Installation and Setup

🕹 Installation

[Option 1] Install from PyPI

To install the base CAMEL library:

pip install camel-ai

Some features require extra dependencies:

• To use the HuggingFace agents:

  pip install 'camel-ai[huggingface-agent]'

• To enable RAG or use agent memory:

  pip install 'camel-ai[tools]'

• To install with all dependencies:

  pip install 'camel-ai[all]'

Install from Source with Conda and Pip

# Create a conda virtual environment
conda create --name camel python=3.10

# Activate camel conda environment
conda activate camel

# Clone github repo
git clone -b v0.1.1 https://github.com/camel-ai/camel.git

# Change directory into project directory
cd camel

# Install camel from source
pip install -e .

# Or if you want to use all other extra packages
pip install -e '.[all]' # (Optional)

🕹 API Setup

Our agents can be deployed with either OpenAI API or your local models.

[Option 1] Using OpenAI API

Assessing the OpenAI API requires the API key, which you may obtained from here. We here provide instructions for different OS.

Unix-like System (Linux / MacOS)

echo 'export OPENAI_API_KEY="your_api_key"' >> ~/.zshrc

# [Optional] if you are using other proxy services like Azure
echo 'export OPENAI_API_BASE_URL="your_base_url"' >> ~/.zshrc

# Let the change take place
source ~/.zshrc

Replace ~/.zshrc with ~/.bashrc if you are using bash.

Windows

If you are using Command Prompt:

set OPENAI_API_KEY=your_api_key
set OPENAI_API_BASE_URL=your_base_url

Or if you are using PowerShell:

$env:OPENAI_API_KEY=your_api_key
$env:OPENAI_API_BASE_URL=your_base_url

These commands on Windows will set the environment variable for the duration of that particular Command Prompt or PowerShell session only. You may use setx or change the system properties dialog for the change to take place in all the new sessions.

[Option 2] Using Local Models

The high-level idea is to deploy a server with the local model in the backend and use it as a local drop-in replacement for the API. We here use FastChat as an example.

0. Install the FastChat package with the following command, or see here for other options.

   pip3 install "fschat[model_worker,webui]"

1. Starting the FastChat server in the backend.

   # Launch the fastchat controller
   python -m fastchat.serve.controller
   
   # Launch the model worker
   python -m fastchat.serve.model_worker \
       --model-path meta-llama/Llama-2-7b-chat-hf  # a local folder or HuggingFace repo Name
   
   # Launch the API server
   python -m fastchat.serve.openai_api_server \
       --host localhost \
       --port 8000

2. Initialize the agent.

   # Import the necessary classes
   from camel.configs import ChatGPTConfig, OpenSourceConfig
   from camel.types import ModelType
   
   # Set the arguments
   agent_kwargs = dict(
       model_type=ModelType.LLAMA_2,                    # Specify the model type
   
       model_config=OpenSourceConfig(
           model_path='meta-llama/Llama-2-7b-chat-hf',  # a local folder or HuggingFace repo Name
           server_url='http://localhost:8000/v1',       # The url with the set port number
       ),
   
       token_limit=2046,                                # [Optional] Choose the ideal limit
   )
   
   # Now your agent is ready to play
   agent = ChatAgent(sys_msg, **agent_kwargs)