andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2026-01-10 16:48:30 +00:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

Migrate docs from MkDocs to Mintlify (#1423)

Browse Source 
* add new mintlify docs

* add favicon.svg

* minor edits

* add github stats
This commit is contained in:
Tony Kipkemboi
2024-10-10 18:14:28 -04:00
committed by GitHub
parent 02718e291b
commit a7696d5aed
108 changed files with 3271 additions and 3103 deletions
Download Patch File Download Diff File Expand all files Collapse all files

159
docs/concepts/agents.mdx Normal file
View File

@@ -0,0 +1,159 @@
---
title: Agents
description: What are CrewAI Agents and how to use them.
icon: robot
---
## What is an agent?
An agent is an **autonomous unit** programmed to:
<ul>
<li class='leading-3'>Perform tasks</li>
<li class='leading-3'>Make decisions</li>
<li class='leading-3'>Communicate with other agents</li>
</ul>
<Tip>
Think of an agent as a member of a team, with specific skills and a particular job to do. Agents can have different roles like `Researcher`, `Writer`, or `Customer Support`, each contributing to the overall goal of the crew.
</Tip>
## Agent attributes
| Attribute | Parameter | Description |
| :------------------------- | :--------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Role** | `role` | Defines the agent's function within the crew. It determines the kind of tasks the agent is best suited for. |
| **Goal** | `goal` | The individual objective that the agent aims to achieve. It guides the agent's decision-making process. |
| **Backstory** | `backstory`| Provides context to the agent's role and goal, enriching the interaction and collaboration dynamics. |
| **LLM** *(optional)* | `llm` | Represents the language model that will run the agent. It dynamically fetches the model name from the `OPENAI_MODEL_NAME` environment variable, defaulting to "gpt-4" if not specified. |
| **Tools** *(optional)* | `tools` | Set of capabilities or functions that the agent can use to perform tasks. Expected to be instances of custom classes compatible with the agent's execution environment. Tools are initialized with a default value of an empty list. |
| **Function Calling LLM** *(optional)* | `function_calling_llm` | Specifies the language model that will handle the tool calling for this agent, overriding the crew function calling LLM if passed. Default is `None`. |
| **Max Iter** *(optional)* | `max_iter` | Max Iter is the maximum number of iterations the agent can perform before being forced to give its best answer. Default is `25`. |
| **Max RPM** *(optional)* | `max_rpm` | Max RPM is the maximum number of requests per minute the agent can perform to avoid rate limits. It's optional and can be left unspecified, with a default value of `None`. |
| **Max Execution Time** *(optional)* | `max_execution_time` | Max Execution Time is the maximum execution time for an agent to execute a task. It's optional and can be left unspecified, with a default value of `None`, meaning no max execution time. |
| **Verbose** *(optional)* | `verbose` | Setting this to `True` configures the internal logger to provide detailed execution logs, aiding in debugging and monitoring. Default is `False`. |
| **Allow Delegation** *(optional)* | `allow_delegation` | Agents can delegate tasks or questions to one another, ensuring that each task is handled by the most suitable agent. Default is `False`.
| **Step Callback** *(optional)* | `step_callback` | A function that is called after each step of the agent. This can be used to log the agent's actions or to perform other operations. It will overwrite the crew `step_callback`. |
| gbv vbn zzdsxcdsdfc**Cache** *(optional)* | `cache` | Indicates if the agent should use a cache for tool usage. Default is `True`. |
| **System Template** *(optional)* | `system_template` | Specifies the system format for the agent. Default is `None`. |
| **Prompt Template** *(optional)* | `prompt_template` | Specifies the prompt format for the agent. Default is `None`. |
| **Response Template** *(optional)* | `response_template` | Specifies the response format for the agent. Default is `None`. |
| **Allow Code Execution** *(optional)* | `allow_code_execution` | Enable code execution for the agent. Default is `False`. |
| **Max Retry Limit** *(optional)* | `max_retry_limit` | Maximum number of retries for an agent to execute a task when an error occurs. Default is `2`.
| **Use System Prompt** *(optional)* | `use_system_prompt` | Adds the ability to not use system prompt (to support o1 models). Default is `True`. |
| **Respect Context Window** *(optional)* | `respect_context_window` | Summary strategy to avoid overflowing the context window. Default is `True`. |
## Creating an agent
<Note>
**Agent interaction**: Agents can interact with each other using CrewAI's built-in delegation and communication mechanisms. This allows for dynamic task management and problem-solving within the crew.
</Note>
To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example including all attributes:
```python Code example
from crewai import Agent
agent = Agent(
role='Data Analyst',
goal='Extract actionable insights',
backstory="""You're a data analyst at a large company.
You're responsible for analyzing data and providing insights
to the business.
You're currently working on a project to analyze the
performance of our marketing campaigns.""",
tools=[my_tool1, my_tool2], # Optional, defaults to an empty list
llm=my_llm, # Optional
function_calling_llm=my_llm, # Optional
max_iter=15, # Optional
max_rpm=None, # Optional
max_execution_time=None, # Optional
verbose=True, # Optional
allow_delegation=False, # Optional
step_callback=my_intermediate_step_callback, # Optional
cache=True, # Optional
system_template=my_system_template, # Optional
prompt_template=my_prompt_template, # Optional
response_template=my_response_template, # Optional
config=my_config, # Optional
crew=my_crew, # Optional
tools_handler=my_tools_handler, # Optional
cache_handler=my_cache_handler, # Optional
callbacks=[callback1, callback2], # Optional
allow_code_execution=True, # Optional
max_retry_limit=2, # Optional
use_system_prompt=True, # Optional
respect_context_window=True, # Optional
)
```
## Setting prompt templates
Prompt templates are used to format the prompt for the agent. You can use to update the system, regular and response templates for the agent. Here's an example of how to set prompt templates:
```python Code example
agent = Agent(
role="{topic} specialist",
goal="Figure {goal} out",
backstory="I am the master of {role}",
system_template="""<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>""",
prompt_template="""<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>""",
response_template="""<|start_header_id|>assistant<|end_header_id|>
{{ .Response }}<|eot_id|>""",
)
```
## Bring your third-party agents
Extend your third-party agents like LlamaIndex, Langchain, Autogen or fully custom agents using the the CrewAI's `BaseAgent` class.
<Note>
**BaseAgent** includes attributes and methods required to integrate with your crews to run and delegate tasks to other agents within your own crew.
</Note>
CrewAI is a universal multi-agent framework that allows for all agents to work together to automate tasks and solve problems.
```python Code example
from crewai import Agent, Task, Crew
from custom_agent import CustomAgent # You need to build and extend your own agent logic with the CrewAI BaseAgent class then import it here.
from langchain.agents import load_tools
langchain_tools = load_tools(["google-serper"], llm=llm)
agent1 = CustomAgent(
role="agent role",
goal="who is {input}?",
backstory="agent backstory",
verbose=True,
)
task1 = Task(
expected_output="a short biography of {input}",
description="a short biography of {input}",
agent=agent1,
)
agent2 = Agent(
role="agent role",
goal="summarize the short bio for {input} and if needed do more research",
backstory="agent backstory",
verbose=True,
)
task2 = Task(
description="a tldr summary of the short biography",
expected_output="5 bullet point summary of the biography",
agent=agent2,
context=[task1],
)
my_crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
crew = my_crew.kickoff(inputs={"input": "Mark Twain"})
```
## Conclusion
Agents are the building blocks of the CrewAI framework. By understanding how to define and interact with agents,
you can create sophisticated AI systems that leverage the power of collaborative intelligence.

148
docs/concepts/cli.mdx Normal file
View File

@@ -0,0 +1,148 @@
---
title: CLI
description: Learn how to use the CrewAI CLI to interact with CrewAI.
icon: terminal
---
# CrewAI CLI Documentation
The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews and pipelines.
## Installation
To use the CrewAI CLI, make sure you have CrewAI & Poetry installed:
```shell
pip install crewai poetry
```
## Basic Usage
The basic structure of a CrewAI CLI command is:
```shell
crewai [COMMAND] [OPTIONS] [ARGUMENTS]
```
## Available Commands
### 1. Create
Create a new crew or pipeline.
```shell
crewai create [OPTIONS] TYPE NAME
```
- `TYPE`: Choose between "crew" or "pipeline"
- `NAME`: Name of the crew or pipeline
- `--router`: (Optional) Create a pipeline with router functionality
Example:
```shell
crewai create crew my_new_crew
crewai create pipeline my_new_pipeline --router
```
### 2. Version
Show the installed version of CrewAI.
```shell
crewai version [OPTIONS]
```
- `--tools`: (Optional) Show the installed version of CrewAI tools
Example:
```shell
crewai version
crewai version --tools
```
### 3. Train
Train the crew for a specified number of iterations.
```shell
crewai train [OPTIONS]
```
- `-n, --n_iterations INTEGER`: Number of iterations to train the crew (default: 5)
- `-f, --filename TEXT`: Path to a custom file for training (default: "trained_agents_data.pkl")
Example:
```shell
crewai train -n 10 -f my_training_data.pkl
```
### 4. Replay
Replay the crew execution from a specific task.
```shell
crewai replay [OPTIONS]
```
- `-t, --task_id TEXT`: Replay the crew from this task ID, including all subsequent tasks
Example:
```shell
crewai replay -t task_123456
```
### 5. Log-tasks-outputs
Retrieve your latest crew.kickoff() task outputs.
```shell
crewai log-tasks-outputs
```
### 6. Reset-memories
Reset the crew memories (long, short, entity, latest_crew_kickoff_outputs).
```shell
crewai reset-memories [OPTIONS]
```
- `-l, --long`: Reset LONG TERM memory
- `-s, --short`: Reset SHORT TERM memory
- `-e, --entities`: Reset ENTITIES memory
- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
- `-a, --all`: Reset ALL memories
Example:
```shell
crewai reset-memories --long --short
crewai reset-memories --all
```
### 7. Test
Test the crew and evaluate the results.
```shell
crewai test [OPTIONS]
```
- `-n, --n_iterations INTEGER`: Number of iterations to test the crew (default: 3)
- `-m, --model TEXT`: LLM Model to run the tests on the Crew (default: "gpt-4o-mini")
Example:
```shell
crewai test -n 5 -m gpt-3.5-turbo
```
### 8. Run
Run the crew.
```shell
crewai run
```
<Note>
Make sure to run these commands from the directory where your CrewAI project is set up.
Some commands may require additional configuration or setup within your project structure.
</Note>

52
docs/concepts/collaboration.mdx Normal file
View File

@@ -0,0 +1,52 @@
---
title: Collaboration
description: Exploring the dynamics of agent collaboration within the CrewAI framework, focusing on the newly integrated features for enhanced functionality.
icon: screen-users
---
## Collaboration Fundamentals
Collaboration in CrewAI is fundamental, enabling agents to combine their skills, share information, and assist each other in task execution, embodying a truly cooperative ecosystem.
- **Information Sharing**: Ensures all agents are well-informed and can contribute effectively by sharing data and findings.
- **Task Assistance**: Allows agents to seek help from peers with the required expertise for specific tasks.
- **Resource Allocation**: Optimizes task execution through the efficient distribution and sharing of resources among agents.
## Enhanced Attributes for Improved Collaboration
The `Crew` class has been enriched with several attributes to support advanced functionalities:
| Feature | Description |
|:-------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **Language Model Management** (`manager_llm`, `function_calling_llm`) | Manages language models for executing tasks and tools. `manager_llm` is required for hierarchical processes, while `function_calling_llm` is optional with a default value for streamlined interactions. |
| **Custom Manager Agent** (`manager_agent`) | Specifies a custom agent as the manager, replacing the default CrewAI manager. |
| **Process Flow** (`process`) | Defines execution logic (e.g., sequential, hierarchical) for task distribution. |
| **Verbose Logging** (`verbose`) | Provides detailed logging for monitoring and debugging. Accepts integer and boolean values to control verbosity level. |
| **Rate Limiting** (`max_rpm`) | Limits requests per minute to optimize resource usage. Setting guidelines depend on task complexity and load. |
| **Internationalization / Customization** (`language`, `prompt_file`) | Supports prompt customization for global usability. [Example of file](https://github.com/joaomdmoura/crewAI/blob/main/src/crewai/translations/en.json) |
| **Execution and Output Handling** (`full_output`) | Controls output granularity, distinguishing between full and final outputs. |
| **Callback and Telemetry** (`step_callback`, `task_callback`) | Enables step-wise and task-level execution monitoring and telemetry for performance analytics. |
| **Crew Sharing** (`share_crew`) | Allows sharing crew data with CrewAI for model improvement. Privacy implications and benefits should be considered. |
| **Usage Metrics** (`usage_metrics`) | Logs all LLM usage metrics during task execution for performance insights. |
| **Memory Usage** (`memory`) | Enables memory for storing execution history, aiding in agent learning and task efficiency. |
| **Embedder Configuration** (`embedder`) | Configures the embedder for language understanding and generation, with support for provider customization. |
| **Cache Management** (`cache`) | Specifies whether to cache tool execution results, enhancing performance. |
| **Output Logging** (`output_log_file`) | Defines the file path for logging crew execution output. |
| **Planning Mode** (`planning`) | Enables action planning before task execution. Set `planning=True` to activate. |
| **Replay Feature** (`replay`) | Provides CLI for listing tasks from the last run and replaying from specific tasks, aiding in task management and troubleshooting. |
## Delegation (Dividing to Conquer)
Delegation enhances functionality by allowing agents to intelligently assign tasks or seek help, thereby amplifying the crew's overall capability.
## Implementing Collaboration and Delegation
Setting up a crew involves defining the roles and capabilities of each agent. CrewAI seamlessly manages their interactions, ensuring efficient collaboration and delegation, with enhanced customization and monitoring features to adapt to various operational needs.
## Example Scenario
Consider a crew with a researcher agent tasked with data gathering and a writer agent responsible for compiling reports. The integration of advanced language model management and process flow attributes allows for more sophisticated interactions, such as the writer delegating complex research tasks to the researcher or querying specific information, thereby facilitating a seamless workflow.
## Conclusion
The integration of advanced attributes and functionalities into the CrewAI framework significantly enriches the agent collaboration ecosystem. These enhancements not only simplify interactions but also offer unprecedented flexibility and control, paving the way for sophisticated AI-driven solutions capable of tackling complex tasks through intelligent collaboration and delegation.

190
docs/concepts/crews.mdx Normal file
View File

@@ -0,0 +1,190 @@
---
title: Crews
description: Understanding and utilizing crews in the crewAI framework with comprehensive attributes and functionalities.
icon: people-group
---
## 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. Default is `sequential`. |
| **Verbose** _(optional)_ | `verbose` | The verbosity level for logging during execution. Defaults to `False`. |
| **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. Defaults to `None`. |
| **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). Defaults to `False`. |
| **Cache** _(optional)_ | `cache` | Specifies whether to use a cache for storing the results of tools' execution. Defaults to `True`. |
| **Embedder** _(optional)_ | `embedder` | Configuration for the embedder to be used by the crew. Mostly used by memory for now. Default is `{"provider": "openai"}`. |
| **Full Output** _(optional)_ | `full_output` | Whether the crew should return the full output with all tasks outputs or just the final output. Defaults to `False`. |
| **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. |
| **Planning LLM** *(optional)* | `planning_llm` | The language model used by the AgentPlanner in a planning process. |
<Tip>
**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.
</Tip>
## Crew Output
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 Code
# Example crew execution
crew = Crew(
agents=[research_agent, writer_agent],
tasks=[research_task, write_article_task],
verbose=True
)
crew_output = 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.
## Cache Utilization
Caches can be employed to store the results of tools' execution, making the process more efficient by reducing the need to re-execute identical tasks.
## Crew Usage Metrics
After the crew execution, you can access the `usage_metrics` attribute to view the language model (LLM) usage metrics for all tasks executed by the crew. This provides insights into operational efficiency and areas for improvement.
```python Code
# Access the crew's usage metrics
crew = Crew(agents=[agent1, agent2], tasks=[task1, task2])
crew.kickoff()
print(crew.usage_metrics)
```
## Crew Execution Process
- **Sequential Process**: Tasks are executed one after another, allowing for a linear flow of work.
- **Hierarchical Process**: A manager agent coordinates the crew, delegating tasks and validating outcomes before proceeding. **Note**: A `manager_llm` or `manager_agent` is required for this process and it's essential for validating the process flow.
### Kicking Off a Crew
Once your crew is assembled, initiate the workflow with the `kickoff()` method. This starts the execution process according to the defined process flow.
```python Code
# Start the crew's task execution
result = my_crew.kickoff()
print(result)
```
### Different Ways to Kick Off a Crew
Once your crew is assembled, initiate the workflow with the appropriate kickoff method. CrewAI provides several methods for better control over the kickoff process: `kickoff()`, `kickoff_for_each()`, `kickoff_async()`, and `kickoff_for_each_async()`.
- `kickoff()`: Starts the execution process according to the defined process flow.
- `kickoff_for_each()`: Executes tasks for each agent individually.
- `kickoff_async()`: Initiates the workflow asynchronously.
- `kickoff_for_each_async()`: Executes tasks for each agent individually in an asynchronous manner.
```python Code
# Start the crew's task execution
result = my_crew.kickoff()
print(result)
# Example of using kickoff_for_each
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
results = my_crew.kickoff_for_each(inputs=inputs_array)
for result in results:
print(result)
# Example of using kickoff_async
inputs = {'topic': 'AI in healthcare'}
async_result = my_crew.kickoff_async(inputs=inputs)
print(async_result)
# Example of using kickoff_for_each_async
inputs_array = [{'topic': 'AI in healthcare'}, {'topic': 'AI in finance'}]
async_results = my_crew.kickoff_for_each_async(inputs=inputs_array)
for async_result in async_results:
print(async_result)
```
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 a 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 a 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 the latest kickoff task IDs, use:
```shell
crewai log-tasks-outputs
```
Then, to replay from a specific task, use:
```shell
crewai replay -t <task_id>
```
These commands let you replay from your latest kickoff tasks, still retaining context from previously executed tasks.

656
docs/concepts/flows.mdx Normal file
View File

@@ -0,0 +1,656 @@
---
title: Flows
description: Learn how to create and manage AI workflows using CrewAI Flows.
icon: arrow-progress
---
## Introduction
CrewAI Flows is a powerful feature designed to streamline the creation and management of AI workflows. Flows allow developers to combine and coordinate coding tasks and Crews efficiently, providing a robust framework for building sophisticated AI automations.
Flows allow you to create structured, event-driven workflows. They provide a seamless way to connect multiple tasks, manage state, and control the flow of execution in your AI applications. With Flows, you can easily design and implement multi-step processes that leverage the full potential of CrewAI's capabilities.
1. **Simplified Workflow Creation**: Easily chain together multiple Crews and tasks to create complex AI workflows.
2. **State Management**: Flows make it super easy to manage and share state between different tasks in your workflow.
3. **Event-Driven Architecture**: Built on an event-driven model, allowing for dynamic and responsive workflows.
4. **Flexible Control Flow**: Implement conditional logic, loops, and branching within your workflows.
## Getting Started
Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
```python Code
import asyncio
from crewai.flow.flow import Flow, listen, start
from litellm import completion
class ExampleFlow(Flow):
model = "gpt-4o-mini"
@start()
def generate_city(self):
print("Starting flow")
response = completion(
model=self.model,
messages=[
{
"role": "user",
"content": "Return the name of a random city in the world.",
},
],
)
random_city = response["choices"][0]["message"]["content"]
print(f"Random City: {random_city}")
return random_city
@listen(generate_city)
def generate_fun_fact(self, random_city):
response = completion(
model=self.model,
messages=[
{
"role": "user",
"content": f"Tell me a fun fact about {random_city}",
},
],
)
fun_fact = response["choices"][0]["message"]["content"]
return fun_fact
async def main():
flow = ExampleFlow()
result = await flow.kickoff()
print(f"Generated fun fact: {result}")
asyncio.run(main())
```
In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
When you run the Flow, it will generate a random city and then generate a fun fact about that city. The output will be printed to the console.
### @start()
The `@start()` decorator is used to mark a method as the starting point of a Flow. When a Flow is started, all the methods decorated with `@start()` are executed in parallel. You can have multiple start methods in a Flow, and they will all be executed when the Flow is started.
### @listen()
The `@listen()` decorator is used to mark a method as a listener for the output of another task in the Flow. The method decorated with `@listen()` will be executed when the specified task emits an output. The method can access the output of the task it is listening to as an argument.
#### Usage
The `@listen()` decorator can be used in several ways:
1. **Listening to a Method by Name**: You can pass the name of the method you want to listen to as a string. When that method completes, the listener method will be triggered.
```python Code
@listen("generate_city")
def generate_fun_fact(self, random_city):
# Implementation
```
2. **Listening to a Method Directly**: You can pass the method itself. When that method completes, the listener method will be triggered.
```python Code
@listen(generate_city)
def generate_fun_fact(self, random_city):
# Implementation
```
### Flow Output
Accessing and handling the output of a Flow is essential for integrating your AI workflows into larger applications or systems. CrewAI Flows provide straightforward mechanisms to retrieve the final output, access intermediate results, and manage the overall state of your Flow.
#### Retrieving the Final Output
When you run a Flow, the final output is determined by the last method that completes. The `kickoff()` method returns the output of this final method.
Here's how you can access the final output:
<CodeGroup>
```python Code
import asyncio
from crewai.flow.flow import Flow, listen, start
class OutputExampleFlow(Flow):
@start()
def first_method(self):
return "Output from first_method"
@listen(first_method)
def second_method(self, first_output):
return f"Second method received: {first_output}"
async def main():
flow = OutputExampleFlow()
final_output = await flow.kickoff()
print("---- Final Output ----")
print(final_output)
asyncio.run(main())
```
``` text Output
---- Final Output ----
Second method received: Output from first_method
```
</CodeGroup>
In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow.
The `kickoff()` method will return the final output, which is then printed to the console.
#### Accessing and Updating State
In addition to retrieving the final output, you can also access and update the state within your Flow. The state can be used to store and share data between different methods in the Flow. After the Flow has run, you can access the state to retrieve any information that was added or updated during the execution.
Here's an example of how to update and access the state:
<CodeGroup>
```python Code
import asyncio
from crewai.flow.flow import Flow, listen, start
from pydantic import BaseModel
class ExampleState(BaseModel):
counter: int = 0
message: str = ""
class StateExampleFlow(Flow[ExampleState]):
@start()
def first_method(self):
self.state.message = "Hello from first_method"
self.state.counter += 1
@listen(first_method)
def second_method(self):
self.state.message += " - updated by second_method"
self.state.counter += 1
return self.state.message
async def main():
flow = StateExampleFlow()
final_output = await flow.kickoff()
print(f"Final Output: {final_output}")
print("Final State:")
print(flow.state)
asyncio.run(main())
```
``` text Output
Final Output: Hello from first_method - updated by second_method
Final State:
counter=2 message='Hello from first_method - updated by second_method'
```
</CodeGroup>
In this example, the state is updated by both `first_method` and `second_method`.
After the Flow has run, you can access the final state to see the updates made by these methods.
By ensuring that the final method's output is returned and providing access to the state, CrewAI Flows make it easy to integrate the results of your AI workflows into larger applications or systems,
while also maintaining and accessing the state throughout the Flow's execution.
## Flow State Management
Managing state effectively is crucial for building reliable and maintainable AI workflows. CrewAI Flows provides robust mechanisms for both unstructured and structured state management,
allowing developers to choose the approach that best fits their application's needs.
### Unstructured State Management
In unstructured state management, all state is stored in the `state` attribute of the `Flow` class.
This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
```python Code
import asyncio
from crewai.flow.flow import Flow, listen, start
class UntructuredExampleFlow(Flow):
@start()
def first_method(self):
self.state.message = "Hello from structured flow"
self.state.counter = 0
@listen(first_method)
def second_method(self):
self.state.counter += 1
self.state.message += " - updated"
@listen(second_method)
def third_method(self):
self.state.counter += 1
self.state.message += " - updated again"
print(f"State after third_method: {self.state}")
async def main():
flow = UntructuredExampleFlow()
await flow.kickoff()
asyncio.run(main())
```
**Key Points:**
- **Flexibility:** You can dynamically add attributes to `self.state` without predefined constraints.
- **Simplicity:** Ideal for straightforward workflows where state structure is minimal or varies significantly.
### Structured State Management
Structured state management leverages predefined schemas to ensure consistency and type safety across the workflow.
By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
```python Code
import asyncio
from crewai.flow.flow import Flow, listen, start
from pydantic import BaseModel
class ExampleState(BaseModel):
counter: int = 0
message: str = ""
class StructuredExampleFlow(Flow[ExampleState]):
@start()
def first_method(self):
self.state.message = "Hello from structured flow"
@listen(first_method)
def second_method(self):
self.state.counter += 1
self.state.message += " - updated"
@listen(second_method)
def third_method(self):
self.state.counter += 1
self.state.message += " - updated again"
print(f"State after third_method: {self.state}")
async def main():
flow = StructuredExampleFlow()
await flow.kickoff()
asyncio.run(main())
```
**Key Points:**
- **Defined Schema:** `ExampleState` clearly outlines the state structure, enhancing code readability and maintainability.
- **Type Safety:** Leveraging Pydantic ensures that state attributes adhere to the specified types, reducing runtime errors.
- **Auto-Completion:** IDEs can provide better auto-completion and error checking based on the defined state model.
### Choosing Between Unstructured and Structured State Management
- **Use Unstructured State Management when:**
- The workflow's state is simple or highly dynamic.
- Flexibility is prioritized over strict state definitions.
- Rapid prototyping is required without the overhead of defining schemas.
- **Use Structured State Management when:**
- The workflow requires a well-defined and consistent state structure.
- Type safety and validation are important for your application's reliability.
- You want to leverage IDE features like auto-completion and type checking for better developer experience.
By providing both unstructured and structured state management options, CrewAI Flows empowers developers to build AI workflows that are both flexible and robust, catering to a wide range of application requirements.
## Flow Control
### Conditional Logic: `or`
The `or_` function in Flows allows you to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
<CodeGroup>
```python Code
import asyncio
from crewai.flow.flow import Flow, listen, or_, start
class OrExampleFlow(Flow):
@start()
def start_method(self):
return "Hello from the start method"
@listen(start_method)
def second_method(self):
return "Hello from the second method"
@listen(or_(start_method, second_method))
def logger(self, result):
print(f"Logger: {result}")
async def main():
flow = OrExampleFlow()
await flow.kickoff()
asyncio.run(main())
```
``` text Output
Logger: Hello from the start method
Logger: Hello from the second method
```
</CodeGroup>
When you run this Flow, the `logger` method will be triggered by the output of either the `start_method` or the `second_method`.
The `or_` function is used to listen to multiple methods and trigger the listener method when any of the specified methods emit an output.
### Conditional Logic: `and`
The `and_` function in Flows allows you to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
<CodeGroup>
```python Code
import asyncio
from crewai.flow.flow import Flow, and_, listen, start
class AndExampleFlow(Flow):
@start()
def start_method(self):
self.state["greeting"] = "Hello from the start method"
@listen(start_method)
def second_method(self):
self.state["joke"] = "What do computers eat? Microchips."
@listen(and_(start_method, second_method))
def logger(self):
print("---- Logger ----")
print(self.state)
async def main():
flow = AndExampleFlow()
await flow.kickoff()
asyncio.run(main())
```
``` text Output
---- Logger ----
{'greeting': 'Hello from the start method', 'joke': 'What do computers eat? Microchips.'}
```
</CodeGroup>
When you run this Flow, the `logger` method will be triggered only when both the `start_method` and the `second_method` emit an output.
The `and_` function is used to listen to multiple methods and trigger the listener method only when all the specified methods emit an output.
### Router
The `@router()` decorator in Flows allows you to define conditional routing logic based on the output of a method.
You can specify different routes based on the output of the method, allowing you to control the flow of execution dynamically.
<CodeGroup>
```python Code
import asyncio
import random
from crewai.flow.flow import Flow, listen, router, start
from pydantic import BaseModel
class ExampleState(BaseModel):
success_flag: bool = False
class RouterFlow(Flow[ExampleState]):
@start()
def start_method(self):
print("Starting the structured flow")
random_boolean = random.choice([True, False])
self.state.success_flag = random_boolean
@router(start_method)
def second_method(self):
if self.state.success_flag:
return "success"
else:
return "failed"
@listen("success")
def third_method(self):
print("Third method running")
@listen("failed")
def fourth_method(self):
print("Fourth method running")
async def main():
flow = RouterFlow()
await flow.kickoff()
asyncio.run(main())
```
``` text Output
Starting the structured flow
Third method running
Fourth method running
```
</CodeGroup>
In the above example, the `start_method` generates a random boolean value and sets it in the state.
The `second_method` uses the `@router()` decorator to define conditional routing logic based on the value of the boolean.
If the boolean is `True`, the method returns `"success"`, and if it is `False`, the method returns `"failed"`.
The `third_method` and `fourth_method` listen to the output of the `second_method` and execute based on the returned value.
When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
## Adding Crews to Flows
Creating a flow with multiple crews in CrewAI is straightforward.
You can generate a new CrewAI project that includes all the scaffolding needed to create a flow with multiple crews by running the following command:
```bash
crewai create flow name_of_flow
```
This command will generate a new CrewAI project with the necessary folder structure. The generated project includes a prebuilt crew called `poem_crew` that is already working. You can use this crew as a template by copying, pasting, and editing it to create other crews.
### Folder Structure
After running the `crewai create flow name_of_flow` command, you will see a folder structure similar to the following:
| Directory/File | Description |
|:---------------------------------|:------------------------------------------------------------------|
| `name_of_flow/` | Root directory for the flow. |
| ├── `crews/` | Contains directories for specific crews. |
| │ └── `poem_crew/` | Directory for the "poem_crew" with its configurations and scripts.|
| │ ├── `config/` | Configuration files directory for the "poem_crew". |
| │ │ ├── `agents.yaml` | YAML file defining the agents for "poem_crew". |
| │ │ └── `tasks.yaml` | YAML file defining the tasks for "poem_crew". |
| │ ├── `poem_crew.py` | Script for "poem_crew" functionality. |
| ├── `tools/` | Directory for additional tools used in the flow. |
| │ └── `custom_tool.py` | Custom tool implementation. |
| ├── `main.py` | Main script for running the flow. |
| ├── `README.md` | Project description and instructions. |
| ├── `pyproject.toml` | Configuration file for project dependencies and settings. |
| └── `.gitignore` | Specifies files and directories to ignore in version control. |
### Building Your Crews
In the `crews` folder, you can define multiple crews. Each crew will have its own folder containing configuration files and the crew definition file. For example, the `poem_crew` folder contains:
- `config/agents.yaml`: Defines the agents for the crew.
- `config/tasks.yaml`: Defines the tasks for the crew.
- `poem_crew.py`: Contains the crew definition, including agents, tasks, and the crew itself.
You can copy, paste, and edit the `poem_crew` to create other crews.
### Connecting Crews in `main.py`
The `main.py` file is where you create your flow and connect the crews together. You can define your flow by using the `Flow` class and the decorators `@start` and `@listen` to specify the flow of execution.
Here's an example of how you can connect the `poem_crew` in the `main.py` file:
```python Code
#!/usr/bin/env python
import asyncio
from random import randint
from pydantic import BaseModel
from crewai.flow.flow import Flow, listen, start
from .crews.poem_crew.poem_crew import PoemCrew
class PoemState(BaseModel):
sentence_count: int = 1
poem: str = ""
class PoemFlow(Flow[PoemState]):
@start()
def generate_sentence_count(self):
print("Generating sentence count")
# Generate a number between 1 and 5
self.state.sentence_count = randint(1, 5)
@listen(generate_sentence_count)
def generate_poem(self):
print("Generating poem")
poem_crew = PoemCrew().crew()
result = poem_crew.kickoff(inputs={"sentence_count": self.state.sentence_count})
print("Poem generated", result.raw)
self.state.poem = result.raw
@listen(generate_poem)
def save_poem(self):
print("Saving poem")
with open("poem.txt", "w") as f:
f.write(self.state.poem)
async def run():
"""
Run the flow.
"""
poem_flow = PoemFlow()
await poem_flow.kickoff()
def main():
asyncio.run(run())
if __name__ == "__main__":
main()
```
In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method.
### Running the Flow
Before running the flow, make sure to install the dependencies by running:
```bash
poetry install
```
Once all of the dependencies are installed, you need to activate the virtual environment by running:
```bash
poetry shell
```
After activating the virtual environment, you can run the flow by executing one of the following commands:
```bash
crewai flow run
```
or
```bash
poetry run run_flow
```
The flow will execute, and you should see the output in the console.
## Plot Flows
Visualizing your AI workflows can provide valuable insights into the structure and execution paths of your flows. CrewAI offers a powerful visualization tool that allows you to generate interactive plots of your flows, making it easier to understand and optimize your AI workflows.
### What are Plots?
Plots in CrewAI are graphical representations of your AI workflows. They display the various tasks, their connections, and the flow of data between them. This visualization helps in understanding the sequence of operations, identifying bottlenecks, and ensuring that the workflow logic aligns with your expectations.
### How to Generate a Plot
CrewAI provides two convenient methods to generate plots of your flows:
#### Option 1: Using the `plot()` Method
If you are working directly with a flow instance, you can generate a plot by calling the `plot()` method on your flow object. This method will create an HTML file containing the interactive plot of your flow.
```python Code
# Assuming you have a flow instance
flow.plot("my_flow_plot")
```
This will generate a file named `my_flow_plot.html` in your current directory. You can open this file in a web browser to view the interactive plot.
#### Option 2: Using the Command Line
If you are working within a structured CrewAI project, you can generate a plot using the command line. This is particularly useful for larger projects where you want to visualize the entire flow setup.
```bash
crewai flow plot
```
This command will generate an HTML file with the plot of your flow, similar to the `plot()` method. The file will be saved in your project directory, and you can open it in a web browser to explore the flow.
### Understanding the Plot
The generated plot will display nodes representing the tasks in your flow, with directed edges indicating the flow of execution. The plot is interactive, allowing you to zoom in and out, and hover over nodes to see additional details.
By visualizing your flows, you can gain a clearer understanding of the workflow's structure, making it easier to debug, optimize, and communicate your AI processes to others.
### Conclusion
Plotting your flows is a powerful feature of CrewAI that enhances your ability to design and manage complex AI workflows. Whether you choose to use the `plot()` method or the command line, generating plots will provide you with a visual representation of your workflows, aiding in both development and presentation.
## Next Steps
If you're interested in exploring additional examples of flows, we have a variety of recommendations in our examples repository. Here are four specific flow examples, each showcasing unique use cases to help you match your current problem type to a specific example:
1. **Email Auto Responder Flow**: This example demonstrates an infinite loop where a background job continually runs to automate email responses. It's a great use case for tasks that need to be performed repeatedly without manual intervention. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow)
2. **Lead Score Flow**: This flow showcases adding human-in-the-loop feedback and handling different conditional branches using the router. It's an excellent example of how to incorporate dynamic decision-making and human oversight into your workflows. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow)
3. **Write a Book Flow**: This example excels at chaining multiple crews together, where the output of one crew is used by another. Specifically, one crew outlines an entire book, and another crew generates chapters based on the outline. Eventually, everything is connected to produce a complete book. This flow is perfect for complex, multi-step processes that require coordination between different tasks. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows)
4. **Meeting Assistant Flow**: This flow demonstrates how to broadcast one event to trigger multiple follow-up actions. For instance, after a meeting is completed, the flow can update a Trello board, send a Slack message, and save the results. It's a great example of handling multiple outcomes from a single event, making it ideal for comprehensive task management and notification systems. [View Example](https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow)
By exploring these examples, you can gain insights into how to leverage CrewAI Flows for various use cases, from automating repetitive tasks to managing complex, multi-step processes with dynamic decision-making and human feedback.

45
docs/concepts/langchain-tools.mdx Normal file
View File

@@ -0,0 +1,45 @@
---
title: Using LangChain Tools
description: Learn how to integrate LangChain tools with CrewAI agents to enhance search-based queries and more.
icon: link
---
## Using LangChain Tools
<Info>
CrewAI seamlessly integrates with LangChain’s comprehensive [list of tools](https://python.langchain.com/docs/integrations/tools/), all of which can be used with CrewAI.
</Info>
```python Code
import os
from crewai import Agent
from langchain.agents import Tool
from langchain.utilities import GoogleSerperAPIWrapper
# Setup API keys
os.environ["SERPER_API_KEY"] = "Your Key"
search = GoogleSerperAPIWrapper()
# Create and assign the search tool to an agent
serper_tool = Tool(
name="Intermediate Answer",
func=search.run,
description="Useful for search-based queries",
)
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[serper_tool]
)
# rest of the code ...
```
## Conclusion
Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling, caching mechanisms,
and the flexibility of tool arguments to optimize your agents' performance and capabilities.

71
docs/concepts/llamaindex-tools.mdx Normal file
View File

@@ -0,0 +1,71 @@
---
title: Using LlamaIndex Tools
description: Learn how to integrate LlamaIndex tools with CrewAI agents to enhance search-based queries and more.
icon: toolbox
---
## Using LlamaIndex Tools
<Info>
CrewAI seamlessly integrates with LlamaIndex’s comprehensive toolkit for RAG (Retrieval-Augmented Generation) and agentic pipelines, enabling advanced search-based queries and more.
</Info>
Here are the available built-in tools offered by LlamaIndex.
```python Code
from crewai import Agent
from crewai_tools import LlamaIndexTool
# Example 1: Initialize from FunctionTool
from llama_index.core.tools import FunctionTool
your_python_function = lambda ...: ...
og_tool = FunctionTool.from_defaults(
your_python_function,
name="<name>",
description='<description>'
)
tool = LlamaIndexTool.from_tool(og_tool)
# Example 2: Initialize from LlamaHub Tools
from llama_index.tools.wolfram_alpha import WolframAlphaToolSpec
wolfram_spec = WolframAlphaToolSpec(app_id="<app_id>")
wolfram_tools = wolfram_spec.to_tool_list()
tools = [LlamaIndexTool.from_tool(t) for t in wolfram_tools]
# Example 3: Initialize Tool from a LlamaIndex Query Engine
query_engine = index.as_query_engine()
query_tool = LlamaIndexTool.from_query_engine(
query_engine,
name="Uber 2019 10K Query Tool",
description="Use this tool to lookup the 2019 Uber 10K Annual Report"
)
# Create and assign the tools to an agent
agent = Agent(
role='Research Analyst',
goal='Provide up-to-date market analysis',
backstory='An expert analyst with a keen eye for market trends.',
tools=[tool, *tools, query_tool]
)
# rest of the code ...
```
## Steps to Get Started
To effectively use the LlamaIndexTool, follow these steps:
<Steps>
<Step title="Package Installation">
Make sure that `crewai[tools]` package is installed in your Python environment:
<CodeGroup>
```shell Terminal
pip install 'crewai[tools]'
```
</CodeGroup>
</Step>
<Step title="Install and Use LlamaIndex">
Follow the LlamaIndex documentation [LlamaIndex Documentation](https://docs.llamaindex.ai/) to set up a RAG/agent pipeline.
</Step>
</Steps>

158
docs/concepts/llms.mdx Normal file
View File

@@ -0,0 +1,158 @@
---
title: LLMs
description: Learn how to configure and optimize LLMs for your CrewAI projects.
icon: microchip-ai
---
# Large Language Models (LLMs) in CrewAI
Large Language Models (LLMs) are the backbone of intelligent agents in the CrewAI framework. This guide will help you understand, configure, and optimize LLM usage for your CrewAI projects.
## Key Concepts
- **LLM**: Large Language Model, the AI powering agent intelligence
- **Agent**: A CrewAI entity that uses an LLM to perform tasks
- **Provider**: A service that offers LLM capabilities (e.g., OpenAI, Anthropic, Ollama, [more providers](https://docs.litellm.ai/docs/providers))
## Configuring LLMs for Agents
CrewAI offers flexible options for setting up LLMs:
### 1. Default Configuration
By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables if no LLM is specified:
- `OPENAI_MODEL_NAME` (defaults to "gpt-4o-mini" if not set)
- `OPENAI_API_BASE`
- `OPENAI_API_KEY`
### 2. String Identifier
```python Code
agent = Agent(llm="gpt-4o", ...)
```
### 3. LLM Instance
List of [more providers](https://docs.litellm.ai/docs/providers).
```python Code
from crewai import LLM
llm = LLM(model="gpt-4", temperature=0.7)
agent = Agent(llm=llm, ...)
```
### 4. Custom LLM Objects
Pass a custom LLM implementation or object from another library.
## Connecting to OpenAI-Compatible LLMs
You can connect to OpenAI-compatible LLMs using either environment variables or by setting specific attributes on the LLM class:
1. Using environment variables:
```python Code
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
```
2. Using LLM class attributes:
```python Code
llm = LLM(
model="custom-model-name",
api_key="your-api-key",
base_url="https://api.your-provider.com/v1"
)
agent = Agent(llm=llm, ...)
```
## LLM Configuration Options
When configuring an LLM for your agent, you have access to a wide range of parameters:
| Parameter | Type | Description |
|:------------------|:---------------:|:-------------------------------------------------------------------------------------------------|
| **model** | `str` | Name of the model to use (e.g., "gpt-4", "gpt-3.5-turbo", "ollama/llama3.1"). For more options, visit the providers documentation. |
| **timeout** | `float, int` | Maximum time (in seconds) to wait for a response. |
| **temperature** | `float` | Controls randomness in output (0.0 to 1.0). |
| **top_p** | `float` | Controls diversity of output (0.0 to 1.0). |
| **n** | `int` | Number of completions to generate. |
| **stop** | `str, List[str]` | Sequence(s) where generation should stop. |
| **max_tokens** | `int` | Maximum number of tokens to generate. |
| **presence_penalty** | `float` | Penalizes new tokens based on their presence in prior text. |
| **frequency_penalty**| `float` | Penalizes new tokens based on their frequency in prior text. |
| **logit_bias** | `Dict[int, float]`| Modifies likelihood of specified tokens appearing. |
| **response_format** | `Dict[str, Any]` | Specifies the format of the response (e.g., JSON object). |
| **seed** | `int` | Sets a random seed for deterministic results. |
| **logprobs** | `bool` | Returns log probabilities of output tokens if enabled. |
| **top_logprobs** | `int` | Number of most likely tokens for which to return log probabilities. |
| **base_url** | `str` | The base URL for the API endpoint. |
| **api_version** | `str` | Version of the API to use. |
| **api_key** | `str` | Your API key for authentication. |
Example:
```python Code
llm = LLM(
model="gpt-4",
temperature=0.8,
max_tokens=150,
top_p=0.9,
frequency_penalty=0.1,
presence_penalty=0.1,
stop=["END"],
seed=42,
base_url="https://api.openai.com/v1",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
## Using Ollama (Local LLMs)
crewAI supports using Ollama for running open-source models locally:
1. Install Ollama: [ollama.ai](https://ollama.ai/)
2. Run a model: `ollama run llama2`
3. Configure agent:
```python Code
agent = Agent(
llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
...
)
```
## Changing the Base API URL
You can change the base API URL for any LLM provider by setting the `base_url` parameter:
```python Code
llm = LLM(
model="custom-model-name",
base_url="https://api.your-provider.com/v1",
api_key="your-api-key"
)
agent = Agent(llm=llm, ...)
```
This is particularly useful when working with OpenAI-compatible APIs or when you need to specify a different endpoint for your chosen provider.
## Best Practices
1. **Choose the right model**: Balance capability and cost.
2. **Optimize prompts**: Clear, concise instructions improve output.
3. **Manage tokens**: Monitor and limit token usage for efficiency.
4. **Use appropriate temperature**: Lower for factual tasks, higher for creative ones.
5. **Implement error handling**: Gracefully manage API errors and rate limits.
## Troubleshooting
- **API Errors**: Check your API key, network connection, and rate limits.
- **Unexpected Outputs**: Refine your prompts and adjust temperature or top_p.
- **Performance Issues**: Consider using a more powerful model or optimizing your queries.
- **Timeout Errors**: Increase the `timeout` parameter or optimize your input.

244
docs/concepts/memory.mdx Normal file
View File

@@ -0,0 +1,244 @@
---
title: Memory
description: Leveraging memory systems in the CrewAI framework to enhance agent capabilities.
icon: database
---
## Introduction to Memory Systems in CrewAI
The crewAI framework introduces a sophisticated memory system designed to significantly enhance the capabilities of AI agents.
This system comprises `short-term memory`, `long-term memory`, `entity memory`, and `contextual memory`, each serving a unique purpose in aiding agents to remember,
reason, and learn from past interactions.
## Memory System Components
| Component | Description |
| :------------------- | :---------------------------------------------------------------------------------------------------------------------- |
| **Short-Term Memory**| Temporarily stores recent interactions and outcomes using `RAG`, enabling agents to recall and utilize information relevant to their current context during the current executions.|
| **Long-Term Memory** | Preserves valuable insights and learnings from past executions, allowing agents to build and refine their knowledge over time. |
| **Entity Memory** | Captures and organizes information about entities (people, places, concepts) encountered during tasks, facilitating deeper understanding and relationship mapping. Uses `RAG` for storing entity information. |
| **Contextual Memory**| Maintains the context of interactions by combining `ShortTermMemory`, `LongTermMemory`, and `EntityMemory`, aiding in the coherence and relevance of agent responses over a sequence of tasks or a conversation. |
## How Memory Systems Empower Agents
1. **Contextual Awareness**: With short-term and contextual memory, agents gain the ability to maintain context over a conversation or task sequence, leading to more coherent and relevant responses.
2. **Experience Accumulation**: Long-term memory allows agents to accumulate experiences, learning from past actions to improve future decision-making and problem-solving.
3. **Entity Understanding**: By maintaining entity memory, agents can recognize and remember key entities, enhancing their ability to process and interact with complex information.
## Implementing Memory in Your Crew
When configuring a crew, you can enable and customize each memory component to suit the crew's objectives and the nature of tasks it will perform.
By default, the memory system is disabled, 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.
It's also possible to initialize the memory instance with your own instance.
The 'embedder' only applies to **Short-Term Memory** which uses Chroma for RAG using the EmbedChain package.
The **Long-Term Memory** uses SQLite3 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 can be overridden using the **CREWAI_STORAGE_DIR** environment variable.
### Example: Configuring Memory for a Crew
```python Code
from crewai import Crew, Agent, Task, Process
# Assemble your crew with memory capabilities
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True
)
```
### Example: Use Custom Memory Instances e.g FAISS as the VectorDB
```python Code
from crewai import Crew, Agent, Task, Process
# Assemble your crew with memory capabilities
my_crew = Crew(
agents=[...],
tasks=[...],
process="Process.sequential",
memory=True,
long_term_memory=EnhanceLongTermMemory(
storage=LTMSQLiteStorage(
db_path="/my_data_dir/my_crew1/long_term_memory_storage.db"
)
),
short_term_memory=EnhanceShortTermMemory(
storage=CustomRAGStorage(
crew_name="my_crew",
storage_type="short_term",
data_dir="//my_data_dir",
model=embedder["model"],
dimension=embedder["dimension"],
),
),
entity_memory=EnhanceEntityMemory(
storage=CustomRAGStorage(
crew_name="my_crew",
storage_type="entities",
data_dir="//my_data_dir",
model=embedder["model"],
dimension=embedder["dimension"],
),
),
verbose=True,
)
```
## Additional Embedding Providers
### Using OpenAI embeddings (already default)
```python Code
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "openai",
"config": {
"model": 'text-embedding-3-small'
}
}
)
```
### Using Google AI embeddings
```python Code
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "google",
"config": {
"model": 'models/embedding-001',
"task_type": "retrieval_document",
"title": "Embeddings for Embedchain"
}
}
)
```
### Using Azure OpenAI embeddings
```python Code
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "azure_openai",
"config": {
"model": 'text-embedding-ada-002',
"deployment_name": "your_embedding_model_deployment_name"
}
}
)
```
### Using GPT4ALL embeddings
```python Code
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "gpt4all"
}
)
```
### Using Vertex AI embeddings
```python Code
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "vertexai",
"config": {
"model": 'textembedding-gecko'
}
}
)
```
### Using Cohere embeddings
```python Code
from crewai import Crew, Agent, Task, Process
my_crew = Crew(
agents=[...],
tasks=[...],
process=Process.sequential,
memory=True,
verbose=True,
embedder={
"provider": "cohere",
"config": {
"model": "embed-english-v3.0",
"vector_dimension": 1024
}
}
)
```
### Resetting Memory
```shell
crewai reset-memories [OPTIONS]
```
#### Resetting Memory Options
| Option | Description | Type | Default |
| :----------------- | :------------------------------- | :------------- | :------ |
| `-l`, `--long` | Reset LONG TERM memory. | Flag (boolean) | False |
| `-s`, `--short` | Reset SHORT TERM memory. | Flag (boolean) | False |
| `-e`, `--entities` | Reset ENTITIES memory. | Flag (boolean) | False |
| `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False |
| `-a`, `--all` | Reset ALL memories. | Flag (boolean) | 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.
## Conclusion
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.

277
docs/concepts/pipeline.mdx Normal file
View File

@@ -0,0 +1,277 @@
---
title: Pipelines
description: Understanding and utilizing pipelines in the crewAI framework for efficient multi-stage task processing.
icon: timeline-arrow
---
## What is a Pipeline?
A pipeline in CrewAI represents a structured workflow that allows for the sequential or parallel execution of multiple crews. It provides a way to organize complex processes involving multiple stages, where the output of one stage can serve as input for subsequent stages.
## Key Terminology
Understanding the following terms is crucial for working effectively with pipelines:
- **Stage**: A distinct part of the pipeline, which can be either sequential (a single crew) or parallel (multiple crews executing concurrently).
- **Kickoff**: A specific execution of the pipeline for a given set of inputs, representing a single instance of processing through the pipeline.
- **Branch**: Parallel executions within a stage (e.g., concurrent crew operations).
- **Trace**: The journey of an individual input through the entire pipeline, capturing the path and transformations it undergoes.
Example pipeline structure:
```bash Pipeline
crew1 >> [crew2, crew3] >> crew4
```
This represents a pipeline with three stages:
1. A sequential stage (crew1)
2. A parallel stage with two branches (crew2 and crew3 executing concurrently)
3. Another sequential stage (crew4)
Each input creates its own kickoff, flowing through all stages of the pipeline. Multiple kickoffs can be processed concurrently, each following the defined pipeline structure.
## Pipeline Attributes
| Attribute | Parameters | Description |
| :--------- | :---------- | :----------------------------------------------------------------------------------------------------------------- |
| **Stages** | `stages` | A list of `PipelineStage` (crews, lists of crews, or routers) representing the stages to be executed in sequence. |
## Creating a Pipeline
When creating a pipeline, you define a series of stages, each consisting of either a single crew or a list of crews for parallel execution.
The pipeline ensures that each stage is executed in order, with the output of one stage feeding into the next.
### Example: Assembling a Pipeline
```python
from crewai import Crew, Process, Pipeline
# Define your crews
research_crew = Crew(
agents=[researcher],
tasks=[research_task],
process=Process.sequential
)
analysis_crew = Crew(
agents=[analyst],
tasks=[analysis_task],
process=Process.sequential
)
writing_crew = Crew(
agents=[writer],
tasks=[writing_task],
process=Process.sequential
)
# Assemble the pipeline
my_pipeline = Pipeline(
stages=[research_crew, analysis_crew, writing_crew]
)
```
## Pipeline Methods
| Method | Description |
| :--------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **kickoff** | Executes the pipeline, processing all stages and returning the results. This method initiates one or more kickoffs through the pipeline, handling the flow of data between stages. |
| **process_runs** | Runs the pipeline for each input provided, handling the flow and transformation of data between stages. |
## Pipeline Output
The output of a pipeline in the CrewAI framework is encapsulated within the `PipelineKickoffResult` class.
This class provides a structured way to access the results of the pipeline's execution, including various formats such as raw strings, JSON, and Pydantic models.
### Pipeline Output Attributes
| Attribute | Parameters | Type | Description |
| :-------------- | :------------ | :------------------------ | :-------------------------------------------------------------------------------------------------------- |
| **ID** | `id` | `UUID4` | A unique identifier for the pipeline output. |
| **Run Results** | `run_results` | `List[PipelineRunResult]` | A list of `PipelineRunResult` objects, each representing the output of a single run through the pipeline. |
### Pipeline Output Methods
| Method/Property | Description |
| :----------------- | :----------------------------------------------------- |
| **add_run_result** | Adds a `PipelineRunResult` to the list of run results. |
### Pipeline Run Result Attributes
| Attribute | Parameters | Type | Description |
| :---------------- | :-------------- | :------------------------- | :-------------------------------------------------------------------------------------------- |
| **ID** | `id` | `UUID4` | A unique identifier for the run result. |
| **Raw** | `raw` | `str` | The raw output of the final stage in the pipeline kickoff. |
| **Pydantic** | `pydantic` | `Any` | A Pydantic model object representing the structured output of the final stage, if applicable. |
| **JSON Dict** | `json_dict` | `Union[Dict[str, Any], None]` | A dictionary representing the JSON output of the final stage, if applicable. |
| **Token Usage** | `token_usage` | `Dict[str, UsageMetrics]` | A summary of token usage across all stages of the pipeline kickoff. |
| **Trace** | `trace` | `List[Any]` | A trace of the journey of inputs through the pipeline kickoff. |
| **Crews Outputs** | `crews_outputs` | `List[CrewOutput]` | A list of `CrewOutput` objects, representing the outputs from each crew in the pipeline kickoff. |
### Pipeline Run Result Methods and Properties
| Method/Property | Description |
| :-------------- | :------------------------------------------------------------------------------------------------------- |
| **json** | Returns the JSON string representation of the run result if the output format of the final task is JSON. |
| **to_dict** | Converts the JSON and Pydantic outputs to a dictionary. |
| **str** | Returns the string representation of the run result, prioritizing Pydantic, then JSON, then raw. |
### Accessing Pipeline Outputs
Once a pipeline has been executed, its output can be accessed through the `PipelineOutput` object returned by the `process_runs` method.
The `PipelineOutput` class provides access to individual `PipelineRunResult` objects, each representing a single run through the pipeline.
#### Example
```python
# Define input data for the pipeline
input_data = [
{"initial_query": "Latest advancements in AI"},
{"initial_query": "Future of robotics"}
]
# Execute the pipeline
pipeline_output = await my_pipeline.process_runs(input_data)
# Access the results
for run_result in pipeline_output.run_results:
print(f"Run ID: {run_result.id}")
print(f"Final Raw Output: {run_result.raw}")
if run_result.json_dict:
print(f"JSON Output: {json.dumps(run_result.json_dict, indent=2)}")
if run_result.pydantic:
print(f"Pydantic Output: {run_result.pydantic}")
print(f"Token Usage: {run_result.token_usage}")
print(f"Trace: {run_result.trace}")
print("Crew Outputs:")
for crew_output in run_result.crews_outputs:
print(f" Crew: {crew_output.raw}")
print("\n")
```
This example demonstrates how to access and work with the pipeline output, including individual run results and their associated data.
## Using Pipelines
Pipelines are particularly useful for complex workflows that involve multiple stages of processing, analysis, or content generation. They allow you to:
1. **Sequence Operations**: Execute crews in a specific order, ensuring that the output of one crew is available as input to the next.
2. **Parallel Processing**: Run multiple crews concurrently within a stage for increased efficiency.
3. **Manage Complex Workflows**: Break down large tasks into smaller, manageable steps executed by specialized crews.
### Example: Running a Pipeline
```python
# Define input data for the pipeline
input_data = [{"initial_query": "Latest advancements in AI"}]
# Execute the pipeline, initiating a run for each input
results = await my_pipeline.process_runs(input_data)
# Access the results
for result in results:
print(f"Final Output: {result.raw}")
print(f"Token Usage: {result.token_usage}")
print(f"Trace: {result.trace}") # Shows the path of the input through all stages
```
## Advanced Features
### Parallel Execution within Stages
You can define parallel execution within a stage by providing a list of crews, creating multiple branches:
```python
parallel_analysis_crew = Crew(agents=[financial_analyst], tasks=[financial_analysis_task])
market_analysis_crew = Crew(agents=[market_analyst], tasks=[market_analysis_task])
my_pipeline = Pipeline(
stages=[
research_crew,
[parallel_analysis_crew, market_analysis_crew], # Parallel execution (branching)
writing_crew
]
)
```
### Routers in Pipelines
Routers are a powerful feature in crewAI pipelines that allow for dynamic decision-making and branching within your workflow.
They enable you to direct the flow of execution based on specific conditions or criteria, making your pipelines more flexible and adaptive.
#### What is a Router?
A router in crewAI is a special component that can be included as a stage in your pipeline. It evaluates the input data and determines which path the execution should take next.
This allows for conditional branching in your pipeline, where different crews or sub-pipelines can be executed based on the router's decision.
#### Key Components of a Router
1. **Routes**: A dictionary of named routes, each associated with a condition and a pipeline to execute if the condition is met.
2. **Default Route**: A fallback pipeline that is executed if none of the defined route conditions are met.
#### Creating a Router
Here's an example of how to create a router:
```python
from crewai import Router, Route, Pipeline, Crew, Agent, Task
# Define your agents
classifier = Agent(name="Classifier", role="Email Classifier")
urgent_handler = Agent(name="Urgent Handler", role="Urgent Email Processor")
normal_handler = Agent(name="Normal Handler", role="Normal Email Processor")
# Define your tasks
classify_task = Task(description="Classify the email based on its content and metadata.")
urgent_task = Task(description="Process and respond to urgent email quickly.")
normal_task = Task(description="Process and respond to normal email thoroughly.")
# Define your crews
classification_crew = Crew(agents=[classifier], tasks=[classify_task]) # classify email between high and low urgency 1-10
urgent_crew = Crew(agents=[urgent_handler], tasks=[urgent_task])
normal_crew = Crew(agents=[normal_handler], tasks=[normal_task])
# Create pipelines for different urgency levels
urgent_pipeline = Pipeline(stages=[urgent_crew])
normal_pipeline = Pipeline(stages=[normal_crew])
# Create a router
email_router = Router(
routes={
"high_urgency": Route(
condition=lambda x: x.get("urgency_score", 0) > 7,
pipeline=urgent_pipeline
),
"low_urgency": Route(
condition=lambda x: x.get("urgency_score", 0) <= 7,
pipeline=normal_pipeline
)
},
default=Pipeline(stages=[normal_pipeline]) # Default to just normal if no urgency score
)
# Use the router in a main pipeline
main_pipeline = Pipeline(stages=[classification_crew, email_router])
inputs = [{"email": "..."}, {"email": "..."}] # List of email data
main_pipeline.kickoff(inputs=inputs)
```
In this example, the router decides between an urgent pipeline and a normal pipeline based on the urgency score of the email. If the urgency score is greater than 7,
it routes to the urgent pipeline; otherwise, it uses the normal pipeline. If the input doesn't include an urgency score, it defaults to just the classification crew.
#### Benefits of Using Routers
1. **Dynamic Workflow**: Adapt your pipeline's behavior based on input characteristics or intermediate results.
2. **Efficiency**: Route urgent tasks to quicker processes, reserving more thorough pipelines for less time-sensitive inputs.
3. **Flexibility**: Easily modify or extend your pipeline's logic without changing the core structure.
4. **Scalability**: Handle a wide range of email types and urgency levels with a single pipeline structure.
### Error Handling and Validation
The `Pipeline` class includes validation mechanisms to ensure the robustness of the pipeline structure:
- Validates that stages contain only Crew instances or lists of Crew instances.
- Prevents double nesting of stages to maintain a clear structure.

151
docs/concepts/planning.mdx Normal file
View File

@@ -0,0 +1,151 @@
---
title: Planning
description: Learn how to add planning to your CrewAI Crew and improve their performance.
icon: brain
---
## 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:
<CodeGroup>
```python Code
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,
)
```
</CodeGroup>
From this point on, your crew will have planning enabled, and the tasks will be planned before each iteration.
#### Planning LLM
Now you can define the LLM that will be used to plan the tasks. You can use any ChatOpenAI LLM model available.
When running the base case example, you will see something like the output below, which represents the output of the `AgentPlanner`
responsible for creating the step-by-step logic to add to the Agents' tasks.
<CodeGroup>
```python Code
from crewai import Crew, Agent, Task, Process
from langchain_openai import ChatOpenAI
# Assemble your crew with planning capabilities and custom LLM
my_crew = Crew(
agents=self.agents,
tasks=self.tasks,
process=Process.sequential,
planning=True,
planning_llm=ChatOpenAI(model="gpt-4o")
)
# Run the crew
my_crew.kickoff()
```
```markdown Result
[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 fledged 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 '```'.
```
</CodeGroup>

67
docs/concepts/processes.mdx Normal file
View File

@@ -0,0 +1,67 @@
---
title: Processes
description: Detailed guide on workflow management through processes in CrewAI, with updated implementation details.
icon: bars-staggered
---
## Understanding Processes
<Tip>
Processes orchestrate the execution of tasks by agents, akin to project management in human teams.
These processes ensure tasks are distributed and executed efficiently, in alignment with a predefined strategy.
</Tip>
## Process Implementations
- **Sequential**: Executes tasks sequentially, ensuring tasks are completed in an orderly progression.
- **Hierarchical**: Organizes tasks in a managerial hierarchy, where tasks are delegated and executed based on a structured chain of command. A manager language model (`manager_llm`) or a custom manager agent (`manager_agent`) must be specified in the crew to enable the hierarchical process, facilitating the creation and management of tasks by the manager.
- **Consensual Process (Planned)**: Aiming for collaborative decision-making among agents on task execution, this process type introduces a democratic approach to task management within CrewAI. It is planned for future development and is not currently implemented in the codebase.
## The Role of Processes in Teamwork
Processes enable individual agents to operate as a cohesive unit, streamlining their efforts to achieve common objectives with efficiency and coherence.
## Assigning Processes to a Crew
To assign a process to a crew, specify the process type upon crew creation to set the execution strategy. For a hierarchical process, ensure to define `manager_llm` or `manager_agent` for the manager agent.
```python
from crewai import Crew
from crewai.process import Process
from langchain_openai import ChatOpenAI
# Example: Creating a crew with a sequential process
crew = Crew(
agents=my_agents,
tasks=my_tasks,
process=Process.sequential
)
# Example: Creating a crew with a hierarchical process
# Ensure to provide a manager_llm or manager_agent
crew = Crew(
agents=my_agents,
tasks=my_tasks,
process=Process.hierarchical,
manager_llm=ChatOpenAI(model="gpt-4")
# or
# manager_agent=my_manager_agent
)
```
**Note:** Ensure `my_agents` and `my_tasks` are defined prior to creating a `Crew` object, and for the hierarchical process, either `manager_llm` or `manager_agent` is also required.
## Sequential Process
This method mirrors dynamic team workflows, progressing through tasks in a thoughtful and systematic manner. Task execution follows the predefined order in the task list, with the output of one task serving as context for the next.
To customize task context, utilize the `context` parameter in the `Task` class to specify outputs that should be used as context for subsequent tasks.
## Hierarchical Process
Emulates a corporate hierarchy, CrewAI allows specifying a custom manager agent or automatically creates one, requiring the specification of a manager language model (`manager_llm`). This agent oversees task execution, including planning, delegation, and validation. Tasks are not pre-assigned; the manager allocates tasks to agents based on their capabilities, reviews outputs, and assesses task completion.
## Process Class: Detailed Overview
The `Process` class is implemented as an enumeration (`Enum`), ensuring type safety and restricting process values to the defined types (`sequential`, `hierarchical`). The consensual process is planned for future inclusion, emphasizing our commitment to continuous development and innovation.
## Conclusion
The structured collaboration facilitated by processes within CrewAI is crucial for enabling systematic teamwork among agents.
This documentation has been updated to reflect the latest features, enhancements, and the planned integration of the Consensual Process, ensuring users have access to the most current and comprehensive information.

323
docs/concepts/tasks.mdx Normal file
View File

@@ -0,0 +1,323 @@
---
title: Tasks
description: Detailed guide on managing and creating tasks within the CrewAI framework, reflecting the latest codebase updates.
icon: list-check
---
## Overview of a Task
In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
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 | Type | Description |
| :------------------------------- | :---------------- | :---------------------------- | :------------------------------------------------------------------------------------------------------------------- |
| **Description** | `description` | `str` | A clear, concise statement of what the task entails. |
| **Agent** | `agent` | `Optional[BaseAgent]` | The agent responsible for the task, assigned either directly or by the crew's process. |
| **Expected Output** | `expected_output` | `str` | A detailed description of what the task's completion looks like. |
| **Tools** _(optional)_ | `tools` | `Optional[List[Any]]` | The functions or capabilities the agent can utilize to perform the task. Defaults to an empty list. |
| **Async Execution** _(optional)_ | `async_execution` | `Optional[bool]` | If set, the task executes asynchronously, allowing progression without waiting for completion. Defaults to False. |
| **Context** _(optional)_ | `context` | `Optional[List["Task"]]` | Specifies tasks whose outputs are used as context for this task. |
| **Config** _(optional)_ | `config` | `Optional[Dict[str, Any]]` | Additional configuration details for the agent executing the task, allowing further customization. Defaults to None. |
| **Output JSON** _(optional)_ | `output_json` | `Optional[Type[BaseModel]]` | Outputs a JSON object, requiring an OpenAI client. Only one output format can be set. |
| **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]` | Outputs a Pydantic model object, requiring an OpenAI client. Only one output format can be set. |
| **Output File** _(optional)_ | `output_file` | `Optional[str]` | Saves the task output to a file. If used with `Output JSON` or `Output Pydantic`, specifies how the output is saved. |
| **Output** _(optional)_ | `output` | `Optional[TaskOutput]` | An instance of `TaskOutput`, containing the raw, JSON, and Pydantic output plus additional details. |
| **Callback** _(optional)_ | `callback` | `Optional[Any]` | A callable that is executed with the task's output upon completion. |
| **Human Input** _(optional)_ | `human_input` | `Optional[bool]` | Indicates if the task should involve human review at the end, useful for tasks needing human oversight. Defaults to False.|
| **Converter Class** _(optional)_ | `converter_cls` | `Optional[Type[Converter]]` | A converter class used to export structured output. Defaults to None. |
## Creating a Task
Creating a task involves defining its scope, responsible agent, and any additional attributes for flexibility:
```python Code
from crewai import Task
task = Task(
description='Find and summarize the latest and most relevant news on AI',
agent=sales_agent,
expected_output='A bullet list summary of the top 5 most important AI news',
)
```
<Tip>
Directly specify an `agent` for assignment or let the `hierarchical` CrewAI's process decide based on roles, availability, etc.
</Tip>
## Task Output
The output of a task in 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 output, 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` | Description of the task. |
| **Summary** | `summary` | `Optional[str]` | Summary of the task, auto-generated from the first 10 words of 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 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 Code
# 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=True
)
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
Leverage tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools) for enhanced task performance and agent interaction.
## Creating a Task with Tools
```python Code
import os
os.environ["OPENAI_API_KEY"] = "Your Key"
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
from crewai import Agent, Task, Crew
from crewai_tools import SerperDevTool
research_agent = Agent(
role='Researcher',
goal='Find and summarize the latest AI news',
backstory="""You're a researcher at a large company.
You're responsible for analyzing data and providing insights
to the business.""",
verbose=True
)
# to perform a semantic search for a specified query from a text's content across the internet
search_tool = SerperDevTool()
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]
)
crew = Crew(
agents=[research_agent],
tasks=[task],
verbose=True
)
result = crew.kickoff()
print(result)
```
This demonstrates how tasks with specific tools can override an agent's default set for tailored task execution.
## Referring to Other Tasks
In CrewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output, including multiple, should be used as context for another task.
This is useful when you have a task that depends on the output of another task that is not performed immediately after it. This is done through the `context` attribute of the task:
```python Code
# ...
research_ai_task = Task(
description='Find and summarize the latest AI news',
expected_output='A bullet list summary of the top 5 most important AI news',
async_execution=True,
agent=research_agent,
tools=[search_tool]
)
research_ops_task = Task(
description='Find and summarize the latest AI Ops news',
expected_output='A bullet list summary of the top 5 most important AI Ops news',
async_execution=True,
agent=research_agent,
tools=[search_tool]
)
write_blog_task = Task(
description="Write a full blog post about the importance of AI and its latest news",
expected_output='Full blog post that is 4 paragraphs long',
agent=writer_agent,
context=[research_ai_task, research_ops_task]
)
#...
```
## Asynchronous Execution
You can define a task to be executed asynchronously. This means that the crew will not wait for it to be completed to continue with the next task. This is useful for tasks that take a long time to be completed, or that are not crucial for the next tasks to be performed.
You can then use the `context` attribute to define in a future task that it should wait for the output of the asynchronous task to be completed.
```python Code
#...
list_ideas = Task(
description="List of 5 interesting ideas to explore for an article about AI.",
expected_output="Bullet point list of 5 ideas for an article.",
agent=researcher,
async_execution=True # Will be executed asynchronously
)
list_important_history = Task(
description="Research the history of AI and give me the 5 most important events.",
expected_output="Bullet point list of 5 important events.",
agent=researcher,
async_execution=True # Will be executed asynchronously
)
write_article = Task(
description="Write an article about AI, its history, and interesting ideas.",
expected_output="A 4 paragraph article about AI.",
agent=writer,
context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
)
#...
```
## Callback Mechanism
The callback function is executed after the task is completed, allowing for actions or notifications to be triggered based on the task's outcome.
```python Code
# ...
def callback_function(output: TaskOutput):
# Do something after the task is completed
# Example: Send an email to the manager
print(f"""
Task completed!
Task: {output.description}
Output: {output.raw}
""")
research_task = Task(
description='Find and summarize the latest AI news',
expected_output='A bullet list summary of the top 5 most important AI news',
agent=research_agent,
tools=[search_tool],
callback=callback_function
)
#...
```
## Accessing a Specific Task Output
Once a crew finishes running, you can access the output of a specific task by using the `output` attribute of the task object:
```python Code
# ...
task1 = 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]
)
#...
crew = Crew(
agents=[research_agent],
tasks=[task1, task2, task3],
verbose=True
)
result = crew.kickoff()
# Returns a TaskOutput object with the description and results of the task
print(f"""
Task completed!
Task: {task1.output.description}
Output: {task1.output.raw}
""")
```
## Tool Override Mechanism
Specifying tools in a task allows for dynamic adaptation of agent capabilities, emphasizing CrewAI's flexibility.
## Error Handling and Validation Mechanisms
While creating and executing tasks, certain validation mechanisms are in place to ensure the robustness and reliability of task attributes. These include but are not limited to:
- Ensuring only one output type is set per task to maintain clear output expectations.
- Preventing the manual assignment of the `id` attribute to uphold the integrity of the unique identifier system.
These validations help in maintaining the consistency and reliability of task executions within the crewAI framework.
## Creating Directories when Saving Files
You can now specify if a task should create directories when saving its output to a file. This is particularly useful for organizing outputs and ensuring that file paths are correctly structured.
```python Code
# ...
save_output_task = Task(
description='Save the summarized AI news to a file',
expected_output='File saved successfully',
agent=research_agent,
tools=[file_save_tool],
output_file='outputs/ai_news_summary.txt',
create_directory=True
)
#...
```
## Conclusion
Tasks are the driving force behind the actions of agents in CrewAI.
By properly defining tasks and their outcomes, you set the stage for your AI agents to work effectively, either independently or as a collaborative unit.
Equipping tasks with appropriate tools, understanding the execution process, and following robust validation practices are crucial for maximizing CrewAI's potential,
ensuring agents are effectively prepared for their assignments and that tasks are executed as intended.

48
docs/concepts/testing.mdx Normal file
View File

@@ -0,0 +1,48 @@
---
title: Testing
description: Learn how to test your CrewAI Crew and evaluate their performance.
icon: vial
---
## Introduction
Testing is a crucial part of the development process, and it is essential to ensure that your crew is performing as expected. With crewAI, you can easily test your crew and evaluate its performance using the built-in testing capabilities.
### Using the Testing Feature
We added the CLI command `crewai test` to make it easy to test your crew. This command will run your crew for a specified number of iterations and provide detailed performance metrics. The parameters are `n_iterations` and `model`, which are optional and default to 2 and `gpt-4o-mini` respectively. For now, the only provider available is OpenAI.
```bash
crewai test
```
If you want to run more iterations or use a different model, you can specify the parameters like this:
```bash
crewai test --n_iterations 5 --model gpt-4o
```
or using the short forms:
```bash
crewai test -n 5 -m gpt-4o
```
When you run the `crewai test` command, the crew will be executed for the specified number of iterations, and the performance metrics will be displayed at the end of the run.
A table of scores at the end will show the performance of the crew in terms of the following metrics:
<center>**Tasks Scores (1-10 Higher is better)**</center>
| Tasks/Crew/Agents | Run 1 | Run 2 | Avg. Total | Agents | Additional Info |
|:------------------|:-----:|:-----:|:----------:|:------------------------------:|:---------------------------------|
| Task 1 | 9.0 | 9.5 | **9.2** | Professional Insights | |
| | | | | Researcher | |
| Task 2 | 9.0 | 10.0 | **9.5** | Company Profile Investigator | |
| Task 3 | 9.0 | 9.0 | **9.0** | Automation Insights | |
| | | | | Specialist | |
| Task 4 | 9.0 | 9.0 | **9.0** | Final Report Compiler | Automation Insights Specialist |
| Crew | 9.00 | 9.38 | **9.2** | | |
| Execution Time (s) | 126 | 145 | **135** | | |
The example above shows the test results for two runs of the crew with two tasks, with the average total score for each task and the crew as a whole.

213
docs/concepts/tools.mdx Normal file
View File

@@ -0,0 +1,213 @@
---
title: Tools
description: Understanding and leveraging tools within the CrewAI framework for agent collaboration and task execution.
icon: screwdriver-wrench
---
## Introduction
CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
This documentation outlines how to create, integrate, and leverage these tools within the CrewAI framework, including a new focus on collaboration tools.
## What is a Tool?
A tool in CrewAI is a skill or function that agents can utilize to perform various actions.
This includes tools from the [CrewAI Toolkit](https://github.com/joaomdmoura/crewai-tools) and [LangChain Tools](https://python.langchain.com/docs/integrations/tools),
enabling everything from simple searches to complex interactions and effective teamwork among agents.
## Key Characteristics of Tools
- **Utility**: Crafted for tasks such as web searching, data analysis, content generation, and agent collaboration.
- **Integration**: Boosts agent capabilities by seamlessly integrating tools into their workflow.
- **Customizability**: Provides the flexibility to develop custom tools or utilize existing ones, catering to the specific needs of agents.
- **Error Handling**: Incorporates robust error handling mechanisms to ensure smooth operation.
- **Caching Mechanism**: Features intelligent caching to optimize performance and reduce redundant operations.
## Using CrewAI Tools
To enhance your agents' capabilities with crewAI tools, begin by installing our extra tools package:
```bash
pip install 'crewai[tools]'
```
Here's an example demonstrating their use:
```python Code
import os
from crewai import Agent, Task, Crew
# Importing crewAI tools
from crewai_tools import (
DirectoryReadTool,
FileReadTool,
SerperDevTool,
WebsiteSearchTool
)
# Set up API keys
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key
os.environ["OPENAI_API_KEY"] = "Your Key"
# Instantiate tools
docs_tool = DirectoryReadTool(directory='./blog-posts')
file_tool = FileReadTool()
search_tool = SerperDevTool()
web_rag_tool = WebsiteSearchTool()
# Create agents
researcher = Agent(
role='Market Research Analyst',
goal='Provide up-to-date market analysis of the AI industry',
backstory='An expert analyst with a keen eye for market trends.',
tools=[search_tool, web_rag_tool],
verbose=True
)
writer = Agent(
role='Content Writer',
goal='Craft engaging blog posts about the AI industry',
backstory='A skilled writer with a passion for technology.',
tools=[docs_tool, file_tool],
verbose=True
)
# Define tasks
research = Task(
description='Research the latest trends in the AI industry and provide a summary.',
expected_output='A summary of the top 3 trending developments in the AI industry with a unique perspective on their significance.',
agent=researcher
)
write = Task(
description='Write an engaging blog post about the AI industry, based on the research analyst’s summary. Draw inspiration from the latest blog posts in the directory.',
expected_output='A 4-paragraph blog post formatted in markdown with engaging, informative, and accessible content, avoiding complex jargon.',
agent=writer,
output_file='blog-posts/new_post.md' # The final blog post will be saved here
)
# Assemble a crew with planning enabled
crew = Crew(
agents=[researcher, writer],
tasks=[research, write],
verbose=True,
planning=True, # Enable planning feature
)
# Execute tasks
crew.kickoff()
```
## Available CrewAI Tools
- **Error Handling**: All tools are built with error handling capabilities, allowing agents to gracefully manage exceptions and continue their tasks.
- **Caching Mechanism**: All tools support caching, enabling agents to efficiently reuse previously obtained results, reducing the load on external resources and speeding up the execution time. You can also define finer control over the caching mechanism using the `cache_function` attribute on the tool.
Here is a list of the available tools and their descriptions:
| Tool | Description |
| :-------------------------- | :-------------------------------------------------------------------------------------------- |
| **BrowserbaseLoadTool** | A tool for interacting with and extracting data from web browsers. |
| **CodeDocsSearchTool** | A RAG tool optimized for searching through code documentation and related technical documents. |
| **CodeInterpreterTool** | A tool for interpreting python code. |
| **ComposioTool** | Enables use of Composio tools. |
| **CSVSearchTool** | A RAG tool designed for searching within CSV files, tailored to handle structured data. |
| **DALL-E Tool** | A tool for generating images using the DALL-E API. |
| **DirectorySearchTool** | A RAG tool for searching within directories, useful for navigating through file systems. |
| **DOCXSearchTool** | A RAG tool aimed at searching within DOCX documents, ideal for processing Word files. |
| **DirectoryReadTool** | Facilitates reading and processing of directory structures and their contents. |
| **EXASearchTool** | A tool designed for performing exhaustive searches across various data sources. |
| **FileReadTool** | Enables reading and extracting data from files, supporting various file formats. |
| **FirecrawlSearchTool** | A tool to search webpages using Firecrawl and return the results. |
| **FirecrawlCrawlWebsiteTool** | A tool for crawling webpages using Firecrawl. |
| **FirecrawlScrapeWebsiteTool** | A tool for scraping webpages URL using Firecrawl and returning its contents. |
| **GithubSearchTool** | A RAG tool for searching within GitHub repositories, useful for code and documentation search.|
| **SerperDevTool** | A specialized tool for development purposes, with specific functionalities under development. |
| **TXTSearchTool** | A RAG tool focused on searching within text (.txt) files, suitable for unstructured data. |
| **JSONSearchTool** | A RAG tool designed for searching within JSON files, catering to structured data handling. |
| **LlamaIndexTool** | Enables the use of LlamaIndex tools. |
| **MDXSearchTool** | A RAG tool tailored for searching within Markdown (MDX) files, useful for documentation. |
| **PDFSearchTool** | A RAG tool aimed at searching within PDF documents, ideal for processing scanned documents. |
| **PGSearchTool** | A RAG tool optimized for searching within PostgreSQL databases, suitable for database queries. |
| **Vision Tool** | A tool for generating images using the DALL-E API. |
| **RagTool** | A general-purpose RAG tool capable of handling various data sources and types. |
| **ScrapeElementFromWebsiteTool** | Enables scraping specific elements from websites, useful for targeted data extraction. |
| **ScrapeWebsiteTool** | Facilitates scraping entire websites, ideal for comprehensive data collection. |
| **WebsiteSearchTool** | A RAG tool for searching website content, optimized for web data extraction. |
| **XMLSearchTool** | A RAG tool designed for searching within XML files, suitable for structured data formats. |
| **YoutubeChannelSearchTool**| A RAG tool for searching within YouTube channels, useful for video content analysis. |
| **YoutubeVideoSearchTool** | A RAG tool aimed at searching within YouTube videos, ideal for video data extraction. |
## Creating your own Tools
<Tip>
Developers can craft `custom tools` tailored for their agent’s needs or utilize pre-built options.
</Tip>
To create your own CrewAI tools you will need to install our extra tools package:
```bash
pip install 'crewai[tools]'
```
Once you do that there are two main ways for one to create a CrewAI tool:
### Subclassing `BaseTool`
```python Code
from crewai_tools import BaseTool
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
description: str = "Clear description for what this tool is useful for, your agent will need this information to use it."
def _run(self, argument: str) -> str:
# Implementation goes here
return "Result from custom tool"
```
### Utilizing the `tool` Decorator
```python Code
from crewai_tools import tool
@tool("Name of my tool")
def my_tool(question: str) -> str:
"""Clear description for what this tool is useful for, your agent will need this information to use it."""
# Function logic here
return "Result from your custom tool"
```
### Custom Caching Mechanism
<Tip>
Tools can optionally implement a `cache_function` to fine-tune caching behavior. This function determines when to cache results based on specific conditions, offering granular control over caching logic.
</Tip>
```python Code
from crewai_tools import tool
@tool
def multiplication_tool(first_number: int, second_number: int) -> str:
"""Useful for when you need to multiply two numbers together."""
return first_number * second_number
def cache_func(args, result):
# In this case, we only cache the result if it's a multiple of 2
cache = result % 2 == 0
return cache
multiplication_tool.cache_function = cache_func
writer1 = Agent(
role="Writer",
goal="You write lessons of math for kids.",
backstory="You're an expert in writing and you love to teach kids but you know nothing of math.",
tools=[multiplication_tool],
allow_delegation=False,
)
#...
```
## Conclusion
Tools are pivotal in extending the capabilities of CrewAI agents, enabling them to undertake a broad spectrum of tasks and collaborate effectively.
When building solutions with CrewAI, leverage both custom and existing tools to empower your agents and enhance the AI ecosystem. Consider utilizing error handling,
caching mechanisms, and the flexibility of tool arguments to optimize your agents' performance and capabilities.

67
docs/concepts/training.mdx Normal file
View File

@@ -0,0 +1,67 @@
---
title: Training
description: Learn how to train your CrewAI agents by giving them feedback early on and get consistent results.
icon: dumbbell
---
## Introduction
The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI).
By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
This helps the agents improve their understanding, decision-making, and problem-solving abilities.
### Training Your Crew Using the CLI
To use the training 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:
```shell
crewai train -n <n_iterations> <filename> (optional)
```
<Tip>
Replace `<n_iterations>` with the desired number of training iterations and `<filename>` with the appropriate filename ending with `.pkl`.
</Tip>
### Training Your Crew Programmatically
To train your crew programmatically, use the following steps:
1. Define the number of iterations for training.
2. Specify the input parameters for the training process.
3. Execute the training command within a try-except block to handle potential errors.
```python Code
n_iterations = 2
inputs = {"topic": "CrewAI Training"}
filename = "your_model.pkl"
try:
YourCrewName_Crew().crew().train(
n_iterations=n_iterations,
inputs=inputs,
filename=filename
)
except Exception as e:
raise Exception(f"An error occurred while training the crew: {e}")
```
### Key Points to Note
- **Positive Integer Requirement:** Ensure that the number of iterations (`n_iterations`) is a positive integer. The code will raise a `ValueError` if this condition is not met.
- **Filename Requirement:** Ensure that the filename ends with `.pkl`. The code will raise a `ValueError` if this condition is not met.
- **Error Handling:** The code handles subprocess errors and unexpected exceptions, providing error messages to the user.
It is important to note that the training process may take some time, depending on the complexity of your agents and will also require your feedback on each iteration.
Once the training is complete, your agents will be equipped with enhanced capabilities and knowledge, ready to tackle complex tasks and provide more consistent and valuable insights.
Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
Happy training with CrewAI! 🚀