Amulya KumarAtomic Agents Framework: Imagine building AI applications as easily as snapping together LEGO blocks. This is the revolutionary concept behind Atomic Agents, a new multi-agent framework inspired by Atomic Design principles. By breaking down AI systems into smaller, self-contained, reusable components, Atomic Agents promises a future where AI development is both modular and predictable.
Understanding Atomic Design
Before diving into Atomic Agents, let’s explore the foundation it’s built upon: Atomic Design. Created by Brad Frost, this methodology revolutionizes UI design by deconstructing interfaces into fundamental, reusable parts—like atoms, molecules, and organisms in chemistry. This approach ensures scalable, consistent user interfaces and simplifies the modification of individual components with minimal impact. Think of it as assembling intricate structures with simple LEGO pieces.
Chaining Agents and Tools
The true potential of the Atomic Agents Framework is unlocked when chaining the inputs and outputs of various tools and agents. By assigning an agent’s output schema as the input schema for a tool or another agent, developers can create complex yet comprehensible AI applications. Changing a tool or schema at any point won’t disrupt the system, ensuring flexibility and robustness.
Building a Simple AI Agent
To illustrate, let’s build a simple AI agent using Atomic Agents.
Install the Atomic Agents Package:
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pip install atomic-agents openai
Alternatively, clone the repository and contribute to its improvement.
Import Necessary Components:
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import os
from atomic_agents.lib.components.system_prompt_generator import SystemPromptGenerator, SystemPromptInfo
Define System Prompt Information:
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system_prompt = SystemPromptInfo(
background=['This assistant is a general-purpose AI designed to be helpful and friendly.'],
steps=[
'Understand the user\'s input and provide a relevant response.',
'Respond to the user.'
],
output_instructions=[
'Provide helpful and relevant information to assist the user.',
'Be friendly and respectful in all interactions.',
'Always answer in rhyming verse.'
]
)
system_prompt_generator = SystemPromptGenerator(system_prompt)
Initialize Chat Memory:
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from atomic_agents.lib.components.chat_memory import ChatMemory
memory = ChatMemory()
initial_memory = [{'role': 'assistant', 'content': 'How do you do? What can I do for you? Tell me, pray, what is your need today?'}]
memory.load(initial_memory)
Create Custom Chatbot:
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from atomic_agents.agents.base_chat_agent import BaseChatAgent, BaseChatAgentConfig
import openai
import instructor
API_KEY = os.getenv('OPENAI_API_KEY', '')
client = instructor.from_openai(openai.OpenAI(api_key=API_KEY))
agent = BaseChatAgent(
config=BaseChatAgentConfig(
client=client,
system_prompt_generator=system_prompt_generator,
model='gpt-3.5-turbo',
memory=memory,
)
)
print(f'Agent: {initial_memory[0]["content"]}')
while True:
user_input = input('You: ')
if user_input.lower() in ['/exit', '/quit']:
print('Exiting chat...')
break
response = agent.run(agent.input_schema(chat_message=user_input))
print(f'Agent: {response.chat_message}')
Behind the Scenes
Underneath, each tool and agent has an input schema and an output schema, extending from the BaseAgentIO class. These schemas facilitate structured responses and documentation generation, enhancing clarity and precision in both agent performance and documentation.
