Skip to main content
You can also check this cookbook in colab here ⭐ Star us on Github, join our Discord or follow our X

​
Philosophical Bits

We believe the essence of intelligence emerges from its dynamic interactions with the external environment, where the use of various tools becomes a pivotal factor in its development and manifestation. The EmbodiedAgent() in CAMEL is an advanced conversational agent that leverages code interpreters and tool agents (e.g., HuggingFaceToolAgent()) to execute diverse tasks efficiently. This agent represents a blend of advanced programming and AI capabilities, and is able to interact and respond within a dynamic environment.

​
Quick Start

Let’s first play with a ChatAgent instance by simply initialize it with a system message and interact with user messages.

​
🕹 Step 0: Preparations

%pip install "camel-ai==0.2.16"

from camel.agents import EmbodiedAgent
from camel.generators import SystemMessageGenerator as sys_msg_gen
from camel.messages import BaseMessage as bm
from camel.types import RoleType

​
Setting Up API Keys

You’ll need to set up your API keys for OpenAI. 
import os
from getpass import getpass

# Prompt for the API key securely
openai_api_key = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_key
Alternatively, if running on Colab, you could save your API keys and tokens as Colab Secrets, and use them across notebooks. To do so, comment out the above manual API key prompt code block(s), and uncomment the following codeblock. ⚠️ Don’t forget granting access to the API key you would be using to the current notebook. 
# import os
# from google.colab import userdata

# os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")

​
🕹 Step 1: Define the Role

We first need to set up the necessary information. 
# Set the role name and the task
role = 'Programmer'
task = 'Writing and executing codes.'

# Create the meta_dict and the role_tuple
meta_dict = dict(role=role, task=task)
role_tuple = (role, RoleType.EMBODIMENT)
The meta_dict and role_type will be used to generate the system message. 
# Generate the system message based on this
sys_msg = sys_msg_gen().from_dict(meta_dict=meta_dict, role_tuple=role_tuple)

​
🕹 Step 2: Initialize the Agent 🐫

Based on the system message, we are ready to initialize our embodied agent. 
embodied_agent = EmbodiedAgent(system_message=sys_msg,
                               tool_agents=None,
                               code_interpreter=None,
                               verbose=True)
Be aware that the default argument values for tool_agents and code_interpreter are None, and the underlying code interpreter is using the SubProcessInterpreter(), which handles the execution of code in Python and Bash within a subprocess.

​
🕹 Step 3: Interact with the Agent with .step()

Use the base message wrapper to generate the user message. 
usr_msg = bm.make_user_message(
    role_name='user',
    content=('1. write a bash script to install numpy. '
             '2. then write a python script to compute '
             'the dot product of [8, 9] and [5, 4], '
             'and print the result. '
             '3. then write a script to search for '
             'the weather at london with wttr.in/london.'))
And feed that into your agents: 
response = embodied_agent.step(usr_msg)
Under the hood, the agent will perform multiple actions within its action space in the OS to fulfill the user request. It will compose code to implement the action – no worries, it will ask for your permission before execution. Ideally you should get the output similar to this, if you allow the agent to perform actions: 
print(response.msg.content)
⌘I