Skip to content

Commit

Permalink
Update README (#1376)
Browse files Browse the repository at this point in the history 
* Change all instaces of crewAI to CrewAI and fix installation step

* Update the  example to use YAML format

* Update  to come after setup and edits

* Remove double tool instance
  • Loading branch information
@tonykipkemboi
tonykipkemboi authored Oct 3, 2024
1 parent c50d62b commit 5059aef
Showing 1 changed file with 211 additions and 72 deletions.
283 changes: 211 additions & 72 deletions README.md
View file
Original file line number Diff line number Diff line change
@@ -1,10 +1,10 @@
<div align="center">

![Logo of crewAI, two people rowing on a boat](./docs/crewai_logo.png)
![Logo of CrewAI, two people rowing on a boat](./docs/crewai_logo.png)

# **crewAI**
# **CrewAI**

🤖 **crewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
🤖 **CrewAI**: Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.

<h3>

Expand Down Expand Up @@ -44,83 +44,222 @@ To get started with CrewAI, follow these simple steps:

### 1. Installation

Ensure you have Python >=3.10 <=3.13 installed on your system. CrewAI uses [Poetry](https://python-poetry.org/) for dependency management and package handling, offering a seamless setup and execution experience.

First, if you haven't already, install Poetry:

```bash
pip install poetry
```

Then, install CrewAI:

```shell
pip install crewai
```

If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command: pip install 'crewai[tools]'. This command installs the basic package and also adds extra components which require more dependencies to function."
If you want to install the 'crewai' package along with its optional features that include additional tools for agents, you can do so by using the following command:

```shell
pip install 'crewai[tools]'
```
The command above installs the basic package and also adds extra components which require more dependencies to function.

### 2. Setting Up Your Crew with the YAML Configuration

To create a new CrewAI project, run the following CLI (Command Line Interface) command:

```shell
crewai create crew <project_name>
```

This command creates a new project folder with the following structure:

```
my_project/
├── .gitignore
├── pyproject.toml
├── README.md
├── .env
└── src/
└── my_project/
├── __init__.py
├── main.py
├── crew.py
├── tools/
│ ├── custom_tool.py
│ └── __init__.py
└── config/
├── agents.yaml
└── tasks.yaml
```

You can now start developing your crew by editing the files in the `src/my_project` folder. The `main.py` file is the entry point of the project, the `crew.py` file is where you define your crew, the `agents.yaml` file is where you define your agents, and the `tasks.yaml` file is where you define your tasks.

#### To customize your project, you can:

- Modify `src/my_project/config/agents.yaml` to define your agents.
- Modify `src/my_project/config/tasks.yaml` to define your tasks.
- Modify `src/my_project/crew.py` to add your own logic, tools, and specific arguments.
- Modify `src/my_project/main.py` to add custom inputs for your agents and tasks.
- Add your environment variables into the `.env` file.

#### Example of a simple crew with a sequential process:

Instatiate your crew:

```shell
crewai create crew latest-ai-development
```

### 2. Setting Up Your Crew
Modify the files as needed to fit your use case:

**agents.yaml**

```yaml
# src/my_project/config/agents.yaml
researcher:
role: >
{topic} Senior Data Researcher
goal: >
Uncover cutting-edge developments in {topic}
backstory: >
You're a seasoned researcher with a knack for uncovering the latest
developments in {topic}. Known for your ability to find the most relevant
information and present it in a clear and concise manner.
reporting_analyst:
role: >
{topic} Reporting Analyst
goal: >
Create detailed reports based on {topic} data analysis and research findings
backstory: >
You're a meticulous analyst with a keen eye for detail. You're known for
your ability to turn complex data into clear and concise reports, making
it easy for others to understand and act on the information you provide.
```
**tasks.yaml**
```yaml
# src/my_project/config/tasks.yaml
research_task:
description: >
Conduct a thorough research about {topic}
Make sure you find any interesting and relevant information given
the current year is 2024.
expected_output: >
A list with 10 bullet points of the most relevant information about {topic}
agent: researcher

reporting_task:
description: >
Review the context you got and expand each topic into a full section for a report.
Make sure the report is detailed and contains any and all relevant information.
expected_output: >
A fully fledge reports with the mains topics, each with a full section of information.
Formatted as markdown without '```'
agent: reporting_analyst
output_file: report.md
```
**crew.py**
```python
import os
from crewai import Agent, Task, Crew, Process
# src/my_project/crew.py
from crewai import Agent, Crew, Process, Task
from crewai.project import CrewBase, agent, crew, task
from crewai_tools import SerperDevTool

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
os.environ["SERPER_API_KEY"] = "Your Key" # serper.dev API key

# It can be a local model through Ollama / LM Studio or a remote
# model like OpenAI, Mistral, Antrophic or others (https://docs.crewai.com/how-to/LLM-Connections/)

# Define your agents with roles and goals
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
backstory="""You work at a leading tech think tank.
Your expertise lies in identifying emerging trends.
You have a knack for dissecting complex data and presenting actionable insights.""",
verbose=True,
allow_delegation=False,
# You can pass an optional llm attribute specifying what model you wanna use.
# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7),
tools=[SerperDevTool()]
)
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
You transform complex concepts into compelling narratives.""",
verbose=True,
allow_delegation=True
)

# Create tasks for your agents
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.""",
expected_output="Full analysis report in bullet points",
agent=researcher
)

task2 = Task(
description="""Using the insights provided, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Make it sound cool, avoid complex words so it doesn't sound like AI.""",
expected_output="Full blog post of at least 4 paragraphs",
agent=writer
)

# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=True,
process = Process.sequential
)

# Get your crew to work!
result = crew.kickoff()

print("######################")
print(result)
@CrewBase
class LatestAiDevelopmentCrew():
"""LatestAiDevelopment crew"""

@agent
def researcher(self) -> Agent:
return Agent(
config=self.agents_config['researcher'],
verbose=True,
tools=[SerperDevTool()]
)

@agent
def reporting_analyst(self) -> Agent:
return Agent(
config=self.agents_config['reporting_analyst'],
verbose=True
)

@task
def research_task(self) -> Task:
return Task(
config=self.tasks_config['research_task'],
)

@task
def reporting_task(self) -> Task:
return Task(
config=self.tasks_config['reporting_task'],
output_file='report.md'
)

@crew
def crew(self) -> Crew:
"""Creates the LatestAiDevelopment crew"""
return Crew(
agents=self.agents, # Automatically created by the @agent decorator
tasks=self.tasks, # Automatically created by the @task decorator
process=Process.sequential,
verbose=True,
)
```

**main.py**

```python
#!/usr/bin/env python
# src/my_project/main.py
import sys
from latest_ai_development.crew import LatestAiDevelopmentCrew

def run():
"""
Run the crew.
"""
inputs = {
'topic': 'AI Agents'
}
LatestAiDevelopmentCrew().crew().kickoff(inputs=inputs)
```

### 3. Running Your Crew

Before running your crew, make sure you have the following keys set as environment variables in your `.env` file:

- An [OpenAI API key](https://platform.openai.com/account/api-keys) (or other LLM API key): `OPENAI_API_KEY=sk-...`
- A [Serper.dev](https://serper.dev/) API key: `SERPER_API_KEY=YOUR_KEY_HERE`

Lock the dependencies and install them by using the CLI command but first, navigate to your project directory:

```shell
cd my_project
crewai install
```

To run your crew, execute the following command in the root of your project:

```bash
crewai run
```

or

```bash
python src/my_project/main.py
```

You should see the output in the console and the `report.md` file should be created in the root of your project with the full final report.

In addition to the sequential process, you can use the hierarchical process, which automatically assigns a manager to the defined crew to properly coordinate the planning and execution of tasks through delegation and validation of results. [See more about the processes here](https://docs.crewai.com/core-concepts/Processes/).

## Key Features
Expand All @@ -131,13 +270,13 @@ In addition to the sequential process, you can use the hierarchical process, whi
- **Processes Driven**: Currently only supports `sequential` task execution and `hierarchical` processes, but more complex processes like consensual and autonomous are being worked on.
- **Save output as file**: Save the output of individual tasks as a file, so you can use it later.
- **Parse output as Pydantic or Json**: Parse the output of individual tasks as a Pydantic model or as a Json if you want to.
- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!
- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring your agents' connections to models, even ones running locally!

![CrewAI Mind Map](./docs/crewAI-mindmap.png "CrewAI Mind Map")

## Examples

You can test different real life examples of AI crews in the [crewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):
You can test different real life examples of AI crews in the [CrewAI-examples repo](https://github.com/crewAIInc/crewAI-examples?tab=readme-ov-file):

- [Landing Page Generator](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator)
- [Having Human input on the execution](https://docs.crewai.com/how-to/Human-Input-on-Execution)
Expand Down Expand Up @@ -168,9 +307,9 @@ You can test different real life examples of AI crews in the [crewAI-examples re

## Connecting Your Crew to a Model

crewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.
CrewAI supports using various LLMs through a variety of connection options. By default your agents will use the OpenAI API when querying the model. However, there are several other ways to allow your agents to connect to models. For example, you can configure your agents to use a local model via the Ollama tool.

Please refer to the [Connect crewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.
Please refer to the [Connect CrewAI to LLMs](https://docs.crewai.com/how-to/LLM-Connections/) page for details on configuring you agents' connections to models.

## How CrewAI Compares

Expand Down Expand Up @@ -241,7 +380,7 @@ It's pivotal to understand that **NO data is collected** concerning prompts, tas

Data collected includes:

- Version of crewAI
- Version of CrewAI
- So we can understand how many users are using the latest version
- Version of Python
- So we can decide on what versions to better support
Expand All @@ -266,7 +405,7 @@ Users can opt-in to Further Telemetry, sharing the complete telemetry data by se

## License

CrewAI is released under the MIT License.
CrewAI is released under the [MIT License](https://github.com/crewAIInc/crewAI/blob/main/LICENSE).

## Frequently Asked Questions (FAQ)

Expand Down Expand Up @@ -299,7 +438,7 @@ A: Yes, CrewAI is open-source and welcomes contributions from the community.
A: CrewAI uses anonymous telemetry to collect usage data for improvement purposes. No sensitive data (like prompts, task descriptions, or API calls) is collected. Users can opt-in to share more detailed data by setting `share_crew=True` on their Crews.

### Q: Where can I find examples of CrewAI in action?
A: You can find various real-life examples in the [crewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.
A: You can find various real-life examples in the [CrewAI-examples repository](https://github.com/crewAIInc/crewAI-examples), including trip planners, stock analysis tools, and more.

### Q: How can I contribute to CrewAI?
A: Contributions are welcome! You can fork the repository, create a new branch for your feature, add your improvement, and send a pull request. Check the Contribution section in the README for more details.

0 comments on commit 5059aef

Please sign in to comment.