,15 +275,14 @@ def test_agent_respect_the_max_rpm_set(capsys):
 
     with patch.object(RPMController, "_wait_for_next_minute") as moveon:
         moveon.return_value = True
-        task = Task(
-            description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
-            expected_output="The final answer",
-        )
         output = agent.execute_task(
-            task=task,
+            task="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
             tools=[get_final_answer],
         )
-        assert output == "42"
+        assert (
+            output
+            == "I've used the `get_final_answer` tool multiple times and it consistently returns the number 42."
+        )
         captured = capsys.readouterr()
         assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called()
@@ -389,8 +310,7 @@ def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
     )
 
     task = Task(
-        description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
-        expected_output="The final answer",
+        description="Don't give a Final Answer, instead keep using the `get_final_answer` tool.",
         tools=[get_final_answer],
         agent=agent,
     )
@@ -423,7 +343,6 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
         backstory="test backstory",
         max_rpm=10,
         verbose=True,
-        allow_delegation=False,
     )
 
     agent2 = Agent(
@@ -432,16 +351,15 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
         backstory="test backstory2",
         max_iter=2,
         verbose=True,
-        allow_delegation=False,
     )
 
     tasks = [
         Task(
-            description="Just say hi.", agent=agent1, expected_output="Your greeting."
+            description="Just say hi.",
+            agent=agent1,
         ),
         Task(
-            description="NEVER give a Final Answer, instead keep using the `get_final_answer` tool non-stop",
-            expected_output="The final answer",
+            description="Don't give a Final Answer, instead keep using the `get_final_answer` tool.",
             tools=[get_final_answer],
             agent=agent2,
         ),
@@ -453,102 +371,23 @@ def test_agent_without_max_rpm_respet_crew_rpm(capsys):
         moveon.return_value = True
         crew.kickoff()
         captured = capsys.readouterr()
-        assert "get_final_answer" in captured.out
+        assert "Action: get_final_answer" in captured.out
         assert "Max RPM reached, waiting for next minute to start." in captured.out
         moveon.assert_called_once()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_error_on_parsing_tool(capsys):
-    from unittest.mock import patch
-
-    from langchain.tools import tool
-
-    @tool
-    def get_final_answer() -> float:
-        """Get the final answer but don't give it yet, just re-use this
-        tool non-stop."""
-        return 42
-
-    agent1 = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-    )
-    tasks = [
-        Task(
-            description="Use the get_final_answer tool.",
-            expected_output="The final answer",
-            agent=agent1,
-            tools=[get_final_answer],
-        )
-    ]
-
-    crew = Crew(
-        agents=[agent1],
-        tasks=tasks,
-        verbose=2,
-        function_calling_llm=ChatOpenAI(model="gpt-4-0125-preview"),
-    )
-
-    with patch.object(ToolUsage, "_render") as force_exception:
-        force_exception.side_effect = Exception("Error on parsing tool.")
-        crew.kickoff()
-        captured = capsys.readouterr()
-        assert "Error on parsing tool." in captured.out
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_remembers_output_format_after_using_tools_too_many_times():
-    from unittest.mock import patch
-
-    from langchain.tools import tool
-
-    @tool
-    def get_final_answer(anything: str) -> float:
-        """Get the final answer but don't give it yet, just re-use this
-        tool non-stop."""
-        return 42
-
-    agent1 = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        max_iter=6,
-        verbose=True,
-    )
-    tasks = [
-        Task(
-            description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
-            expected_output="The final answer",
-            agent=agent1,
-            tools=[get_final_answer],
-        )
-    ]
-
-    crew = Crew(agents=[agent1], tasks=tasks, verbose=2)
-
-    with patch.object(ToolUsage, "_remember_format") as remember_format:
-        crew.kickoff()
-        remember_format.assert_called()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_agent_use_specific_tasks_output_as_context(capsys):
+    pass
+
     agent1 = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     agent2 = Agent(role="test role2", goal="test goal2", backstory="test backstory2")
 
-    say_hi_task = Task(
-        description="Just say hi.", agent=agent1, expected_output="Your greeting."
-    )
-    say_bye_task = Task(
-        description="Just say bye.", agent=agent1, expected_output="Your farewell."
-    )
+    say_hi_task = Task(description="Just say hi.", agent=agent1)
+    say_bye_task = Task(description="Just say bye.", agent=agent1)
     answer_task = Task(
         description="Answer accordingly to the context you got.",
-        expected_output="Your answer.",
         context=[say_hi_task],
         agent=agent2,
     )
@@ -559,107 +398,3 @@ def test_agent_use_specific_tasks_output_as_context(capsys):
     result = crew.kickoff()
     assert "bye" not in result.lower()
     assert "hi" in result.lower() or "hello" in result.lower()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_step_callback():
-    class StepCallback:
-        def callback(self, step):
-            print(step)
-
-    with patch.object(StepCallback, "callback") as callback:
-
-        @tool
-        def learn_about_AI(topic) -> float:
-            """Useful for when you need to learn about AI to write an paragraph about it."""
-            return "AI is a very broad field."
-
-        agent1 = Agent(
-            role="test role",
-            goal="test goal",
-            backstory="test backstory",
-            tools=[learn_about_AI],
-            step_callback=StepCallback().callback,
-        )
-
-        essay = Task(
-            description="Write and then review an small paragraph on AI until it's AMAZING",
-            expected_output="The final paragraph.",
-            agent=agent1,
-        )
-        tasks = [essay]
-        crew = Crew(agents=[agent1], tasks=tasks)
-
-        callback.return_value = "ok"
-        crew.kickoff()
-        callback.assert_called()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_function_calling_llm():
-    from langchain_openai import ChatOpenAI
-
-    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
-
-    with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
-
-        @tool
-        def learn_about_AI(topic) -> float:
-            """Useful for when you need to learn about AI to write an paragraph about it."""
-            return "AI is a very broad field."
-
-        agent1 = Agent(
-            role="test role",
-            goal="test goal",
-            backstory="test backstory",
-            tools=[learn_about_AI],
-            llm=ChatOpenAI(model="gpt-4-0125-preview"),
-            function_calling_llm=llm,
-        )
-
-        essay = Task(
-            description="Write and then review an small paragraph on AI until it's AMAZING",
-            expected_output="The final paragraph.",
-            agent=agent1,
-        )
-        tasks = [essay]
-        crew = Crew(agents=[agent1], tasks=tasks)
-
-        crew.kickoff()
-        private_mock.assert_called()
-
-
-def test_agent_count_formatting_error():
-    from unittest.mock import patch
-
-    agent1 = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-    )
-
-    parser = CrewAgentParser()
-    parser.agent = agent1
-
-    with patch.object(Agent, "increment_formatting_errors") as mock_count_errors:
-        test_text = "This text does not match expected formats."
-        with pytest.raises(OutputParserException):
-            parser.parse(test_text)
-        mock_count_errors.assert_called_once()
-
-
-def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
-    agent1 = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        verbose=True,
-    )
-
-    assert len(agent1.llm.callbacks) == 1
-    assert agent1.llm.callbacks[0].__class__.__name__ == "TokenCalcHandler"
-    assert agent1.llm.callbacks[0].model == "gpt-4"
-    assert (
-        agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
-    )

tests/agent_tools/agent_tools_test.py

@@ -17,52 +17,58 @@ tools = AgentTools(agents=[researcher])
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegate_work():
     result = tools.delegate_work(
-        coworker="researcher",
-        task="share your take on AI Agents",
-        context="I heard you hate them",
+        command="researcher|share your take on AI Agents|I heard you hate them"
     )
 
     assert (
         result
-        == "As a researcher, my opinions are based on facts and extensive study. Regarding AI Agents, they are a fundamental part of the advancement in technology. AI agents are essentially the entities that perceive their environment and take actions to maximize their chances of success. They have a wide range of applications from self-driving cars to intelligent personal assistants like Siri and Alexa. They have the potential to greatly improve our lives by automating mundane tasks, helping us make better decisions, and even potentially solving complex problems. However, like any technology, they have their own set of challenges such as the risk of job displacement and the ethical implications of their use. My goal as a researcher is not to love or hate AI agents, but to understand them, their benefits, and their implications. It's about maintaining an objective view in order to provide the most accurate and comprehensive analysis."
+        == "I apologize if my previous statements have given you the impression that I hate AI agents. As a technology researcher, I don't hold personal sentiments towards AI or any other technology. Rather, I analyze them objectively based on their capabilities, applications, and implications. AI agents, in particular, are a fascinating domain of research. They hold tremendous potential in automating and optimizing various tasks across industries. However, like any other technology, they come with their own set of challenges, such as ethical considerations around privacy and decision-making. My objective is to understand these technologies in depth and provide a balanced view."
     )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_ask_question():
     result = tools.ask_question(
-        coworker="researcher",
-        question="do you hate AI Agents?",
-        context="I heard you LOVE them",
+        command="researcher|do you hate AI Agents?|I heard you LOVE them"
     )
 
     assert (
         result
-        == "As an AI researcher, I don't have personal feelings or emotions like love or hate. However, I recognize the importance of AI Agents in today's technological landscape. They have the potential to greatly enhance our lives and make tasks more efficient. At the same time, it is crucial to consider the ethical implications and societal impacts that come with their use. My role is to provide objective research and analysis on these topics."
+        == "As an AI, I don't possess feelings or emotions, so I don't love or hate anything. However, I can provide detailed analysis and research on AI agents. They are a fascinating field of study with the potential to revolutionize many industries, although they also present certain challenges and ethical considerations."
+    )
+
+
+def test_can_not_self_delegate():
+    # TODO: Add test for self delegation
+    pass
+
+
+def test_delegate_work_with_wrong_input():
+    result = tools.ask_question(command="writer|share your take on AI Agents")
+
+    assert (
+        result
+        == "\nError executing tool. Missing exact 3 pipe (|) separated values. For example, `coworker|task|context`. I need to make sure to pass context as context.\n"
     )
 
 
 def test_delegate_work_to_wrong_agent():
     result = tools.ask_question(
-        coworker="writer",
-        question="share your take on AI Agents",
-        context="I heard you hate them",
+        command="writer|share your take on AI Agents|I heard you hate them"
     )
 
     assert (
         result
-        == "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n- researcher\n"
+        == "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: researcher.\n"
     )
 
 
 def test_ask_question_to_wrong_agent():
     result = tools.ask_question(
-        coworker="writer",
-        question="do you hate AI Agents?",
-        context="I heard you LOVE them",
+        command="writer|do you hate AI Agents?|I heard you LOVE them"
     )
 
     assert (
         result
-        == "\nError executing tool. Co-worker mentioned not found, it must to be one of the following options:\n- researcher\n"
+        == "\nError executing tool. Co-worker mentioned on the Action Input not found, it must to be one of the following options: researcher.\n"
     )

tests/agent_tools/cassettes/test_ask_question.yaml

@@ -2,25 +2,23 @@ interactions:
 - request:
     body: '{"messages": [{"role": "user", "content": "You are researcher.\nYou''re
       an expert researcher, specialized in technology\n\nYour personal goal is: make
-      the best research and analysis on content about AI and AI agentsTOOLS:\n------\nYou
-      have access to only the following tools:\n\n\n\nTo use a tool, please use the
-      exact following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction:
-      the tool you wanna use, should be one of [], just the name.\nAction Input: Any
-      and all relevant information input and context for using the tool\nObservation:
-      the result of using the tool\n```\n\nWhen you have a response for your task,
-      or if you do not need to use a tool, you MUST use the format:\n\n```\nThought:
-      Do I need to use a tool? No\nFinal Answer: [your response here]```This is the
-      summary of your work so far:\nThe human asks the AI''s opinion on AI Agents,
-      suggesting that the AI dislikes them. The AI, identifying as a researcher, clarifies
-      that its opinions are based on research and study. It views AI Agents as a key
-      part of technological advancement, with potential to improve lives through automation
-      and decision-making assistance. However, it also acknowledges challenges, including
-      job displacement risk and ethical implications. The AI aims to maintain an objective
-      view for accurate analysis, rather than loving or hating AI Agents.Begin! This
-      is VERY important to you, your job depends on it!\n\nCurrent Task: do you hate
-      AI Agents?\nThis is the context you''re working with:\nI heard you LOVE them\n"}],
-      "model": "gpt-4", "n": 1, "stop": ["\nObservation"], "stream": true, "temperature":
-      0.7}'
+      the best research and analysis on content about AI and AI agents\n\nTOOLS:\n------\nYou
+      have access to the following tools:\n\n\n\nTo use a tool, please use the exact
+      following format:\n\n```\nThought: Do I need to use a tool? Yes\nAction: the
+      action to take, should be one of []\nAction Input: the input to the action\nObservation:
+      the result of the action\n```\n\nWhen you have a response for your task, or
+      if you do not need to use a tool, you MUST use the format:\n\n```\nThought:
+      Do I need to use a tool? No\nFinal Answer: [your response here]\n```\n\t\tThis
+      is the summary of your work so far:\n    The human asks the AI for its opinion
+      on AI agents, based on the impression that the AI dislikes them. The AI clarifies
+      that it doesn''t hold personal sentiments towards AI or any technology, but
+      instead analyzes them objectively. The AI finds AI agents a fascinating domain
+      of research with great potential for task automation and optimization across
+      industries, but acknowledges they present challenges such as ethical considerations
+      around privacy and decision-making.\nBegin! This is VERY important to you, your
+      job depends on it!\n\nCurrent Task: do you hate AI Agents?\n\nThis is the context
+      you are working with:\nI heard you LOVE them\n\n"}], "model": "gpt-4", "n":
+      1, "stop": ["\nObservation"], "stream": false, "temperature": 0.7}'
     headers:
       accept:
       - application/json
@@ -29,16 +27,16 @@ interactions:
       connection:
       - keep-alive
       content-length:
-      - '1607'
+      - '1494'
       content-type:
       - application/json
       cookie:
-      - __cf_bm=h0bOt9YDs6yXE_oMKhs3agQtymHaKWVUaKmUU1JTjF0-1707817571-1-ASLnLk23NWsEgocWyiwez3Ekvu0XRYdiq/aaJBeVWO+FtIxo1aStNrgbDNkJJMN6zBfVppkCrs6/YvM5SPomQkw=;
-        _cfuvid=JXBcxKdSP7U2jrK3OVg2NRCw5efh3.IEvakR_W8ac90-1707817571102-0-604800000
+      - __cf_bm=k2HUdEp80irAkv.3wl0c6unbzRUujrE1TnJeObxyuHw-1703102483-1-AZe8OKi9NWunQ9x4f3lkdOpb/hJIp/3oyXUqPhkcmcEHXvFTkMcv77NSclcoz9DjRhwC62ZvANkWImyVRM4seH4=;
+        _cfuvid=8qN4npFFWXAqn.wugd0jrQ36YkreDcTGH14We.FcBjg-1703102483136-0-604800000
       host:
       - api.openai.com
       user-agent:
-      - OpenAI/Python 1.12.0
+      - OpenAI/Python 1.6.0
       x-stainless-arch:
       - arm64
       x-stainless-async:
@@ -48,7 +46,7 @@ interactions:
       x-stainless-os:
       - MacOS
       x-stainless-package-version:
-      - 1.12.0
+      - 1.6.0
       x-stainless-runtime:
       - CPython
       x-stainless-runtime-version:
@@ -56,406 +54,32 @@ interactions:
     method: POST
     uri: https://api.openai.com/v1/chat/completions
   response:
-    body:
-      string: 'data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"Thought"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        Do"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        I"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        need"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        to"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        use"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        a"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        tool"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"?"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        No"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"\n"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"Final"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"
-        Answer"},"logprobs":null,"finish_reason":null}]}
-
-
-        data: {"id":"chatcmpl-8rjey60Ubh4qJkkFFqnsmTZWzsGWf","object":"chat.completion.chunk","created":1707817592,"model":"gpt-4-0613","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
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tests/crew_test.py

@@ -55,12 +55,10 @@ def test_crew_config_conditional_requirement():
             "tasks": [
                 {
                     "description": "Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-                    "expected_output": "Bullet point list of 5 important events.",
                     "agent": "Senior Researcher",
                 },
                 {
                     "description": "Write a 1 amazing paragraph highlight for each idead that showcases how good an article about this topic could be, check references if necessary or search for more content but make sure it's unique, interesting and well written. Return the list of ideas with their paragraph and your notes.",
-                    "expected_output": "A 4 paragraph article about AI.",
                     "agent": "Senior Writer",
                 },
             ],
@@ -117,12 +115,10 @@ def test_crew_creation():
     tasks = [
         Task(
             description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-            expected_output="Bullet point list of 5 important events.",
             agent=researcher,
         ),
         Task(
             description="Write a 1 amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
-            expected_output="A 4 paragraph article about AI.",
             agent=writer,
         ),
     ]
@@ -135,7 +131,15 @@ def test_crew_creation():
 
     assert (
         crew.kickoff()
-        == "1. **The Rise of AI in Healthcare**: The convergence of AI and healthcare is a promising frontier, offering unprecedented opportunities for disease diagnosis and patient outcome prediction. AI's potential to revolutionize healthcare lies in its capacity to synthesize vast amounts of data, generating precise and efficient results. This technological breakthrough, however, is not just about improving accuracy and efficiency; it's about saving lives. As we stand on the precipice of this transformative era, we must prepare for the complex challenges and ethical questions it poses, while embracing its ability to reshape healthcare as we know it.\n\n2. **Ethical Implications of AI**: As AI intertwines with our daily lives, it presents a complex web of ethical dilemmas. This fusion of technology, philosophy, and ethics is not merely academically intriguing but profoundly impacts the fabric of our society. The questions raised range from decision-making transparency to accountability, and from privacy to potential biases. As we navigate this ethical labyrinth, it is crucial to establish robust frameworks and regulations to ensure that AI serves humanity, and not the other way around.\n\n3. **AI and Data Privacy**: The rise of AI brings with it an insatiable appetite for data, spawning new debates around privacy rights. Balancing the potential benefits of AI with the right to privacy is a unique challenge that intersects technology, law, and human rights. In an increasingly digital world, where personal information forms the backbone of many services, we must grapple with these issues. It's time to redefine the concept of privacy and devise innovative solutions that ensure our digital footprints are not abused.\n\n4. **AI in Job Market**: The discourse around AI's impact on employment is a narrative of contrast, a tale of displacement and creation. On one hand, AI threatens to automate a multitude of jobs, on the other, it promises to create new roles that we cannot yet imagine. This intersection of technology, economics, and labor rights is a critical dialogue that will shape our future. As we stand at this crossroads, we must not only brace ourselves for the changes but also seize the opportunities that this technological wave brings.\n\n5. **Future of AI Agents**: The evolution of AI agents signifies a leap towards a future where AI is not just a tool, but a partner. These sophisticated AI agents, employed in customer service to personal assistants, are redefining our interactions with technology. As we gaze into the future of AI agents, we see a landscape of possibilities and challenges. This journey will be about harnessing the potential of AI agents while navigating the issues of trust, dependence, and ethical use."
+        == """1. **The Evolution of AI: From Old Concepts to New Frontiers** - Journey with us as we traverse the fascinating timeline of artificial intelligence - from its philosophical and mathematical infancy to the sophisticated, problem-solving tool it has become today. This riveting account will not only educate but also inspire, as we delve deep into the milestones that brought us here and shine a beacon on the potential that lies ahead.
+
+2. **AI Agents in Healthcare: The Future of Medicine** - Imagine a world where illnesses are diagnosed before symptoms appear, where patient outcomes are not mere guesses but accurate predictions. This is the world AI is crafting in healthcare - a revolution that's saving lives and changing the face of medicine as we know it. This article will spotlight this transformative journey, underlining the profound impact AI is having on our health and well-being.
+
+3. **AI and Ethics: Navigating the Moral Landscape of Artificial Intelligence** - As AI becomes an integral part of our lives, it brings along a plethora of ethical dilemmas. This thought-provoking piece will navigate the complex moral landscape of AI, addressing critical concerns like privacy, job displacement, and decision-making biases. It serves as a much-needed discussion platform for the societal implications of AI, urging us to look beyond the technology and into the mirror.
+
+4. **Demystifying AI Algorithms: A Deep Dive into Machine Learning** - Ever wondered what goes on behind the scenes of AI? This enlightening article will break down the complex world of machine learning algorithms into digestible insights, unraveling the mystery of AI's 'black box'. It's a rare opportunity for the non-technical audience to appreciate the inner workings of AI, fostering a deeper understanding of this revolutionary technology.
+
+5. **AI Startups: The Game Changers of the Tech Industry** - In the world of tech, AI startups are the bold pioneers charting new territories. This article will spotlight these game changers, showcasing how their innovative products and services are driving the AI revolution. It's a unique opportunity to catch a glimpse of the entrepreneurial side of AI, offering inspiration for the tech enthusiasts and dreamers alike."""
     )
 
 
@@ -145,7 +149,6 @@ def test_hierarchical_process():
 
     task = Task(
         description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
-        expected_output="5 bullet points with a paragraph for each idea.",
     )
 
     crew = Crew(
@@ -157,14 +160,28 @@ def test_hierarchical_process():
 
     assert (
         crew.kickoff()
-        == "1. 'Demystifying AI: An in-depth exploration of Artificial Intelligence for the layperson' - In this piece, we will unravel the enigma of AI, simplifying its complexities into digestible information for the everyday individual. By using relatable examples and analogies, we will journey through the neural networks and machine learning algorithms that define AI, without the jargon and convoluted explanations that often accompany such topics.\n\n2. 'The Role of AI in Startups: A Game Changer?' - Startups today are harnessing the power of AI to revolutionize their businesses. This article will delve into how AI, as an innovative force, is shaping the startup ecosystem, transforming everything from customer service to product development. We'll explore real-life case studies of startups that have leveraged AI to accelerate their growth and disrupt their respective industries.\n\n3. 'AI and Ethics: Navigating the Complex Landscape' - AI brings with it not just technological advancements, but ethical dilemmas as well. This article will engage readers in a thought-provoking discussion on the ethical implications of AI, exploring issues like bias in algorithms, privacy concerns, job displacement, and the moral responsibility of AI developers. We will also discuss potential solutions and frameworks to address these challenges.\n\n4. 'Unveiling the AI Agents: The Future of Customer Service' - AI agents are poised to reshape the customer service landscape, offering businesses the ability to provide round-the-clock support and personalized experiences. In this article, we'll dive deep into the world of AI agents, examining how they work, their benefits and limitations, and how they're set to redefine customer interactions in the digital age.\n\n5. 'From Science Fiction to Reality: AI in Everyday Life' - AI, once a concept limited to the realm of sci-fi, has now permeated our daily lives. This article will highlight the ubiquitous presence of AI, from voice assistants and recommendation algorithms, to autonomous vehicles and smart homes. We'll explore how AI, in its various forms, is transforming our everyday experiences, making the future seem a lot closer than we imagined."
+        == """Here are the 5 interesting ideas with a highlight paragraph for each:
+
+1. "The Future of AI in Healthcare: Predicting Diseases Before They Happen"
+   - "Imagine a future where AI empowers us to detect diseases before they arise, transforming healthcare from reactive to proactive. Machine learning algorithms, trained on vast amounts of patient data, could potentially predict heart diseases, strokes, or cancers before they manifest, allowing for early interventions and significantly improving patient outcomes. This article will delve into the rapid advancements in AI within the healthcare sector and how these technologies are ushering us into a new era of predictive medicine."
+
+2. "How AI is Changing the Way We Cook: An Insight into Smart Kitchens"
+   - "From the humble home kitchen to grand culinary stages, AI is revolutionizing the way we cook. Smart appliances, equipped with advanced sensors and predictive algorithms, are turning kitchens into creative playgrounds, offering personalized recipes, precise cooking instructions, and even automated meal preparation. This article explores the fascinating intersection of AI and gastronomy, revealing how technology is transforming our culinary experiences."
+
+3. "Redefining Fitness with AI: Personalized Workout Plans and Nutritional Advice"
+   - "Fitness reimagined – that's the promise of AI in the wellness industry. Picture a personal trainer who knows your strengths, weaknesses, and nutritional needs intimately. An AI-powered fitness app can provide this personalized experience, adapting your workout plans and dietary recommendations in real-time based on your progress and feedback. Join us as we unpack how AI is revolutionizing the fitness landscape, offering personalized, data-driven approaches to health and well-being."
+
+4. "AI and the Art World: How Technology is Shaping Creativity"
+   - "Art and AI may seem like unlikely partners, but their synergy is sparking a creative revolution. AI algorithms are now creating mesmerizing artworks, challenging our perceptions of creativity and originality. From AI-assisted painting to generative music composition, this article will take you on a journey through the fascinating world of AI in art, exploring how technology is reshaping the boundaries of human creativity."
+
+5. "AI in Space Exploration: The Next Frontier"
+   - "The vast expanse of space, once the sole domain of astronauts and rovers, is the next frontier for AI. AI technology is playing an increasingly vital role in space exploration, from predicting space weather to assisting in interstellar navigation. This article will delve into the exciting intersection of AI and space exploration, exploring how these advanced technologies are helping us uncover the mysteries of the cosmos.\""""
     )
 
 
 def test_manager_llm_requirement_for_hierarchical_process():
     task = Task(
         description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
-        expected_output="5 bullet points with a paragraph for each idea.",
     )
 
     with pytest.raises(pydantic_core._pydantic_core.ValidationError):
@@ -180,7 +197,6 @@ def test_crew_with_delegating_agents():
     tasks = [
         Task(
             description="Produce and amazing 1 paragraph draft of an article about AI Agents.",
-            expected_output="A 4 paragraph article about AI.",
             agent=ceo,
         )
     ]
@@ -193,23 +209,15 @@ def test_crew_with_delegating_agents():
 
     assert (
         crew.kickoff()
-        == "AI Agents, simply put, are intelligent systems that can perceive their environment and take actions to reach specific goals. Imagine them as digital assistants that can learn, adapt and make decisions. They operate in the realms of software or hardware, like a chatbot on a website or a self-driving car. The key to their intelligence is their ability to learn from their experiences, making them better at their tasks over time. In today's interconnected world, AI agents are transforming our lives. They enhance customer service experiences, streamline business processes, and even predict trends in data. Vehicles equipped with AI agents are making transportation safer. In healthcare, AI agents are helping to diagnose diseases, personalizing treatment plans, and monitoring patient health. As we embrace the digital era, these AI agents are not just important, they're becoming indispensable, shaping a future where technology works intuitively and intelligently to meet our needs."
+        == '"AI agents, the digital masterminds at the heart of the 21st-century revolution, are shaping a new era of intelligence and innovation. They are autonomous entities, capable of observing their environment, making decisions, and acting on them, all in pursuit of a specific goal. From streamlining operations in logistics to personalizing customer experiences in retail, AI agents are transforming how businesses operate. But their potential extends far beyond the corporate world. They are the sentinels protecting our digital frontiers, the virtual assistants making our lives easier, and the unseen hands guiding autonomous vehicles. As this technology evolves, AI agents will play an increasingly central role in our world, ushering in an era of unprecedented efficiency, personalization, and productivity. But with great power comes great responsibility, and understanding and harnessing this potential responsibly will be one of our greatest challenges and opportunities in the coming years."'
     )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_verbose_output(capsys):
     tasks = [
-        Task(
-            description="Research AI advancements.",
-            expected_output="A full report on AI advancements.",
-            agent=researcher,
-        ),
-        Task(
-            description="Write about AI in healthcare.",
-            expected_output="A 4 paragraph article about AI.",
-            agent=writer,
-        ),
+        Task(description="Research AI advancements.", agent=researcher),
+        Task(description="Write about AI in healthcare.", agent=writer),
     ]
 
     crew = Crew(
@@ -242,13 +250,7 @@ def test_crew_verbose_output(capsys):
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_verbose_levels_output(capsys):
-    tasks = [
-        Task(
-            description="Write about AI advancements.",
-            expected_output="A 4 paragraph article about AI.",
-            agent=researcher,
-        )
-    ]
+    tasks = [Task(description="Write about AI advancements.", agent=researcher)]
 
     crew = Crew(agents=[researcher], tasks=tasks, process=Process.sequential, verbose=1)
 
@@ -275,25 +277,27 @@ def test_crew_verbose_levels_output(capsys):
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_cache_hitting_between_agents():
-    from unittest.mock import call, patch
+    from unittest.mock import patch
 
     from langchain.tools import tool
 
     @tool
-    def multiplier(first_number: int, second_number: int) -> float:
-        """Useful for when you need to multiply two numbers together."""
-        return first_number * second_number
+    def multiplier(numbers) -> float:
+        """Useful for when you need to multiply two numbers together.
+        The input to this tool should be a comma separated list of numbers of
+        length two, representing the two numbers you want to multiply together.
+        For example, `1,2` would be the input if you wanted to multiply 1 by 2."""
+        a, b = numbers.split(",")
+        return int(a) * int(b)
 
     tasks = [
         Task(
             description="What is 2 tims 6? Return only the number.",
-            expected_output="the result of multiplication",
             tools=[multiplier],
             agent=ceo,
         ),
         Task(
             description="What is 2 times 6? Return only the number.",
-            expected_output="the result of multiplication",
             tools=[multiplier],
             agent=researcher,
         ),
@@ -304,16 +308,15 @@ def test_cache_hitting_between_agents():
         tasks=tasks,
     )
 
+    assert crew._cache_handler._cache == {}
+    output = crew.kickoff()
+    assert crew._cache_handler._cache == {"multiplier-2,6": "12"}
+    assert output == "12"
+
     with patch.object(CacheHandler, "read") as read:
         read.return_value = "12"
         crew.kickoff()
-        assert read.call_count == 2, "read was not called exactly twice"
-        # Check if read was called with the expected arguments
-        expected_calls = [
-            call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
-            call(tool="multiplier", input={"first_number": 2, "second_number": 6}),
-        ]
-        read.assert_has_calls(expected_calls, any_order=False)
+        read.assert_called_with("multiplier", "2,6")
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -321,10 +324,9 @@ def test_api_calls_throttling(capsys):
     from unittest.mock import patch
 
     from langchain.tools import tool
-    from langchain_openai import ChatOpenAI
 
     @tool
-    def get_final_answer(anything) -> float:
+    def get_final_answer(numbers) -> float:
         """Get the final answer but don't give it yet, just re-use this
         tool non-stop."""
         return 42
@@ -336,12 +338,10 @@ def test_api_calls_throttling(capsys):
         max_iter=5,
         allow_delegation=False,
         verbose=True,
-        llm=ChatOpenAI(model="gpt-4-0125-preview"),
     )
 
     task = Task(
         description="Don't give a Final Answer, instead keep using the `get_final_answer` tool.",
-        expected_output="The final answer.",
         tools=[get_final_answer],
         agent=agent,
     )
@@ -356,36 +356,6 @@ def test_api_calls_throttling(capsys):
         moveon.assert_called()
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_full_ouput():
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        allow_delegation=False,
-        verbose=True,
-    )
-
-    task1 = Task(
-        description="just say hi!",
-        expected_output="your greeting",
-        agent=agent,
-    )
-    task2 = Task(
-        description="just say hello!",
-        expected_output="your greeting",
-        agent=agent,
-    )
-
-    crew = Crew(agents=[agent], tasks=[task1, task2], full_output=True)
-
-    result = crew.kickoff()
-    assert result == {
-        "final_output": "Hello! It is a delight to receive your message. I trust this response finds you in good spirits. It's indeed a pleasure to connect with you too.",
-        "tasks_outputs": [task1.output, task2.output],
-    }
-
-
 def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
     agent = Agent(
         role="test role",
@@ -397,7 +367,6 @@ def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
 
     task = Task(
         description="just say hi!",
-        expected_output="your greeting",
         agent=agent,
     )
 
@@ -442,297 +411,13 @@ def test_async_task_execution():
         with patch.object(threading.Thread, "start") as start:
             thread = threading.Thread(target=lambda: None, args=()).start()
             start.return_value = thread
-            with patch.object(threading.Thread, "join", wraps=thread.join()) as join:
+            with patch.object(threading.Thread, "join", wraps=thread.join()) as join:  # type: ignore
                 list_ideas.output = TaskOutput(
-                    description="A 4 paragraph article about AI.", raw_output="ok"
+                    description="A 4 paragraph article about AI.", result="ok"
                 )
                 list_important_history.output = TaskOutput(
-                    description="A 4 paragraph article about AI.", raw_output="ok"
+                    description="A 4 paragraph article about AI.", result="ok"
                 )
                 crew.kickoff()
                 start.assert_called()
                 join.assert_called()
-
-
-def test_set_agents_step_callback():
-    from unittest.mock import patch
-
-    researcher_agent = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        allow_delegation=False,
-    )
-
-    list_ideas = Task(
-        description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 important events.",
-        agent=researcher_agent,
-        async_execution=True,
-    )
-
-    crew = Crew(
-        agents=[researcher_agent],
-        process=Process.sequential,
-        tasks=[list_ideas],
-        step_callback=lambda: None,
-    )
-
-    with patch.object(Agent, "execute_task") as execute:
-        execute.return_value = "ok"
-        crew.kickoff()
-        assert researcher_agent.step_callback is not None
-
-
-def test_dont_set_agents_step_callback_if_already_set():
-    from unittest.mock import patch
-
-    def agent_callback(_):
-        pass
-
-    def crew_callback(_):
-        pass
-
-    researcher_agent = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        allow_delegation=False,
-        step_callback=agent_callback,
-    )
-
-    list_ideas = Task(
-        description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 important events.",
-        agent=researcher_agent,
-        async_execution=True,
-    )
-
-    crew = Crew(
-        agents=[researcher_agent],
-        process=Process.sequential,
-        tasks=[list_ideas],
-        step_callback=crew_callback,
-    )
-
-    with patch.object(Agent, "execute_task") as execute:
-        execute.return_value = "ok"
-        crew.kickoff()
-        assert researcher_agent.step_callback is not crew_callback
-        assert researcher_agent.step_callback is agent_callback
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_function_calling_llm():
-    from unittest.mock import patch
-
-    from langchain.tools import tool
-    from langchain_openai import ChatOpenAI
-
-    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
-
-    with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:
-
-        @tool
-        def learn_about_AI(topic) -> float:
-            """Useful for when you need to learn about AI to write an paragraph about it."""
-            return "AI is a very broad field."
-
-        agent1 = Agent(
-            role="test role",
-            goal="test goal",
-            backstory="test backstory",
-            llm=ChatOpenAI(model="gpt-4-0125-preview"),
-            tools=[learn_about_AI],
-        )
-
-        essay = Task(
-            description="Write and then review an small paragraph on AI until it's AMAZING",
-            expected_output="A 4 paragraph article about AI.",
-            agent=agent1,
-        )
-        tasks = [essay]
-        crew = Crew(agents=[agent1], tasks=tasks, function_calling_llm=llm)
-        crew.kickoff()
-        private_mock.assert_called()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_task_with_no_arguments():
-    from langchain.tools import tool
-
-    @tool
-    def return_data() -> str:
-        "Useful to get the sales related data"
-        return "January: 5, February: 10, March: 15, April: 20, May: 25"
-
-    researcher = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        tools=[return_data],
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Look at the available data nd give me a sense on the total number of sales.",
-        expected_output="The total number of sales as an integer",
-        agent=researcher,
-    )
-
-    crew = Crew(agents=[researcher], tasks=[task])
-
-    result = crew.kickoff()
-    assert result == "75"
-
-
-def test_delegation_is_not_enabled_if_there_are_only_one_agent():
-    from unittest.mock import patch
-
-    researcher = Agent(
-        role="Researcher",
-        goal="Make the best research and analysis on content about AI and AI agents",
-        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
-        allow_delegation=True,
-    )
-
-    task = Task(
-        description="Look at the available data nd give me a sense on the total number of sales.",
-        expected_output="The total number of sales as an integer",
-        agent=researcher,
-    )
-
-    crew = Crew(agents=[researcher], tasks=[task])
-
-    with patch.object(Task, "execute") as execute:
-        execute.return_value = "ok"
-        crew.kickoff()
-        assert task.tools == []
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agents_do_not_get_delegation_tools_with_there_is_only_one_agent():
-    agent = Agent(
-        role="Researcher",
-        goal="Be super empathetic.",
-        backstory="You're love to sey howdy.",
-        allow_delegation=False,
-    )
-
-    task = Task(description="say howdy", expected_output="Howdy!", agent=agent)
-
-    crew = Crew(agents=[agent], tasks=[task])
-
-    result = crew.kickoff()
-    assert (
-        result
-        == "Howdy! I hope this message finds you well and brings a smile to your face. Have a fantastic day!"
-    )
-    assert len(agent.tools) == 0
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_usage_metrics_are_captured_for_sequential_process():
-    agent = Agent(
-        role="Researcher",
-        goal="Be super empathetic.",
-        backstory="You're love to sey howdy.",
-        allow_delegation=False,
-    )
-
-    task = Task(description="say howdy", expected_output="Howdy!", agent=agent)
-
-    crew = Crew(agents=[agent], tasks=[task])
-
-    result = crew.kickoff()
-    assert (
-        result
-        == "Howdy! I hope you're having a wonderful day. I'm here ready to make your day even better. Let's have a great time together!"
-    )
-    assert crew.usage_metrics == {
-        "completion_tokens": 91,
-        "prompt_tokens": 164,
-        "successful_requests": 1,
-        "total_tokens": 255,
-    }
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_agent_usage_metrics_are_captured_for_hierarchical_process():
-    from langchain_openai import ChatOpenAI
-
-    agent = Agent(
-        role="Researcher",
-        goal="Be super empathetic.",
-        backstory="You're love to sey howdy.",
-        allow_delegation=False,
-    )
-
-    task = Task(description="Ask the researched to say hi!", expected_output="Howdy!")
-
-    crew = Crew(
-        agents=[agent],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),
-    )
-
-    result = crew.kickoff()
-    assert result == "Howdy!"
-    assert crew.usage_metrics == {
-        "total_tokens": 2476,
-        "prompt_tokens": 2191,
-        "completion_tokens": 285,
-        "successful_requests": 5,
-    }
-
-
-def test_crew_inputs_interpolate_both_agents_and_tasks():
-    agent = Agent(
-        role="{topic} Researcher",
-        goal="Express hot takes on {topic}.",
-        backstory="You have a lot of experience with {topic}.",
-    )
-
-    task = Task(
-        description="Give me an analysis around {topic}.",
-        expected_output="{points} bullet points about {topic}.",
-    )
-
-    crew = Crew(agents=[agent], tasks=[task], inputs={"topic": "AI", "points": 5})
-
-    assert crew.tasks[0].description == "Give me an analysis around AI."
-    assert crew.tasks[0].expected_output == "5 bullet points about AI."
-    assert crew.agents[0].role == "AI Researcher"
-    assert crew.agents[0].goal == "Express hot takes on AI."
-    assert crew.agents[0].backstory == "You have a lot of experience with AI."
-
-
-def test_crew_inputs_interpolate_both_agents_and_tasks():
-    from unittest.mock import patch
-
-    agent = Agent(
-        role="{topic} Researcher",
-        goal="Express hot takes on {topic}.",
-        backstory="You have a lot of experience with {topic}.",
-    )
-
-    task = Task(
-        description="Give me an analysis around {topic}.",
-        expected_output="{points} bullet points about {topic}.",
-        agent=agent,
-    )
-
-    crew = Crew(agents=[agent], tasks=[task])
-
-    with patch.object(Agent, "execute_task") as execute:
-        with patch.object(
-            Agent, "interpolate_inputs", wraps=agent.interpolate_inputs
-        ) as interpolate_agent_inputs:
-            with patch.object(
-                Task, "interpolate_inputs", wraps=task.interpolate_inputs
-            ) as interpolate_task_inputs:
-                execute.return_value = "ok"
-                crew.kickoff(inputs={"topic": "AI", "points": 5})
-                interpolate_agent_inputs.assert_called()
-                interpolate_task_inputs.assert_called()

tests/task_test.py

@@ -2,11 +2,8 @@
 
 from unittest.mock import MagicMock, patch
 
-import pytest
-from pydantic import BaseModel
-from pydantic_core import ValidationError
-
-from crewai import Agent, Crew, Process, Task
+from crewai.agent import Agent
+from crewai.task import Task
 
 
 def test_task_tool_reflect_agent_tools():
@@ -26,7 +23,6 @@ def test_task_tool_reflect_agent_tools():
 
     task = Task(
         description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 ideas.",
         agent=researcher,
     )
 
@@ -54,7 +50,6 @@ def test_task_tool_takes_precedence_over_agent_tools():
 
     task = Task(
         description="Give me a list of 5 interesting ideas to explore for an article, what makes them unique and interesting.",
-        expected_output="Bullet point list of 5 ideas.",
         agent=researcher,
         tools=[fake_task_tool],
     )
@@ -79,7 +74,7 @@ def test_task_prompt_includes_expected_output():
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
         task.execute()
-        execute.assert_called_once_with(task=task, context=None, tools=[])
+        execute.assert_called_once_with(task=task._prompt(), context=None, tools=[])
 
 
 def test_task_callback():
@@ -120,7 +115,7 @@ def test_execute_with_agent():
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
         task.execute(agent=researcher)
-        execute.assert_called_once_with(task=task, context=None, tools=[])
+        execute.assert_called_once_with(task=task._prompt(), context=None, tools=[])
 
 
 def test_async_execution():
@@ -140,312 +135,4 @@ def test_async_execution():
 
     with patch.object(Agent, "execute_task", return_value="ok") as execute:
         task.execute(agent=researcher)
-        execute.assert_called_once_with(task=task, context=None, tools=[])
-
-
-def test_multiple_output_type_error():
-    class Output(BaseModel):
-        field: str
-
-    with pytest.raises(ValidationError):
-        Task(
-            description="Give me a list of 5 interesting ideas to explore for na article, what makes them unique and interesting.",
-            expected_output="Bullet point list of 5 interesting ideas.",
-            output_json=Output,
-            output_pydantic=Output,
-        )
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_pydantic():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_pydantic=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task])
-    result = crew.kickoff()
-    assert isinstance(result, ScoreOutput)
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task])
-    result = crew.kickoff()
-    assert '{\n  "score": 4\n}' == result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_pydantic_to_another_task():
-    from langchain_openai import ChatOpenAI
-
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-        llm=ChatOpenAI(model="gpt-4-0125-preview"),
-        function_calling_llm=ChatOpenAI(model="gpt-3.5-turbo-0125"),
-        verbose=True,
-    )
-
-    task1 = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_pydantic=ScoreOutput,
-        agent=scorer,
-    )
-
-    task2 = Task(
-        description="Given the score the title 'The impact of AI in the future of work' got, give me an integer score between 1-5 for the following title: 'Return of the Jedi', you MUST give it a score, use your best judgment",
-        expected_output="The score of the title.",
-        output_pydantic=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task1, task2], verbose=2)
-    result = crew.kickoff()
-    assert 5 == result.score
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_output_json_to_another_task():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task1 = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    task2 = Task(
-        description="Given the score the title 'The impact of AI in the future of work' got, give me an integer score between 1-5 for the following title: 'Return of the Jedi'",
-        expected_output="The score of the title.",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task1, task2])
-    result = crew.kickoff()
-    assert '{\n  "score": 3\n}' == result
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_save_task_output():
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_file="score.json",
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task])
-
-    with patch.object(Task, "_save_file") as save_file:
-        save_file.return_value = None
-        crew.kickoff()
-        save_file.assert_called_once()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_save_task_json_output():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_file="score.json",
-        output_json=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task])
-
-    with patch.object(Task, "_save_file") as save_file:
-        save_file.return_value = None
-        crew.kickoff()
-        save_file.assert_called_once_with('{\n  "score": 4\n}')
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_save_task_pydantic_output():
-    class ScoreOutput(BaseModel):
-        score: int
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        output_file="score.json",
-        output_pydantic=ScoreOutput,
-        agent=scorer,
-    )
-
-    crew = Crew(agents=[scorer], tasks=[task])
-
-    with patch.object(Task, "_save_file") as save_file:
-        save_file.return_value = None
-        crew.kickoff()
-        save_file.assert_called_once_with('{"score":4}')
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_increment_delegations_for_hierarchical_process():
-    from langchain_openai import ChatOpenAI
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4-0125-preview"),
-    )
-
-    with patch.object(Task, "increment_delegations") as increment_delegations:
-        increment_delegations.return_value = None
-        crew.kickoff()
-        increment_delegations.assert_called_once
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_increment_delegations_for_sequential_process():
-    pass
-
-    manager = Agent(
-        role="Manager",
-        goal="Coordinate scoring processes",
-        backstory="You're great at delegating work about scoring.",
-        allow_delegation=False,
-    )
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        allow_delegation=False,
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work'",
-        expected_output="The score of the title.",
-        agent=manager,
-    )
-
-    crew = Crew(
-        agents=[manager, scorer],
-        tasks=[task],
-        process=Process.sequential,
-    )
-
-    with patch.object(Task, "increment_delegations") as increment_delegations:
-        increment_delegations.return_value = None
-        crew.kickoff()
-        increment_delegations.assert_called_once
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_increment_tool_errors():
-    from crewai_tools import tool
-    from langchain_openai import ChatOpenAI
-
-    @tool
-    def scoring_examples() -> None:
-        "Useful examples for scoring titles."
-        raise Exception("Error")
-
-    scorer = Agent(
-        role="Scorer",
-        goal="Score the title",
-        backstory="You're an expert scorer, specialized in scoring titles.",
-        tools=[scoring_examples],
-    )
-
-    task = Task(
-        description="Give me an integer score between 1-5 for the following title: 'The impact of AI in the future of work', check examples to based your evaluation.",
-        expected_output="The score of the title.",
-    )
-
-    crew = Crew(
-        agents=[scorer],
-        tasks=[task],
-        process=Process.hierarchical,
-        manager_llm=ChatOpenAI(model="gpt-4-0125-preview"),
-    )
-
-    with patch.object(Task, "increment_tools_errors") as increment_tools_errors:
-        increment_tools_errors.return_value = None
-        crew.kickoff()
-        increment_tools_errors.assert_called_once
+        execute.assert_called_once_with(task=task._prompt(), context=None, tools=[])