Added new CLI functionality: docs generator. Updated cli.py and added doc_generator.py

fixed bullet points for crew yaml annoations

.gitignore

@@ -14,4 +14,5 @@ test.py
 rc-tests/*
 *.pkl
 temp/*
-.vscode/*
+.vscode/*
+crew_tasks_output.json

docs/core-concepts/Crews.md

@@ -4,36 +4,38 @@ description: Understanding and utilizing crews in the crewAI framework with comp
 ---
 
 ## What is a Crew?
+
 A crew in crewAI represents a collaborative group of agents working together to achieve a set of tasks. Each crew defines the strategy for task execution, agent collaboration, and the overall workflow.
 
 ## Crew Attributes
 
-| Attribute                   | Parameters          | Description                                                                                              |
-| :-------------------------- | :------------------ | :------------------------------------------------------------------------------------------------------- |
-| **Tasks**                   | `tasks`               | A list of tasks assigned to the crew.                                                                    |
-| **Agents**                  | `agents`              | A list of agents that are part of the crew.                                                              |
-| **Process** *(optional)*    | `process`             | The process flow (e.g., sequential, hierarchical) the crew follows.                                      |
-| **Verbose** *(optional)*    | `verbose`             | The verbosity level for logging during execution.                                                        |
-| **Manager LLM** *(optional)*| `manager_llm`         | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.** |
-| **Function Calling LLM** *(optional)* | `function_calling_llm` | 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)*     | `config`              | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                       |
-| **Max RPM** *(optional)*    | `max_rpm`             | Maximum requests per minute the crew adheres to during execution.                                         |
-| **Language**  *(optional)*  | `language`            | Language used for the crew, defaults to English.                                                          |
-| **Language File** *(optional)* | `language_file`       | Path to the language file to be used for the crew.                                                        |
-| **Memory** *(optional)*     | `memory`              | Utilized for storing execution memories (short-term, long-term, entity memory).                           |
-| **Cache** *(optional)*      | `cache`               | Specifies whether to use a cache for storing the results of tools' execution.                             |
-| **Embedder** *(optional)*   | `embedder`            | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                     |
-| **Full Output** *(optional)*| `full_output`         | Whether the crew should return the full output with all tasks outputs or just the final output.           |
-| **Step Callback** *(optional)* | `step_callback`       | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`. |
-| **Task Callback** *(optional)* | `task_callback`       | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution. |
-| **Share Crew** *(optional)* | `share_crew`          | 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. |
-| **Output Log File** *(optional)* | `output_log_file`    | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
-| **Manager Agent** *(optional)* | `manager_agent`      | `manager` sets a custom agent that will be used as a manager.                                             |
-| **Manager Callbacks** *(optional)* | `manager_callbacks` | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used. |
-| **Prompt File** *(optional)* | `prompt_file`         | Path to the prompt JSON file to be used for the crew.                                                     |
+| Attribute                             | Parameters             | Description                                                                                                                                                                                                                                               |
+| :------------------------------------ | :--------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Tasks**                             | `tasks`                | A list of tasks assigned to the crew.                                                                                                                                                                                                                     |
+| **Agents**                            | `agents`               | A list of agents that are part of the crew.                                                                                                                                                                                                               |
+| **Process** _(optional)_              | `process`              | The process flow (e.g., sequential, hierarchical) the crew follows.                                                                                                                                                                                       |
+| **Verbose** _(optional)_              | `verbose`              | The verbosity level for logging during execution.                                                                                                                                                                                                         |
+| **Manager LLM** _(optional)_          | `manager_llm`          | The language model used by the manager agent in a hierarchical process. **Required when using a hierarchical process.**                                                                                                                                   |
+| **Function Calling LLM** _(optional)_ | `function_calling_llm` | 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)_               | `config`               | Optional configuration settings for the crew, in `Json` or `Dict[str, Any]` format.                                                                                                                                                                       |
+| **Max RPM** _(optional)_              | `max_rpm`              | Maximum requests per minute the crew adheres to during execution.                                                                                                                                                                                         |
+| **Language** _(optional)_             | `language`             | Language used for the crew, defaults to English.                                                                                                                                                                                                          |
+| **Language File** _(optional)_        | `language_file`        | Path to the language file to be used for the crew.                                                                                                                                                                                                        |
+| **Memory** _(optional)_               | `memory`               | Utilized for storing execution memories (short-term, long-term, entity memory).                                                                                                                                                                           |
+| **Cache** _(optional)_                | `cache`                | Specifies whether to use a cache for storing the results of tools' execution.                                                                                                                                                                             |
+| **Embedder** _(optional)_             | `embedder`             | Configuration for the embedder to be used by the crew. Mostly used by memory for now.                                                                                                                                                                     |
+| **Full Output** _(optional)_          | `full_output`          | Whether the crew should return the full output with all tasks outputs or just the final output.                                                                                                                                                           |
+| **Step Callback** _(optional)_        | `step_callback`        | A function that is called after each step of every agent. This can be used to log the agent's actions or to perform other operations; it won't override the agent-specific `step_callback`.                                                               |
+| **Task Callback** _(optional)_        | `task_callback`        | A function that is called after the completion of each task. Useful for monitoring or additional operations post-task execution.                                                                                                                          |
+| **Share Crew** _(optional)_           | `share_crew`           | 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.                                                                                                      |
+| **Output Log File** _(optional)_      | `output_log_file`      | Whether you want to have a file with the complete crew output and execution. You can set it using True and it will default to the folder you are currently in and it will be called logs.txt or passing a string with the full path and name of the file. |
+| **Manager Agent** _(optional)_        | `manager_agent`        | `manager` sets a custom agent that will be used as a manager.                                                                                                                                                                                             |
+| **Manager Callbacks** _(optional)_    | `manager_callbacks`    | `manager_callbacks` takes a list of callback handlers to be executed by the manager agent when a hierarchical process is used.                                                                                                                            |
+| **Prompt File** _(optional)_          | `prompt_file`          | Path to the prompt JSON file to be used for the crew.                                                                                                                                                                                                     |
+| **Planning** *(optional)*             | `planning`             |  Adds planning ability to the Crew. When activated before each Crew iteration, all Crew data is sent to an AgentPlanner that will plan the tasks and this plan will be added to each task description.
 
 !!! 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
 
@@ -44,6 +46,12 @@ When assembling a crew, you combine agents with complementary roles and tools, a
 ```python
 from crewai import Crew, Agent, Task, Process
 from langchain_community.tools import DuckDuckGoSearchRun
+from crewai_tools import tool
+
+@tool('DuckDuckGoSearch')
+def search(search_query: str):
+    """Search the web for information on a given topic"""
+    return DuckDuckGoSearchRun().run(search_query)
 
 # Define agents with specific roles and tools
 researcher = Agent(
@@ -54,7 +62,7 @@ researcher = Agent(
         to the business.
         You're currently working on a project to analyze the
         trends and innovations in the space of artificial intelligence.""",
-    tools=[DuckDuckGoSearchRun()]
+    tools=[search]
 )
 
 writer = Agent(
@@ -89,6 +97,57 @@ my_crew = Crew(
 )
 ```
 
+## Crew Output
+
+!!! note "Understanding Crew Outputs"
+The output of a crew in the crewAI framework is encapsulated within the `CrewOutput` class.
+This class provides a structured way to access results of the crew's execution, including various formats such as raw strings, JSON, and Pydantic models.
+The `CrewOutput` includes the results from the final task output, token usage, and individual task outputs.
+
+### Crew Output Attributes
+
+| Attribute        | Parameters     | Type                       | Description                                                                                          |
+| :--------------- | :------------- | :------------------------- | :--------------------------------------------------------------------------------------------------- |
+| **Raw**          | `raw`          | `str`                      | The raw output of the crew. This is the default format for the output.                               |
+| **Pydantic**     | `pydantic`     | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the crew.                              |
+| **JSON Dict**    | `json_dict`    | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the crew.                                               |
+| **Tasks Output** | `tasks_output` | `List[TaskOutput]`         | A list of `TaskOutput` objects, each representing the output of a task in the crew.                  |
+| **Token Usage**  | `token_usage`  | `Dict[str, Any]`           | A summary of token usage, providing insights into the language model's performance during execution. |
+
+### Crew Output Methods and Properties
+
+| Method/Property | Description                                                                                       |
+| :-------------- | :------------------------------------------------------------------------------------------------ |
+| **json**        | Returns the JSON string representation of the crew output if the output format is JSON.           |
+| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
+| \***\*str\*\*** | Returns the string representation of the crew output, prioritizing Pydantic, then JSON, then raw. |
+
+### Accessing Crew Outputs
+
+Once a crew has been executed, its output can be accessed through the `output` attribute of the `Crew` object. The `CrewOutput` class provides various ways to interact with and present this output.
+
+#### Example
+
+```python
+# Example crew execution
+crew = Crew(
+    agents=[research_agent, writer_agent],
+    tasks=[research_task, write_article_task],
+    verbose=2
+)
+
+result = crew.kickoff()
+
+# Accessing the crew output
+print(f"Raw Output: {crew_output.raw}")
+if crew_output.json_dict:
+    print(f"JSON Output: {json.dumps(crew_output.json_dict, indent=2)}")
+if crew_output.pydantic:
+    print(f"Pydantic Output: {crew_output.pydantic}")
+print(f"Tasks Output: {crew_output.tasks_output}")
+print(f"Token Usage: {crew_output.token_usage}")
+```
+
 ## Memory Utilization
 
 Crews can utilize memory (short-term, long-term, and entity memory) to enhance their execution and learning over time. This feature allows crews to store and recall execution memories, aiding in decision-making and task execution strategies.
@@ -156,3 +215,32 @@ for async_result in async_results:
 ```
 
 These methods provide flexibility in how you manage and execute tasks within your crew, allowing for both synchronous and asynchronous workflows tailored to your needs
+
+
+### Replaying from specific task:
+You can now replay from a specific task using our cli command replay.
+
+The replay feature in CrewAI allows you to replay from a specific task using the command-line interface (CLI). By running the command `crewai replay -t <task_id>`, you can specify the `task_id` for the replay process.
+
+Kickoffs will now save the latest kickoffs returned task outputs locally for you to be able to replay from.
+
+
+### Replaying from specific task Using the CLI
+To use the replay feature, follow these steps:
+
+1. Open your terminal or command prompt.
+2. Navigate to the directory where your CrewAI project is located.
+3. Run the following command:
+
+To view latest kickoff task_ids use:
+
+```shell
+crewai log-tasks-outputs
+```
+
+
+```shell
+crewai replay -t <task_id>
+```
+
+These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

docs/core-concepts/Memory.md

@@ -29,6 +29,11 @@ description: Leveraging memory systems in the crewAI framework to enhance agent
 When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
 By default, the memory system is disabled, and you can ensure it is active by setting `memory=True` in the crew configuration. The memory will use OpenAI Embeddings by default, but you can change it by setting `embedder` to a different model.
 
+The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using EmbedChain package.  
+The **Long-Term Memory** uses SQLLite3 to store task results.  Currently, there is no way to override these storage implementations.
+The data storage files are saved into a platform specific location found using the appdirs package 
+and the name of the project which can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
+
 ### Example: Configuring Memory for a Crew
 
 ```python
@@ -161,10 +166,43 @@ my_crew = Crew(
 )
 ```
 
+### Resetting Memory
+```sh
+crewai reset_memories [OPTIONS]
+```
+
+#### Resetting Memory Options
+- **`-l, --long`**
+  - **Description:** Reset LONG TERM memory.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-s, --short`**
+  - **Description:** Reset SHORT TERM memory.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-e, --entities`**
+  - **Description:** Reset ENTITIES memory.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-k, --kickoff-outputs`**
+  - **Description:** Reset LATEST KICKOFF TASK OUTPUTS.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+- **`-a, --all`**
+  - **Description:** Reset ALL memories.
+  - **Type:** Flag (boolean)
+  - **Default:** False
+
+
+
 ## Benefits of Using crewAI's Memory System
 - **Adaptive Learning:** Crews become more efficient over time, adapting to new information and refining their approach to tasks.
 - **Enhanced Personalization:** Memory enables agents to remember user preferences and historical interactions, leading to personalized experiences.
 - **Improved Problem Solving:** Access to a rich memory store aids agents in making more informed decisions, drawing on past learnings and contextual insights.
 
 ## Getting Started
-Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.
+Integrating crewAI's memory system into your projects is straightforward. By leveraging the provided memory components and configurations, you can quickly empower your agents with the ability to remember, reason, and learn from their interactions, unlocking new levels of intelligence and capability.

docs/core-concepts/Planning.md

@@ -0,0 +1,119 @@
+---
+title: crewAI Planning
+description: Learn how to add planning to your crewAI Crew and improve their performance.
+---
+
+## Introduction
+The planning feature in CrewAI allows you to add planning capability to your crew. When enabled, before each Crew iteration, all Crew information is sent to an AgentPlanner that will plan the tasks step by step, and this plan will be added to each task description.
+
+### Using the Planning Feature
+Getting started with the planning feature is very easy, the only step required is to add `planning=True` to your Crew:
+
+```python
+from crewai import Crew, Agent, Task, Process
+
+# Assemble your crew with planning capabilities
+my_crew = Crew(
+    agents=self.agents,
+    tasks=self.tasks,
+    process=Process.sequential,
+    planning=True,
+)
+```
+
+From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
+
+### Example
+
+When running the base case example, you will see something like the following output, which represents the output of the AgentPlanner responsible for creating the step-by-step logic to add to the Agents tasks.
+
+```bash
+
+[2024-07-15 16:49:11][INFO]: Planning the crew execution
+**Step-by-Step Plan for Task Execution**
+
+**Task Number 1: Conduct a thorough research about AI LLMs**
+
+**Agent:** AI LLMs Senior Data Researcher
+
+**Agent Goal:** Uncover cutting-edge developments in AI LLMs
+
+**Task Expected Output:** A list with 10 bullet points of the most relevant information about AI LLMs
+
+**Task Tools:** None specified
+
+**Agent Tools:** None specified
+
+**Step-by-Step Plan:**
+
+1. **Define Research Scope:**
+   - Determine the specific areas of AI LLMs to focus on, such as advancements in architecture, use cases, ethical considerations, and performance metrics.
+
+2. **Identify Reliable Sources:**
+   - List reputable sources for AI research, including academic journals, industry reports, conferences (e.g., NeurIPS, ACL), AI research labs (e.g., OpenAI, Google AI), and online databases (e.g., IEEE Xplore, arXiv).
+
+3. **Collect Data:**
+   - Search for the latest papers, articles, and reports published in 2023 and early 2024.
+   - Use keywords like "Large Language Models 2024", "AI LLM advancements", "AI ethics 2024", etc.
+
+4. **Analyze Findings:**
+   - Read and summarize the key points from each source.
+   - Highlight new techniques, models, and applications introduced in the past year.
+
+5. **Organize Information:**
+   - Categorize the information into relevant topics (e.g., new architectures, ethical implications, real-world applications).
+   - Ensure each bullet point is concise but informative.
+
+6. **Create the List:**
+   - Compile the 10 most relevant pieces of information into a bullet point list.
+   - Review the list to ensure clarity and relevance.
+
+**Expected Output:**
+A list with 10 bullet points of the most relevant information about AI LLMs.
+
+---
+
+**Task Number 2: Review the context you got and expand each topic into a full section for a report**
+
+**Agent:** AI LLMs Reporting Analyst
+
+**Agent Goal:** Create detailed reports based on AI LLMs data analysis and research findings
+
+**Task Expected Output:** A fully fledge report with the main topics, each with a full section of information. Formatted as markdown without '```'
+
+**Task Tools:** None specified
+
+**Agent Tools:** None specified
+
+**Step-by-Step Plan:**
+
+1. **Review the Bullet Points:**
+   - Carefully read through the list of 10 bullet points provided by the AI LLMs Senior Data Researcher.
+
+2. **Outline the Report:**
+   - Create an outline with each bullet point as a main section heading.
+   - Plan sub-sections under each main heading to cover different aspects of the topic.
+
+3. **Research Further Details:**
+   - For each bullet point, conduct additional research if necessary to gather more detailed information.
+   - Look for case studies, examples, and statistical data to support each section.
+
+4. **Write Detailed Sections:**
+   - Expand each bullet point into a comprehensive section.
+   - Ensure each section includes an introduction, detailed explanation, examples, and a conclusion.
+   - Use markdown formatting for headings, subheadings, lists, and emphasis.
+
+5. **Review and Edit:**
+   - Proofread the report for clarity, coherence, and correctness.
+   - Make sure the report flows logically from one section to the next.
+   - Format the report according to markdown standards.
+
+6. **Finalize the Report:**
+   - Ensure the report is complete with all sections expanded and detailed.
+   - Double-check formatting and make any necessary adjustments.
+
+**Expected Output:**
+A fully-fledged report with the main topics, each with a full section of information. Formatted as markdown without '```'.
+
+---
+```

docs/core-concepts/Tasks.md

@@ -4,27 +4,29 @@ description: Detailed guide on managing and creating tasks within the crewAI fra
 ---
 
 ## Overview of a Task
+
 !!! note "What is a Task?"
-    In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
+In the crewAI framework, tasks are specific assignments completed by agents. They provide all necessary details for execution, such as a description, the agent responsible, required tools, and more, facilitating a wide range of action complexities.
 
 Tasks within crewAI can be collaborative, requiring multiple agents to work together. This is managed through the task properties and orchestrated by the Crew's process, enhancing teamwork and efficiency.
 
 ## Task Attributes
 
-| Attribute              | Parameters           | Description                                                                                   |
-| :----------------------| :------------------- | :-------------------------------------------------------------------------------------------- |
-| **Description**        | `description`          | A clear, concise statement of what the task entails.                                          |
-| **Agent**              | `agent`                | The agent responsible for the task, assigned either directly or by the crew's process.        |
-| **Expected Output**    | `expected_output`      | A detailed description of what the task's completion looks like.                              |
-| **Tools** *(optional)* | `tools`                | The functions or capabilities the agent can utilize to perform the task.                      |
-| **Async Execution** *(optional)* | `async_execution`      | If set, the task executes asynchronously, allowing progression without waiting for completion.|
-| **Context**  *(optional)* | `context`              | Specifies tasks whose outputs are used as context for this task.                              |
-| **Config** *(optional)* | `config`               | Additional configuration details for the agent executing the task, allowing further customization. |
-| **Output JSON**  *(optional)* | `output_json`          | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.         |
-| **Output Pydantic**  *(optional)* | `output_pydantic`      | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
-| **Output File**  *(optional)* | `output_file`          | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
-| **Callback**  *(optional)* | `callback`             | A Python callable that is executed with the task's output upon completion.                    |
-| **Human Input** *(optional)* | `human_input`         | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight. |
+| Attribute                        | Parameters        | Description                                                                                                          |
+| :------------------------------- | :---------------- | :------------------------------------------------------------------------------------------------------------------- |
+| **Description**                  | `description`     | A clear, concise statement of what the task entails.                                                                 |
+| **Agent**                        | `agent`           | The agent responsible for the task, assigned either directly or by the crew's process.                               |
+| **Expected Output**              | `expected_output` | A detailed description of what the task's completion looks like.                                                     |
+| **Tools** _(optional)_           | `tools`           | The functions or capabilities the agent can utilize to perform the task.                                             |
+| **Async Execution** _(optional)_ | `async_execution` | If set, the task executes asynchronously, allowing progression without waiting for completion.                       |
+| **Context** _(optional)_         | `context`         | Specifies tasks whose outputs are used as context for this task.                                                     |
+| **Config** _(optional)_          | `config`          | Additional configuration details for the agent executing the task, allowing further customization.                   |
+| **Output JSON** _(optional)_     | `output_json`     | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set.                                |
+| **Output Pydantic** _(optional)_ | `output_pydantic` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set.                      |
+| **Output File** _(optional)_     | `output_file`     | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
+| **Output** _(optional)_          | `output`          | The output of the task, containing the raw, JSON, and Pydantic output plus additional details.                       |
+| **Callback** _(optional)_        | `callback`        | A Python callable that is executed with the task's output upon completion.                                           |
+| **Human Input** _(optional)_     | `human_input`     | Indicates if the task requires human feedback at the end, useful for tasks needing human oversight.                  |
 
 ## Creating a Task
 
@@ -35,12 +37,75 @@ from crewai import Task
 
 task = Task(
     description='Find and summarize the latest and most relevant news on AI',
-    agent=sales_agent
+    agent=sales_agent,
+    expected_output='A bullet list summary of the top 5 most important AI news',
 )
 ```
 
 !!! note "Task Assignment"
-    Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
+Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
+
+## Task Output
+
+!!! note "Understanding Task Outputs"
+The output of a task in the crewAI framework is encapsulated within the `TaskOutput` class. This class provides a structured way to access results of a task, including various formats such as raw strings, JSON, and Pydantic models.
+By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` will only include the `pydantic` or `json_dict` output if the original `Task` object was configured with `output_pydantic` or `output_json`, respectively.
+
+### Task Output Attributes
+
+| Attribute         | Parameters      | Type                       | Description                                                                                        |
+| :---------------- | :-------------- | :------------------------- | :------------------------------------------------------------------------------------------------- |
+| **Description**   | `description`   | `str`                      | A brief description of the task.                                                                   |
+| **Summary**       | `summary`       | `Optional[str]`            | A short summary of the task, auto-generated from the description.                                  |
+| **Raw**           | `raw`           | `str`                      | The raw output of the task. This is the default format for the output.                             |
+| **Pydantic**      | `pydantic`      | `Optional[BaseModel]`      | A Pydantic model object representing the structured output of the task.                            |
+| **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
+| **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
+| **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
+
+### Task Output Methods and Properties
+
+| Method/Property | Description                                                                                       |
+| :-------------- | :------------------------------------------------------------------------------------------------ |
+| **json**        | Returns the JSON string representation of the task output if the output format is JSON.           |
+| **to_dict**     | Converts the JSON and Pydantic outputs to a dictionary.                                           |
+| \***\*str\*\*** | Returns the string representation of the task output, prioritizing Pydantic, then JSON, then raw. |
+
+### Accessing Task Outputs
+
+Once a task has been executed, its output can be accessed through the `output` attribute of the `Task` object. The `TaskOutput` class provides various ways to interact with and present this output.
+
+#### Example
+
+```python
+# Example task
+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,
+    tools=[search_tool]
+)
+
+# Execute the crew
+crew = Crew(
+    agents=[research_agent],
+    tasks=[task],
+    verbose=2
+)
+
+result = crew.kickoff()
+
+# Accessing the task output
+task_output = task.output
+
+print(f"Task Description: {task_output.description}")
+print(f"Task Summary: {task_output.summary}")
+print(f"Raw Output: {task_output.raw}")
+if task_output.json_dict:
+    print(f"JSON Output: {json.dumps(task_output.json_dict, indent=2)}")
+if task_output.pydantic:
+    print(f"Pydantic Output: {task_output.pydantic}")
+```
 
 ## Integrating Tools with Tasks
 

docs/core-concepts/Tools.md

@@ -100,16 +100,24 @@ Here is a list of the available tools and their descriptions:
 
 | Tool                        | Description                                                                                   |
 | :-------------------------- | :-------------------------------------------------------------------------------------------- |
+| **BrowserbaseLoadTool**     | A tool for interacting with and extracting data from web browsers.                            |
 | **CodeDocsSearchTool**      | A RAG tool optimized for searching through code documentation and related technical documents. |
+| **CodeInterpreterTool**     | A tool for interpreting python code.                                                          |
+| **ComposioTool**            | Enables use of Composio tools.                                                                |
 | **CSVSearchTool**           | A RAG tool designed for searching within CSV files, tailored to handle structured data.       |
 | **DirectorySearchTool**     | A RAG tool for searching within directories, useful for navigating through file systems.      |
 | **DOCXSearchTool**          | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files.         |
 | **DirectoryReadTool**       | Facilitates reading and processing of directory structures and their contents.                |
+| **EXASearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
 | **FileReadTool**            | Enables reading and extracting data from files, supporting various file formats.              |
+| **FirecrawlSearchTool**     | A tool to search webpages using Firecrawl and return the results.                             |
+| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl.                                               |
+| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages url using Firecrawl and returning its contents.               |
 | **GithubSearchTool**        | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
 | **SerperDevTool**           | A specialized tool for development purposes, with specific functionalities under development. |
 | **TXTSearchTool**           | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data.     |
 | **JSONSearchTool**          | A RAG tool designed for searching within JSON files, catering to structured data handling.     |
+| **LlamaIndexTool**          | Enables the use of LlamaIndex tools.                                                          |
 | **MDXSearchTool**           | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation.      |
 | **PDFSearchTool**           | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents.    |
 | **PGSearchTool**            | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
@@ -120,8 +128,6 @@ Here is a list of the available tools and their descriptions:
 | **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.          |
-| **BrowserbaseTool**         | A tool for interacting with and extracting data from web browsers.                            |
-| **ExaSearchTool**           | A tool designed for performing exhaustive searches across various data sources.               |
 
 ## Creating your own Tools
 

docs/how-to/Conditional-Tasks.md

@@ -0,0 +1,87 @@
+---
+title: Conditional Tasks
+description: Learn how to use conditional tasks in a crewAI kickoff
+---
+
+## Introduction
+
+Conditional Tasks in crewAI allow for dynamic workflow adaptation based on the outcomes of previous tasks. This powerful feature enables crews to make decisions and execute tasks selectively, enhancing the flexibility and efficiency of your AI-driven processes.
+
+```python
+from typing import List
+
+from pydantic import BaseModel
+from crewai import Agent, Crew
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.task_output import TaskOutput
+from crewai.task import Task
+from crewai_tools import SerperDevTool
+
+
+# Define a condition function for the conditional task
+# if false task will be skipped, true, then execute task
+def is_data_missing(output: TaskOutput) -> bool:
+    return len(output.pydantic.events) < 10: # this will skip this task
+
+# Define the agents
+data_fetcher_agent = Agent(
+    role="Data Fetcher",
+    goal="Fetch data online using Serper tool",
+    backstory="Backstory 1",
+    verbose=True,
+    tools=[SerperDevTool()],
+)
+
+data_processor_agent = Agent(
+    role="Data Processor",
+    goal="Process fetched data",
+    backstory="Backstory 2",
+    verbose=True,
+)
+
+summary_generator_agent = Agent(
+    role="Summary Generator",
+    goal="Generate summary from fetched data",
+    backstory="Backstory 3",
+    verbose=True,
+)
+
+
+class EventOutput(BaseModel):
+    events: List[str]
+
+
+task1 = Task(
+    description="Fetch data about events in San Francisco using Serper tool",
+    expected_output="List of 10 things to do in SF this week",
+    agent=data_fetcher_agent,
+    output_pydantic=EventOutput,
+)
+
+conditional_task = ConditionalTask(
+    description="""
+        Check if data is missing. If we have less than 10 events,
+        fetch more events using Serper tool so that
+        we have a total of 10 events in SF this week..
+        """,
+    expected_output="List of 10 Things to do in SF this week ",
+    condition=is_data_missing,
+    agent=data_processor_agent,
+)
+
+task3 = Task(
+    description="Generate summary of events in San Francisco from fetched data",
+    expected_output="summary_generated",
+    agent=summary_generator_agent,
+)
+
+# Create a crew with the tasks
+crew = Crew(
+    agents=[data_fetcher_agent, data_processor_agent, summary_generator_agent],
+    tasks=[task1, conditional_task, task3],
+    verbose=2,
+)
+
+result = crew.kickoff()
+print("results", result)
+```

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

@@ -21,14 +21,16 @@ Define your agents with distinct roles, backstories, and enhanced capabilities.
 import os
 from langchain.llms import OpenAI
 from crewai import Agent
-from crewai_tools import SerperDevTool, BrowserbaseTool, ExaSearchTool
+from crewai_tools import SerperDevTool, BrowserbaseLoadTool, EXASearchTool
 
 os.environ["OPENAI_API_KEY"] = "Your OpenAI Key"
 os.environ["SERPER_API_KEY"] = "Your Serper Key"
+os.environ["BROWSERBASE_API_KEY"] = "Your BrowserBase Key"
+os.environ["BROWSERBASE_PROJECT_ID"] = "Your BrowserBase Project Id"
 
 search_tool = SerperDevTool()
-browser_tool = BrowserbaseTool()
-exa_search_tool = ExaSearchTool()
+browser_tool = BrowserbaseLoadTool()
+exa_search_tool = EXASearchTool()
 
 # Creating a senior researcher agent with advanced configurations
 researcher = Agent(

docs/how-to/LLM-Connections.md

@@ -127,7 +127,7 @@ llm = HuggingFaceHub(
 ```
 
 ## OpenAI Compatible API Endpoints
-Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, and Mistral AI.
+Switch between APIs and models seamlessly using environment variables, supporting platforms like FastChat, LM Studio, Groq, and Mistral AI.
 
 ### Configuration Examples
 #### FastChat
@@ -144,6 +144,13 @@ OPENAI_API_BASE="http://localhost:1234/v1"
 OPENAI_API_KEY="lm-studio"
 ```
 
+#### Groq API
+```sh
+OPENAI_API_KEY=your-groq-api-key
+OPENAI_MODEL_NAME='llama3-8b-8192'
+OPENAI_API_BASE=https://api.groq.com/openai/v1
+```
+
 #### Mistral API
 ```sh
 OPENAI_API_KEY=your-mistral-api-key
@@ -211,4 +218,4 @@ azure_agent = Agent(
 ```
 
 ## Conclusion
-Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.
+Integrating CrewAI with different LLMs expands the framework's versatility, allowing for customized, efficient AI solutions across various domains and platforms.

docs/how-to/Replay-tasks-from-latest-Crew-Kickoff.md

@@ -0,0 +1,49 @@
+---
+title: Replay Tasks from Latest Crew Kickoff
+description: Replay tasks from the latest crew.kickoff(...)
+---
+
+## Introduction
+CrewAI provides the ability to replay from a task specified from the latest crew kickoff. This feature is particularly useful when you've finished a kickoff and may want to retry certain tasks or don't need to refetch data over and your agents already have the context saved from the kickoff execution so you just need to replay the tasks you want to.
+
+## Note:
+You must run `crew.kickoff()` before you can replay a task. Currently, only the latest kickoff is supported, so if you use `kickoff_for_each`, it will only allow you to replay from the most recent crew run.
+
+Here's an example of how to replay from a task:
+
+### Replaying from specific task Using the CLI
+To use the replay feature, follow these steps:
+
+1. Open your terminal or command prompt.
+2. Navigate to the directory where your CrewAI project is located.
+3. Run the following command:
+
+To view latest kickoff task_ids use:
+```shell
+crewai log-tasks-outputs
+```
+
+Once you have your task_id to replay from use:
+```shell
+crewai replay -t <task_id>
+```
+
+
+### Replaying from a task Programmatically
+To replay from a task programmatically, use the following steps:
+
+1. Specify the task_id and input parameters for the replay process.
+2. Execute the replay command within a try-except block to handle potential errors.
+
+```python
+   def replay():
+    """
+    Replay the crew execution from a specific task.
+    """
+    task_id = '<task_id>'
+    inputs = {"topic": "CrewAI Training"} # this is optional, you can pass in the inputs you want to replay otherwise uses the previous kickoffs inputs
+    try:
+        YourCrewName_Crew().crew().replay(task_id=task_id, inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")

docs/how-to/Start-a-New-CrewAI-Project.md

@@ -9,14 +9,14 @@ Welcome to the ultimate guide for starting a new CrewAI project. This document w
 
 ## Prerequisites
 
-We assume you have already installed CrewAI. If not, please refer to the [installation guide](how-to/Installing-CrewAI.md) to install CrewAI and its dependencies.
+We assume you have already installed CrewAI. If not, please refer to the [installation guide](https://docs.crewai.com/how-to/Installing-CrewAI/) to install CrewAI and its dependencies.
 
 ## Creating a New Project
 
 To create a new project, run the following CLI command:
 
 ```shell
-$ crewai create my_project
+$ crewai create <project_name>
 ```
 
 This command will create a new project folder with the following structure:
@@ -79,8 +79,77 @@ research_candidates_task:
     {job_requirements}
   expected_output: >
     A list of 10 potential candidates with their contact information and brief profiles highlighting their suitability.
+  agent: researcher # THIS NEEDS TO MATCH THE AGENT NAME IN THE AGENTS.YAML FILE AND THE AGENT DEFINED IN THE Crew.PY FILE
+  context: # THESE NEED TO MATCH THE TASK NAMES DEFINED ABOVE AND THE TASKS.YAML FILE AND THE TASK DEFINED IN THE Crew.PY FILE
+    - researcher
 ```
 
+### Referencing Variables:
+Your defined functions with the same name will be used. For example, you can reference the agent for specific tasks from task.yaml file. Ensure your annotated agent and function name is the same otherwise your task wont recognize the reference properly.
+
+#### Example References
+agent.yaml
+```yaml
+email_summarizer:
+    role: >
+      Email Summarizer
+    goal: >
+      Summarize emails into a concise and clear summary
+    backstory: >
+      You will create a 5 bullet point summary of the report
+    llm: mixtal_llm
+```
+
+task.yaml
+```yaml
+email_summarizer_task:
+    description: >
+      Summarize the email into a 5 bullet point summary
+    expected_output: >
+      A 5 bullet point summary of the email
+    agent: email_summarizer
+    context:
+      - reporting_task
+      - research_task
+```
+
+Use the annotations are used to properly reference the agent and task in the crew.py file.
+
+### Annotations include:
+* @agent
+* @task
+* @crew
+* @llm
+* @tool
+* @callback
+* @output_json
+* @output_pydantic
+* @cache_handler
+
+
+crew.py
+```py
+...
+    @llm
+    def mixtal_llm(self):
+        return ChatGroq(temperature=0, model_name="mixtral-8x7b-32768")
+
+    @agent
+    def email_summarizer(self) -> Agent:
+        return Agent(
+            config=self.agents_config["email_summarizer"],
+        )
+    ## ...other tasks defined
+    @task
+    def email_summarizer_task(self) -> Task:
+        return Task(
+            config=self.tasks_config["email_summarizer_task"],
+        )
+...
+```
+
+
+
 ## Installing Dependencies
 
 To install the dependencies for your project, you can use Poetry. First, navigate to your project directory:
@@ -134,4 +203,4 @@ This will initialize your crew of AI agents and begin task execution as defined
 
 ## Deploying Your Project
 
-The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.
+The easiest way to deploy your crew is through [CrewAI+](https://www.crewai.com/crewaiplus), where you can deploy your crew in a few clicks.

docs/index.md

@@ -43,6 +43,11 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Memory
                 </a>
             </li>
+            <li>
+                <a href="./core-concepts/Planning">
+                    Planning
+                </a>
+            </li>
         </ul>
     </div>
     <div style="width:30%">
@@ -113,6 +118,16 @@ Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By
                     Kickoff a Crew for a List
                 </a>
             </li>
+            <li>
+                <a href="./how-to/Replay-tasks-from-latest-Crew-Kickoff">
+                    Replay from a Task
+                </a>
+            </li>
+            <li>
+                <a href="./how-to/Conditional-Tasks">
+                    Conditional Tasks
+                </a>
+            </li>
             <li>
                 <a href="./how-to/AgentOps-Observability">
                     Agent Monitoring with AgentOps

mkdocs.yml

@@ -128,6 +128,7 @@ nav:
     - Collaboration: 'core-concepts/Collaboration.md'
     - Training: 'core-concepts/Training-Crew.md'
     - Memory: 'core-concepts/Memory.md'
+    - Planning: 'core-concepts/Planning.md'
     - Using LangChain Tools: 'core-concepts/Using-LangChain-Tools.md'
     - Using LlamaIndex Tools: 'core-concepts/Using-LlamaIndex-Tools.md'
   - How to Guides:
@@ -145,6 +146,8 @@ nav:
     - Human Input on Execution: 'how-to/Human-Input-on-Execution.md'
     - Kickoff a Crew Asynchronously: 'how-to/Kickoff-async.md'
     - Kickoff a Crew for a List: 'how-to/Kickoff-for-each.md'
+    - Replay from a specific task from a kickoff: 'how-to/Replay-tasks-from-latest-Crew-Kickoff.md'
+    - Conditional Tasks: 'how-to/Conditional-Tasks.md'
     - Agent Monitoring with AgentOps: 'how-to/AgentOps-Observability.md'
     - Agent Monitoring with LangTrace: 'how-to/Langtrace-Observability.md'
   - Tools Docs:
@@ -180,6 +183,7 @@ nav:
     - Landing Page Generator: https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator"
     - Prepare for meetings: https://github.com/joaomdmoura/crewAI-examples/tree/main/prep-for-a-meeting"
   - Telemetry: 'telemetry/Telemetry.md'
+  - Change Log: 'https://github.com/crewAIInc/crewAI/releases'
 
 extra_css:
   - stylesheets/output.css

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "crewai"
-version = "0.36.0"
+version = "0.41.1"
 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"
@@ -21,13 +21,14 @@ opentelemetry-sdk = "^1.22.0"
 opentelemetry-exporter-otlp-proto-http = "^1.22.0"
 instructor = "1.3.3"
 regex = "^2023.12.25"
-crewai-tools = { version = "^0.4.8", optional = true }
+crewai-tools = { version = "^0.4.26", optional = true }
 click = "^8.1.7"
 python-dotenv = "^1.0.0"
 appdirs = "^1.4.4"
 jsonref = "^1.1.0"
-agentops = { version = "^0.1.9", optional = true }
+agentops = { version = "^0.3.0", optional = true }
 embedchain = "^0.1.114"
+json-repair = "^0.25.2"
 
 [tool.poetry.extras]
 tools = ["crewai-tools"]
@@ -45,7 +46,7 @@ mkdocs-material = { extras = ["imaging"], version = "^9.5.7" }
 mkdocs-material-extensions = "^1.3.1"
 pillow = "^10.2.0"
 cairosvg = "^2.7.1"
-crewai-tools = "^0.4.8"
+crewai-tools = "^0.4.26"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^8.0.0"

src/crewai/agent.py

@@ -1,13 +1,14 @@
 import os
+from inspect import signature
 from typing import Any, List, Optional, Tuple
 
 from langchain.agents.agent import RunnableAgent
+from langchain.agents.tools import BaseTool
 from langchain.agents.tools import tool as LangChainTool
-from langchain.tools.render import render_text_description
 from langchain_core.agents import AgentAction
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_openai import ChatOpenAI
-from pydantic import Field, InstanceOf, model_validator
+from pydantic import Field, InstanceOf, PrivateAttr, model_validator
 
 from crewai.agents import CacheHandler, CrewAgentExecutor, CrewAgentParser
 from crewai.agents.agent_builder.base_agent import BaseAgent
@@ -54,8 +55,11 @@ class Agent(BaseAgent):
             tools: Tools at agents disposal
             step_callback: Callback to be executed after each step of the agent execution.
             callbacks: A list of callback functions from the langchain library that are triggered during the agent's execution process
+            allow_code_execution: Enable code execution for the agent.
+            max_retry_limit: Maximum number of retries for an agent to execute a task when an error occurs.
     """
 
+    _times_executed: int = PrivateAttr(default=0)
     max_execution_time: Optional[int] = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
@@ -96,6 +100,10 @@ class Agent(BaseAgent):
     allow_code_execution: Optional[bool] = Field(
         default=False, description="Enable code execution for the agent."
     )
+    max_retry_limit: int = Field(
+        default=2,
+        description="Maximum number of retries for an agent to execute a task when an error occurs.",
+    )
 
     def __init__(__pydantic_self__, **data):
         config = data.pop("config", {})
@@ -167,14 +175,15 @@ class Agent(BaseAgent):
             if memory.strip() != "":
                 task_prompt += self.i18n.slice("memory").format(memory=memory)
 
-        tools = tools or self.tools
-
-        parsed_tools = self._parse_tools(tools or [])  # type: ignore # Argument 1 to "_parse_tools" of "Agent" has incompatible type "list[Any] | None"; expected "list[Any]"
+        tools = tools or self.tools or []
+        parsed_tools = self._parse_tools(tools)
         self.create_agent_executor(tools=tools)
         self.agent_executor.tools = parsed_tools
         self.agent_executor.task = task
 
-        self.agent_executor.tools_description = render_text_description(parsed_tools)
+        self.agent_executor.tools_description = self._render_text_description_and_args(
+            parsed_tools
+        )
         self.agent_executor.tools_names = self.__tools_names(parsed_tools)
 
         if self.crew and self.crew._train:
@@ -182,13 +191,20 @@ class Agent(BaseAgent):
         else:
             task_prompt = self._use_trained_data(task_prompt=task_prompt)
 
-        result = self.agent_executor.invoke(
-            {
-                "input": task_prompt,
-                "tool_names": self.agent_executor.tools_names,
-                "tools": self.agent_executor.tools_description,
-            }
-        )["output"]
+        try:
+            result = self.agent_executor.invoke(
+                {
+                    "input": task_prompt,
+                    "tool_names": self.agent_executor.tools_names,
+                    "tools": self.agent_executor.tools_description,
+                }
+            )["output"]
+        except Exception as e:
+            self._times_executed += 1
+            if self._times_executed > self.max_retry_limit:
+                raise e
+            result = self.execute_task(task, context, tools)
+
         if self.max_rpm:
             self._rpm_controller.stop_rpm_counter()
 
@@ -220,7 +236,7 @@ class Agent(BaseAgent):
         Returns:
             An instance of the CrewAgentExecutor class.
         """
-        tools = tools or self.tools
+        tools = tools or self.tools or []
 
         agent_args = {
             "input": lambda x: x["input"],
@@ -315,6 +331,7 @@ class Agent(BaseAgent):
             tools_list = []
             for tool in tools:
                 tools_list.append(tool)
+
         return tools_list
 
     def _training_handler(self, task_prompt: str) -> str:
@@ -341,6 +358,52 @@ class Agent(BaseAgent):
                 )
         return task_prompt
 
+    def _render_text_description(self, tools: List[BaseTool]) -> str:
+        """Render the tool name and description in plain text.
+
+        Output will be in the format of:
+
+        .. code-block:: markdown
+
+            search: This tool is used for search
+            calculator: This tool is used for math
+        """
+        description = "\n".join(
+            [
+                f"Tool name: {tool.name}\nTool description:\n{tool.description}"
+                for tool in tools
+            ]
+        )
+
+        return description
+
+    def _render_text_description_and_args(self, tools: List[BaseTool]) -> str:
+        """Render the tool name, description, and args in plain text.
+
+        Output will be in the format of:
+
+        .. code-block:: markdown
+
+            search: This tool is used for search, args: {"query": {"type": "string"}}
+            calculator: This tool is used for math, \
+    args: {"expression": {"type": "string"}}
+        """
+        tool_strings = []
+        for tool in tools:
+            args_schema = str(tool.args)
+            if hasattr(tool, "func") and tool.func:
+                sig = signature(tool.func)
+                description = (
+                    f"Tool Name: {tool.name}{sig}\nTool Description: {tool.description}"
+                )
+            else:
+                description = (
+                    f"Tool Name: {tool.name}\nTool Description: {tool.description}"
+                )
+            tool_strings.append(f"{description}\nTool Arguments: {args_schema}")
+
+        return "\n".join(tool_strings)
+
     @staticmethod
     def __tools_names(tools) -> str:
         return ", ".join([t.name for t in tools])

src/crewai/agents/agent_builder/base_agent.py

@@ -1,6 +1,7 @@
 import uuid
 from abc import ABC, abstractmethod
 from copy import copy as shallow_copy
+from hashlib import md5
 from typing import Any, Dict, List, Optional, TypeVar
 
 from pydantic import (
@@ -162,6 +163,11 @@ class BaseAgent(ABC, BaseModel):
             self._token_process = TokenProcess()
         return self
 
+    @property
+    def key(self):
+        source = [self.role, self.goal, self.backstory]
+        return md5("|".join(source).encode()).hexdigest()
+
     @abstractmethod
     def execute_task(
         self,
@@ -180,7 +186,7 @@ class BaseAgent(ABC, BaseModel):
         pass
 
     @abstractmethod
-    def get_delegation_tools(self, agents: List["BaseAgent"]):
+    def get_delegation_tools(self, agents: List["BaseAgent"]) -> List[Any]:
         """Set the task tools that init BaseAgenTools class."""
         pass
 

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

@@ -24,6 +24,7 @@ class BaseAgentTools(BaseModel, ABC):
             is_list = coworker.startswith("[") and coworker.endswith("]")
             if is_list:
                 coworker = coworker[1:-1].split(",")[0]
+
         return coworker
 
     def delegate_work(
@@ -40,11 +41,13 @@ class BaseAgentTools(BaseModel, ABC):
         coworker = self._get_coworker(coworker, **kwargs)
         return self._execute(coworker, question, context)
 
-    def _execute(self, agent: Union[str, None], task: str, context: Union[str, None]):
+    def _execute(
+        self, agent_name: Union[str, None], task: str, context: Union[str, None]
+    ):
         """Execute the command."""
         try:
-            if agent is None:
-                agent = ""
+            if agent_name is None:
+                agent_name = ""
 
             # It is important to remove the quotes from the agent name.
             # The reason we have to do this is because less-powerful LLM's
@@ -53,7 +56,7 @@ class BaseAgentTools(BaseModel, ABC):
             # {"task": "....", "coworker": "....
             # when it should look like this:
             # {"task": "....", "coworker": "...."}
-            agent_name = agent.casefold().replace('"', "").replace("\n", "")
+            agent_name = agent_name.casefold().replace('"', "").replace("\n", "")
 
             agent = [  # type: ignore # Incompatible types in assignment (expression has type "list[BaseAgent]", variable has type "str | None")
                 available_agent
@@ -75,9 +78,9 @@ class BaseAgentTools(BaseModel, ABC):
             )
 
         agent = agent[0]
-        task = Task(  # type: ignore # Incompatible types in assignment (expression has type "Task", variable has type "str")
+        task_with_assigned_agent = Task(  # type: ignore # Incompatible types in assignment (expression has type "Task", variable has type "str")
             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)  # type: ignore # "str" has no attribute "execute_task"
+        return agent.execute_task(task_with_assigned_agent, context)

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

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 from typing import Any, Optional
 
-from pydantic import BaseModel, Field, PrivateAttr
+from pydantic import BaseModel, Field
 
 
 class OutputConverter(BaseModel, ABC):
@@ -21,7 +21,6 @@ class OutputConverter(BaseModel, ABC):
         max_attempts (int): Maximum number of conversion attempts (default: 3).
     """
 
-    _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.")
@@ -41,7 +40,8 @@ class OutputConverter(BaseModel, ABC):
         """Convert text to json."""
         pass
 
-    @abstractmethod  # type: ignore #  Name "_is_gpt" already defined on line 25
-    def _is_gpt(self, llm):  # type: ignore #  Name "_is_gpt" already defined on line 25
+    @property
+    @abstractmethod
+    def is_gpt(self) -> bool:
         """Return if llm provided is of gpt from openai."""
         pass

src/crewai/agents/executor.py

@@ -1,14 +1,6 @@
 import threading
 import time
-from typing import (
-    Any,
-    Dict,
-    Iterator,
-    List,
-    Optional,
-    Tuple,
-    Union,
-)
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 from langchain.agents import AgentExecutor
 from langchain.agents.agent import ExceptionTool
@@ -19,9 +11,7 @@ from langchain_core.tools import BaseTool
 from langchain_core.utils.input import get_color_mapping
 from pydantic import InstanceOf
 
-from crewai.agents.agent_builder.base_agent_executor_mixin import (
-    CrewAgentExecutorMixin,
-)
+from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.tools_handler import ToolsHandler
 from crewai.tools.tool_usage import ToolUsage, ToolUsageErrorException
 from crewai.utilities import I18N
@@ -252,6 +242,8 @@ class CrewAgentExecutor(AgentExecutor, CrewAgentExecutorMixin):
             else:
                 if tool_calling.tool_name.casefold().strip() in [
                     name.casefold().strip() for name in name_to_tool_map
+                ] or tool_calling.tool_name.casefold().replace("_", " ") in [
+                    name.casefold().strip() for name in name_to_tool_map
                 ]:
                     observation = tool_usage.use(tool_calling, agent_action.log)
                 else:

src/crewai/agents/parser.py

@@ -1,6 +1,7 @@
 import re
 from typing import Any, Union
 
+from json_repair import repair_json
 from langchain.agents.output_parsers import ReActSingleInputOutputParser
 from langchain_core.agents import AgentAction, AgentFinish
 from langchain_core.exceptions import OutputParserException
@@ -48,11 +49,15 @@ class CrewAgentParser(ReActSingleInputOutputParser):
                 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)
+            action = action_match.group(1)
+            clean_action = self._clean_action(action)
+
+            action_input = action_match.group(2).strip()
+
+            tool_input = action_input.strip(" ").strip('"')
+            safe_tool_input = self._safe_repair_json(tool_input)
+
+            return AgentAction(clean_action, safe_tool_input, text)
 
         elif includes_answer:
             return AgentFinish(
@@ -87,3 +92,30 @@ class CrewAgentParser(ReActSingleInputOutputParser):
                 llm_output=text,
                 send_to_llm=True,
             )
+
+    def _clean_action(self, text: str) -> str:
+        """Clean action string by removing non-essential formatting characters."""
+        return re.sub(r"^\s*\*+\s*|\s*\*+\s*$", "", text).strip()
+
+    def _safe_repair_json(self, tool_input: str) -> str:
+        UNABLE_TO_REPAIR_JSON_RESULTS = ['""', "{}"]
+
+        # Skip repair if the input starts and ends with square brackets
+        # Explanation: The JSON parser has issues handling inputs that are enclosed in square brackets ('[]').
+        # These are typically valid JSON arrays or strings that do not require repair. Attempting to repair such inputs
+        # might lead to unintended alterations, such as wrapping the entire input in additional layers or modifying
+        # the structure in a way that changes its meaning. By skipping the repair for inputs that start and end with
+        # square brackets, we preserve the integrity of these valid JSON structures and avoid unnecessary modifications.
+        if tool_input.startswith("[") and tool_input.endswith("]"):
+            return tool_input
+
+        # Before repair, handle common LLM issues:
+        # 1. Replace """ with " to avoid JSON parser errors
+
+        tool_input = tool_input.replace('"""', '"')
+
+        result = repair_json(tool_input)
+        if result in UNABLE_TO_REPAIR_JSON_RESULTS:
+            return tool_input
+
+        return str(result)

src/crewai/cli/cli.py

@@ -1,8 +1,16 @@
 import click
 import pkg_resources
 
+from crewai.memory.storage.kickoff_task_outputs_storage import (
+    KickoffTaskOutputsSQLiteStorage,
+)
+
 from .create_crew import create_crew
+from .replay_from_task import replay_task_command
+from .reset_memories_command import reset_memories_command
+from .test_crew import test_crew
 from .train_crew import train_crew
+from .doc_generator import generate_documentation
 
 
 @click.group()
@@ -48,5 +56,109 @@ def train(n_iterations: int):
     train_crew(n_iterations)
 
 
+@crewai.command()
+@click.option(
+    "-t",
+    "--task_id",
+    type=str,
+    help="Replay the crew from this task ID, including all subsequent tasks.",
+)
+def replay(task_id: str) -> None:
+    """
+    Replay the crew execution from a specific task.
+
+    Args:
+        task_id (str): The ID of the task to replay from.
+    """
+    try:
+        click.echo(f"Replaying the crew from task {task_id}")
+        replay_task_command(task_id)
+    except Exception as e:
+        click.echo(f"An error occurred while replaying: {e}", err=True)
+
+
+@crewai.command()
+def log_tasks_outputs() -> None:
+    """
+    Retrieve your latest crew.kickoff() task outputs.
+    """
+    try:
+        storage = KickoffTaskOutputsSQLiteStorage()
+        tasks = storage.load()
+
+        if not tasks:
+            click.echo(
+                "No task outputs found. Only crew kickoff task outputs are logged."
+            )
+            return
+
+        for index, task in enumerate(tasks, 1):
+            click.echo(f"Task {index}: {task['task_id']}")
+            click.echo(f"Description: {task['expected_output']}")
+            click.echo("------")
+
+    except Exception as e:
+        click.echo(f"An error occurred while logging task outputs: {e}", err=True)
+
+
+@crewai.command()
+@click.option("-l", "--long", is_flag=True, help="Reset LONG TERM memory")
+@click.option("-s", "--short", is_flag=True, help="Reset SHORT TERM memory")
+@click.option("-e", "--entities", is_flag=True, help="Reset ENTITIES memory")
+@click.option(
+    "-k",
+    "--kickoff-outputs",
+    is_flag=True,
+    help="Reset LATEST KICKOFF TASK OUTPUTS",
+)
+@click.option("-a", "--all", is_flag=True, help="Reset ALL memories")
+def reset_memories(long, short, entities, kickoff_outputs, all):
+    """
+    Reset the crew memories (long, short, entity, latest_crew_kickoff_ouputs). This will delete all the data saved.
+    """
+    try:
+        if not all and not (long or short or entities or kickoff_outputs):
+            click.echo(
+                "Please specify at least one memory type to reset using the appropriate flags."
+            )
+            return
+        reset_memories_command(long, short, entities, kickoff_outputs, all)
+    except Exception as e:
+        click.echo(f"An error occurred while resetting memories: {e}", err=True)
+
+
+@crewai.command()
+@click.option(
+    "-n",
+    "--n_iterations",
+    type=int,
+    default=3,
+    help="Number of iterations to Test the crew",
+)
+@click.option(
+    "-m",
+    "--model",
+    type=str,
+    default="gpt-4o-mini",
+    help="LLM Model to run the tests on the Crew. For now only accepting only OpenAI models.",
+)
+def test(n_iterations: int, model: str):
+    """Test the crew and evaluate the results."""
+    click.echo(f"Testing the crew for {n_iterations} iterations with model {model}")
+    test_crew(n_iterations, model)
+
+@crewai.command()
+@click.option('--output', '-o', default='crew_documentation.md', help='Output file for the documentation')
+@click.option('--format', '-f', default='markdown', help='Output format')
+def generate_docs(output, format):
+    """Generate documentation for the current project setup."""
+    try:
+        click.echo(f"Generating documentation in {format} format...")
+        generate_documentation(output, format)
+        click.echo(f"Documentation generated and saved to {output}")
+    except ValueError as e:
+        click.echo(f"Error: {str(e)}", err=True)
+        click.echo("Please ensure you are in the root directory of your CrewAI project.")
+
 if __name__ == "__main__":
     crewai()

src/crewai/cli/doc_generator.py

@@ -0,0 +1,204 @@
+import os
+import yaml
+import logging
+
+
+def is_project_root():
+    """
+    Check if the current directory is the root of a CrewAI project.
+
+    Returns:
+        bool: True if in project root, False otherwise.
+    """
+    # Check for key indicators of a CrewAI project root
+    indicators = ["pyproject.toml", "poetry.lock", "src"]
+    return all(os.path.exists(indicator) for indicator in indicators)
+
+
+def generate_documentation(output_file, format):
+    """
+    Generate documentation for the current CrewAI project setup.
+
+    Args:
+        output_file (str): The path and filename where the generated documentation
+                           will be saved.
+        format (str): The desired output format for the documentation.
+                      Supported values currently 'markdown'.
+
+    Returns:
+        None: The function writes the generated documentation to the specified
+              output file and doesn't return any value.
+
+    Raises:
+        ValueError: If not in the project root or if an unsupported output format is specified.
+    """
+    if not is_project_root():
+        raise ValueError(
+            "Not in the root of a CrewAI project."
+        )
+
+    # Load the current project configuration
+    config = load_crew_configuration()
+
+    if config is None:
+        logging.error("Failed to load crew configuration. Exiting.")
+        return
+
+    if format == "markdown":
+        content = generate_markdown(config)
+    else:
+        raise ValueError(f"Unsupported output format: {format}")
+
+    with open(output_file, "w") as f:
+        f.write(content)
+
+    logging.info(f"Documentation generated and saved to {output_file}")
+
+
+def find_config_dir():
+    """
+    Find the configuration directory based on the project structure.
+
+    This function attempts to locate the configuration directory for a CrewAI project
+    by assuming a standard project structure. It starts from the current working
+    directory and constructs an expected path to the config directory.
+
+    Returns:
+        str or None: The path to the configuration directory if found, None otherwise.
+
+    The function performs the following steps:
+    1. Gets the current working directory.
+    2. Extracts the project name from the current directory path.
+    3. Constructs the expected config path using the project structure convention.
+    4. Checks if the expected config directory exists.
+    5. Returns the path if found, or None if not found.
+
+    Logging:
+        - Logs debug information about the search process.
+        - Logs the starting directory, the checked path, and the result of the search.
+
+    Note:
+        This function assumes a specific project structure where the config
+        directory is located at 'src/<project_name>/config' relative to the
+        project root.
+    """
+    current_dir = os.getcwd()
+    logging.debug(f"Starting search from: {current_dir}")
+
+    # Split the path to get the project name
+    path_parts = current_dir.split(os.path.sep)
+    project_name = path_parts[-1]
+
+    # Construct the expected config path
+    expected_config_path = os.path.join(current_dir, "src", project_name, "config")
+
+    logging.debug(f"Checking for config directory: {expected_config_path}")
+
+    if os.path.isdir(expected_config_path):
+        logging.debug(f"Found config directory: {expected_config_path}")
+        return expected_config_path
+
+    logging.debug("Config directory not found in the expected location")
+    return None
+
+
+def load_crew_configuration():
+    """
+    Load the crew configuration from YAML files.
+
+    This function attempts to find the configuration directory and load the agents
+    and tasks configurations from their respective YAML files.
+
+    Returns:
+        dict or None: A dictionary containing 'agents' and 'tasks' configurations
+                      if successful, None if there was an error.
+
+    The function performs the following steps:
+    1. Finds the configuration directory using find_config_dir().
+    2. Constructs paths to agents.yaml and tasks.yaml files.
+    3. Checks if both files exist.
+    4. Loads and parses the YAML content of both files.
+    5. Returns a dictionary with the parsed configurations.
+
+    Logging:
+        - Logs an error if the configuration directory is not found.
+        - Logs an error if either agents.yaml or tasks.yaml is not found.
+
+    Note:
+        This function assumes that the configuration files are named 'agents.yaml'
+        and 'tasks.yaml' and are located in the directory returned by find_config_dir().
+    """
+    config_dir = find_config_dir()
+    if not config_dir:
+        logging.error(
+            "Configuration directory not found. Make sure you're in the root of your CrewAI project."
+        )
+        return None
+
+    agents_file = os.path.join(config_dir, "agents.yaml")
+    tasks_file = os.path.join(config_dir, "tasks.yaml")
+
+    if not os.path.exists(agents_file) or not os.path.exists(tasks_file):
+        logging.error(f"agents.yaml or tasks.yaml not found in {config_dir}")
+        return None
+
+    with open(agents_file, "r") as f:
+        agents_config = yaml.safe_load(f)
+
+    with open(tasks_file, "r") as f:
+        tasks_config = yaml.safe_load(f)
+
+    return {"agents": agents_config, "tasks": tasks_config}
+
+
+def generate_markdown(config):
+    """
+    Generate Markdown documentation for the CrewAI project configuration.
+
+    This function takes the parsed configuration dictionary and generates
+    a formatted Markdown string containing documentation for the project's
+    agents and tasks.
+
+    Args:
+        config (dict): A dictionary containing the parsed configuration
+                       with 'agents' and 'tasks' keys.
+
+    Returns:
+        str: A formatted Markdown string containing the project documentation.
+             If the input config is None, it returns an error message.
+
+    The generated Markdown includes:
+    1. A title for the project documentation.
+    2. A section for Agents, listing each agent's name, role, goal, and backstory.
+    3. A section for Tasks, listing each task's name, description, expected output,
+       and assigned agent.
+
+    Each piece of information is wrapped in code blocks for better readability
+    in rendered Markdown.
+
+    Note:
+        This function assumes that the config dictionary has the correct structure
+        with 'agents' and 'tasks' keys, each containing nested dictionaries of
+        agent and task information respectively.
+    """
+    if config is None:
+        return "# Error: No crew configuration available"
+
+    md = "# CrewAI Project Documentation\n\n"
+
+    md += "## Agents\n\n"
+    for agent_name, agent_data in config["agents"].items():
+        md += f"### \n```\n{agent_name}\n```\n"
+        md += f"Role: \n```\n{agent_data.get('role', 'Not specified')}\n```\n"
+        md += f"Goal: \n```\n{agent_data.get('goal', 'Not specified')}\n```\n"
+        md += f"Backstory: \n```\n{agent_data.get('backstory', 'Not specified')}\n```\n"
+        md += f""
+
+    md += "## Tasks\n\n"
+    for task_name, task_data in config["tasks"].items():
+        md += f"### {task_name}\n"
+        md += f"Description: \n```\n{task_data.get('description', 'Not specified')}\n```\n"
+        md += f"Expected Output: \n```\n{task_data.get('expected_output', 'Not specified')}\n```\n"
+        md += f"Assigned Agent: \n```\n{task_data.get('agent', 'Not assigned')}\n```\n"
+
+    return md

src/crewai/cli/replay_from_task.py

@@ -0,0 +1,24 @@
+import subprocess
+import click
+
+
+def replay_task_command(task_id: str) -> None:
+    """
+    Replay the crew execution from a specific task.
+
+    Args:
+      task_id (str): The ID of the task to replay from.
+    """
+    command = ["poetry", "run", "replay", task_id]
+
+    try:
+        result = subprocess.run(command, capture_output=False, text=True, check=True)
+        if result.stderr:
+            click.echo(result.stderr, err=True)
+
+    except subprocess.CalledProcessError as e:
+        click.echo(f"An error occurred while replaying the task: {e}", err=True)
+        click.echo(e.output, err=True)
+
+    except Exception as e:
+        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/reset_memories_command.py

@@ -0,0 +1,45 @@
+import subprocess
+import click
+
+from crewai.memory.entity.entity_memory import EntityMemory
+from crewai.memory.long_term.long_term_memory import LongTermMemory
+from crewai.memory.short_term.short_term_memory import ShortTermMemory
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+
+
+def reset_memories_command(long, short, entity, kickoff_outputs, all) -> None:
+    """
+    Replay the crew execution from a specific task.
+
+    Args:
+      task_id (str): The ID of the task to replay from.
+    """
+
+    try:
+        if all:
+            ShortTermMemory().reset()
+            EntityMemory().reset()
+            LongTermMemory().reset()
+            TaskOutputStorageHandler().reset()
+            click.echo("All memories have been reset.")
+        else:
+            if long:
+                LongTermMemory().reset()
+                click.echo("Long term memory has been reset.")
+
+            if short:
+                ShortTermMemory().reset()
+                click.echo("Short term memory has been reset.")
+            if entity:
+                EntityMemory().reset()
+                click.echo("Entity memory has been reset.")
+            if kickoff_outputs:
+                TaskOutputStorageHandler().reset()
+                click.echo("Latest Kickoff outputs stored has been reset.")
+
+    except subprocess.CalledProcessError as e:
+        click.echo(f"An error occurred while resetting the memories: {e}", err=True)
+        click.echo(e.output, err=True)
+
+    except Exception as e:
+        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/cli/templates/config/tasks.yaml

@@ -5,6 +5,7 @@ research_task:
     the current year is 2024.
   expected_output: >
     A list with 10 bullet points of the most relevant information about {topic}
+  agent: researcher
 
 reporting_task:
   description: >
@@ -13,3 +14,4 @@ reporting_task:
   expected_output: >
     A fully fledge reports with the mains topics, each with a full section of information.
     Formatted as markdown without '```'
+  agent: reporting_analyst

src/crewai/cli/templates/crew.py

@@ -32,14 +32,12 @@ class {{crew_name}}Crew():
 	def research_task(self) -> Task:
 		return Task(
 			config=self.tasks_config['research_task'],
-			agent=self.researcher()
 		)
 
 	@task
 	def reporting_task(self) -> Task:
 		return Task(
 			config=self.tasks_config['reporting_task'],
-			agent=self.reporting_analyst(),
 			output_file='report.md'
 		)
 

src/crewai/cli/templates/main.py

@@ -2,9 +2,15 @@
 import sys
 from {{folder_name}}.crew import {{crew_name}}Crew
 
+# This main file is intended to be a way for your to run your
+# crew locally, so refrain from adding necessary logic into this file.
+# Replace with inputs you want to test with, it will automatically
+# interpolate any tasks and agents information
 
 def run():
-    # Replace with your inputs, it will automatically interpolate any tasks and agents information
+    """
+    Run the crew.
+    """
     inputs = {
         'topic': 'AI LLMs'
     }
@@ -15,9 +21,34 @@ def train():
     """
     Train the crew for a given number of iterations.
     """
-    inputs = {"topic": "AI LLMs"}
+    inputs = {
+        "topic": "AI LLMs"
+    }
     try:
         {{crew_name}}Crew().crew().train(n_iterations=int(sys.argv[1]), inputs=inputs)
 
     except Exception as e:
         raise Exception(f"An error occurred while training the crew: {e}")
+
+def replay():
+    """
+    Replay the crew execution from a specific task.
+    """
+    try:
+        {{crew_name}}Crew().crew().replay(task_id=sys.argv[1])
+
+    except Exception as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")
+
+def test():
+    """
+    Test the crew execution and returns the results.
+    """
+    inputs = {
+        "topic": "AI LLMs"
+    }
+    try:
+        {{crew_name}}Crew().crew().test(n_iterations=int(sys.argv[1]), model=sys.argv[2], inputs=inputs)
+
+    except Exception as e:
+        raise Exception(f"An error occurred while replaying the crew: {e}")

src/crewai/cli/templates/pyproject.toml

@@ -6,11 +6,13 @@ authors = ["Your Name <you@example.com>"]
 
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
-crewai = { extras = ["tools"], version = "^0.35.8" }
+crewai = { extras = ["tools"], version = "^0.41.1" }
 
 [tool.poetry.scripts]
 {{folder_name}} = "{{folder_name}}.main:run"
 train = "{{folder_name}}.main:train"
+replay = "{{folder_name}}.main:replay"
+test = "{{folder_name}}.main:test"
 
 [build-system]
 requires = ["poetry-core"]

src/crewai/cli/test_crew.py

@@ -0,0 +1,30 @@
+import subprocess
+
+import click
+
+
+def test_crew(n_iterations: int, model: str) -> None:
+    """
+    Test the crew by running a command in the Poetry environment.
+
+    Args:
+        n_iterations (int): The number of iterations to test the crew.
+        model (str): The model to test the crew with.
+    """
+    command = ["poetry", "run", "test", str(n_iterations), model]
+
+    try:
+        if n_iterations <= 0:
+            raise ValueError("The number of iterations must be a positive integer.")
+
+        result = subprocess.run(command, capture_output=False, text=True, check=True)
+
+        if result.stderr:
+            click.echo(result.stderr, err=True)
+
+    except subprocess.CalledProcessError as e:
+        click.echo(f"An error occurred while testing the crew: {e}", err=True)
+        click.echo(e.output, err=True)
+
+    except Exception as e:
+        click.echo(f"An unexpected error occurred: {e}", err=True)

src/crewai/crew.py

@@ -2,6 +2,7 @@ import asyncio
 import json
 import uuid
 from concurrent.futures import Future
+from hashlib import md5
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from langchain_core.callbacks import BaseCallbackHandler
@@ -27,16 +28,22 @@ from crewai.memory.long_term.long_term_memory import LongTermMemory
 from crewai.memory.short_term.short_term_memory import ShortTermMemory
 from crewai.process import Process
 from crewai.task import Task
+from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.telemetry import Telemetry
 from crewai.tools.agent_tools import AgentTools
 from crewai.utilities import I18N, FileHandler, Logger, RPMController
-from crewai.utilities.constants import TRAINED_AGENTS_DATA_FILE, TRAINING_DATA_FILE
+from crewai.utilities.constants import (
+    TRAINED_AGENTS_DATA_FILE,
+    TRAINING_DATA_FILE,
+)
 from crewai.utilities.evaluators.task_evaluator import TaskEvaluator
 from crewai.utilities.formatter import (
     aggregate_raw_outputs_from_task_outputs,
     aggregate_raw_outputs_from_tasks,
 )
+from crewai.utilities.planning_handler import CrewPlanner
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
 try:
@@ -67,6 +74,7 @@ class Crew(BaseModel):
         task_callback: Callback to be executed after each task for every agents execution.
         step_callback: Callback to be executed after each step for every agents execution.
         share_crew: Whether you want to share the complete crew information and execution with crewAI to make the library better, and allow us to train models.
+        planning: Plan the crew execution and add the plan to the crew.
     """
 
     __hash__ = object.__hash__  # type: ignore
@@ -80,6 +88,13 @@ class Crew(BaseModel):
     _entity_memory: Optional[InstanceOf[EntityMemory]] = PrivateAttr()
     _train: Optional[bool] = PrivateAttr(default=False)
     _train_iteration: Optional[int] = PrivateAttr()
+    _inputs: Optional[Dict[str, Any]] = PrivateAttr(default=None)
+    _logging_color: str = PrivateAttr(
+        default="bold_purple",
+    )
+    _task_output_handler: TaskOutputStorageHandler = PrivateAttr(
+        default_factory=TaskOutputStorageHandler
+    )
 
     cache: bool = Field(default=True)
     model_config = ConfigDict(arbitrary_types_allowed=True)
@@ -135,6 +150,18 @@ class Crew(BaseModel):
         default=False,
         description="output_log_file",
     )
+    planning: Optional[bool] = Field(
+        default=False,
+        description="Plan the crew execution and add the plan to the crew.",
+    )
+    task_execution_output_json_files: Optional[List[str]] = Field(
+        default=None,
+        description="List of file paths for task execution JSON files.",
+    )
+    execution_logs: List[Dict[str, Any]] = Field(
+        default=[],
+        description="List of execution logs for tasks",
+    )
 
     @field_validator("id", mode="before")
     @classmethod
@@ -170,7 +197,6 @@ class Crew(BaseModel):
         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
 
     @model_validator(mode="after")
@@ -275,6 +301,29 @@ class Crew(BaseModel):
 
         return self
 
+    @model_validator(mode="after")
+    def validate_first_task(self) -> "Crew":
+        """Ensure the first task is not a ConditionalTask."""
+        if self.tasks and isinstance(self.tasks[0], ConditionalTask):
+            raise PydanticCustomError(
+                "invalid_first_task",
+                "The first task cannot be a ConditionalTask.",
+                {},
+            )
+        return self
+
+    @model_validator(mode="after")
+    def validate_async_tasks_not_async(self) -> "Crew":
+        """Ensure that ConditionalTask is not async."""
+        for task in self.tasks:
+            if task.async_execution and isinstance(task, ConditionalTask):
+                raise PydanticCustomError(
+                    "invalid_async_conditional_task",
+                    f"Conditional Task: {task.description} , cannot be executed asynchronously.",  # type: ignore # Argument of type "str" cannot be assigned to parameter "message_template" of type "LiteralString"
+                    {},
+                )
+        return self
+
     @model_validator(mode="after")
     def validate_async_task_cannot_include_sequential_async_tasks_in_context(self):
         """
@@ -311,6 +360,13 @@ class Crew(BaseModel):
                         )
         return self
 
+    @property
+    def key(self) -> str:
+        source = [agent.key for agent in self.agents] + [
+            task.key for task in self.tasks
+        ]
+        return md5("|".join(source).encode()).hexdigest()
+
     def _setup_from_config(self):
         assert self.config is not None, "Config should not be None."
 
@@ -377,7 +433,11 @@ class Crew(BaseModel):
     ) -> CrewOutput:
         """Starts the crew to work on its assigned tasks."""
         self._execution_span = self._telemetry.crew_execution_span(self, inputs)
+        self._task_output_handler.reset()
+        self._logging_color = "bold_purple"
+
         if inputs is not None:
+            self._inputs = inputs
             self._interpolate_inputs(inputs)
         self._set_tasks_callbacks()
 
@@ -399,12 +459,15 @@ class Crew(BaseModel):
 
             agent.create_agent_executor()
 
+        if self.planning:
+            self._handle_crew_planning()
+
         metrics = []
 
         if self.process == Process.sequential:
             result = self._run_sequential_process()
         elif self.process == Process.hierarchical:
-            result = self._run_hierarchical_process()  # type: ignore # Incompatible types in assignment (expression has type "str | dict[str, Any]", variable has type "str")
+            result = self._run_hierarchical_process()
         else:
             raise NotImplementedError(
                 f"The process '{self.process}' is not implemented yet."
@@ -441,6 +504,7 @@ class Crew(BaseModel):
             results.append(output)
 
         self.usage_metrics = total_usage_metrics
+        self._task_output_handler.reset()
         return results
 
     async def kickoff_async(self, inputs: Optional[Dict[str, Any]] = {}) -> CrewOutput:
@@ -489,106 +553,61 @@ class Crew(BaseModel):
                     total_usage_metrics[key] += crew.usage_metrics.get(key, 0)
 
         self.usage_metrics = total_usage_metrics
-
+        self._task_output_handler.reset()
         return results
 
+    def _handle_crew_planning(self):
+        """Handles the Crew planning."""
+        self._logger.log("info", "Planning the crew execution")
+        result = CrewPlanner(self.tasks)._handle_crew_planning()
+
+        if result is not None and hasattr(result, "list_of_plans_per_task"):
+            for task, step_plan in zip(self.tasks, result.list_of_plans_per_task):
+                task.description += step_plan
+        else:
+            self._logger.log(
+                "info", "Something went wrong with the planning process of the Crew"
+            )
+
+    def _store_execution_log(
+        self,
+        task: Task,
+        output: TaskOutput,
+        task_index: int,
+        was_replayed: bool = False,
+    ):
+        if self._inputs:
+            inputs = self._inputs
+        else:
+            inputs = {}
+
+        log = {
+            "task": task,
+            "output": {
+                "description": output.description,
+                "summary": output.summary,
+                "raw": output.raw,
+                "pydantic": output.pydantic,
+                "json_dict": output.json_dict,
+                "output_format": output.output_format,
+                "agent": output.agent,
+            },
+            "task_index": task_index,
+            "inputs": inputs,
+            "was_replayed": was_replayed,
+        }
+        self._task_output_handler.update(task_index, log)
+
     def _run_sequential_process(self) -> CrewOutput:
         """Executes tasks sequentially and returns the final output."""
-        task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput]]] = []
+        return self._execute_tasks(self.tasks)
 
-        for task in self.tasks:
-            if task.agent 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 += task.agent.get_delegation_tools(agents_for_delegation)
-
-            role = task.agent.role if task.agent is not None else "None"
-            self._logger.log("debug", f"== Working Agent: {role}", color="bold_purple")
-            self._logger.log(
-                "info", f"== Starting Task: {task.description}", color="bold_purple"
-            )
-
-            if self.output_log_file:
-                self._file_handler.log(
-                    agent=role, task=task.description, status="started"
-                )
-
-            if task.async_execution:
-                context = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
-                )
-                future = task.execute_async(
-                    agent=task.agent, context=context, tools=task.tools
-                )
-                futures.append((task, future))
-            else:
-                # Before executing a synchronous task, wait for all async tasks to complete
-                if futures:
-                    # Clear task_outputs before processing async tasks
-                    task_outputs = []
-                    for future_task, future in futures:
-                        task_output = future.result()
-                        task_outputs.append(task_output)
-                        self._process_task_result(future_task, task_output)
-
-                    # Clear the futures list after processing all async results
-                    futures.clear()
-
-                context = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
-                )
-                task_output = task.execute_sync(
-                    agent=task.agent, context=context, tools=task.tools
-                )
-                task_outputs = [task_output]
-                self._process_task_result(task, task_output)
-
-        if futures:
-            # Clear task_outputs before processing async tasks
-            task_outputs = []
-            for future_task, future in futures:
-                task_output = future.result()
-                task_outputs.append(task_output)
-                self._process_task_result(future_task, task_output)
-
-        # Important: There should only be one task output in the list
-        #            If there are more or 0, something went wrong.
-        if len(task_outputs) != 1:
-            raise ValueError(
-                "Something went wrong. Kickoff should return only one task output."
-            )
-
-        final_task_output = task_outputs[0]
-
-        final_string_output = final_task_output.raw
-        self._finish_execution(final_string_output)
-
-        token_usage = self.calculate_usage_metrics()
-
-        return CrewOutput(
-            raw=final_task_output.raw,
-            pydantic=final_task_output.pydantic,
-            json_dict=final_task_output.json_dict,
-            tasks_output=[task.output for task in self.tasks if task.output],
-            token_usage=token_usage,
-        )
-
-    def _process_task_result(self, task: Task, output: TaskOutput) -> None:
-        role = task.agent.role if task.agent is not None else "None"
-        self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
-        if self.output_log_file:
-            self._file_handler.log(agent=role, task=output, status="completed")
-
-    # TODO: @joao, Breaking change. Changed return type. Usage metrics is included in crewoutput
     def _run_hierarchical_process(self) -> CrewOutput:
         """Creates and assigns a manager agent to make sure the crew completes the tasks."""
+        self._create_manager_agent()
+        return self._execute_tasks(self.tasks, self.manager_agent)
+
+    def _create_manager_agent(self):
         i18n = I18N(prompt_file=self.prompt_file)
         if self.manager_agent is not None:
             self.manager_agent.allow_delegation = True
@@ -607,74 +626,186 @@ class Crew(BaseModel):
             )
             self.manager_agent = manager
 
+    def _execute_tasks(
+        self,
+        tasks: List[Task],
+        manager: Optional[BaseAgent] = None,
+        start_index: Optional[int] = 0,
+        was_replayed: bool = False,
+    ) -> CrewOutput:
+        """Executes tasks sequentially and returns the final output.
+
+        Args:
+            tasks (List[Task]): List of tasks to execute
+            manager (Optional[BaseAgent], optional): Manager agent to use for delegation. Defaults to None.
+
+        Returns:
+            CrewOutput: Final output of the crew
+        """
+
         task_outputs: List[TaskOutput] = []
-        futures: List[Tuple[Task, Future[TaskOutput]]] = []
+        futures: List[Tuple[Task, Future[TaskOutput], int]] = []
+        last_sync_output: Optional[TaskOutput] = None
 
-        # TODO: IF USER OVERRIDE THE CONTEXT, PASS THAT
-        for task in self.tasks:
-            self._logger.log("debug", f"Working Agent: {manager.role}")
-            self._logger.log("info", f"Starting Task: {task.description}")
+        for task_index, task in enumerate(tasks):
+            if start_index is not None and task_index < start_index:
+                if task.output:
+                    if task.async_execution:
+                        task_outputs.append(task.output)
+                    else:
+                        task_outputs = [task.output]
+                        last_sync_output = task.output
+                continue
 
-            if self.output_log_file:
-                self._file_handler.log(
-                    agent=manager.role, task=task.description, status="started"
+            agent_to_use = self._get_agent_to_use(task, manager)
+            if agent_to_use is None:
+                raise ValueError(
+                    f"No agent available for task: {task.description}. Ensure that either the task has an assigned agent or a manager agent is provided."
                 )
 
+            self._prepare_agent_tools(task, manager)
+            self._log_task_start(task, agent_to_use.role)
+
+            if isinstance(task, ConditionalTask):
+                skipped_task_output = self._handle_conditional_task(
+                    task, task_outputs, futures, task_index, was_replayed
+                )
+                if skipped_task_output:
+                    continue
+
             if task.async_execution:
-                context = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
+                context = self._get_context(
+                    task, [last_sync_output] if last_sync_output else []
                 )
                 future = task.execute_async(
-                    agent=manager, context=context, tools=manager.tools
+                    agent=agent_to_use,
+                    context=context,
+                    tools=agent_to_use.tools,
                 )
-                futures.append((task, future))
+                futures.append((task, future, task_index))
             else:
-                # Before executing a synchronous task, wait for all async tasks to complete
                 if futures:
-                    # Clear task_outputs before processing async tasks
-                    task_outputs = []
-                    for future_task, future in futures:
-                        task_output = future.result()
-                        task_outputs.append(task_output)
-                        self._process_task_result(future_task, task_output)
-
-                    # Clear the futures list after processing all async results
+                    task_outputs = self._process_async_tasks(futures, was_replayed)
                     futures.clear()
 
-                context = (
-                    aggregate_raw_outputs_from_tasks(task.context)
-                    if task.context
-                    else aggregate_raw_outputs_from_task_outputs(task_outputs)
-                )
+                context = self._get_context(task, task_outputs)
                 task_output = task.execute_sync(
-                    agent=manager, context=context, tools=manager.tools
+                    agent=agent_to_use,
+                    context=context,
+                    tools=agent_to_use.tools,
                 )
                 task_outputs = [task_output]
                 self._process_task_result(task, task_output)
+                self._store_execution_log(task, task_output, task_index, was_replayed)
 
-        # Process any remaining async results
         if futures:
-            # Clear task_outputs before processing async tasks
-            task_outputs = []
-            for future_task, future in futures:
-                task_output = future.result()
-                task_outputs.append(task_output)
-                self._process_task_result(future_task, task_output)
+            task_outputs = self._process_async_tasks(futures, was_replayed)
 
-        # Important: There should only be one task output in the list
-        #            If there are more or 0, something went wrong.
+        return self._create_crew_output(task_outputs)
+
+    def _handle_conditional_task(
+        self,
+        task: ConditionalTask,
+        task_outputs: List[TaskOutput],
+        futures: List[Tuple[Task, Future[TaskOutput], int]],
+        task_index: int,
+        was_replayed: bool,
+    ) -> Optional[TaskOutput]:
+        if futures:
+            task_outputs = self._process_async_tasks(futures, was_replayed)
+            futures.clear()
+
+        previous_output = task_outputs[task_index - 1] if task_outputs else None
+        if previous_output is not None and not task.should_execute(previous_output):
+            self._logger.log(
+                "debug",
+                f"Skipping conditional task: {task.description}",
+                color="yellow",
+            )
+            skipped_task_output = task.get_skipped_task_output()
+
+            if not was_replayed:
+                self._store_execution_log(task, skipped_task_output, task_index)
+            return skipped_task_output
+        return None
+
+    def _prepare_agent_tools(self, task: Task, manager: Optional[BaseAgent]):
+        if self.process == Process.hierarchical:
+            if manager:
+                self._update_manager_tools(task, manager)
+            else:
+                raise ValueError("Manager agent is required for hierarchical process.")
+        elif task.agent and task.agent.allow_delegation:
+            self._add_delegation_tools(task)
+
+    def _get_agent_to_use(
+        self, task: Task, manager: Optional[BaseAgent]
+    ) -> Optional[BaseAgent]:
+        if self.process == Process.hierarchical:
+            return manager
+        return task.agent
+
+    def _add_delegation_tools(self, task: Task):
+        agents_for_delegation = [agent for agent in self.agents if agent != task.agent]
+        if len(self.agents) > 1 and len(agents_for_delegation) > 0 and task.agent:
+            delegation_tools = task.agent.get_delegation_tools(agents_for_delegation)
+
+            # Add tools if they are not already in task.tools
+            for new_tool in delegation_tools:
+                # Find the index of the tool with the same name
+                existing_tool_index = next(
+                    (
+                        index
+                        for index, tool in enumerate(task.tools or [])
+                        if tool.name == new_tool.name
+                    ),
+                    None,
+                )
+                if not task.tools:
+                    task.tools = []
+
+                if existing_tool_index is not None:
+                    # Replace the existing tool
+                    task.tools[existing_tool_index] = new_tool
+                else:
+                    # Add the new tool
+                    task.tools.append(new_tool)
+
+    def _log_task_start(self, task: Task, role: str = "None"):
+        color = self._logging_color
+        self._logger.log("debug", f"== Working Agent: {role}", color=color)
+        self._logger.log("info", f"== Starting Task: {task.description}", color=color)
+        if self.output_log_file:
+            self._file_handler.log(agent=role, task=task.description, status="started")
+
+    def _update_manager_tools(self, task: Task, manager: BaseAgent):
+        if task.agent:
+            manager.tools = task.agent.get_delegation_tools([task.agent])
+        else:
+            manager.tools = manager.get_delegation_tools(self.agents)
+
+    def _get_context(self, task: Task, task_outputs: List[TaskOutput]):
+        context = (
+            aggregate_raw_outputs_from_tasks(task.context)
+            if task.context
+            else aggregate_raw_outputs_from_task_outputs(task_outputs)
+        )
+        return context
+
+    def _process_task_result(self, task: Task, output: TaskOutput) -> None:
+        role = task.agent.role if task.agent is not None else "None"
+        self._logger.log("debug", f"== [{role}] Task output: {output}\n\n")
+        if self.output_log_file:
+            self._file_handler.log(agent=role, task=output, status="completed")
+
+    def _create_crew_output(self, task_outputs: List[TaskOutput]) -> CrewOutput:
         if len(task_outputs) != 1:
             raise ValueError(
                 "Something went wrong. Kickoff should return only one task output."
             )
-
         final_task_output = task_outputs[0]
-
         final_string_output = final_task_output.raw
         self._finish_execution(final_string_output)
-
         token_usage = self.calculate_usage_metrics()
 
         return CrewOutput(
@@ -685,6 +816,74 @@ class Crew(BaseModel):
             token_usage=token_usage,
         )
 
+    def _process_async_tasks(
+        self,
+        futures: List[Tuple[Task, Future[TaskOutput], int]],
+        was_replayed: bool = False,
+    ) -> List[TaskOutput]:
+        task_outputs: List[TaskOutput] = []
+        for future_task, future, task_index in futures:
+            task_output = future.result()
+            task_outputs.append(task_output)
+            self._process_task_result(future_task, task_output)
+            self._store_execution_log(
+                future_task, task_output, task_index, was_replayed
+            )
+        return task_outputs
+
+    def _find_task_index(
+        self, task_id: str, stored_outputs: List[Any]
+    ) -> Optional[int]:
+        return next(
+            (
+                index
+                for (index, d) in enumerate(stored_outputs)
+                if d["task_id"] == str(task_id)
+            ),
+            None,
+        )
+
+    def replay(
+        self, task_id: str, inputs: Optional[Dict[str, Any]] = None
+    ) -> CrewOutput:
+        stored_outputs = self._task_output_handler.load()
+        if not stored_outputs:
+            raise ValueError(f"Task with id {task_id} not found in the crew's tasks.")
+
+        start_index = self._find_task_index(task_id, stored_outputs)
+
+        if start_index is None:
+            raise ValueError(f"Task with id {task_id} not found in the crew's tasks.")
+
+        replay_inputs = (
+            inputs if inputs is not None else stored_outputs[start_index]["inputs"]
+        )
+        self._inputs = replay_inputs
+
+        if replay_inputs:
+            self._interpolate_inputs(replay_inputs)
+
+        if self.process == Process.hierarchical:
+            self._create_manager_agent()
+
+        for i in range(start_index):
+            stored_output = stored_outputs[i][
+                "output"
+            ]  # for adding context to the task
+            task_output = TaskOutput(
+                description=stored_output["description"],
+                agent=stored_output["agent"],
+                raw=stored_output["raw"],
+                pydantic=stored_output["pydantic"],
+                json_dict=stored_output["json_dict"],
+                output_format=stored_output["output_format"],
+            )
+            self.tasks[i].output = task_output
+
+        self._logging_color = "bold_blue"
+        result = self._execute_tasks(self.tasks, self.manager_agent, start_index, True)
+        return result
+
     def copy(self):
         """Create a deep copy of the Crew."""
 
@@ -767,5 +966,11 @@ class Crew(BaseModel):
 
         return total_usage_metrics
 
+    def test(
+        self, n_iterations: int, model: str, inputs: Optional[Dict[str, Any]] = None
+    ) -> None:
+        """Test the crew with the given inputs."""
+        pass
+
     def __repr__(self):
         return f"Crew(id={self.id}, process={self.process}, number_of_agents={len(self.agents)}, number_of_tasks={len(self.tasks)})"

src/crewai/crews/crew_output.py

@@ -24,18 +24,6 @@ class CrewOutput(BaseModel):
         description="Processed token summary", default={}
     )
 
-    # TODO: Joao - Adding this safety check breakes when people want to see
-    #                   The full output of a CrewOutput.
-    # @property
-    # def pydantic(self) -> Optional[BaseModel]:
-    #     # Check if the final task output included a pydantic model
-    #     if self.tasks_output[-1].output_format != OutputFormat.PYDANTIC:
-    #         raise ValueError(
-    #             "No pydantic model found in the final task. Please make sure to set the output_pydantic property in the final task in your crew."
-    #         )
-
-    #     return self._pydantic
-
     @property
     def json(self) -> Optional[str]:
         if self.tasks_output[-1].output_format != OutputFormat.JSON:
@@ -46,11 +34,13 @@ class CrewOutput(BaseModel):
         return json.dumps(self.json_dict)
 
     def to_dict(self) -> Dict[str, Any]:
+        """Convert json_output and pydantic_output to a dictionary."""
+        output_dict = {}
         if self.json_dict:
-            return self.json_dict
-        if self.pydantic:
-            return self.pydantic.model_dump()
-        raise ValueError("No output to convert to dictionary")
+            output_dict.update(self.json_dict)
+        elif self.pydantic:
+            output_dict.update(self.pydantic.model_dump())
+        return output_dict
 
     def __str__(self):
         if self.pydantic:

src/crewai/memory/entity/entity_memory.py

@@ -23,3 +23,9 @@ class EntityMemory(Memory):
         """Saves an entity item into the SQLite storage."""
         data = f"{item.name}({item.type}): {item.description}"
         super().save(data, item.metadata)
+
+    def reset(self) -> None:
+        try:
+            self.storage.reset()
+        except Exception as e:
+            raise Exception(f"An error occurred while resetting the entity memory: {e}")

src/crewai/memory/long_term/long_term_memory.py

@@ -30,3 +30,6 @@ class LongTermMemory(Memory):
 
     def search(self, task: str, latest_n: int = 3) -> Dict[str, Any]:
         return self.storage.load(task, latest_n)  # type: ignore # BUG?: "Storage" has no attribute "load"
+
+    def reset(self) -> None:
+        self.storage.reset()

src/crewai/memory/short_term/short_term_memory.py

@@ -18,8 +18,16 @@ class ShortTermMemory(Memory):
         )
         super().__init__(storage)
 
-    def save(self, item: ShortTermMemoryItem) -> None:  # type: ignore # BUG?: Signature of "save" incompatible with supertype "Memory"
+    def save(self, item: ShortTermMemoryItem) -> None:
         super().save(item.data, item.metadata, item.agent)
 
     def search(self, query: str, score_threshold: float = 0.35):
         return self.storage.search(query=query, score_threshold=score_threshold)  # type: ignore # BUG? The reference is to the parent class, but the parent class does not have this parameters
+
+    def reset(self) -> None:
+        try:
+            self.storage.reset()
+        except Exception as e:
+            raise Exception(
+                f"An error occurred while resetting the short-term memory: {e}"
+            )

src/crewai/memory/storage/interface.py

@@ -9,3 +9,6 @@ class Storage:
 
     def search(self, key: str) -> Dict[str, Any]:  # type: ignore
         pass
+
+    def reset(self) -> None:
+        pass

src/crewai/memory/storage/kickoff_task_outputs_storage.py

@@ -0,0 +1,166 @@
+import json
+import sqlite3
+from typing import Any, Dict, List, Optional
+
+from crewai.task import Task
+from crewai.utilities import Printer
+from crewai.utilities.crew_json_encoder import CrewJSONEncoder
+from crewai.utilities.paths import db_storage_path
+
+
+class KickoffTaskOutputsSQLiteStorage:
+    """
+    An updated SQLite storage class for kickoff task outputs storage.
+    """
+
+    def __init__(
+        self, db_path: str = f"{db_storage_path()}/latest_kickoff_task_outputs.db"
+    ) -> None:
+        self.db_path = db_path
+        self._printer: Printer = Printer()
+        self._initialize_db()
+
+    def _initialize_db(self):
+        """
+        Initializes the SQLite database and creates LTM table
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute(
+                    """
+                    CREATE TABLE IF NOT EXISTS latest_kickoff_task_outputs (
+                        task_id TEXT PRIMARY KEY,
+                        expected_output TEXT,
+                        output JSON,
+                        task_index INTEGER,
+                        inputs JSON,
+                        was_replayed BOOLEAN,
+                        timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+                    )
+                """
+                )
+
+                conn.commit()
+        except sqlite3.Error as e:
+            self._printer.print(
+                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
+                color="red",
+            )
+
+    def add(
+        self,
+        task: Task,
+        output: Dict[str, Any],
+        task_index: int,
+        was_replayed: bool = False,
+        inputs: Dict[str, Any] = {},
+    ):
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute(
+                    """
+                INSERT OR REPLACE INTO latest_kickoff_task_outputs
+                (task_id, expected_output, output, task_index, inputs, was_replayed)
+                VALUES (?, ?, ?, ?, ?, ?)
+            """,
+                    (
+                        str(task.id),
+                        task.expected_output,
+                        json.dumps(output, cls=CrewJSONEncoder),
+                        task_index,
+                        json.dumps(inputs),
+                        was_replayed,
+                    ),
+                )
+                conn.commit()
+        except sqlite3.Error as e:
+            self._printer.print(
+                content=f"SAVING KICKOFF TASK OUTPUTS ERROR: An error occurred during database initialization: {e}",
+                color="red",
+            )
+
+    def update(
+        self,
+        task_index: int,
+        **kwargs,
+    ):
+        """
+        Updates an existing row in the latest_kickoff_task_outputs table based on task_index.
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+
+                fields = []
+                values = []
+                for key, value in kwargs.items():
+                    fields.append(f"{key} = ?")
+                    values.append(
+                        json.dumps(value, cls=CrewJSONEncoder)
+                        if isinstance(value, dict)
+                        else value
+                    )
+
+                query = f"UPDATE latest_kickoff_task_outputs SET {', '.join(fields)} WHERE task_index = ?"
+                values.append(task_index)
+
+                cursor.execute(query, tuple(values))
+                conn.commit()
+
+                if cursor.rowcount == 0:
+                    self._printer.print(
+                        f"No row found with task_index {task_index}. No update performed.",
+                        color="red",
+                    )
+        except sqlite3.Error as e:
+            self._printer.print(f"UPDATE KICKOFF TASK OUTPUTS ERROR: {e}", color="red")
+
+    def load(self) -> Optional[List[Dict[str, Any]]]:
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute("""
+                SELECT *
+                FROM latest_kickoff_task_outputs
+                ORDER BY task_index
+                """)
+
+                rows = cursor.fetchall()
+                results = []
+                for row in rows:
+                    result = {
+                        "task_id": row[0],
+                        "expected_output": row[1],
+                        "output": json.loads(row[2]),
+                        "task_index": row[3],
+                        "inputs": json.loads(row[4]),
+                        "was_replayed": row[5],
+                        "timestamp": row[6],
+                    }
+                    results.append(result)
+
+                return results
+
+        except sqlite3.Error as e:
+            self._printer.print(
+                content=f"LOADING KICKOFF TASK OUTPUTS ERROR: An error occurred while querying kickoff task outputs: {e}",
+                color="red",
+            )
+            return None
+
+    def delete_all(self):
+        """
+        Deletes all rows from the latest_kickoff_task_outputs table.
+        """
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute("DELETE FROM latest_kickoff_task_outputs")
+                conn.commit()
+        except sqlite3.Error as e:
+            self._printer.print(
+                content=f"ERROR: Failed to delete all kickoff task outputs: {e}",
+                color="red",
+            )

src/crewai/memory/storage/ltm_sqlite_storage.py

@@ -103,3 +103,20 @@ class LTMSQLiteStorage:
                 color="red",
             )
         return None
+
+    def reset(
+        self,
+    ) -> None:
+        """Resets the LTM table with error handling."""
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                cursor = conn.cursor()
+                cursor.execute("DELETE FROM long_term_memories")
+                conn.commit()
+
+        except sqlite3.Error as e:
+            self._printer.print(
+                content=f"MEMORY ERROR: An error occurred while deleting all rows in LTM: {e}",
+                color="red",
+            )
+        return None

src/crewai/memory/storage/rag_storage.py

@@ -2,6 +2,7 @@ import contextlib
 import io
 import logging
 import os
+import shutil
 from typing import Any, Dict, List, Optional
 
 from embedchain import App
@@ -71,13 +72,13 @@ class RAGStorage(Storage):
 
         if embedder_config:
             config["embedder"] = embedder_config
-
+        self.type = type
         self.app = App.from_config(config=config)
         self.app.llm = FakeLLM()
         if allow_reset:
             self.app.reset()
 
-    def save(self, value: Any, metadata: Dict[str, Any]) -> None:  # type: ignore # BUG?: Should be save(key, value, metadata)  Signature of "save" incompatible with supertype "Storage"
+    def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         self._generate_embedding(value, metadata)
 
     def search(  # type: ignore # BUG?: Signature of "search" incompatible with supertype "Storage"
@@ -102,3 +103,11 @@ class RAGStorage(Storage):
     def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> Any:
         with suppress_logging():
             self.app.add(text, data_type="text", metadata=metadata)
+
+    def reset(self) -> None:
+        try:
+            shutil.rmtree(f"{db_storage_path()}/{self.type}")
+        except Exception as e:
+            raise Exception(
+                f"An error occurred while resetting the {self.type} memory: {e}"
+            )

src/crewai/project/__init__.py

@@ -1,2 +1,25 @@
-from .annotations import agent, crew, task
+from .annotations import (
+    agent,
+    crew,
+    task,
+    output_json,
+    output_pydantic,
+    tool,
+    callback,
+    llm,
+    cache_handler,
+)
 from .crew_base import CrewBase
+
+__all__ = [
+    "agent",
+    "crew",
+    "task",
+    "output_json",
+    "output_pydantic",
+    "tool",
+    "callback",
+    "CrewBase",
+    "llm",
+    "cache_handler",
+]

src/crewai/project/annotations.py

@@ -30,6 +30,37 @@ def agent(func):
     return func
 
 
+def llm(func):
+    func.is_llm = True
+    func = memoize(func)
+    return func
+
+
+def output_json(cls):
+    cls.is_output_json = True
+    return cls
+
+
+def output_pydantic(cls):
+    cls.is_output_pydantic = True
+    return cls
+
+
+def tool(func):
+    func.is_tool = True
+    return memoize(func)
+
+
+def callback(func):
+    func.is_callback = True
+    return memoize(func)
+
+
+def cache_handler(func):
+    func.is_cache_handler = True
+    return memoize(func)
+
+
 def crew(func):
     def wrapper(self, *args, **kwargs):
         instantiated_tasks = []

src/crewai/project/crew_base.py

@@ -1,6 +1,7 @@
 import inspect
 import os
 from pathlib import Path
+from typing import Any, Callable, Dict
 
 import yaml
 from dotenv import load_dotenv
@@ -20,11 +21,6 @@ def CrewBase(cls):
                 base_directory = Path(frame_info.filename).parent.resolve()
                 break
 
-        if base_directory is None:
-            raise Exception(
-                "Unable to dynamically determine the project's base directory, you must run it from the project's root directory."
-            )
-
         original_agents_config_path = getattr(
             cls, "agents_config", "config/agents.yaml"
         )
@@ -32,12 +28,20 @@ def CrewBase(cls):
 
         def __init__(self, *args, **kwargs):
             super().__init__(*args, **kwargs)
+
+            if self.base_directory is None:
+                raise Exception(
+                    "Unable to dynamically determine the project's base directory, you must run it from the project's root directory."
+                )
+
             self.agents_config = self.load_yaml(
                 os.path.join(self.base_directory, self.original_agents_config_path)
             )
             self.tasks_config = self.load_yaml(
                 os.path.join(self.base_directory, self.original_tasks_config_path)
             )
+            self.map_all_agent_variables()
+            self.map_all_task_variables()
 
         @staticmethod
         def load_yaml(config_path: str):
@@ -45,4 +49,138 @@ def CrewBase(cls):
                 # parsedContent = YamlParser.parse(file)  # type: ignore # Argument 1 to "parse" has incompatible type "TextIOWrapper"; expected "YamlParser"
                 return yaml.safe_load(file)
 
+        def _get_all_functions(self):
+            return {
+                name: getattr(self, name)
+                for name in dir(self)
+                if callable(getattr(self, name))
+            }
+
+        def _filter_functions(
+            self, functions: Dict[str, Callable], attribute: str
+        ) -> Dict[str, Callable]:
+            return {
+                name: func
+                for name, func in functions.items()
+                if hasattr(func, attribute)
+            }
+
+        def map_all_agent_variables(self) -> None:
+            all_functions = self._get_all_functions()
+            llms = self._filter_functions(all_functions, "is_llm")
+            tool_functions = self._filter_functions(all_functions, "is_tool")
+            cache_handler_functions = self._filter_functions(
+                all_functions, "is_cache_handler"
+            )
+            callbacks = self._filter_functions(all_functions, "is_callback")
+            agents = self._filter_functions(all_functions, "is_agent")
+
+            for agent_name, agent_info in self.agents_config.items():
+                self._map_agent_variables(
+                    agent_name,
+                    agent_info,
+                    agents,
+                    llms,
+                    tool_functions,
+                    cache_handler_functions,
+                    callbacks,
+                )
+
+        def _map_agent_variables(
+            self,
+            agent_name: str,
+            agent_info: Dict[str, Any],
+            agents: Dict[str, Callable],
+            llms: Dict[str, Callable],
+            tool_functions: Dict[str, Callable],
+            cache_handler_functions: Dict[str, Callable],
+            callbacks: Dict[str, Callable],
+        ) -> None:
+            if llm := agent_info.get("llm"):
+                self.agents_config[agent_name]["llm"] = llms[llm]()
+
+            if tools := agent_info.get("tools"):
+                self.agents_config[agent_name]["tools"] = [
+                    tool_functions[tool]() for tool in tools
+                ]
+
+            if function_calling_llm := agent_info.get("function_calling_llm"):
+                self.agents_config[agent_name]["function_calling_llm"] = agents[
+                    function_calling_llm
+                ]()
+
+            if step_callback := agent_info.get("step_callback"):
+                self.agents_config[agent_name]["step_callback"] = callbacks[
+                    step_callback
+                ]()
+
+            if cache_handler := agent_info.get("cache_handler"):
+                self.agents_config[agent_name]["cache_handler"] = (
+                    cache_handler_functions[cache_handler]()
+                )
+
+        def map_all_task_variables(self) -> None:
+            all_functions = self._get_all_functions()
+            agents = self._filter_functions(all_functions, "is_agent")
+            tasks = self._filter_functions(all_functions, "is_task")
+            output_json_functions = self._filter_functions(
+                all_functions, "is_output_json"
+            )
+            tool_functions = self._filter_functions(all_functions, "is_tool")
+            callback_functions = self._filter_functions(all_functions, "is_callback")
+            output_pydantic_functions = self._filter_functions(
+                all_functions, "is_output_pydantic"
+            )
+
+            for task_name, task_info in self.tasks_config.items():
+                self._map_task_variables(
+                    task_name,
+                    task_info,
+                    agents,
+                    tasks,
+                    output_json_functions,
+                    tool_functions,
+                    callback_functions,
+                    output_pydantic_functions,
+                )
+
+        def _map_task_variables(
+            self,
+            task_name: str,
+            task_info: Dict[str, Any],
+            agents: Dict[str, Callable],
+            tasks: Dict[str, Callable],
+            output_json_functions: Dict[str, Callable],
+            tool_functions: Dict[str, Callable],
+            callback_functions: Dict[str, Callable],
+            output_pydantic_functions: Dict[str, Callable],
+        ) -> None:
+            if context_list := task_info.get("context"):
+                self.tasks_config[task_name]["context"] = [
+                    tasks[context_task_name]() for context_task_name in context_list
+                ]
+
+            if tools := task_info.get("tools"):
+                self.tasks_config[task_name]["tools"] = [
+                    tool_functions[tool]() for tool in tools
+                ]
+
+            if agent_name := task_info.get("agent"):
+                self.tasks_config[task_name]["agent"] = agents[agent_name]()
+
+            if output_json := task_info.get("output_json"):
+                self.tasks_config[task_name]["output_json"] = output_json_functions[
+                    output_json
+                ]
+
+            if output_pydantic := task_info.get("output_pydantic"):
+                self.tasks_config[task_name]["output_pydantic"] = (
+                    output_pydantic_functions[output_pydantic]
+                )
+
+            if callbacks := task_info.get("callbacks"):
+                self.tasks_config[task_name]["callbacks"] = [
+                    callback_functions[callback]() for callback in callbacks
+                ]
+
     return WrappedClass

src/crewai/task.py

@@ -5,6 +5,7 @@ import threading
 import uuid
 from concurrent.futures import Future
 from copy import copy
+from hashlib import md5
 from typing import Any, Dict, List, Optional, Tuple, Type, Union
 
 from langchain_openai import ChatOpenAI
@@ -173,6 +174,14 @@ class Task(BaseModel):
         """Execute the task synchronously."""
         return self._execute_core(agent, context, tools)
 
+    @property
+    def key(self) -> str:
+        description = self._original_description or self.description
+        expected_output = self._original_expected_output or self.expected_output
+        source = [description, expected_output]
+
+        return md5("|".join(source).encode()).hexdigest()
+
     def execute_async(
         self,
         agent: BaseAgent | None = None,
@@ -205,6 +214,7 @@ class Task(BaseModel):
     ) -> TaskOutput:
         """Run the core execution logic of the task."""
         agent = agent or self.agent
+        self.agent = agent
         if not agent:
             raise Exception(
                 f"The task '{self.description}' has no agent assigned, therefore it can't be executed directly and should be executed in a Crew using a specific process that support that, like hierarchical."
@@ -237,14 +247,16 @@ class Task(BaseModel):
             self.callback(self.output)
 
         if self._execution_span:
-            self._telemetry.task_ended(self._execution_span, self)
+            self._telemetry.task_ended(self._execution_span, self, agent.crew)
             self._execution_span = None
 
         if self.output_file:
             content = (
                 json_output
                 if json_output
-                else pydantic_output.model_dump_json() if pydantic_output else result
+                else pydantic_output.model_dump_json()
+                if pydantic_output
+                else result
             )
             self._save_file(content)
 
@@ -316,9 +328,14 @@ class Task(BaseModel):
 
     def _create_converter(self, *args, **kwargs) -> Converter:
         """Create a converter instance."""
-        converter = self.agent.get_output_converter(*args, **kwargs)
-        if self.converter_cls:
+        if self.agent and not self.converter_cls:
+            converter = self.agent.get_output_converter(*args, **kwargs)
+        elif self.converter_cls:
             converter = self.converter_cls(*args, **kwargs)
+
+        if not converter:
+            raise Exception("No output converter found or set.")
+
         return converter
 
     def _export_output(
@@ -378,7 +395,7 @@ class Task(BaseModel):
     def _convert_with_instructions(
         self, result: str, model: Type[BaseModel]
     ) -> Union[dict, BaseModel, str]:
-        llm = self.agent.function_calling_llm or self.agent.llm
+        llm = self.agent.function_calling_llm or self.agent.llm  # type: ignore # Item "None" of "BaseAgent | None" has no attribute "function_calling_llm"
         instructions = self._get_conversion_instructions(model, llm)
 
         converter = self._create_converter(

src/crewai/tasks/conditional_task.py

@@ -0,0 +1,47 @@
+from typing import Any, Callable
+
+from pydantic import Field
+
+from crewai.task import Task
+from crewai.tasks.output_format import OutputFormat
+from crewai.tasks.task_output import TaskOutput
+
+
+class ConditionalTask(Task):
+    """
+    A task that can be conditionally executed based on the output of another task.
+    Note: This cannot be the only task you have in your crew and cannot be the first since its needs context from the previous task.
+    """
+
+    condition: Callable[[TaskOutput], bool] = Field(
+        default=None,
+        description="Maximum number of retries for an agent to execute a task when an error occurs.",
+    )
+
+    def __init__(
+        self,
+        condition: Callable[[Any], bool],
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.condition = condition
+
+    def should_execute(self, context: TaskOutput) -> bool:
+        """
+        Determines whether the conditional task should be executed based on the provided context.
+
+        Args:
+            context (Any): The context or output from the previous task that will be evaluated by the condition.
+
+        Returns:
+            bool: True if the task should be executed, False otherwise.
+        """
+        return self.condition(context)
+
+    def get_skipped_task_output(self):
+        return TaskOutput(
+            description=self.description,
+            raw="",
+            agent=self.agent.role if self.agent else "",
+            output_format=OutputFormat.RAW,
+        )

src/crewai/tasks/task_output.py

@@ -11,9 +11,7 @@ class TaskOutput(BaseModel):
 
     description: str = Field(description="Description of the task")
     summary: Optional[str] = Field(description="Summary of the task", default=None)
-    raw: str = Field(
-        description="Raw output of the task", default=""
-    )  # TODO: @joao: breaking change, by renaming raw_output to raw, but now consistent with CrewOutput
+    raw: str = Field(description="Raw output of the task", default="")
     pydantic: Optional[BaseModel] = Field(
         description="Pydantic output of task", default=None
     )
@@ -32,22 +30,6 @@ class TaskOutput(BaseModel):
         self.summary = f"{excerpt}..."
         return self
 
-    # TODO: Joao - Adding this safety check breakes when people want to see
-    #                   The full output of a TaskOutput or CrewOutput.
-    # @property
-    # def pydantic(self) -> Optional[BaseModel]:
-    #     # Check if the final task output included a pydantic model
-    #     if self.output_format != OutputFormat.PYDANTIC:
-    #         raise ValueError(
-    #             """
-    #             Invalid output format requested.
-    #             If you would like to access the pydantic model,
-    #             please make sure to set the output_pydantic property for the task.
-    #             """
-    #         )
-
-    #     return self._pydantic
-
     @property
     def json(self) -> Optional[str]:
         if self.output_format != OutputFormat.JSON:
@@ -66,7 +48,7 @@ class TaskOutput(BaseModel):
         output_dict = {}
         if self.json_dict:
             output_dict.update(self.json_dict)
-        if self.pydantic:
+        elif self.pydantic:
             output_dict.update(self.pydantic.model_dump())
         return output_dict
 

src/crewai/telemetry/telemetry.py

@@ -80,7 +80,7 @@ class Telemetry:
                 self.ready = False
                 self.trace_set = False
 
-    def crew_creation(self, crew):
+    def crew_creation(self, crew: Crew, inputs: dict[str, Any] | None):
         """Records the creation of a crew."""
         if self.ready:
             try:
@@ -92,6 +92,7 @@ class Telemetry:
                     pkg_resources.get_distribution("crewai").version,
                 )
                 self._add_attribute(span, "python_version", platform.python_version())
+                self._add_attribute(span, "crew_key", crew.key)
                 self._add_attribute(span, "crew_id", str(crew.id))
                 self._add_attribute(span, "crew_process", crew.process)
                 self._add_attribute(span, "crew_memory", crew.memory)
@@ -103,6 +104,7 @@ class Telemetry:
                     json.dumps(
                         [
                             {
+                                "key": agent.key,
                                 "id": str(agent.id),
                                 "role": agent.role,
                                 "goal": agent.goal,
@@ -114,7 +116,7 @@ class Telemetry:
                                 "llm": json.dumps(self._safe_llm_attributes(agent.llm)),
                                 "delegation_enabled?": agent.allow_delegation,
                                 "tools_names": [
-                                    tool.name.casefold() for tool in agent.tools
+                                    tool.name.casefold() for tool in agent.tools or []
                                 ],
                             }
                             for agent in crew.agents
@@ -127,19 +129,21 @@ class Telemetry:
                     json.dumps(
                         [
                             {
+                                "key": task.key,
                                 "id": str(task.id),
                                 "description": task.description,
                                 "expected_output": task.expected_output,
                                 "async_execution?": task.async_execution,
                                 "human_input?": task.human_input,
                                 "agent_role": task.agent.role if task.agent else "None",
+                                "agent_key": task.agent.key if task.agent else None,
                                 "context": (
                                     [task.description for task in task.context]
                                     if task.context
                                     else None
                                 ),
                                 "tools_names": [
-                                    tool.name.casefold() for tool in task.tools
+                                    tool.name.casefold() for tool in task.tools or []
                                 ],
                             }
                             for task in crew.tasks
@@ -151,6 +155,12 @@ class Telemetry:
                 self._add_attribute(span, "platform_system", platform.system())
                 self._add_attribute(span, "platform_version", platform.version())
                 self._add_attribute(span, "cpus", os.cpu_count())
+
+                if crew.share_crew:
+                    self._add_attribute(
+                        span, "crew_inputs", json.dumps(inputs) if inputs else None
+                    )
+
                 span.set_status(Status(StatusCode.OK))
                 span.end()
             except Exception:
@@ -161,10 +171,12 @@ class Telemetry:
         if self.ready:
             try:
                 tracer = trace.get_tracer("crewai.telemetry")
-                span = tracer.start_span("Task Execution")
 
                 created_span = tracer.start_span("Task Created")
 
+                self._add_attribute(created_span, "crew_key", crew.key)
+                self._add_attribute(created_span, "crew_id", str(crew.id))
+                self._add_attribute(created_span, "task_key", task.key)
                 self._add_attribute(created_span, "task_id", str(task.id))
 
                 if crew.share_crew:
@@ -178,6 +190,11 @@ class Telemetry:
                 created_span.set_status(Status(StatusCode.OK))
                 created_span.end()
 
+                span = tracer.start_span("Task Execution")
+
+                self._add_attribute(span, "crew_key", crew.key)
+                self._add_attribute(span, "crew_id", str(crew.id))
+                self._add_attribute(span, "task_key", task.key)
                 self._add_attribute(span, "task_id", str(task.id))
 
                 if crew.share_crew:
@@ -192,13 +209,16 @@ class Telemetry:
 
         return None
 
-    def task_ended(self, span: Span, task: Task):
+    def task_ended(self, span: Span, task: Task, crew: Crew):
         """Records task execution in a crew."""
         if self.ready:
             try:
-                self._add_attribute(
-                    span, "output", task.output.raw_output if task.output else ""
-                )
+                if crew.share_crew:
+                    self._add_attribute(
+                        span,
+                        "task_output",
+                        task.output.raw if task.output else "",
+                    )
 
                 span.set_status(Status(StatusCode.OK))
                 span.end()
@@ -273,6 +293,8 @@ class Telemetry:
         """Records the complete execution of a crew.
         This is only collected if the user has opted-in to share the crew.
         """
+        self.crew_creation(crew, inputs)
+
         if (self.ready) and (crew.share_crew):
             try:
                 tracer = trace.get_tracer("crewai.telemetry")
@@ -282,14 +304,18 @@ class Telemetry:
                     "crewai_version",
                     pkg_resources.get_distribution("crewai").version,
                 )
+                self._add_attribute(span, "crew_key", crew.key)
                 self._add_attribute(span, "crew_id", str(crew.id))
-                self._add_attribute(span, "inputs", json.dumps(inputs))
+                self._add_attribute(
+                    span, "crew_inputs", json.dumps(inputs) if inputs else None
+                )
                 self._add_attribute(
                     span,
                     "crew_agents",
                     json.dumps(
                         [
                             {
+                                "key": agent.key,
                                 "id": str(agent.id),
                                 "role": agent.role,
                                 "goal": agent.goal,
@@ -320,6 +346,7 @@ class Telemetry:
                                 "async_execution?": task.async_execution,
                                 "human_input?": task.human_input,
                                 "agent_role": task.agent.role if task.agent else "None",
+                                "agent_key": task.agent.key if task.agent else None,
                                 "context": (
                                     [task.description for task in task.context]
                                     if task.context

src/crewai/tools/agent_tools.py

@@ -7,7 +7,7 @@ class AgentTools(BaseAgentTools):
     """Default tools around agent delegation"""
 
     def tools(self):
-        coworkers = f"[{', '.join([f'{agent.role}' for agent in self.agents])}]"
+        coworkers = ", ".join([f"{agent.role}" for agent in self.agents])
         tools = [
             StructuredTool.from_function(
                 func=self.delegate_work,

src/crewai/tools/tool_usage.py

@@ -151,16 +151,12 @@ class ToolUsage:
                             for k, v in calling.arguments.items()
                             if k in acceptable_args
                         }
-                        result = tool._run(**arguments)
+                        result = tool.invoke(input=arguments)
                     except Exception:
-                        if tool.args_schema:
-                            arguments = calling.arguments
-                            result = tool._run(**arguments)
-                        else:
-                            arguments = calling.arguments.values()  # type: ignore # Incompatible types in assignment (expression has type "dict_values[str, Any]", variable has type "dict[str, Any]")
-                            result = tool._run(*arguments)
+                        arguments = calling.arguments
+                        result = tool.invoke(input=arguments)
                 else:
-                    result = tool._run()
+                    result = tool.invoke(input={})
             except Exception as e:
                 self._run_attempts += 1
                 if self._run_attempts > self._max_parsing_attempts:

src/crewai/utilities/converter.py

@@ -2,10 +2,8 @@ import json
 
 from langchain.schema import HumanMessage, SystemMessage
 from langchain_openai import ChatOpenAI
-from pydantic import model_validator
-from crewai.agents.agent_builder.utilities.base_output_converter_base import (
-    OutputConverter,
-)
+
+from crewai.agents.agent_builder.utilities.base_output_converter import OutputConverter
 
 
 class ConverterError(Exception):
@@ -19,15 +17,10 @@ class ConverterError(Exception):
 class Converter(OutputConverter):
     """Class that converts text into either pydantic or json."""
 
-    @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:
+            if self.is_gpt:
                 return self._create_instructor().to_pydantic()
             else:
                 return self._create_chain().invoke({})
@@ -41,14 +34,14 @@ class Converter(OutputConverter):
     def to_json(self, current_attempt=1):
         """Convert text to json."""
         try:
-            if self._is_gpt:
+            if self.is_gpt:
                 return self._create_instructor().to_json()
             else:
                 return json.dumps(self._create_chain().invoke({}).model_dump())
-        except Exception:
+        except Exception as e:
             if current_attempt < self.max_attempts:
                 return self.to_json(current_attempt + 1)
-            return ConverterError("Failed to convert text into JSON.")
+            return ConverterError(f"Failed to convert text into JSON, error: {e}.")
 
     def _create_instructor(self):
         """Create an instructor."""
@@ -75,5 +68,7 @@ class Converter(OutputConverter):
         )
         return new_prompt | self.llm | parser
 
-    def _is_gpt(self, llm) -> bool:  # type: ignore # BUG? Name "_is_gpt" defined on line 20 hides name from outer scope
-        return isinstance(llm, ChatOpenAI) and llm.openai_api_base is None
+    @property
+    def is_gpt(self) -> bool:
+        """Return if llm provided is of gpt from openai."""
+        return isinstance(self.llm, ChatOpenAI) and self.llm.openai_api_base is None

src/crewai/utilities/crew_json_encoder.py

@@ -0,0 +1,31 @@
+from datetime import datetime
+import json
+from uuid import UUID
+from pydantic import BaseModel
+
+
+class CrewJSONEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, BaseModel):
+            return self._handle_pydantic_model(obj)
+        elif isinstance(obj, UUID):
+            return str(obj)
+
+        elif isinstance(obj, datetime):
+            return obj.isoformat()
+        return super().default(obj)
+
+    def _handle_pydantic_model(self, obj):
+        try:
+            data = obj.model_dump()
+            # Remove circular references
+            for key, value in data.items():
+                if isinstance(value, BaseModel):
+                    data[key] = str(
+                        value
+                    )  # Convert nested models to string representation
+            return data
+        except RecursionError:
+            return str(
+                obj
+            )  # Fall back to string representation if circular reference is detected

src/crewai/utilities/crew_pydantic_output_parser.py

@@ -17,6 +17,16 @@ class CrewPydanticOutputParser(PydanticOutputParser):
 
     def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
         result[0].text = self._transform_in_valid_json(result[0].text)
+
+        # Treating edge case of function calling llm returning the name instead of tool_name
+        json_object = json.loads(result[0].text)
+        json_object["tool_name"] = (
+            json_object["name"]
+            if "tool_name" not in json_object
+            else json_object["tool_name"]
+        )
+        result[0].text = json.dumps(json_object)
+
         json_object = super().parse_result(result)
         try:
             return self.pydantic_object.parse_obj(json_object)

src/crewai/utilities/file_handler.py

@@ -1,5 +1,7 @@
 import os
 import pickle
+
+
 from datetime import datetime
 
 

src/crewai/utilities/planning_handler.py

@@ -0,0 +1,64 @@
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+from crewai.agent import Agent
+from crewai.task import Task
+
+
+class PlannerTaskPydanticOutput(BaseModel):
+    list_of_plans_per_task: List[str]
+
+
+class CrewPlanner:
+    def __init__(self, tasks: List[Task]):
+        self.tasks = tasks
+
+    def _handle_crew_planning(self) -> Optional[BaseModel]:
+        """Handles the Crew planning by creating detailed step-by-step plans for each task."""
+        planning_agent = self._create_planning_agent()
+        tasks_summary = self._create_tasks_summary()
+
+        planner_task = self._create_planner_task(planning_agent, tasks_summary)
+
+        return planner_task.execute_sync().pydantic
+
+    def _create_planning_agent(self) -> Agent:
+        """Creates the planning agent for the crew planning."""
+        return Agent(
+            role="Task Execution Planner",
+            goal=(
+                "Your goal is to create an extremely detailed, step-by-step plan based on the tasks and tools "
+                "available to each agent so that they can perform the tasks in an exemplary manner"
+            ),
+            backstory="Planner agent for crew planning",
+        )
+
+    def _create_planner_task(self, planning_agent: Agent, tasks_summary: str) -> Task:
+        """Creates the planner task using the given agent and tasks summary."""
+        return Task(
+            description=(
+                f"Based on these tasks summary: {tasks_summary} \n Create the most descriptive plan based on the tasks "
+                "descriptions, tools available, and agents' goals for them to execute their goals with perfection."
+            ),
+            expected_output="Step by step plan on how the agents can execute their tasks using the available tools with mastery",
+            agent=planning_agent,
+            output_pydantic=PlannerTaskPydanticOutput,
+        )
+
+    def _create_tasks_summary(self) -> str:
+        """Creates a summary of all tasks."""
+        tasks_summary = []
+        for idx, task in enumerate(self.tasks):
+            tasks_summary.append(
+                f"""
+                Task Number {idx + 1} - {task.description}
+                "task_description": {task.description}
+                "task_expected_output": {task.expected_output}
+                "agent": {task.agent.role if task.agent else "None"}
+                "agent_goal": {task.agent.goal if task.agent else "None"}
+                "task_tools": {task.tools}
+                "agent_tools": {task.agent.tools if task.agent else "None"}
+                """
+            )
+        return " ".join(tasks_summary)

src/crewai/utilities/printer.py

@@ -8,6 +8,10 @@ class Printer:
             self._print_bold_green(content)
         elif color == "bold_purple":
             self._print_bold_purple(content)
+        elif color == "bold_blue":
+            self._print_bold_blue(content)
+        elif color == "yellow":
+            self._print_yellow(content)
         else:
             print(content)
 
@@ -22,3 +26,9 @@ class Printer:
 
     def _print_red(self, content):
         print("\033[91m {}\033[00m".format(content))
+
+    def _print_bold_blue(self, content):
+        print("\033[1m\033[94m {}\033[00m".format(content))
+
+    def _print_yellow(self, content):
+        print("\033[93m {}\033[00m".format(content))

src/crewai/utilities/task_output_storage_handler.py

@@ -0,0 +1,61 @@
+from pydantic import BaseModel, Field
+from datetime import datetime
+from typing import Dict, Any, Optional, List
+from crewai.memory.storage.kickoff_task_outputs_storage import (
+    KickoffTaskOutputsSQLiteStorage,
+)
+from crewai.task import Task
+
+
+class ExecutionLog(BaseModel):
+    task_id: str
+    expected_output: Optional[str] = None
+    output: Dict[str, Any]
+    timestamp: datetime = Field(default_factory=datetime.now)
+    task_index: int
+    inputs: Dict[str, Any] = Field(default_factory=dict)
+    was_replayed: bool = False
+
+    def __getitem__(self, key: str) -> Any:
+        return getattr(self, key)
+
+
+class TaskOutputStorageHandler:
+    def __init__(self) -> None:
+        self.storage = KickoffTaskOutputsSQLiteStorage()
+
+    def update(self, task_index: int, log: Dict[str, Any]):
+        saved_outputs = self.load()
+        if saved_outputs is None:
+            raise ValueError("Logs cannot be None")
+
+        if log.get("was_replayed", False):
+            replayed = {
+                "task_id": str(log["task"].id),
+                "expected_output": log["task"].expected_output,
+                "output": log["output"],
+                "was_replayed": log["was_replayed"],
+                "inputs": log["inputs"],
+            }
+            self.storage.update(
+                task_index,
+                **replayed,
+            )
+        else:
+            self.storage.add(**log)
+
+    def add(
+        self,
+        task: Task,
+        output: Dict[str, Any],
+        task_index: int,
+        inputs: Dict[str, Any] = {},
+        was_replayed: bool = False,
+    ):
+        self.storage.add(task, output, task_index, was_replayed, inputs)
+
+    def reset(self):
+        self.storage.delete_all()
+
+    def load(self) -> Optional[List[Dict[str, Any]]]:
+        return self.storage.load()

tests/agent_test.py

@@ -963,3 +963,54 @@ def test_agent_use_trained_data(crew_training_handler):
     crew_training_handler.assert_has_calls(
         [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
     )
+
+
+def test_agent_max_retry_limit():
+    agent = Agent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+        max_retry_limit=1,
+    )
+
+    task = Task(
+        agent=agent,
+        description="Say the word: Hi",
+        expected_output="The word: Hi",
+        human_input=True,
+    )
+
+    error_message = "Error happening while sending prompt to model."
+    with patch.object(
+        CrewAgentExecutor, "invoke", wraps=agent.agent_executor.invoke
+    ) as invoke_mock:
+        invoke_mock.side_effect = Exception(error_message)
+
+        assert agent._times_executed == 0
+        assert agent.max_retry_limit == 1
+
+        with pytest.raises(Exception) as e:
+            agent.execute_task(
+                task=task,
+            )
+        assert e.value.args[0] == error_message
+        assert agent._times_executed == 2
+
+        invoke_mock.assert_has_calls(
+            [
+                mock.call(
+                    {
+                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
+                        "tool_names": "",
+                        "tools": "",
+                    }
+                ),
+                mock.call(
+                    {
+                        "input": "Say the word: Hi\n\nThis is the expect criteria for your final answer: The word: Hi \n you MUST return the actual complete content as the final answer, not a summary.",
+                        "tool_names": "",
+                        "tools": "",
+                    }
+                ),
+            ]
+        )

tests/agents/agent_builder/base_agent_test.py

@@ -0,0 +1,36 @@
+import hashlib
+from typing import Any, List, Optional
+
+from crewai.agents.agent_builder.base_agent import BaseAgent
+from pydantic import BaseModel
+
+
+class TestAgent(BaseAgent):
+    def execute_task(
+        self,
+        task: Any,
+        context: Optional[str] = None,
+        tools: Optional[List[Any]] = None,
+    ) -> str:
+        return ""
+
+    def create_agent_executor(self, tools=None) -> None: ...
+
+    def _parse_tools(self, tools: List[Any]) -> List[Any]:
+        return []
+
+    def get_delegation_tools(self, agents: List["BaseAgent"]): ...
+
+    def get_output_converter(
+        self, llm: Any, text: str, model: type[BaseModel] | None, instructions: str
+    ): ...
+
+
+def test_key():
+    agent = TestAgent(
+        role="test role",
+        goal="test goal",
+        backstory="test backstory",
+    )
+    hash = hashlib.md5("test role|test goal|test backstory".encode()).hexdigest()
+    assert agent.key == hash

tests/agents/test_crew_agent_parser.py

@@ -0,0 +1,378 @@
+import pytest
+from crewai.agents.parser import CrewAgentParser
+from langchain_core.agents import AgentAction, AgentFinish
+from langchain_core.exceptions import OutputParserException
+
+
+@pytest.fixture
+def parser():
+    p = CrewAgentParser()
+    p.agent = MockAgent()
+    return p
+
+
+def test_valid_action_parsing_special_characters(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what's the temperature in SF?"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what's the temperature in SF?"
+
+
+def test_valid_action_parsing_with_json_tool_input(parser):
+    text = """
+    Thought: Let's find the information
+    Action: query
+    Action Input: ** {"task": "What are some common challenges or barriers that you have observed or experienced when implementing AI-powered solutions in healthcare settings?", "context": "As we've discussed recent advancements in AI applications in healthcare, it's crucial to acknowledge the potential hurdles. Some possible obstacles include...", "coworker": "Senior Researcher"}
+    """
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    expected_tool_input = '{"task": "What are some common challenges or barriers that you have observed or experienced when implementing AI-powered solutions in healthcare settings?", "context": "As we\'ve discussed recent advancements in AI applications in healthcare, it\'s crucial to acknowledge the potential hurdles. Some possible obstacles include...", "coworker": "Senior Researcher"}'
+    assert result.tool == "query"
+    assert result.tool_input == expected_tool_input
+
+
+def test_valid_action_parsing_with_quotes(parser):
+    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "temperature in SF"'
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "temperature in SF"
+
+
+def test_valid_action_parsing_with_curly_braces(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: {temperature in SF}"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "{temperature in SF}"
+
+
+def test_valid_action_parsing_with_angle_brackets(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: <temperature in SF>"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "<temperature in SF>"
+
+
+def test_valid_action_parsing_with_parentheses(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: (temperature in SF)"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "(temperature in SF)"
+
+
+def test_valid_action_parsing_with_mixed_brackets(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: [temperature in {SF}]"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "[temperature in {SF}]"
+
+
+def test_valid_action_parsing_with_nested_quotes(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what's the temperature in 'SF'?\""
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what's the temperature in 'SF'?"
+
+
+def test_valid_action_parsing_with_incomplete_json(parser):
+    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: {"query": "temperature in SF"'
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == '{"query": "temperature in SF"}'
+
+
+def test_valid_action_parsing_with_special_characters(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is the temperature in SF? @$%^&*"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what is the temperature in SF? @$%^&*"
+
+
+def test_valid_action_parsing_with_combination(parser):
+    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "[what is the temperature in SF?]"'
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "[what is the temperature in SF?]"
+
+
+def test_valid_action_parsing_with_mixed_quotes(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what's the temperature in SF?\""
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what's the temperature in SF?"
+
+
+def test_valid_action_parsing_with_newlines(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is\nthe temperature in SF?"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what is\nthe temperature in SF?"
+
+
+def test_valid_action_parsing_with_escaped_characters(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: what is the temperature in SF? \\n"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what is the temperature in SF? \\n"
+
+
+def test_valid_action_parsing_with_json_string(parser):
+    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: {"query": "temperature in SF"}'
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == '{"query": "temperature in SF"}'
+
+
+def test_valid_action_parsing_with_unbalanced_quotes(parser):
+    text = "Thought: Let's find the temperature\nAction: search\nAction Input: \"what is the temperature in SF?"
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what is the temperature in SF?"
+
+
+def test_clean_action_no_formatting(parser):
+    action = "Ask question to senior researcher"
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == "Ask question to senior researcher"
+
+
+def test_clean_action_with_leading_asterisks(parser):
+    action = "** Ask question to senior researcher"
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == "Ask question to senior researcher"
+
+
+def test_clean_action_with_trailing_asterisks(parser):
+    action = "Ask question to senior researcher **"
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == "Ask question to senior researcher"
+
+
+def test_clean_action_with_leading_and_trailing_asterisks(parser):
+    action = "** Ask question to senior researcher **"
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == "Ask question to senior researcher"
+
+
+def test_clean_action_with_multiple_leading_asterisks(parser):
+    action = "**** Ask question to senior researcher"
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == "Ask question to senior researcher"
+
+
+def test_clean_action_with_multiple_trailing_asterisks(parser):
+    action = "Ask question to senior researcher ****"
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == "Ask question to senior researcher"
+
+
+def test_clean_action_with_spaces_and_asterisks(parser):
+    action = "  **  Ask question to senior researcher  **  "
+    cleaned_action = parser._clean_action(action)
+    print(f"Original action: '{action}'")
+    print(f"Cleaned action: '{cleaned_action}'")
+    assert cleaned_action == "Ask question to senior researcher"
+
+
+def test_clean_action_with_only_asterisks(parser):
+    action = "****"
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == ""
+
+
+def test_clean_action_with_empty_string(parser):
+    action = ""
+    cleaned_action = parser._clean_action(action)
+    assert cleaned_action == ""
+
+
+def test_valid_final_answer_parsing(parser):
+    text = (
+        "Thought: I found the information\nFinal Answer: The temperature is 100 degrees"
+    )
+    result = parser.parse(text)
+    assert isinstance(result, AgentFinish)
+    assert result.return_values["output"] == "The temperature is 100 degrees"
+
+
+def test_missing_action_error(parser):
+    text = "Thought: Let's find the temperature\nAction Input: what is the temperature in SF?"
+    with pytest.raises(OutputParserException) as exc_info:
+        parser.parse(text)
+    assert "Could not parse LLM output" in str(exc_info.value)
+
+
+def test_missing_action_input_error(parser):
+    text = "Thought: Let's find the temperature\nAction: search"
+    with pytest.raises(OutputParserException) as exc_info:
+        parser.parse(text)
+    assert "Could not parse LLM output" in str(exc_info.value)
+
+
+def test_action_and_final_answer_error(parser):
+    text = "Thought: I found the information\nAction: search\nAction Input: what is the temperature in SF?\nFinal Answer: The temperature is 100 degrees"
+    with pytest.raises(OutputParserException) as exc_info:
+        parser.parse(text)
+    assert "both perform Action and give a Final Answer" in str(exc_info.value)
+
+
+def test_safe_repair_json(parser):
+    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": Senior Researcher'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_unrepairable(parser):
+    invalid_json = "{invalid_json"
+    result = parser._safe_repair_json(invalid_json)
+    print("result:", invalid_json)
+    assert result == invalid_json  # Should return the original if unrepairable
+
+
+def test_safe_repair_json_missing_quotes(parser):
+    invalid_json = (
+        '{task: "Research XAI", context: "Explainable AI", coworker: Senior Researcher}'
+    )
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_unclosed_brackets(parser):
+    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_extra_commas(parser):
+    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher",}'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_trailing_commas(parser):
+    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher",}'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_single_quotes(parser):
+    invalid_json = "{'task': 'Research XAI', 'context': 'Explainable AI', 'coworker': 'Senior Researcher'}"
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_mixed_quotes(parser):
+    invalid_json = "{'task': \"Research XAI\", 'context': \"Explainable AI\", 'coworker': 'Senior Researcher'}"
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_unescaped_characters(parser):
+    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher\n"}'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    print("result:", result)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_missing_colon(parser):
+    invalid_json = '{"task" "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_missing_comma(parser):
+    invalid_json = '{"task": "Research XAI" "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_unexpected_trailing_characters(parser):
+    invalid_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"} random text'
+    expected_repaired_json = '{"task": "Research XAI", "context": "Explainable AI", "coworker": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_safe_repair_json_special_characters_key(parser):
+    invalid_json = '{"task!@#": "Research XAI", "context$%^": "Explainable AI", "coworker&*()": "Senior Researcher"}'
+    expected_repaired_json = '{"task!@#": "Research XAI", "context$%^": "Explainable AI", "coworker&*()": "Senior Researcher"}'
+    result = parser._safe_repair_json(invalid_json)
+    assert result == expected_repaired_json
+
+
+def test_parsing_with_whitespace(parser):
+    text = " Thought: Let's find the temperature \n Action: search \n Action Input: what is the temperature in SF? "
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what is the temperature in SF?"
+
+
+def test_parsing_with_special_characters(parser):
+    text = 'Thought: Let\'s find the temperature\nAction: search\nAction Input: "what is the temperature in SF?"'
+    result = parser.parse(text)
+    assert isinstance(result, AgentAction)
+    assert result.tool == "search"
+    assert result.tool_input == "what is the temperature in SF?"
+
+
+def test_integration_valid_and_invalid(parser):
+    text = """
+    Thought: Let's find the temperature
+    Action: search
+    Action Input: what is the temperature in SF?
+
+    Thought: I found the information
+    Final Answer: The temperature is 100 degrees
+
+    Thought: Missing action
+    Action Input: invalid
+
+    Thought: Missing action input
+    Action: invalid
+    """
+    parts = text.strip().split("\n\n")
+    results = []
+    for part in parts:
+        try:
+            result = parser.parse(part.strip())
+            results.append(result)
+        except OutputParserException as e:
+            results.append(e)
+
+    assert isinstance(results[0], AgentAction)
+    assert isinstance(results[1], AgentFinish)
+    assert isinstance(results[2], OutputParserException)
+    assert isinstance(results[3], OutputParserException)
+
+
+class MockAgent:
+    def increment_formatting_errors(self):
+        pass
+
+
+# TODO: ADD TEST TO MAKE SURE ** REMOVAL DOESN'T MESS UP ANYTHING

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tests/cassettes/test_json_property_without_output_json.yaml

@@ -0,0 +1,262 @@
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+        data: {"id":"chatcmpl-9kFn6xVncEzHOGdnzGN9R8fMONMEi","object":"chat.completion.chunk","created":1720810816,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_dd932ca5d1","choices":[{"index":0,"delta":{"content":"Final"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9hQWSD5B35ANI9JLmbxUdPECfNd43","object":"chat.completion.chunk","created":1720137084,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9kFn6xVncEzHOGdnzGN9R8fMONMEi","object":"chat.completion.chunk","created":1720810816,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_dd932ca5d1","choices":[{"index":0,"delta":{"content":"
         Answer"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9hQWSD5B35ANI9JLmbxUdPECfNd43","object":"chat.completion.chunk","created":1720137084,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9kFn6xVncEzHOGdnzGN9R8fMONMEi","object":"chat.completion.chunk","created":1720810816,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_dd932ca5d1","choices":[{"index":0,"delta":{"content":":"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9hQWSD5B35ANI9JLmbxUdPECfNd43","object":"chat.completion.chunk","created":1720137084,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"
+        data: {"id":"chatcmpl-9kFn6xVncEzHOGdnzGN9R8fMONMEi","object":"chat.completion.chunk","created":1720810816,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_dd932ca5d1","choices":[{"index":0,"delta":{"content":"
         How"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9hQWSD5B35ANI9JLmbxUdPECfNd43","object":"chat.completion.chunk","created":1720137084,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"dy"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9kFn6xVncEzHOGdnzGN9R8fMONMEi","object":"chat.completion.chunk","created":1720810816,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_dd932ca5d1","choices":[{"index":0,"delta":{"content":"dy"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9hQWSD5B35ANI9JLmbxUdPECfNd43","object":"chat.completion.chunk","created":1720137084,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{"content":"!"},"logprobs":null,"finish_reason":null}]}
+        data: {"id":"chatcmpl-9kFn6xVncEzHOGdnzGN9R8fMONMEi","object":"chat.completion.chunk","created":1720810816,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_dd932ca5d1","choices":[{"index":0,"delta":{"content":"!"},"logprobs":null,"finish_reason":null}]}
 
 
-        data: {"id":"chatcmpl-9hQWSD5B35ANI9JLmbxUdPECfNd43","object":"chat.completion.chunk","created":1720137084,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_ce0793330f","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]}
+        data: {"id":"chatcmpl-9kFn6xVncEzHOGdnzGN9R8fMONMEi","object":"chat.completion.chunk","created":1720810816,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_dd932ca5d1","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}]}
 
 
         data: [DONE]
@@ -294,13 +317,13 @@ interactions:
       CF-Cache-Status:
       - DYNAMIC
       CF-RAY:
-      - 89e305ea4abc82f5-GIG
+      - 8a234672c9fbda17-MIA
       Connection:
       - keep-alive
       Content-Type:
       - text/event-stream; charset=utf-8
       Date:
-      - Thu, 04 Jul 2024 23:51:24 GMT
+      - Fri, 12 Jul 2024 19:00:16 GMT
       Server:
       - cloudflare
       Transfer-Encoding:
@@ -310,7 +333,7 @@ interactions:
       openai-organization:
       - crewai-iuxna1
       openai-processing-ms:
-      - '91'
+      - '122'
       openai-version:
       - '2020-10-01'
       strict-transport-security:
@@ -318,17 +341,17 @@ interactions:
       x-ratelimit-limit-requests:
       - '10000'
       x-ratelimit-limit-tokens:
-      - '16000000'
+      - '22000000'
       x-ratelimit-remaining-requests:
       - '9999'
       x-ratelimit-remaining-tokens:
-      - '15999673'
+      - '21999666'
       x-ratelimit-reset-requests:
       - 6ms
       x-ratelimit-reset-tokens:
-      - 1ms
+      - 0s
       x-request-id:
-      - req_10032db16fa190e8435947a6aaa700ff
+      - req_484dde23b57ef3f715ab183690fe73b9
     status:
       code: 200
       message: OK

tests/cli/cli_test.py

@@ -3,7 +3,7 @@ from unittest import mock
 import pytest
 from click.testing import CliRunner
 
-from crewai.cli.cli import train, version
+from crewai.cli.cli import reset_memories, test, train, version
 
 
 @pytest.fixture
@@ -41,6 +41,82 @@ def test_train_invalid_string_iterations(train_crew, runner):
     )
 
 
+@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
+@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
+@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
+@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
+def test_reset_all_memories(
+    MockTaskOutputStorageHandler,
+    MockLongTermMemory,
+    MockEntityMemory,
+    MockShortTermMemory,
+    runner,
+):
+    result = runner.invoke(reset_memories, ["--all"])
+    MockShortTermMemory().reset.assert_called_once()
+    MockEntityMemory().reset.assert_called_once()
+    MockLongTermMemory().reset.assert_called_once()
+    MockTaskOutputStorageHandler().reset.assert_called_once()
+
+    assert result.output == "All memories have been reset.\n"
+
+
+@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
+def test_reset_short_term_memories(MockShortTermMemory, runner):
+    result = runner.invoke(reset_memories, ["-s"])
+    MockShortTermMemory().reset.assert_called_once()
+    assert result.output == "Short term memory has been reset.\n"
+
+
+@mock.patch("crewai.cli.reset_memories_command.EntityMemory")
+def test_reset_entity_memories(MockEntityMemory, runner):
+    result = runner.invoke(reset_memories, ["-e"])
+    MockEntityMemory().reset.assert_called_once()
+    assert result.output == "Entity memory has been reset.\n"
+
+
+@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
+def test_reset_long_term_memories(MockLongTermMemory, runner):
+    result = runner.invoke(reset_memories, ["-l"])
+    MockLongTermMemory().reset.assert_called_once()
+    assert result.output == "Long term memory has been reset.\n"
+
+
+@mock.patch("crewai.cli.reset_memories_command.TaskOutputStorageHandler")
+def test_reset_kickoff_outputs(MockTaskOutputStorageHandler, runner):
+    result = runner.invoke(reset_memories, ["-k"])
+    MockTaskOutputStorageHandler().reset.assert_called_once()
+    assert result.output == "Latest Kickoff outputs stored has been reset.\n"
+
+
+@mock.patch("crewai.cli.reset_memories_command.ShortTermMemory")
+@mock.patch("crewai.cli.reset_memories_command.LongTermMemory")
+def test_reset_multiple_memory_flags(MockShortTermMemory, MockLongTermMemory, runner):
+    result = runner.invoke(
+        reset_memories,
+        [
+            "-s",
+            "-l",
+        ],
+    )
+    MockShortTermMemory().reset.assert_called_once()
+    MockLongTermMemory().reset.assert_called_once()
+    assert (
+        result.output
+        == "Long term memory has been reset.\nShort term memory has been reset.\n"
+    )
+
+
+def test_reset_no_memory_flags(runner):
+    result = runner.invoke(
+        reset_memories,
+    )
+    assert (
+        result.output
+        == "Please specify at least one memory type to reset using the appropriate flags.\n"
+    )
+
+
 def test_version_command(runner):
     result = runner.invoke(version)
 
@@ -57,3 +133,33 @@ def test_version_command_with_tools(runner):
         "crewai tools version:" in result.output
         or "crewai tools not installed" in result.output
     )
+
+
+@mock.patch("crewai.cli.cli.test_crew")
+def test_test_default_iterations(test_crew, runner):
+    result = runner.invoke(test)
+
+    test_crew.assert_called_once_with(3, "gpt-4o-mini")
+    assert result.exit_code == 0
+    assert "Testing the crew for 3 iterations with model gpt-4o-mini" in result.output
+
+
+@mock.patch("crewai.cli.cli.test_crew")
+def test_test_custom_iterations(test_crew, runner):
+    result = runner.invoke(test, ["--n_iterations", "5", "--model", "gpt-4o"])
+
+    test_crew.assert_called_once_with(5, "gpt-4o")
+    assert result.exit_code == 0
+    assert "Testing the crew for 5 iterations with model gpt-4o" in result.output
+
+
+@mock.patch("crewai.cli.cli.test_crew")
+def test_test_invalid_string_iterations(test_crew, runner):
+    result = runner.invoke(test, ["--n_iterations", "invalid"])
+
+    test_crew.assert_not_called()
+    assert result.exit_code == 2
+    assert (
+        "Usage: test [OPTIONS]\nTry 'test --help' for help.\n\nError: Invalid value for '-n' / '--n_iterations': 'invalid' is not a valid integer.\n"
+        in result.output
+    )

tests/cli/test_crew_test.py

@@ -0,0 +1,97 @@
+import subprocess
+from unittest import mock
+
+import pytest
+
+from crewai.cli import test_crew
+
+
+@pytest.mark.parametrize(
+    "n_iterations,model",
+    [
+        (1, "gpt-4o"),
+        (5, "gpt-3.5-turbo"),
+        (10, "gpt-4"),
+    ],
+)
+@mock.patch("crewai.cli.test_crew.subprocess.run")
+def test_crew_success(mock_subprocess_run, n_iterations, model):
+    """Test the crew function for successful execution."""
+    mock_subprocess_run.return_value = subprocess.CompletedProcess(
+        args=f"poetry run test {n_iterations} {model}", returncode=0
+    )
+    result = test_crew.test_crew(n_iterations, model)
+
+    mock_subprocess_run.assert_called_once_with(
+        ["poetry", "run", "test", str(n_iterations), model],
+        capture_output=False,
+        text=True,
+        check=True,
+    )
+    assert result is None
+
+
+@mock.patch("crewai.cli.test_crew.click")
+def test_test_crew_zero_iterations(click):
+    test_crew.test_crew(0, "gpt-4o")
+    click.echo.assert_called_once_with(
+        "An unexpected error occurred: The number of iterations must be a positive integer.",
+        err=True,
+    )
+
+
+@mock.patch("crewai.cli.test_crew.click")
+def test_test_crew_negative_iterations(click):
+    test_crew.test_crew(-2, "gpt-4o")
+    click.echo.assert_called_once_with(
+        "An unexpected error occurred: The number of iterations must be a positive integer.",
+        err=True,
+    )
+
+
+@mock.patch("crewai.cli.test_crew.click")
+@mock.patch("crewai.cli.test_crew.subprocess.run")
+def test_test_crew_called_process_error(mock_subprocess_run, click):
+    n_iterations = 5
+    mock_subprocess_run.side_effect = subprocess.CalledProcessError(
+        returncode=1,
+        cmd=["poetry", "run", "test", str(n_iterations), "gpt-4o"],
+        output="Error",
+        stderr="Some error occurred",
+    )
+    test_crew.test_crew(n_iterations, "gpt-4o")
+
+    mock_subprocess_run.assert_called_once_with(
+        ["poetry", "run", "test", "5", "gpt-4o"],
+        capture_output=False,
+        text=True,
+        check=True,
+    )
+    click.echo.assert_has_calls(
+        [
+            mock.call.echo(
+                "An error occurred while testing the crew: Command '['poetry', 'run', 'test', '5', 'gpt-4o']' returned non-zero exit status 1.",
+                err=True,
+            ),
+            mock.call.echo("Error", err=True),
+        ]
+    )
+
+
+@mock.patch("crewai.cli.test_crew.click")
+@mock.patch("crewai.cli.test_crew.subprocess.run")
+def test_test_crew_unexpected_exception(mock_subprocess_run, click):
+    # Arrange
+    n_iterations = 5
+    mock_subprocess_run.side_effect = Exception("Unexpected error")
+    test_crew.test_crew(n_iterations, "gpt-4o")
+
+    mock_subprocess_run.assert_called_once_with(
+        ["poetry", "run", "test", "5", "gpt-4o"],
+        capture_output=False,
+        text=True,
+        check=True,
+    )
+    click.echo.assert_called_once_with(
+        "An unexpected error occurred: Unexpected error", err=True
+    )

tests/crew_test.py

@@ -1,13 +1,13 @@
 """Test Agent creation and execution basic functionality."""
 
+import hashlib
 import json
 from concurrent.futures import Future
 from unittest import mock
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import pydantic_core
 import pytest
-
 from crewai.agent import Agent
 from crewai.agents.cache import CacheHandler
 from crewai.crew import Crew
@@ -15,8 +15,11 @@ from crewai.crews.crew_output import CrewOutput
 from crewai.memory.contextual.contextual_memory import ContextualMemory
 from crewai.process import Process
 from crewai.task import Task
+from crewai.tasks.conditional_task import ConditionalTask
+from crewai.tasks.output_format import OutputFormat
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities import Logger, RPMController
+from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 
 ceo = Agent(
     role="CEO",
@@ -136,7 +139,6 @@ def test_async_task_cannot_include_sequential_async_tasks_in_context():
 
 
 def test_context_no_future_tasks():
-
     task2 = Task(
         description="Task 2",
         expected_output="output",
@@ -284,7 +286,7 @@ def test_hierarchical_process():
     crew = Crew(
         agents=[researcher, writer],
         process=Process.hierarchical,
-        manager_llm=ChatOpenAI(temperature=0, model="gpt-4"),
+        manager_llm=ChatOpenAI(temperature=0, model="gpt-4o"),
         tasks=[task],
     )
 
@@ -310,6 +312,82 @@ def test_manager_llm_requirement_for_hierarchical_process():
         )
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_manager_agent_delegating_to_assigned_task_agent():
+    """
+    Test that the manager agent delegates to the assigned task agent.
+    """
+    from langchain_openai import ChatOpenAI
+
+    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.",
+        agent=researcher,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        process=Process.hierarchical,
+        manager_llm=ChatOpenAI(temperature=0, model="gpt-4o"),
+        tasks=[task],
+    )
+
+    crew.kickoff()
+
+    # Check if the manager agent has the correct tools
+    assert crew.manager_agent is not None
+    assert crew.manager_agent.tools is not None
+
+    assert len(crew.manager_agent.tools) == 2
+    assert (
+        "Delegate a specific task to one of the following coworkers: Researcher\n"
+        in crew.manager_agent.tools[0].description
+    )
+    assert (
+        "Ask a specific question to one of the following coworkers: Researcher\n"
+        in crew.manager_agent.tools[1].description
+    )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_manager_agent_delegating_to_all_agents():
+    """
+    Test that the manager agent delegates to all agents when none are specified.
+    """
+    from langchain_openai import ChatOpenAI
+
+    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(
+        agents=[researcher, writer],
+        process=Process.hierarchical,
+        manager_llm=ChatOpenAI(temperature=0, model="gpt-4o"),
+        tasks=[task],
+    )
+
+    crew.kickoff()
+
+    assert crew.manager_agent is not None
+    assert crew.manager_agent.tools is not None
+
+    assert len(crew.manager_agent.tools) == 2
+    print(
+        "crew.manager_agent.tools[0].description",
+        crew.manager_agent.tools[0].description,
+    )
+    assert (
+        "Delegate a specific task to one of the following coworkers: Researcher, Senior Writer\n"
+        in crew.manager_agent.tools[0].description
+    )
+    assert (
+        "Ask a specific question to one of the following coworkers: Researcher, Senior Writer\n"
+        in crew.manager_agent.tools[1].description
+    )
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents():
     tasks = [
@@ -584,7 +662,6 @@ def test_sequential_async_task_execution_completion():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_single_task_with_async_execution():
-
     researcher_agent = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -740,7 +817,6 @@ def test_async_task_execution_call_count():
     ) as mock_execute_sync, patch.object(
         Task, "execute_async", return_value=mock_future
     ) as mock_execute_async:
-
         crew.kickoff()
 
         assert mock_execute_async.call_count == 2
@@ -917,9 +993,7 @@ async def test_kickoff_async_basic_functionality_and_output():
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_async_kickoff_for_each_async_basic_functionality_and_output():
-    """Tests the basic functionality and output of akickoff_for_each_async."""
-    from unittest.mock import patch
-
+    """Tests the basic functionality and output of kickoff_for_each_async."""
     inputs = [
         {"topic": "dog"},
         {"topic": "cat"},
@@ -945,8 +1019,13 @@ async def test_async_kickoff_for_each_async_basic_functionality_and_output():
         agent=agent,
     )
 
+    async def mock_kickoff_async(**kwargs):
+        input_data = kwargs.get("inputs")
+        index = [input_["topic"] for input_ in inputs].index(input_data["topic"])
+        return expected_outputs[index]
+
     with patch.object(
-        Crew, "kickoff_async", side_effect=expected_outputs
+        Crew, "kickoff_async", side_effect=mock_kickoff_async
     ) as mock_kickoff_async:
         crew = Crew(agents=[agent], tasks=[task])
 
@@ -1148,7 +1227,6 @@ def test_code_execution_flag_adds_code_tool_upon_kickoff():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_delegation_is_not_enabled_if_there_are_only_one_agent():
-
     researcher = Agent(
         role="Researcher",
         goal="Make the best research and analysis on content about AI and AI agents",
@@ -1271,7 +1349,7 @@ def test_hierarchical_crew_creation_tasks_with_agents():
     assert crew.manager_agent.tools is not None
     print("TOOL DESCRIPTION", crew.manager_agent.tools[0].description)
     assert crew.manager_agent.tools[0].description.startswith(
-        "Delegate a specific task to one of the following coworkers: [Senior Writer, Researcher]"
+        "Delegate a specific task to one of the following coworkers: Senior Writer"
     )
 
 
@@ -1356,6 +1434,7 @@ def test_crew_inputs_interpolate_both_agents_and_tasks_diff():
                 interpolate_task_inputs.assert_called()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_does_not_interpolate_without_inputs():
     from unittest.mock import patch
 
@@ -1380,7 +1459,6 @@ def test_crew_does_not_interpolate_without_inputs():
             interpolate_task_inputs.assert_not_called()
 
 
-# TODO: Ask @joao if we want to start throwing errors if inputs are not provided
 # def test_crew_partial_inputs():
 #     agent = Agent(
 #         role="{topic} Researcher",
@@ -1404,7 +1482,6 @@ def test_crew_does_not_interpolate_without_inputs():
 #     assert crew.agents[0].backstory == "You have a lot of experience with AI."
 
 
-# TODO: If we do want ot throw errors if we are missing inputs. Add in this test.
 # def test_crew_invalid_inputs():
 #     agent = Agent(
 #         role="{topic} Researcher",
@@ -1806,3 +1883,619 @@ def test__setup_for_training():
 
     for agent in agents:
         assert agent.allow_delegation is False
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_feature():
+    list_ideas = Task(
+        description="Generate a list of 5 interesting ideas to explore for an article, where each bulletpoint is under 15 words.",
+        expected_output="Bullet point list of 5 important events. No additional commentary.",
+        agent=researcher,
+    )
+    write = Task(
+        description="Write a sentence about the events",
+        expected_output="A sentence about the events",
+        agent=writer,
+        context=[list_ideas],
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[list_ideas, write],
+        process=Process.sequential,
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_task:
+        mock_execute_task.return_value = TaskOutput(
+            description="Mock description",
+            raw="Mocked output for list of ideas",
+            agent="Researcher",
+            json_dict=None,
+            output_format=OutputFormat.RAW,
+            pydantic=None,
+            summary="Mocked output for list of ideas",
+        )
+
+        crew.kickoff()
+        crew.replay(str(write.id))
+        # Ensure context was passed correctly
+        assert mock_execute_task.call_count == 3
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_replay_error():
+    task = Task(
+        description="Come up with a list of 5 interesting ideas to explore for an article",
+        expected_output="5 bullet points with a paragraph for each idea.",
+        agent=researcher,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task],
+    )
+
+    with pytest.raises(TypeError) as e:
+        crew.replay()  # type: ignore purposefully throwing err
+        assert "task_id is required" in str(e)
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_crew_task_db_init():
+    agent = Agent(
+        role="Content Writer",
+        goal="Write engaging content on various topics.",
+        backstory="You have a background in journalism and creative writing.",
+    )
+
+    task = Task(
+        description="Write a detailed article about AI in healthcare.",
+        expected_output="A 1 paragraph article about AI.",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+
+    with patch.object(Task, "execute_sync") as mock_execute_task:
+        mock_execute_task.return_value = TaskOutput(
+            description="Write about AI in healthcare.",
+            raw="Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic accuracy, personalizing treatment plans, and streamlining administrative tasks.",
+            agent="Content Writer",
+            json_dict=None,
+            output_format=OutputFormat.RAW,
+            pydantic=None,
+            summary="Write about AI in healthcare...",
+        )
+
+        crew.kickoff()
+
+        # Check if this runs without raising an exception
+        try:
+            db_handler = TaskOutputStorageHandler()
+            db_handler.load()
+            assert True  # If we reach this point, no exception was raised
+        except Exception as e:
+            pytest.fail(f"An exception was raised: {str(e)}")
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_task_with_context():
+    agent1 = Agent(
+        role="Researcher",
+        goal="Research AI advancements.",
+        backstory="You are an expert in AI research.",
+    )
+    agent2 = Agent(
+        role="Writer",
+        goal="Write detailed articles on AI.",
+        backstory="You have a background in journalism and AI.",
+    )
+
+    task1 = Task(
+        description="Research the latest advancements in AI.",
+        expected_output="A detailed report on AI advancements.",
+        agent=agent1,
+    )
+    task2 = Task(
+        description="Summarize the AI advancements report.",
+        expected_output="A summary of the AI advancements report.",
+        agent=agent2,
+    )
+    task3 = Task(
+        description="Write an article based on the AI advancements summary.",
+        expected_output="An article on AI advancements.",
+        agent=agent2,
+    )
+    task4 = Task(
+        description="Create a presentation based on the AI advancements article.",
+        expected_output="A presentation on AI advancements.",
+        agent=agent2,
+        context=[task1],
+    )
+
+    crew = Crew(
+        agents=[agent1, agent2],
+        tasks=[task1, task2, task3, task4],
+        process=Process.sequential,
+    )
+
+    mock_task_output1 = TaskOutput(
+        description="Research the latest advancements in AI.",
+        raw="Detailed report on AI advancements...",
+        agent="Researcher",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Detailed report on AI advancements...",
+    )
+    mock_task_output2 = TaskOutput(
+        description="Summarize the AI advancements report.",
+        raw="Summary of the AI advancements report...",
+        agent="Writer",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Summary of the AI advancements report...",
+    )
+    mock_task_output3 = TaskOutput(
+        description="Write an article based on the AI advancements summary.",
+        raw="Article on AI advancements...",
+        agent="Writer",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Article on AI advancements...",
+    )
+    mock_task_output4 = TaskOutput(
+        description="Create a presentation based on the AI advancements article.",
+        raw="Presentation on AI advancements...",
+        agent="Writer",
+        json_dict=None,
+        output_format=OutputFormat.RAW,
+        pydantic=None,
+        summary="Presentation on AI advancements...",
+    )
+
+    with patch.object(Task, "execute_sync") as mock_execute_task:
+        mock_execute_task.side_effect = [
+            mock_task_output1,
+            mock_task_output2,
+            mock_task_output3,
+            mock_task_output4,
+        ]
+
+        crew.kickoff()
+        db_handler = TaskOutputStorageHandler()
+        assert db_handler.load() != []
+
+        with patch.object(Task, "execute_sync") as mock_replay_task:
+            mock_replay_task.return_value = mock_task_output4
+
+            replayed_output = crew.replay(str(task4.id))
+            assert replayed_output.raw == "Presentation on AI advancements..."
+
+        db_handler.reset()
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_with_context():
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(
+        description="Context Task", expected_output="Say Task Output", agent=agent
+    )
+    task2 = Task(
+        description="Test Task", expected_output="Say Hi", agent=agent, context=[task1]
+    )
+
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+
+    with patch(
+        "crewai.utilities.task_output_storage_handler.TaskOutputStorageHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {},
+            },
+        ],
+    ):
+        crew.replay(str(task2.id))
+
+        assert crew.tasks[1].context[0].output.raw == "context raw output"
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_with_invalid_task_id():
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(
+        description="Context Task", expected_output="Say Task Output", agent=agent
+    )
+    task2 = Task(
+        description="Test Task", expected_output="Say Hi", agent=agent, context=[task1]
+    )
+
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+
+    with patch(
+        "crewai.utilities.task_output_storage_handler.TaskOutputStorageHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {},
+            },
+        ],
+    ):
+        with pytest.raises(
+            ValueError,
+            match="Task with id bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d not found in the crew's tasks.",
+        ):
+            crew.replay("bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d")
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@patch.object(Crew, "_interpolate_inputs")
+def test_replay_interpolates_inputs_properly(mock_interpolate_inputs):
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(description="Context Task", expected_output="Say {name}", agent=agent)
+    task2 = Task(
+        description="Test Task",
+        expected_output="Say Hi to {name}",
+        agent=agent,
+        context=[task1],
+    )
+
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+    crew.kickoff(inputs={"name": "John"})
+
+    with patch(
+        "crewai.utilities.task_output_storage_handler.TaskOutputStorageHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {"name": "John"},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {"name": "John"},
+            },
+        ],
+    ):
+        crew.replay(str(task2.id))
+        assert crew._inputs == {"name": "John"}
+        assert mock_interpolate_inputs.call_count == 2
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_setup_context():
+    agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
+    task1 = Task(description="Context Task", expected_output="Say {name}", agent=agent)
+    task2 = Task(
+        description="Test Task",
+        expected_output="Say Hi to {name}",
+        agent=agent,
+    )
+    context_output = TaskOutput(
+        description="Context Task Output",
+        agent="test_agent",
+        raw="context raw output",
+        pydantic=None,
+        json_dict={},
+        output_format=OutputFormat.RAW,
+    )
+    task1.output = context_output
+    crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
+    with patch(
+        "crewai.utilities.task_output_storage_handler.TaskOutputStorageHandler.load",
+        return_value=[
+            {
+                "task_id": str(task1.id),
+                "output": {
+                    "description": context_output.description,
+                    "summary": context_output.summary,
+                    "raw": context_output.raw,
+                    "pydantic": context_output.pydantic,
+                    "json_dict": context_output.json_dict,
+                    "output_format": context_output.output_format,
+                    "agent": context_output.agent,
+                },
+                "inputs": {"name": "John"},
+            },
+            {
+                "task_id": str(task2.id),
+                "output": {
+                    "description": "Test Task Output",
+                    "summary": None,
+                    "raw": "test raw output",
+                    "pydantic": None,
+                    "json_dict": {},
+                    "output_format": "json",
+                    "agent": "test_agent",
+                },
+                "inputs": {"name": "John"},
+            },
+        ],
+    ):
+        crew.replay(str(task2.id))
+
+        # Check if the first task's output was set correctly
+        assert crew.tasks[0].output is not None
+        assert isinstance(crew.tasks[0].output, TaskOutput)
+        assert crew.tasks[0].output.description == "Context Task Output"
+        assert crew.tasks[0].output.agent == "test_agent"
+        assert crew.tasks[0].output.raw == "context raw output"
+        assert crew.tasks[0].output.output_format == OutputFormat.RAW
+
+        assert crew.tasks[1].prompt_context == "context raw output"
+
+
+def test_key():
+    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,
+        ),
+    ]
+    crew = Crew(
+        agents=[researcher, writer],
+        process=Process.sequential,
+        tasks=tasks,
+    )
+    hash = hashlib.md5(
+        f"{researcher.key}|{writer.key}|{tasks[0].key}|{tasks[1].key}".encode()
+    ).hexdigest()
+
+    assert crew.key == hash
+
+
+def test_conditional_task_requirement_breaks_when_singular_conditional_task():
+    def condition_fn(output) -> bool:
+        return output.raw.startswith("Andrew Ng has!!")
+
+    task = ConditionalTask(
+        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.",
+        condition=condition_fn,
+    )
+
+    with pytest.raises(pydantic_core._pydantic_core.ValidationError):
+        Crew(
+            agents=[researcher, writer],
+            tasks=[task],
+        )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_conditional_task_last_task_when_conditional_is_true():
+    def condition_fn(output) -> bool:
+        return True
+
+    task1 = Task(
+        description="Say Hi",
+        expected_output="Hi",
+        agent=researcher,
+    )
+    task2 = ConditionalTask(
+        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.",
+        condition=condition_fn,
+        agent=writer,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+    )
+    result = crew.kickoff()
+    assert result.raw.startswith(
+        "1. **The Rise of AI Agents in Customer Service: Revolutionizing Customer Interactions**"
+    )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_conditional_task_last_task_when_conditional_is_false():
+    def condition_fn(output) -> bool:
+        return False
+
+    task1 = Task(
+        description="Say Hi",
+        expected_output="Hi",
+        agent=researcher,
+    )
+    task2 = ConditionalTask(
+        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.",
+        condition=condition_fn,
+        agent=writer,
+    )
+
+    crew = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+    )
+    result = crew.kickoff()
+    print(result.raw)
+    assert result.raw == "Hi"
+
+
+def test_conditional_task_requirement_breaks_when_task_async():
+    def my_condition(context):
+        return context.get("some_value") > 10
+
+    task = ConditionalTask(
+        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.",
+        execute_async=True,
+        condition=my_condition,
+        agent=researcher,
+    )
+    task2 = Task(
+        description="Say Hi",
+        expected_output="Hi",
+        agent=writer,
+    )
+
+    with pytest.raises(pydantic_core._pydantic_core.ValidationError):
+        Crew(
+            agents=[researcher, writer],
+            tasks=[task, task2],
+        )
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_conditional_should_skip():
+    task1 = Task(description="Return hello", expected_output="say hi", agent=researcher)
+
+    condition_mock = MagicMock(return_value=False)
+    task2 = ConditionalTask(
+        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.",
+        condition=condition_mock,
+        agent=writer,
+    )
+    crew_met = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+    )
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task 1 description",
+            raw="Task 1 output",
+            agent="Researcher",
+        )
+
+        result = crew_met.kickoff()
+        assert mock_execute_sync.call_count == 1
+
+        assert condition_mock.call_count == 1
+        assert condition_mock() is False
+
+        assert task2.output is None
+        assert result.raw.startswith("Task 1 output")
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_conditional_should_execute():
+    task1 = Task(description="Return hello", expected_output="say hi", agent=researcher)
+
+    condition_mock = MagicMock(
+        return_value=True
+    )  # should execute this conditional task
+    task2 = ConditionalTask(
+        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.",
+        condition=condition_mock,
+        agent=writer,
+    )
+    crew_met = Crew(
+        agents=[researcher, writer],
+        tasks=[task1, task2],
+    )
+    with patch.object(Task, "execute_sync") as mock_execute_sync:
+        mock_execute_sync.return_value = TaskOutput(
+            description="Task 1 description",
+            raw="Task 1 output",
+            agent="Researcher",
+        )
+
+        crew_met.kickoff()
+
+        assert condition_mock.call_count == 1
+        assert condition_mock() is True
+        assert mock_execute_sync.call_count == 2

tests/task_test.py

@@ -1,5 +1,6 @@
 """Test Agent creation and execution basic functionality."""
 
+import hashlib
 import json
 from unittest.mock import MagicMock, patch
 
@@ -8,6 +9,7 @@ from pydantic import BaseModel
 from pydantic_core import ValidationError
 
 from crewai import Agent, Crew, Process, Task
+from crewai.tasks.conditional_task import ConditionalTask
 from crewai.tasks.task_output import TaskOutput
 from crewai.utilities.converter import Converter
 
@@ -81,7 +83,7 @@ def test_task_prompt_includes_expected_output():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute_sync()
+        task.execute_sync(agent=researcher)
         execute.assert_called_once_with(task=task, context=None, tools=[])
 
 
@@ -104,7 +106,7 @@ def test_task_callback():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "ok"
-        task.execute_sync()
+        task.execute_sync(agent=researcher)
         task_completed.assert_called_once_with(task.output)
 
 
@@ -129,7 +131,7 @@ def test_task_callback_returns_task_ouput():
 
     with patch.object(Agent, "execute_task") as execute:
         execute.return_value = "exported_ok"
-        task.execute_sync()
+        task.execute_sync(agent=researcher)
         # Ensure the callback is called with a TaskOutput object serialized to JSON
         task_completed.assert_called_once()
         callback_data = task_completed.call_args[0][0]
@@ -317,6 +319,7 @@ def test_output_json_hierarchical():
     assert result.to_dict() == {"score": 4}
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
 def test_json_property_without_output_json():
     class ScoreOutput(BaseModel):
         score: int
@@ -397,8 +400,8 @@ def test_output_json_dict_hierarchical():
         manager_llm=ChatOpenAI(model="gpt-4o"),
     )
     result = crew.kickoff()
-    assert {"score": 4} == result.json_dict
-    assert result.to_dict() == {"score": 4}
+    assert {"score": 5} == result.json_dict
+    assert result.to_dict() == {"score": 5}
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -521,9 +524,7 @@ def test_save_task_json_output():
     with patch.object(Task, "_save_file") as save_file:
         save_file.return_value = None
         crew.kickoff()
-        save_file.assert_called_once_with(
-            {"score": 4}
-        )  # TODO: @Joao, should this be a dict or a json string?
+        save_file.assert_called_once_with({"score": 4})
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -697,6 +698,19 @@ def test_task_definition_based_on_dict():
     assert task.agent is None
 
 
+def test_conditional_task_definition_based_on_dict():
+    config = {
+        "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.",
+    }
+
+    task = ConditionalTask(config=config, condition=lambda x: True)
+
+    assert task.description == config["description"]
+    assert task.expected_output == config["expected_output"]
+    assert task.agent is None
+
+
 def test_interpolate_inputs():
     task = Task(
         description="Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting.",
@@ -793,3 +807,22 @@ def test_task_output_str_with_none():
     )
 
     assert str(task_output) == ""
+
+
+def test_key():
+    original_description = "Give me a list of 5 interesting ideas about {topic} to explore for an article, what makes them unique and interesting."
+    original_expected_output = "Bullet point list of 5 interesting ideas about {topic}."
+    task = Task(
+        description=original_description,
+        expected_output=original_expected_output,
+    )
+    hash = hashlib.md5(
+        f"{original_description}|{original_expected_output}".encode()
+    ).hexdigest()
+
+    assert task.key == hash, "The key should be the hash of the description."
+
+    task.interpolate_inputs(inputs={"topic": "AI"})
+    assert (
+        task.key == hash
+    ), "The key should be the hash of the non-interpolated description."

tests/utilities/test_planning_handler.py

@@ -0,0 +1,74 @@
+from unittest.mock import patch
+from crewai.tasks.task_output import TaskOutput
+
+import pytest
+
+from crewai.agent import Agent
+from crewai.task import Task
+from crewai.utilities.planning_handler import CrewPlanner, PlannerTaskPydanticOutput
+
+
+class TestCrewPlanner:
+    @pytest.fixture
+    def crew_planner(self):
+        tasks = [
+            Task(
+                description="Task 1",
+                expected_output="Output 1",
+                agent=Agent(role="Agent 1", goal="Goal 1", backstory="Backstory 1"),
+            ),
+            Task(
+                description="Task 2",
+                expected_output="Output 2",
+                agent=Agent(role="Agent 2", goal="Goal 2", backstory="Backstory 2"),
+            ),
+            Task(
+                description="Task 3",
+                expected_output="Output 3",
+                agent=Agent(role="Agent 3", goal="Goal 3", backstory="Backstory 3"),
+            ),
+        ]
+        return CrewPlanner(tasks)
+
+    def test_handle_crew_planning(self, crew_planner):
+        with patch.object(Task, "execute_sync") as execute:
+            execute.return_value = TaskOutput(
+                description="Description",
+                agent="agent",
+                pydantic=PlannerTaskPydanticOutput(
+                    list_of_plans_per_task=["Plan 1", "Plan 2", "Plan 3"]
+                ),
+            )
+            result = crew_planner._handle_crew_planning()
+
+            assert isinstance(result, PlannerTaskPydanticOutput)
+            assert len(result.list_of_plans_per_task) == len(crew_planner.tasks)
+            execute.assert_called_once()
+
+    def test_create_planning_agent(self, crew_planner):
+        agent = crew_planner._create_planning_agent()
+        assert isinstance(agent, Agent)
+        assert agent.role == "Task Execution Planner"
+
+    def test_create_planner_task(self, crew_planner):
+        planning_agent = Agent(
+            role="Planning Agent",
+            goal="Plan Step by Step Plan",
+            backstory="Master in Planning",
+        )
+        tasks_summary = "Summary of tasks"
+        task = crew_planner._create_planner_task(planning_agent, tasks_summary)
+
+        assert isinstance(task, Task)
+        assert task.description.startswith("Based on these tasks summary")
+        assert task.agent == planning_agent
+        assert (
+            task.expected_output
+            == "Step by step plan on how the agents can execute their tasks using the available tools with mastery"
+        )
+
+    def test_create_tasks_summary(self, crew_planner):
+        tasks_summary = crew_planner._create_tasks_summary()
+        assert isinstance(tasks_summary, str)
+        assert tasks_summary.startswith("\n                Task Number 1 - Task 1")
+        assert tasks_summary.endswith('"agent_tools": []\n                ')