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Feature: Documentation Site (#188)
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IT Lackey
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README.md
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README.md
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🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
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- [Why CrewAI](#why-crewai)
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- [Getting Started](#getting-started)
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- [Key Features](#key-features)
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- [Examples](#examples)
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- [Local Open Source Models](#local-open-source-models)
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- [CrewAI x AutoGen x ChatDev](#how-crewai-compares)
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- [Contribution](#contribution)
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- [💬 CrewAI Discord Community](https://discord.com/invite/X4JWnZnxPb)
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- [Hire CrewAI](#hire-crewai)
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- [License](#license)
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- [crewAI](#crewai)
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- [Why CrewAI?](#why-crewai)
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- [Getting Started](#getting-started)
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- [Key Features](#key-features)
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- [Examples](#examples)
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- [Code](#code)
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- [Video](#video)
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- [Quick Tutorial](#quick-tutorial)
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- [Trip Planner](#trip-planner)
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- [Stock Analysis](#stock-analysis)
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- [Connecting Your Crew to a Model](#connecting-your-crew-to-a-model)
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- [How CrewAI Compares](#how-crewai-compares)
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- [Contribution](#contribution)
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- [Installing Dependencies](#installing-dependencies)
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- [Virtual Env](#virtual-env)
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- [Pre-commit hooks](#pre-commit-hooks)
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- [Running Tests](#running-tests)
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- [Packaging](#packaging)
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- [Installing Locally](#installing-locally)
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- [Hire CrewAI](#hire-crewai)
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- [License](#license)
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## Why CrewAI?
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CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions.
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- 🤖 [Talk with the Docs](https://chatg.pt/DWjSBZn)
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- 📄 [Documentation Wiki](https://github.com/joaomdmoura/CrewAI/wiki)
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- 📄 [Documentation Wiki](https://joaomdmoura.github.io/crewAI/)
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## Getting Started
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os.environ["OPENAI_API_KEY"] = "YOUR KEY"
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# You can choose to use a local model through Ollama for example.
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#
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# You can choose to use a local model through Ollama for example. See ./docs/llm-connections.md for more information.
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# from langchain.llms import Ollama
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# ollama_llm = Ollama(model="openhermes")
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Currently the only supported process is `Process.sequential`, where one task is executed after the other and the outcome of one is passed as extra content into this next.
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## Key Features
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- **Role-Based Agent Design**: Customize agents with specific roles, goals, and tools.
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- **Autonomous Inter-Agent Delegation**: Agents can autonomously delegate tasks and inquire amongst themselves, enhancing problem-solving efficiency.
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- **Flexible Task Management**: Define tasks with customizable tools and assign them to agents dynamically.
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- **Processes Driven**: Currently only supports `sequential` task execution but more complex processes like consensual and hierarchical being worked on.
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- **Works with Open Source Models**: Run your crew using Open AI or open source models refer to the [Connect crewAI to LLMs](./docs/llm-connections.md) page for details on configuring you agents' connections to models, even ones running locally!
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- [Trip Planner](https://github.com/joaomdmoura/crewAI-examples/tree/main/trip_planner)
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- [Stock Analysis](https://github.com/joaomdmoura/crewAI-examples/tree/main/stock_analysis)
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- [Landing Page Generator](https://github.com/joaomdmoura/crewAI-examples/tree/main/landing_page_generator)
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- [Having Human input on the execution](https://github.com/joaomdmoura/crewAI/wiki/Human-Input-on-Execution)
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- [Having Human input on the execution](./docs/how-to/Human-Input-on-Execution.md)
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### Video
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#### Quick Tutorial
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#### Stock Analysis
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[](https://www.youtube.com/watch?v=e0Uj4yWdaAg "Stock Analysis")
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## Local Open Source Models
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crewAI supports integration with local models, thorugh tools such as [Ollama](https://ollama.ai/), for enhanced flexibility and customization. This allows you to utilize your own models, which can be particularly useful for specialized tasks or data privacy concerns.
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## Connecting Your Crew to a Model
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### Setting Up Ollama
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- **Install Ollama**: Ensure that Ollama is properly installed in your environment. Follow the installation guide provided by Ollama for detailed instructions.
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- **Configure Ollama**: Set up Ollama to work with your local model. You will probably need to [tweak the model using a Modelfile](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md). I'd recommend adding `Observation` as a stop word and playing with `top_p` and `temperature`.
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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.
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### Integrating Ollama with CrewAI
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- Instantiate Ollama Model: Create an instance of the Ollama model. You can specify the model and the base URL during instantiation. For example:
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```python
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from langchain.llms import Ollama
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ollama_openhermes = Ollama(model="openhermes")
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# Pass Ollama Model to Agents: When creating your agents within the CrewAI framework, you can pass the Ollama model as an argument to the Agent constructor. For instance:
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local_expert = Agent(
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role='Local Expert at this city',
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goal='Provide the BEST insights about the selected city',
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backstory="""A knowledgeable local guide with extensive information
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about the city, it's attractions and customs""",
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tools=[
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SearchTools.search_internet,
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BrowserTools.scrape_and_summarize_website,
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],
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llm=ollama_openhermes, # Ollama model passed here
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verbose=True
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)
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```
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Please refer to the [Connect crewAI to LLMs](./docs/how-to/llm-connections.md) page for details on configuring you agents' connections to models.
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## How CrewAI Compares
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