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129
docs/core-concepts/Crews.md
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@@ -4,36 +4,37 @@ 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. |
!!! 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
@@ -89,6 +90,57 @@ 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 +208,32 @@ 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_from_tasks 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.

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docs/core-concepts/Tasks.md
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@@ -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

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docs/how-to/Replay-tasks-from-latest-Crew-Kickoff.md Normal file
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@@ -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_from_task():
"""
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_from_task(task_id=task_id, inputs=inputs)
except Exception as e:
raise Exception(f"An error occurred while replaying the crew: {e}")

5
docs/index.md
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@@ -113,6 +113,11 @@ 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/AgentOps-Observability">
Agent Monitoring with AgentOps