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Tony Kipkemboi
2025-05-21 11:24:10 -04:00
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2
docs/concepts/agents.mdx
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@@ -21,7 +21,7 @@ Think of an agent as a specialized team member with specific skills, expertise,
<Note type="info" title="Enterprise Enhancement: Visual Agent Builder">
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.
![Visual Agent Builder Screenshot](../images/enterprise/crew-studio-quickstart)
![Visual Agent Builder Screenshot](/images/enterprise/crew-studio-interface.png)
The Visual Agent Builder enables:
- Intuitive agent configuration with form-based interfaces

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docs/concepts/cli.mdx
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@@ -4,7 +4,7 @@ description: Learn how to use the CrewAI CLI to interact with CrewAI.
icon: terminal
---
# CrewAI CLI Documentation
## Overview
The CrewAI CLI provides a set of commands to interact with CrewAI, allowing you to create, train, run, and manage crews & flows.

2
docs/concepts/collaboration.mdx
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@@ -4,7 +4,7 @@ description: Exploring the dynamics of agent collaboration within the CrewAI fra
icon: screen-users
---
## Collaboration Fundamentals
## Overview
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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docs/concepts/crews.mdx
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@@ -4,7 +4,7 @@ description: Understanding and utilizing crews in the crewAI framework with comp
icon: people-group
---
## What is a Crew?
## Overview
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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docs/concepts/event-listener.mdx
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@@ -4,7 +4,7 @@ description: 'Tap into CrewAI events to build custom integrations and monitoring
icon: spinner
---
# Event Listeners
## Overview
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.
@@ -21,7 +21,7 @@ When specific actions occur in CrewAI (like a Crew starting execution, an Agent
<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.
![Prompt Tracing Dashboard](../images/enterprise/prompt-tracing.png)
![Prompt Tracing Dashboard](/images/enterprise/traces-overview.png)
With Prompt Tracing you can:
- View the complete history of all prompts sent to your LLM
@@ -224,6 +224,15 @@ CrewAI provides a wide range of events that you can listen for:
- **ToolExecutionErrorEvent**: Emitted when a tool execution encounters an error
- **ToolSelectionErrorEvent**: Emitted when there's an error selecting a tool
### Knowledge Events
- **KnowledgeRetrievalStartedEvent**: Emitted when a knowledge retrieval is started
- **KnowledgeRetrievalCompletedEvent**: Emitted when a knowledge retrieval is completed
- **KnowledgeQueryStartedEvent**: Emitted when a knowledge query is started
- **KnowledgeQueryCompletedEvent**: Emitted when a knowledge query is completed
- **KnowledgeQueryFailedEvent**: Emitted when a knowledge query fails
- **KnowledgeSearchQueryFailedEvent**: Emitted when a knowledge search query fails
### Flow Events
- **FlowCreatedEvent**: Emitted when a Flow is created

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docs/concepts/flows.mdx
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@@ -4,7 +4,7 @@ description: Learn how to create and manage AI workflows using CrewAI Flows.
icon: arrow-progress
---
## Introduction
## Overview
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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docs/concepts/knowledge.mdx
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@@ -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.
@@ -432,6 +435,46 @@ Query rewriting happens transparently using a system prompt that instructs the L
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

7
docs/concepts/llms.mdx
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@@ -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?

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docs/concepts/memory.mdx
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@@ -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,

4
docs/concepts/planning.mdx
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@@ -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.

3
docs/concepts/processes.mdx
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@@ -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.

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docs/concepts/reasoning.mdx
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@@ -1,12 +1,14 @@
---
title: "Agent Reasoning"
title: Reasoning
description: "Learn how to enable and use agent reasoning to improve task execution."
icon: brain
---
# Agent Reasoning
## 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.
## How to Use Agent Reasoning
## Usage
To enable reasoning for an agent, simply set `reasoning=True` when creating the agent:
@@ -35,8 +37,13 @@ This process helps the agent break down complex tasks into manageable steps and
## Configuration Options
- `reasoning` (bool): Enable or disable reasoning (default: False)
- `max_reasoning_attempts` (int, optional): 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 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

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docs/concepts/tasks.mdx
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@@ -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.
![Task Builder Screenshot](../images/enterprise/crew-studio-quickstart.png)
![Task Builder Screenshot](/images/enterprise/crew-studio-interface.png)
The Visual Task Builder enables:
- Drag-and-drop task creation

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docs/concepts/testing.mdx
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@@ -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.

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docs/concepts/tools.mdx
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@@ -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.
![Tools Repository Screenshot](../images/enterprise/tools-repository.png)
The Enterprise Tools Repository includes:
- Pre-built connectors for popular enterprise systems
- Custom tool creation interface

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docs/concepts/training.mdx
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@@ -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.

1
docs/docs.json
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@@ -74,6 +74,7 @@
"concepts/collaboration",
"concepts/training",
"concepts/memory",
"concepts/reasoning",
"concepts/planning",
"concepts/testing",
"concepts/cli",

2
src/crewai/cli/utils.py
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@@ -161,7 +161,7 @@ def tree_find_and_replace(directory, find, replace):
for filename in files:
filepath = os.path.join(path, filename)
with open(filepath, "r") as file:
with open(filepath, "r", encoding="utf-8", errors="ignore") as file:
contents = file.read()
with open(filepath, "w") as file:
file.write(contents.replace(find, replace))

14
src/crewai/utilities/events/event_types.py
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@@ -48,6 +48,14 @@ from .reasoning_events import (
AgentReasoningCompletedEvent,
AgentReasoningFailedEvent,
)
from .knowledge_events import (
KnowledgeRetrievalStartedEvent,
KnowledgeRetrievalCompletedEvent,
KnowledgeQueryStartedEvent,
KnowledgeQueryCompletedEvent,
KnowledgeQueryFailedEvent,
KnowledgeSearchQueryFailedEvent,
)
EventTypes = Union[
CrewKickoffStartedEvent,
@@ -82,4 +90,10 @@ EventTypes = Union[
AgentReasoningStartedEvent,
AgentReasoningCompletedEvent,
AgentReasoningFailedEvent,
KnowledgeRetrievalStartedEvent,
KnowledgeRetrievalCompletedEvent,
KnowledgeQueryStartedEvent,
KnowledgeQueryCompletedEvent,
KnowledgeQueryFailedEvent,
KnowledgeSearchQueryFailedEvent,
]