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Merge branch 'main' into heitor/login-with-workos
This commit is contained in:
Heitor Carvalho
@@ -21,7 +21,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
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<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
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CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
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|
||||
|
||||
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||||
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The Visual Agent Builder enables:
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- Intuitive agent configuration with form-based interfaces
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|
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@@ -4,7 +4,7 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
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icon: terminal
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---
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||||
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# CrewAI CLI Documentation
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## Overview
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The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.
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@@ -110,6 +110,8 @@ crewai reset-memories [OPTIONS]
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- `-s, --short`: Reset SHORT TERM memory
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- `-e, --entities`: Reset ENTITIES memory
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- `-k, --kickoff-outputs`: Reset LATEST KICKOFF TASK OUTPUTS
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- `-kn, --knowledge`: Reset KNOWLEDGE storage
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- `-akn, --agent-knowledge`: Reset AGENT KNOWLEDGE storage
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- `-a, --all`: Reset ALL memories
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|
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Example:
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@@ -4,7 +4,7 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
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icon: screen-users
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---
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## Collaboration Fundamentals
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## Overview
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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.
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@@ -4,7 +4,7 @@ description: Understanding and utilizing crews in the crewAI framework with comp
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icon: people-group
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---
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## What is a Crew?
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## Overview
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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.
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@@ -27,7 +27,7 @@ A crew in crewAI represents a collaborative group of agents working together to
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| **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`. |
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| **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. |
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| **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. |
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| **Output Log File** _(optional)_ | `output_log_file` | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defautls to `None`. |
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| **Output Log File** _(optional)_ | `output_log_file` | Set to True to save logs as logs.txt in the current directory or provide a file path. Logs will be in JSON format if the filename ends in .json, otherwise .txt. Defaults to `None`. |
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| **Manager Agent** _(optional)_ | `manager_agent` | `manager` sets a custom agent that will be used as a manager. |
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| **Prompt File** _(optional)_ | `prompt_file` | Path to the prompt JSON file to be used for the crew. |
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| **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. |
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@@ -117,6 +117,12 @@ class YourCrewName:
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)
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```
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How to run the above code:
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```python code
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YourCrewName().crew().kickoff(inputs={"any": "input here"})
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```
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<Note>
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Tasks will be executed in the order they are defined.
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</Note>
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@@ -184,6 +190,11 @@ class YourCrewName:
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verbose=True
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)
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```
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How to run the above code:
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|
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```python code
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YourCrewName().crew().kickoff(inputs={})
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```
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|
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In this example:
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@@ -246,7 +257,7 @@ print(f"Token Usage: {crew_output.token_usage}")
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You can see real time log of the crew execution, by setting `output_log_file` as a `True(Boolean)` or a `file_name(str)`. Supports logging of events as both `file_name.txt` and `file_name.json`.
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In case of `True(Boolean)` will save as `logs.txt`.
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In case of `output_log_file` is set as `False(Booelan)` or `None`, the logs will not be populated.
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In case of `output_log_file` is set as `False(Boolean)` or `None`, the logs will not be populated.
|
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|
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```python Code
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# Save crew logs
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|
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@@ -4,7 +4,7 @@ description: 'Tap into CrewAI events to build custom integrations and monitoring
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icon: spinner
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---
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|
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# Event Listeners
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## Overview
|
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|
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CrewAI provides a powerful event system that allows you to listen for and react to various events that occur during the execution of your Crew. This feature enables you to build custom integrations, monitoring solutions, logging systems, or any other functionality that needs to be triggered based on CrewAI's internal events.
|
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|
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@@ -21,7 +21,7 @@ When specific actions occur in CrewAI (like a Crew starting execution, an Agent
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<Note type="info" title="Enterprise Enhancement: Prompt Tracing">
|
||||
CrewAI Enterprise provides a built-in Prompt Tracing feature that leverages the event system to track, store, and visualize all prompts, completions, and associated metadata. This provides powerful debugging capabilities and transparency into your agent operations.
|
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|
||||
|
||||
|
||||
|
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With Prompt Tracing you can:
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- View the complete history of all prompts sent to your LLM
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@@ -224,6 +224,15 @@ CrewAI provides a wide range of events that you can listen for:
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- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
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- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
|
||||
|
||||
### Knowledge Events
|
||||
|
||||
- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
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- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
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- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
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- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
|
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- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
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||||
- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
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|
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### Flow Events
|
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- **FlowCreatedEvent**: Emitted when a Flow is created
|
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|
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@@ -4,7 +4,7 @@ description: Learn how to create and manage AI workflows using CrewAI Flows.
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icon: arrow-progress
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---
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||||
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## Introduction
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## Overview
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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.
|
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@@ -75,11 +75,12 @@ class ExampleFlow(Flow):
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flow = ExampleFlow()
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flow.plot()
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result = flow.kickoff()
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print(f"Generated fun fact: {result}")
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```
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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.
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Each Flow instance automatically receives a unique identifier (UUID) in its state, which helps track and manage flow executions. The state can also store additional data (like the generated city and fun fact) that persists throughout the flow's execution.
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@@ -146,6 +147,7 @@ class OutputExampleFlow(Flow):
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flow = OutputExampleFlow()
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flow.plot("my_flow_plot")
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final_output = flow.kickoff()
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print("---- Final Output ----")
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@@ -158,9 +160,10 @@ Second method received: Output from first_method
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```
|
||||
|
||||
</CodeGroup>
|
||||
|
||||
|
||||
In this example, the `second_method` is the last method to complete, so its output will be the final output of the Flow.
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||||
The `kickoff()` method will return the final output, which is then printed to the console.
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||||
The `kickoff()` method will return the final output, which is then printed to the console. The `plot()` method will generate the HTML file, which will help you understand the flow.
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|
||||
#### Accessing and Updating State
|
||||
|
||||
@@ -192,6 +195,7 @@ class StateExampleFlow(Flow[ExampleState]):
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return self.state.message
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|
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flow = StateExampleFlow()
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flow.plot("my_flow_plot")
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final_output = flow.kickoff()
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print(f"Final Output: {final_output}")
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print("Final State:")
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||||
@@ -206,6 +210,8 @@ counter=2 message='Hello from first_method - updated by second_method'
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|
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</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.
|
||||
|
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@@ -249,9 +255,12 @@ class UnstructuredExampleFlow(Flow):
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|
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|
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flow = UnstructuredExampleFlow()
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flow.plot("my_flow_plot")
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flow.kickoff()
|
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```
|
||||
|
||||
|
||||
|
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**Note:** The `id` field is automatically generated and preserved throughout the flow's execution. You don't need to manage or set it manually, and it will be maintained even when updating the state with new data.
|
||||
|
||||
**Key Points:**
|
||||
@@ -302,6 +311,8 @@ flow = StructuredExampleFlow()
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flow.kickoff()
|
||||
```
|
||||
|
||||
|
||||
|
||||
**Key Points:**
|
||||
|
||||
- **Defined Schema:** `ExampleState` clearly outlines the state structure, enhancing code readability and maintainability.
|
||||
@@ -436,6 +447,7 @@ class OrExampleFlow(Flow):
|
||||
|
||||
|
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flow = OrExampleFlow()
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flow.plot("my_flow_plot")
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flow.kickoff()
|
||||
```
|
||||
|
||||
@@ -446,6 +458,8 @@ 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.
|
||||
|
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@@ -474,6 +488,7 @@ class AndExampleFlow(Flow):
|
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print(self.state)
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|
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flow = AndExampleFlow()
|
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flow.plot()
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flow.kickoff()
|
||||
```
|
||||
|
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@@ -484,6 +499,8 @@ flow.kickoff()
|
||||
|
||||
</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.
|
||||
|
||||
@@ -527,6 +544,7 @@ class RouterFlow(Flow[ExampleState]):
|
||||
|
||||
|
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flow = RouterFlow()
|
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flow.plot("my_flow_plot")
|
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flow.kickoff()
|
||||
```
|
||||
|
||||
@@ -538,6 +556,8 @@ 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"`.
|
||||
@@ -641,6 +661,7 @@ class MarketResearchFlow(Flow[MarketResearchState]):
|
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# Usage example
|
||||
async def run_flow():
|
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flow = MarketResearchFlow()
|
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flow.plot("MarketResearchFlowPlot")
|
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result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
|
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return result
|
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|
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@@ -650,6 +671,8 @@ if __name__ == "__main__":
|
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asyncio.run(run_flow())
|
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```
|
||||
|
||||
|
||||
|
||||
This example demonstrates several key features of using Agents in flows:
|
||||
|
||||
1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
|
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@@ -746,13 +769,16 @@ def kickoff():
|
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|
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def plot():
|
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poem_flow = PoemFlow()
|
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poem_flow.plot()
|
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poem_flow.plot("PoemFlowPlot")
|
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|
||||
if __name__ == "__main__":
|
||||
kickoff()
|
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plot()
|
||||
```
|
||||
|
||||
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.
|
||||
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. The PoemFlowPlot will be generated by `plot()` method.
|
||||
|
||||
|
||||
|
||||
### Running the Flow
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@ description: What is knowledge in CrewAI and how to use it.
|
||||
icon: book
|
||||
---
|
||||
|
||||
## What is Knowledge?
|
||||
## Overview
|
||||
|
||||
Knowledge in CrewAI is a powerful system that allows AI agents to access and utilize external information sources during their tasks.
|
||||
Think of it as giving your agents a reference library they can consult while working.
|
||||
@@ -36,12 +36,15 @@ CrewAI supports various types of knowledge sources out of the box:
|
||||
|
||||
## Supported Knowledge Parameters
|
||||
|
||||
| Parameter | Type | Required | Description |
|
||||
| :--------------------------- | :---------------------------------- | :------- | :---------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `sources` | **List[BaseKnowledgeSource]** | Yes | List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content. |
|
||||
| `collection_name` | **str** | No | Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to "knowledge" if not provided. |
|
||||
| `storage` | **Optional[KnowledgeStorage]** | No | Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created. |
|
||||
|
||||
<ParamField body="sources" type="List[BaseKnowledgeSource]" required="Yes">
|
||||
List of knowledge sources that provide content to be stored and queried. Can include PDF, CSV, Excel, JSON, text files, or string content.
|
||||
</ParamField>
|
||||
<ParamField body="collection_name" type="str">
|
||||
Name of the collection where the knowledge will be stored. Used to identify different sets of knowledge. Defaults to \"knowledge\" if not provided.
|
||||
</ParamField>
|
||||
<ParamField body="storage" type="Optional[KnowledgeStorage]">
|
||||
Custom storage configuration for managing how the knowledge is stored and retrieved. If not provided, a default storage will be created.
|
||||
</ParamField>
|
||||
|
||||
<Tip>
|
||||
Unlike retrieval from a vector database using a tool, agents preloaded with knowledge will not need a retrieval persona or task.
|
||||
@@ -397,6 +400,93 @@ result = crew.kickoff(inputs={"question": "What city does John live in and how o
|
||||
John is 30 years old and lives in San Francisco.
|
||||
```
|
||||
</CodeGroup>
|
||||
|
||||
## Query Rewriting
|
||||
|
||||
CrewAI implements an intelligent query rewriting mechanism to optimize knowledge retrieval. When an agent needs to search through knowledge sources, the raw task prompt is automatically transformed into a more effective search query.
|
||||
|
||||
### How Query Rewriting Works
|
||||
|
||||
1. When an agent executes a task with knowledge sources available, the `_get_knowledge_search_query` method is triggered
|
||||
2. The agent's LLM is used to transform the original task prompt into an optimized search query
|
||||
3. This optimized query is then used to retrieve relevant information from knowledge sources
|
||||
|
||||
### Benefits of Query Rewriting
|
||||
|
||||
<CardGroup cols={2}>
|
||||
<Card title="Improved Retrieval Accuracy" icon="bullseye-arrow">
|
||||
By focusing on key concepts and removing irrelevant content, query rewriting helps retrieve more relevant information.
|
||||
</Card>
|
||||
<Card title="Context Awareness" icon="brain">
|
||||
The rewritten queries are designed to be more specific and context-aware for vector database retrieval.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
### Implementation Details
|
||||
|
||||
Query rewriting happens transparently using a system prompt that instructs the LLM to:
|
||||
|
||||
- Focus on key words of the intended task
|
||||
- Make the query more specific and context-aware
|
||||
- Remove irrelevant content like output format instructions
|
||||
- Generate only the rewritten query without preamble or postamble
|
||||
|
||||
<Tip>
|
||||
This mechanism is fully automatic and requires no configuration from users. The agent's LLM is used to perform the query rewriting, so using a more capable LLM can improve the quality of rewritten queries.
|
||||
</Tip>
|
||||
|
||||
## Knowledge Events
|
||||
|
||||
CrewAI emits events during the knowledge retrieval process that you can listen for using the event system. These events allow you to monitor, debug, and analyze how knowledge is being retrieved and used by your agents.
|
||||
|
||||
### Available Knowledge Events
|
||||
|
||||
- **KnowledgeRetrievalStartedEvent**: Emitted when an agent starts retrieving knowledge from sources
|
||||
- **KnowledgeRetrievalCompletedEvent**: Emitted when knowledge retrieval is completed, including the query used and the retrieved content
|
||||
- **KnowledgeQueryStartedEvent**: Emitted when a query to knowledge sources begins
|
||||
- **KnowledgeQueryCompletedEvent**: Emitted when a query completes successfully
|
||||
- **KnowledgeQueryFailedEvent**: Emitted when a query to knowledge sources fails
|
||||
- **KnowledgeSearchQueryFailedEvent**: Emitted when a search query fails
|
||||
|
||||
### Example: Monitoring Knowledge Retrieval
|
||||
|
||||
```python
|
||||
from crewai.utilities.events import (
|
||||
KnowledgeRetrievalStartedEvent,
|
||||
KnowledgeRetrievalCompletedEvent,
|
||||
)
|
||||
from crewai.utilities.events.base_event_listener import BaseEventListener
|
||||
|
||||
class KnowledgeMonitorListener(BaseEventListener):
|
||||
def setup_listeners(self, crewai_event_bus):
|
||||
@crewai_event_bus.on(KnowledgeRetrievalStartedEvent)
|
||||
def on_knowledge_retrieval_started(source, event):
|
||||
print(f"Agent '{event.agent.role}' started retrieving knowledge")
|
||||
|
||||
@crewai_event_bus.on(KnowledgeRetrievalCompletedEvent)
|
||||
def on_knowledge_retrieval_completed(source, event):
|
||||
print(f"Agent '{event.agent.role}' completed knowledge retrieval")
|
||||
print(f"Query: {event.query}")
|
||||
print(f"Retrieved {len(event.retrieved_knowledge)} knowledge chunks")
|
||||
|
||||
# Create an instance of your listener
|
||||
knowledge_monitor = KnowledgeMonitorListener()
|
||||
```
|
||||
|
||||
For more information on using events, see the [Event Listeners](https://docs.crewai.com/concepts/event-listener) documentation.
|
||||
|
||||
### Example
|
||||
|
||||
```python
|
||||
# Original task prompt
|
||||
task_prompt = "Answer the following questions about the user's favorite movies: What movie did John watch last week? Format your answer in JSON."
|
||||
|
||||
# Behind the scenes, this might be rewritten as:
|
||||
rewritten_query = "What movies did John watch last week?"
|
||||
```
|
||||
|
||||
The rewritten query is more focused on the core information need and removes irrelevant instructions about output formatting.
|
||||
|
||||
## Clearing Knowledge
|
||||
|
||||
If you need to clear the knowledge stored in CrewAI, you can use the `crewai reset-memories` command with the `--knowledge` option.
|
||||
@@ -450,6 +540,13 @@ crew = Crew(
|
||||
result = crew.kickoff(
|
||||
inputs={"question": "What is the storage capacity of the XPS 13?"}
|
||||
)
|
||||
|
||||
# Resetting the agent specific knowledge via crew object
|
||||
crew.reset_memories(command_type = 'agent_knowledge')
|
||||
|
||||
# Resetting the agent specific knowledge via CLI
|
||||
crewai reset-memories --agent-knowledge
|
||||
crewai reset-memories -akn
|
||||
```
|
||||
|
||||
<Info>
|
||||
@@ -653,4 +750,11 @@ recent_news = SpaceNewsKnowledgeSource(
|
||||
- Configure appropriate embedding models
|
||||
- Consider using local embedding providers for faster processing
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="One Time Knowledge">
|
||||
- With the typical file structure provided by CrewAI, knowledge sources are embedded every time the kickoff is triggered.
|
||||
- If the knowledge sources are large, this leads to inefficiency and increased latency, as the same data is embedded each time.
|
||||
- To resolve this, directly initialize the knowledge parameter instead of the knowledge_sources parameter.
|
||||
- Link to the issue to get complete idea [Github Issue](https://github.com/crewAIInc/crewAI/issues/2755)
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
@@ -4,9 +4,10 @@ description: 'A comprehensive guide to configuring and using Large Language Mode
|
||||
icon: 'microchip-ai'
|
||||
---
|
||||
|
||||
<Note>
|
||||
CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
|
||||
</Note>
|
||||
## Overview
|
||||
|
||||
CrewAI integrates with multiple LLM providers through LiteLLM, giving you the flexibility to choose the right model for your specific use case. This guide will help you understand how to configure and use different LLM providers in your CrewAI projects.
|
||||
|
||||
|
||||
## What are LLMs?
|
||||
|
||||
@@ -27,23 +28,19 @@ Large Language Models (LLMs) are the core intelligence behind CrewAI agents. The
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
## Setting Up Your LLM
|
||||
## Setting up your LLM
|
||||
|
||||
There are three ways to configure LLMs in CrewAI. Choose the method that best fits your workflow:
|
||||
There are different places in CrewAI code where you can specify the model to use. Once you specify the model you are using, you will need to provide the configuration (like an API key) for each of the model providers you use. See the [provider configuration examples](#provider-configuration-examples) section for your provider.
|
||||
|
||||
<Tabs>
|
||||
<Tab title="1. Environment Variables">
|
||||
The simplest way to get started. Set these variables in your environment:
|
||||
The simplest way to get started. Set the model in your environment directly, through an `.env` file or in your app code. If you used `crewai create` to bootstrap your project, it will be set already.
|
||||
|
||||
```bash
|
||||
# Required: Your API key for authentication
|
||||
OPENAI_API_KEY=<your-api-key>
|
||||
```bash .env
|
||||
MODEL=model-id # e.g. gpt-4o, gemini-2.0-flash, claude-3-sonnet-...
|
||||
|
||||
# Optional: Default model selection
|
||||
OPENAI_MODEL_NAME=gpt-4o-mini # Default if not set
|
||||
|
||||
# Optional: Organization ID (if applicable)
|
||||
OPENAI_ORGANIZATION_ID=<your-org-id>
|
||||
# Be sure to set your API keys here too. See the Provider
|
||||
# section below.
|
||||
```
|
||||
|
||||
<Warning>
|
||||
@@ -53,13 +50,13 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
|
||||
<Tab title="2. YAML Configuration">
|
||||
Create a YAML file to define your agent configurations. This method is great for version control and team collaboration:
|
||||
|
||||
```yaml
|
||||
```yaml agents.yaml {6}
|
||||
researcher:
|
||||
role: Research Specialist
|
||||
goal: Conduct comprehensive research and analysis
|
||||
backstory: A dedicated research professional with years of experience
|
||||
verbose: true
|
||||
llm: openai/gpt-4o-mini # your model here
|
||||
llm: provider/model-id # e.g. openai/gpt-4o, google/gemini-2.0-flash, anthropic/claude...
|
||||
# (see provider configuration examples below for more)
|
||||
```
|
||||
|
||||
@@ -74,23 +71,23 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
|
||||
<Tab title="3. Direct Code">
|
||||
For maximum flexibility, configure LLMs directly in your Python code:
|
||||
|
||||
```python
|
||||
```python {4,8}
|
||||
from crewai import LLM
|
||||
|
||||
# Basic configuration
|
||||
llm = LLM(model="gpt-4")
|
||||
llm = LLM(model="model-id-here") # gpt-4o, gemini-2.0-flash, anthropic/claude...
|
||||
|
||||
# Advanced configuration with detailed parameters
|
||||
llm = LLM(
|
||||
model="gpt-4o-mini",
|
||||
model="model-id-here", # gpt-4o, gemini-2.0-flash, anthropic/claude...
|
||||
temperature=0.7, # Higher for more creative outputs
|
||||
timeout=120, # Seconds to wait for response
|
||||
max_tokens=4000, # Maximum length of response
|
||||
top_p=0.9, # Nucleus sampling parameter
|
||||
frequency_penalty=0.1, # Reduce repetition
|
||||
presence_penalty=0.1, # Encourage topic diversity
|
||||
timeout=120, # Seconds to wait for response
|
||||
max_tokens=4000, # Maximum length of response
|
||||
top_p=0.9, # Nucleus sampling parameter
|
||||
frequency_penalty=0.1 , # Reduce repetition
|
||||
presence_penalty=0.1, # Encourage topic diversity
|
||||
response_format={"type": "json"}, # For structured outputs
|
||||
seed=42 # For reproducible results
|
||||
seed=42 # For reproducible results
|
||||
)
|
||||
```
|
||||
|
||||
@@ -110,7 +107,6 @@ There are three ways to configure LLMs in CrewAI. Choose the method that best fi
|
||||
|
||||
## Provider Configuration Examples
|
||||
|
||||
|
||||
CrewAI supports a multitude of LLM providers, each offering unique features, authentication methods, and model capabilities.
|
||||
In this section, you'll find detailed examples that help you select, configure, and optimize the LLM that best fits your project's needs.
|
||||
|
||||
@@ -174,19 +170,55 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
```
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Google">
|
||||
Set the following environment variables in your `.env` file:
|
||||
<Accordion title="Google (Gemini API)">
|
||||
Set your API key in your `.env` file. If you need a key, or need to find an
|
||||
existing key, check [AI Studio](https://aistudio.google.com/apikey).
|
||||
|
||||
```toml Code
|
||||
# Option 1: Gemini accessed with an API key.
|
||||
```toml .env
|
||||
# https://ai.google.dev/gemini-api/docs/api-key
|
||||
GEMINI_API_KEY=<your-api-key>
|
||||
|
||||
# Option 2: Vertex AI IAM credentials for Gemini, Anthropic, and Model Garden.
|
||||
# https://cloud.google.com/vertex-ai/generative-ai/docs/overview
|
||||
```
|
||||
|
||||
Get credentials from your Google Cloud Console and save it to a JSON file with the following code:
|
||||
Example usage in your CrewAI project:
|
||||
```python Code
|
||||
from crewai import LLM
|
||||
|
||||
llm = LLM(
|
||||
model="gemini/gemini-2.0-flash",
|
||||
temperature=0.7,
|
||||
)
|
||||
```
|
||||
|
||||
### Gemini models
|
||||
|
||||
Google offers a range of powerful models optimized for different use cases.
|
||||
|
||||
| Model | Context Window | Best For |
|
||||
|--------------------------------|----------------|-------------------------------------------------------------------|
|
||||
| gemini-2.5-flash-preview-04-17 | 1M tokens | Adaptive thinking, cost efficiency |
|
||||
| gemini-2.5-pro-preview-05-06 | 1M tokens | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
|
||||
| gemini-2.0-flash | 1M tokens | Next generation features, speed, thinking, and realtime streaming |
|
||||
| gemini-2.0-flash-lite | 1M tokens | Cost efficiency and low latency |
|
||||
| gemini-1.5-flash | 1M tokens | Balanced multimodal model, good for most tasks |
|
||||
| gemini-1.5-flash-8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
|
||||
| gemini-1.5-pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
|
||||
|
||||
The full list of models is available in the [Gemini model docs](https://ai.google.dev/gemini-api/docs/models).
|
||||
|
||||
### Gemma
|
||||
|
||||
The Gemini API also allows you to use your API key to access [Gemma models](https://ai.google.dev/gemma/docs) hosted on Google infrastructure.
|
||||
|
||||
| Model | Context Window |
|
||||
|----------------|----------------|
|
||||
| gemma-3-1b-it | 32k tokens |
|
||||
| gemma-3-4b-it | 32k tokens |
|
||||
| gemma-3-12b-it | 32k tokens |
|
||||
| gemma-3-27b-it | 128k tokens |
|
||||
|
||||
</Accordion>
|
||||
<Accordion title="Google (Vertex AI)">
|
||||
Get credentials from your Google Cloud Console and save it to a JSON file, then load it with the following code:
|
||||
```python Code
|
||||
import json
|
||||
|
||||
@@ -210,14 +242,18 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
vertex_credentials=vertex_credentials_json
|
||||
)
|
||||
```
|
||||
|
||||
Google offers a range of powerful models optimized for different use cases:
|
||||
|
||||
| Model | Context Window | Best For |
|
||||
|-----------------------|----------------|------------------------------------------------------------------|
|
||||
| gemini-2.0-flash-exp | 1M tokens | Higher quality at faster speed, multimodal model, good for most tasks |
|
||||
| gemini-1.5-flash | 1M tokens | Balanced multimodal model, good for most tasks |
|
||||
| gemini-1.5-flash-8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
|
||||
| gemini-1.5-pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
|
||||
| Model | Context Window | Best For |
|
||||
|--------------------------------|----------------|-------------------------------------------------------------------|
|
||||
| gemini-2.5-flash-preview-04-17 | 1M tokens | Adaptive thinking, cost efficiency |
|
||||
| gemini-2.5-pro-preview-05-06 | 1M tokens | Enhanced thinking and reasoning, multimodal understanding, advanced coding, and more |
|
||||
| gemini-2.0-flash | 1M tokens | Next generation features, speed, thinking, and realtime streaming |
|
||||
| gemini-2.0-flash-lite | 1M tokens | Cost efficiency and low latency |
|
||||
| gemini-1.5-flash | 1M tokens | Balanced multimodal model, good for most tasks |
|
||||
| gemini-1.5-flash-8B | 1M tokens | Fastest, most cost-efficient, good for high-frequency tasks |
|
||||
| gemini-1.5-pro | 2M tokens | Best performing, wide variety of reasoning tasks including logical reasoning, coding, and creative collaboration |
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Azure">
|
||||
@@ -383,7 +419,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
| microsoft/phi-3-medium-4k-instruct | 4,096 tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
|
||||
| microsoft/phi-3-medium-128k-instruct | 128K tokens | Lightweight, state-of-the-art open LLM with strong math and logical reasoning skills. |
|
||||
| microsoft/phi-3.5-mini-instruct | 128K tokens | Lightweight multilingual LLM powering AI applications in latency bound, memory/compute constrained environments |
|
||||
| microsoft/phi-3.5-moe-instruct | 128K tokens | Advanced LLM based on Mixture of Experts architecure to deliver compute efficient content generation |
|
||||
| microsoft/phi-3.5-moe-instruct | 128K tokens | Advanced LLM based on Mixture of Experts architecture to deliver compute efficient content generation |
|
||||
| microsoft/kosmos-2 | 1,024 tokens | Groundbreaking multimodal model designed to understand and reason about visual elements in images. |
|
||||
| microsoft/phi-3-vision-128k-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
|
||||
| microsoft/phi-3.5-vision-instruct | 128k tokens | Cutting-edge open multimodal model exceling in high-quality reasoning from images. |
|
||||
@@ -407,19 +443,19 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Local NVIDIA NIM Deployed using WSL2">
|
||||
|
||||
NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
|
||||
This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
|
||||
|
||||
NVIDIA NIM enables you to run powerful LLMs locally on your Windows machine using WSL2 (Windows Subsystem for Linux).
|
||||
This approach allows you to leverage your NVIDIA GPU for private, secure, and cost-effective AI inference without relying on cloud services.
|
||||
Perfect for development, testing, or production scenarios where data privacy or offline capabilities are required.
|
||||
|
||||
|
||||
Here is a step-by-step guide to setting up a local NVIDIA NIM model:
|
||||
|
||||
|
||||
1. Follow installation instructions from [NVIDIA Website](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)
|
||||
|
||||
2. Install the local model. For Llama 3.1-8b follow [instructions](https://build.nvidia.com/meta/llama-3_1-8b-instruct/deploy)
|
||||
|
||||
3. Configure your crewai local models:
|
||||
|
||||
|
||||
```python Code
|
||||
from crewai.llm import LLM
|
||||
|
||||
@@ -441,7 +477,7 @@ In this section, you'll find detailed examples that help you select, configure,
|
||||
config=self.agents_config['researcher'], # type: ignore[index]
|
||||
llm=local_nvidia_nim_llm
|
||||
)
|
||||
|
||||
|
||||
# ...
|
||||
```
|
||||
</Accordion>
|
||||
@@ -637,23 +673,29 @@ CrewAI supports streaming responses from LLMs, allowing your application to rece
|
||||
|
||||
When streaming is enabled, responses are delivered in chunks as they're generated, creating a more responsive user experience.
|
||||
</Tab>
|
||||
|
||||
|
||||
<Tab title="Event Handling">
|
||||
CrewAI emits events for each chunk received during streaming:
|
||||
|
||||
|
||||
```python
|
||||
from crewai import LLM
|
||||
from crewai.utilities.events import EventHandler, LLMStreamChunkEvent
|
||||
|
||||
class MyEventHandler(EventHandler):
|
||||
def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
|
||||
# Process each chunk as it arrives
|
||||
print(f"Received chunk: {event.chunk}")
|
||||
|
||||
# Register the event handler
|
||||
from crewai.utilities.events import crewai_event_bus
|
||||
crewai_event_bus.register_handler(MyEventHandler())
|
||||
from crewai.utilities.events import (
|
||||
LLMStreamChunkEvent
|
||||
)
|
||||
from crewai.utilities.events.base_event_listener import BaseEventListener
|
||||
|
||||
class MyCustomListener(BaseEventListener):
|
||||
def setup_listeners(self, crewai_event_bus):
|
||||
@crewai_event_bus.on(LLMStreamChunkEvent)
|
||||
def on_llm_stream_chunk(self, event: LLMStreamChunkEvent):
|
||||
# Process each chunk as it arrives
|
||||
print(f"Received chunk: {event.chunk}")
|
||||
|
||||
my_listener = MyCustomListener()
|
||||
```
|
||||
|
||||
<Tip>
|
||||
[Click here](https://docs.crewai.com/concepts/event-listener#event-listeners) for more details
|
||||
</Tip>
|
||||
</Tab>
|
||||
</Tabs>
|
||||
|
||||
@@ -750,6 +792,24 @@ Learn how to get the most out of your LLM configuration:
|
||||
Remember to regularly monitor your token usage and adjust your configuration as needed to optimize costs and performance.
|
||||
</Info>
|
||||
</Accordion>
|
||||
|
||||
<Accordion title="Drop Additional Parameters">
|
||||
CrewAI internally uses Litellm for LLM calls, which allows you to drop additional parameters that are not needed for your specific use case. This can help simplify your code and reduce the complexity of your LLM configuration.
|
||||
For example, if you don't need to send the <code>stop</code> parameter, you can simply omit it from your LLM call:
|
||||
|
||||
```python
|
||||
from crewai import LLM
|
||||
import os
|
||||
|
||||
os.environ["OPENAI_API_KEY"] = "<api-key>"
|
||||
|
||||
o3_llm = LLM(
|
||||
model="o3",
|
||||
drop_params=True,
|
||||
additional_drop_params=["stop"]
|
||||
)
|
||||
```
|
||||
</Accordion>
|
||||
</AccordionGroup>
|
||||
|
||||
## Common Issues and Solutions
|
||||
@@ -785,7 +845,7 @@ Learn how to get the most out of your LLM configuration:
|
||||
<Tip>
|
||||
Use larger context models for extensive tasks
|
||||
</Tip>
|
||||
|
||||
|
||||
```python
|
||||
# Large context model
|
||||
llm = LLM(model="openai/gpt-4o") # 128K tokens
|
||||
|
||||
@@ -4,7 +4,7 @@ description: Leveraging memory systems in the CrewAI framework to enhance agent
|
||||
icon: database
|
||||
---
|
||||
|
||||
## Introduction to Memory Systems in CrewAI
|
||||
## Overview
|
||||
|
||||
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,
|
||||
@@ -679,6 +679,7 @@ crewai reset-memories [OPTIONS]
|
||||
| `-e`, `--entities` | Reset ENTITIES memory. | Flag (boolean) | False |
|
||||
| `-k`, `--kickoff-outputs` | Reset LATEST KICKOFF TASK OUTPUTS. | Flag (boolean) | False |
|
||||
| `-kn`, `--knowledge` | Reset KNOWLEDEGE storage | Flag (boolean) | False |
|
||||
| `-akn`, `--agent-knowledge` | Reset AGENT KNOWLEDGE storage | Flag (boolean) | False |
|
||||
| `-a`, `--all` | Reset ALL memories. | Flag (boolean) | False |
|
||||
|
||||
Note: To use the cli command you need to have your crew in a file called crew.py in the same directory.
|
||||
@@ -716,9 +717,11 @@ my_crew.reset_memories(command_type = 'all') # Resets all the memory
|
||||
| `entities` | Reset ENTITIES memory. |
|
||||
| `kickoff_outputs` | Reset LATEST KICKOFF TASK OUTPUTS. |
|
||||
| `knowledge` | Reset KNOWLEDGE memory. |
|
||||
| `agent_knowledge` | Reset AGENT KNOWLEDGE memory. |
|
||||
| `all` | Reset ALL memories. |
|
||||
|
||||
|
||||
|
||||
## 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.
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
---
|
||||
title: Planning
|
||||
description: Learn how to add planning to your CrewAI Crew and improve their performance.
|
||||
icon: brain
|
||||
icon: ruler-combined
|
||||
---
|
||||
|
||||
## Introduction
|
||||
## Overview
|
||||
|
||||
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.
|
||||
|
||||
@@ -4,7 +4,8 @@ description: Detailed guide on workflow management through processes in CrewAI,
|
||||
icon: bars-staggered
|
||||
---
|
||||
|
||||
## Understanding Processes
|
||||
## Overview
|
||||
|
||||
<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.
|
||||
|
||||
147
docs/concepts/reasoning.mdx
Normal file
147
docs/concepts/reasoning.mdx
Normal file
@@ -0,0 +1,147 @@
|
||||
---
|
||||
title: Reasoning
|
||||
description: "Learn how to enable and use agent reasoning to improve task execution."
|
||||
icon: brain
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Agent reasoning is a feature that allows agents to reflect on a task and create a plan before execution. This helps agents approach tasks more methodically and ensures they're ready to perform the assigned work.
|
||||
|
||||
## Usage
|
||||
|
||||
To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
|
||||
|
||||
```python
|
||||
from crewai import Agent
|
||||
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze complex datasets and provide insights",
|
||||
backstory="You are an experienced data analyst with expertise in finding patterns in complex data.",
|
||||
reasoning=True, # Enable reasoning
|
||||
max_reasoning_attempts=3 # Optional: Set a maximum number of reasoning attempts
|
||||
)
|
||||
```
|
||||
|
||||
## How It Works
|
||||
|
||||
When reasoning is enabled, before executing a task, the agent will:
|
||||
|
||||
1. Reflect on the task and create a detailed plan
|
||||
2. Evaluate whether it's ready to execute the task
|
||||
3. Refine the plan as necessary until it's ready or max_reasoning_attempts is reached
|
||||
4. Inject the reasoning plan into the task description before execution
|
||||
|
||||
This process helps the agent break down complex tasks into manageable steps and identify potential challenges before starting.
|
||||
|
||||
## Configuration Options
|
||||
|
||||
<ParamField body="reasoning" type="bool" default="False">
|
||||
Enable or disable reasoning
|
||||
</ParamField>
|
||||
|
||||
<ParamField body="max_reasoning_attempts" type="int" default="None">
|
||||
Maximum number of attempts to refine the plan before proceeding with execution. If None (default), the agent will continue refining until it's ready.
|
||||
</ParamField>
|
||||
|
||||
## Example
|
||||
|
||||
Here's a complete example:
|
||||
|
||||
```python
|
||||
from crewai import Agent, Task, Crew
|
||||
|
||||
# Create an agent with reasoning enabled
|
||||
analyst = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze data and provide insights",
|
||||
backstory="You are an expert data analyst.",
|
||||
reasoning=True,
|
||||
max_reasoning_attempts=3 # Optional: Set a limit on reasoning attempts
|
||||
)
|
||||
|
||||
# Create a task
|
||||
analysis_task = Task(
|
||||
description="Analyze the provided sales data and identify key trends.",
|
||||
expected_output="A report highlighting the top 3 sales trends.",
|
||||
agent=analyst
|
||||
)
|
||||
|
||||
# Create a crew and run the task
|
||||
crew = Crew(agents=[analyst], tasks=[analysis_task])
|
||||
result = crew.kickoff()
|
||||
|
||||
print(result)
|
||||
```
|
||||
|
||||
## Error Handling
|
||||
|
||||
The reasoning process is designed to be robust, with error handling built in. If an error occurs during reasoning, the agent will proceed with executing the task without the reasoning plan. This ensures that tasks can still be executed even if the reasoning process fails.
|
||||
|
||||
Here's how to handle potential errors in your code:
|
||||
|
||||
```python
|
||||
from crewai import Agent, Task
|
||||
import logging
|
||||
|
||||
# Set up logging to capture any reasoning errors
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
|
||||
# Create an agent with reasoning enabled
|
||||
agent = Agent(
|
||||
role="Data Analyst",
|
||||
goal="Analyze data and provide insights",
|
||||
reasoning=True,
|
||||
max_reasoning_attempts=3
|
||||
)
|
||||
|
||||
# Create a task
|
||||
task = Task(
|
||||
description="Analyze the provided sales data and identify key trends.",
|
||||
expected_output="A report highlighting the top 3 sales trends.",
|
||||
agent=agent
|
||||
)
|
||||
|
||||
# Execute the task
|
||||
# If an error occurs during reasoning, it will be logged and execution will continue
|
||||
result = agent.execute_task(task)
|
||||
```
|
||||
|
||||
## Example Reasoning Output
|
||||
|
||||
Here's an example of what a reasoning plan might look like for a data analysis task:
|
||||
|
||||
```
|
||||
Task: Analyze the provided sales data and identify key trends.
|
||||
|
||||
Reasoning Plan:
|
||||
I'll analyze the sales data to identify the top 3 trends.
|
||||
|
||||
1. Understanding of the task:
|
||||
I need to analyze sales data to identify key trends that would be valuable for business decision-making.
|
||||
|
||||
2. Key steps I'll take:
|
||||
- First, I'll examine the data structure to understand what fields are available
|
||||
- Then I'll perform exploratory data analysis to identify patterns
|
||||
- Next, I'll analyze sales by time periods to identify temporal trends
|
||||
- I'll also analyze sales by product categories and customer segments
|
||||
- Finally, I'll identify the top 3 most significant trends
|
||||
|
||||
3. Approach to challenges:
|
||||
- If the data has missing values, I'll decide whether to fill or filter them
|
||||
- If the data has outliers, I'll investigate whether they're valid data points or errors
|
||||
- If trends aren't immediately obvious, I'll apply statistical methods to uncover patterns
|
||||
|
||||
4. Use of available tools:
|
||||
- I'll use data analysis tools to explore and visualize the data
|
||||
- I'll use statistical tools to identify significant patterns
|
||||
- I'll use knowledge retrieval to access relevant information about sales analysis
|
||||
|
||||
5. Expected outcome:
|
||||
A concise report highlighting the top 3 sales trends with supporting evidence from the data.
|
||||
|
||||
READY: I am ready to execute the task.
|
||||
```
|
||||
|
||||
This reasoning plan helps the agent organize its approach to the task, consider potential challenges, and ensure it delivers the expected output.
|
||||
@@ -4,7 +4,7 @@ description: Detailed guide on managing and creating tasks within the CrewAI fra
|
||||
icon: list-check
|
||||
---
|
||||
|
||||
## Overview of a Task
|
||||
## Overview
|
||||
|
||||
In the CrewAI framework, a `Task` is a specific assignment completed by an `Agent`.
|
||||
|
||||
@@ -15,7 +15,7 @@ Tasks within CrewAI can be collaborative, requiring multiple agents to work toge
|
||||
<Note type="info" title="Enterprise Enhancement: Visual Task Builder">
|
||||
CrewAI Enterprise includes a Visual Task Builder in Crew Studio that simplifies complex task creation and chaining. Design your task flows visually and test them in real-time without writing code.
|
||||
|
||||
|
||||
|
||||
|
||||
The Visual Task Builder enables:
|
||||
- Drag-and-drop task creation
|
||||
|
||||
@@ -4,7 +4,7 @@ description: Learn how to test your CrewAI Crew and evaluate their performance.
|
||||
icon: vial
|
||||
---
|
||||
|
||||
## Introduction
|
||||
## Overview
|
||||
|
||||
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.
|
||||
|
||||
|
||||
@@ -4,7 +4,7 @@ description: Understanding and leveraging tools within the CrewAI framework for
|
||||
icon: screwdriver-wrench
|
||||
---
|
||||
|
||||
## Introduction
|
||||
## Overview
|
||||
|
||||
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.
|
||||
@@ -18,8 +18,6 @@ enabling everything from simple searches to complex interactions and effective t
|
||||
<Note type="info" title="Enterprise Enhancement: Tools Repository">
|
||||
CrewAI Enterprise provides a comprehensive Tools Repository with pre-built integrations for common business systems and APIs. Deploy agents with enterprise tools in minutes instead of days.
|
||||
|
||||
|
||||
|
||||
The Enterprise Tools Repository includes:
|
||||
- Pre-built connectors for popular enterprise systems
|
||||
- Custom tool creation interface
|
||||
|
||||
@@ -4,7 +4,7 @@ description: Learn how to train your CrewAI agents by giving them feedback early
|
||||
icon: dumbbell
|
||||
---
|
||||
|
||||
## Introduction
|
||||
## Overview
|
||||
|
||||
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.
|
||||
|
||||
Reference in New Issue
Block a user