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Merge branch 'main' into brandon/cre-402-add-input-into-flow-kickoff

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Brandon Hancock
2024-11-01 16:00:45 -04:00
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26
docs/concepts/flows.mdx
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@@ -572,18 +572,20 @@ This command will create a new directory for your crew within the `crews` folder
After adding a new crew, your folder structure will look like this:
name_of_flow/
├── crews/
│ ├── poem_crew/
│ │ ├── config/
│ │ │ ├── agents.yaml
│ │ │ └── tasks.yaml
│ │ └── poem_crew.py
│ └── name_of_crew/
│ ├── config/
│ │ ├── agents.yaml
│ │ └── tasks.yaml
│ └── name_of_crew.py
| 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. |
| └── `name_of_crew/` | Directory for the new crew. |
| ├── `config/` | Configuration files directory for the new crew. |
| │ ├── `agents.yaml` | YAML file defining the agents for the new crew. |
| │ └── `tasks.yaml` | YAML file defining the tasks for the new crew. |
| └── `name_of_crew.py` | Script for the new crew functionality. |
You can then customize the `agents.yaml` and `tasks.yaml` files to define the agents and tasks for your new crew. The `name_of_crew.py` file will contain the crew's logic, which you can modify to suit your needs.

257
docs/concepts/llms.mdx
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@@ -25,52 +25,55 @@ By default, CrewAI uses the `gpt-4o-mini` model. It uses environment variables i
- `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
### 2. Custom LLM Objects
Pass a custom LLM implementation or object from another library.
See below for examples.
<Tabs>
<Tab title="String Identifier">
```python Code
agent = Agent(llm="gpt-4o", ...)
```
</Tab>
<Tab title="LLM Instance">
```python Code
from crewai import LLM
llm = LLM(model="gpt-4", temperature=0.7)
agent = Agent(llm=llm, ...)
```
</Tab>
</Tabs>
## 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:
<Tabs>
<Tab title="Using Environment Variables">
```python Code
import os
```python Code
import os
os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["OPENAI_API_BASE"] = "https://api.your-provider.com/v1"
```
</Tab>
<Tab title="Using LLM Class Attributes">
```python Code
from crewai import LLM
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
from crewai import LLM
llm = LLM(
model="custom-model-name",
api_key="your-api-key",
base_url="https://api.your-provider.com/v1"
)
agent = Agent(llm=llm, ...)
```
llm = LLM(
model="custom-model-name",
api_key="your-api-key",
base_url="https://api.your-provider.com/v1"
)
agent = Agent(llm=llm, ...)
```
</Tab>
</Tabs>
## LLM Configuration Options
@@ -97,55 +100,157 @@ When configuring an LLM for your agent, you have access to a wide range of param
| **api_key** | `str` | Your API key for authentication. |
## OpenAI Example Configuration
These are examples of how to configure LLMs for your agent.
```python Code
from crewai import LLM
<AccordionGroup>
<Accordion title="OpenAI">
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, ...)
```
```python Code
from crewai import LLM
## Cerebras Example Configuration
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, ...)
```
</Accordion>
```python Code
from crewai import LLM
<Accordion title="Cerebras">
llm = LLM(
model="cerebras/llama-3.1-70b",
base_url="https://api.cerebras.ai/v1",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
```python Code
from crewai import LLM
## Using Ollama (Local LLMs)
llm = LLM(
model="cerebras/llama-3.1-70b",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="Ollama (Local LLMs)">
CrewAI supports using Ollama for running open-source models locally:
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:
1. Install Ollama: [ollama.ai](https://ollama.ai/)
2. Run a model: `ollama run llama2`
3. Configure agent:
```python Code
from crewai import LLM
```python Code
from crewai import LLM
agent = Agent(
llm=LLM(model="ollama/llama3.1", base_url="http://localhost:11434"),
...
)
```
agent = Agent(
llm=LLM(
model="ollama/llama3.1",
base_url="http://localhost:11434"
),
...
)
```
</Accordion>
<Accordion title="Groq">
```python Code
from crewai import LLM
llm = LLM(
model="groq/llama3-8b-8192",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="Anthropic">
```python Code
from crewai import LLM
llm = LLM(
model="anthropic/claude-3-5-sonnet-20241022",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="Fireworks AI">
```python Code
from crewai import LLM
llm = LLM(
model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="Gemini">
```python Code
from crewai import LLM
llm = LLM(
model="gemini/gemini-1.5-pro-002",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="Perplexity AI (pplx-api)">
```python Code
from crewai import LLM
llm = LLM(
model="perplexity/mistral-7b-instruct",
base_url="https://api.perplexity.ai/v1",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="IBM watsonx.ai">
```python Code
from crewai import LLM
llm = LLM(
model="watsonx/ibm/granite-13b-chat-v2",
base_url="https://api.watsonx.ai/v1",
api_key="your-api-key-here"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
<Accordion title="Hugging Face">
```python Code
from crewai import LLM
llm = LLM(
model="huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct",
api_key="your-api-key-here",
base_url="your_api_endpoint"
)
agent = Agent(llm=llm, ...)
```
</Accordion>
</AccordionGroup>
## Changing the Base API URL

99
docs/concepts/tools.mdx
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@@ -5,13 +5,14 @@ icon: screwdriver-wrench
---
## Introduction
CrewAI tools empower agents with capabilities ranging from web searching and data analysis to collaboration and delegating tasks among coworkers.
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),
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
@@ -103,57 +104,53 @@ crew.kickoff()
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. |
| 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.
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:
There are two main ways for one to create a CrewAI tool:
### Subclassing `BaseTool`
```python Code
from crewai_tools import BaseTool
from crewai.tools import BaseTool
class MyCustomTool(BaseTool):
name: str = "Name of my tool"
@@ -167,7 +164,7 @@ class MyCustomTool(BaseTool):
### Utilizing the `tool` Decorator
```python Code
from crewai_tools import tool
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."""
@@ -178,11 +175,13 @@ def my_tool(question: str) -> str:
### 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.
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
from crewai.tools import tool
@tool
def multiplication_tool(first_number: int, second_number: int) -> str:
@@ -208,6 +207,6 @@ writer1 = Agent(
## 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.
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.

18
docs/how-to/create-custom-tools.mdx
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@@ -6,25 +6,17 @@ icon: hammer
## Creating and Utilizing Tools in CrewAI
This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools,
incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
This guide provides detailed instructions on creating custom tools for the CrewAI framework and how to efficiently manage and utilize these tools,
incorporating the latest functionalities such as tool delegation, error handling, and dynamic tool calling. It also highlights the importance of collaboration tools,
enabling agents to perform a wide range of actions.
### Prerequisites
Before creating your own tools, ensure you have the crewAI extra tools package installed:
```bash
pip install 'crewai[tools]'
```
### Subclassing `BaseTool`
To create a personalized tool, inherit from `BaseTool` and define the necessary attributes, including the `args_schema` for input validation, and the `_run` method.
```python Code
from typing import Type
from crewai_tools import BaseTool
from crewai.tools import BaseTool
from pydantic import BaseModel, Field
class MyToolInput(BaseModel):
@@ -47,7 +39,7 @@ Alternatively, you can use the tool decorator `@tool`. This approach allows you
offering a concise and efficient way to create specialized tools tailored to your needs.
```python Code
from crewai_tools import tool
from crewai.tools import tool
@tool("Tool Name")
def my_simple_tool(question: str) -> str:
@@ -73,5 +65,5 @@ def my_cache_strategy(arguments: dict, result: str) -> bool:
cached_tool.cache_function = my_cache_strategy
```
By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
By adhering to these guidelines and incorporating new functionalities and collaboration tools into your tool creation and management processes,
you can leverage the full capabilities of the CrewAI framework, enhancing both the development experience and the efficiency of your AI agents.