Installation and Setup

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 install with all dependencies:

    pip install 'camel-ai[all]'

  • To use the HuggingFace agents:

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

  • To enable RAG or use agent memory:

    pip install 'camel-ai[tools]'

[Option 2] Install from Source#

Install from Source with Poetry#

# Make sure your python version is later than 3.10
# You can use pyenv to manage multiple python verisons in your sytstem

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

# Change directory into project directory
cd camel

# If you didn't install peotry before
pip install poetry  # (Optional)

# We suggest using python 3.10
poetry env use python3.10  # (Optional)

# Activate CAMEL virtual environment
poetry shell

# Install the base CAMEL library
# It takes about 90 seconds
poetry install

# Install CAMEL with all dependencies
poetry install -E all  # (Optional)

# Exit the virtual environment
exit

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 v0.2.1a 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

# If you are using other proxy services like Azure
echo 'export OPENAI_API_BASE_URL="your_base_url"' >> ~/.zshrc # (Optional)

# 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"

# If you are using other proxy services like Azure
set OPENAI_API_BASE_URL="your_base_url" # (Optional)

Or if you are using PowerShell:

$env:OPENAI_API_KEY="your_api_key"

# If you are using other proxy services like Azure
$env:OPENAI_API_BASE_URL="your_base_url" # (Optional)

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#

In the current landscape, for those seeking highly stable content generation, OpenAI’s GPT-3.5 turbo, GPT-4o are often recommended. However, the field is rich with many other outstanding open-source models that also yield commendable results. CAMEL can support developers to delve into integrating these open-source large language models (LLMs) to achieve project outputs based on unique input ideas.

Example: Using Ollama to set Llama 3 locally#

  • Download Ollama.

  • After setting up Ollama, pull the Llama3 model by typing the following command into the terminal:

ollama pull llama3

  • Create a ModelFile similar the one below in your project directory.

FROM llama3

# Set parameters
PARAMETER temperature 0.8
PARAMETER stop Result

# Sets a custom system message to specify the behavior of the chat assistant

# Leaving it blank for now.

SYSTEM """ """

  • Create a script to get the base model (llama3) and create a custom model using the ModelFile above. Save this as a .sh file:

#!/bin/zsh

# variables
model_name="llama3"
custom_model_name="camel-llama3"

#get the base model
ollama pull $model_name

#create the model file
ollama create $custom_model_name -f ./Llama3ModelFile

  • Navigate to the directory where the script and ModelFile are located and run the script. Enjoy your Llama3 model, enhanced by CAMEL’s excellent agents.

from camel.agents import ChatAgent
from camel.messages import BaseMessage
from camel.models import ModelFactory
from camel.types import ModelPlatformType

ollama_model = ModelFactory.create(
    model_platform=ModelPlatformType.OLLAMA,
    model_type="llama3",
    url="http://localhost:11434/v1",
    model_config_dict={"temperature": 0.4},
)

assistant_sys_msg = BaseMessage.make_assistant_message(
    role_name="Assistant",
    content="You are a helpful assistant.",
)
agent = ChatAgent(assistant_sys_msg, model=ollama_model, token_limit=4096)

user_msg = BaseMessage.make_user_message(
    role_name="User", content="Say hi to CAMEL"
)
assistant_response = agent.step(user_msg)
print(assistant_response.msg.content)
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