atomic-agents
Building AI agents, atomically
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The Atomic Agents framework is a modular and extensible tool designed for creating powerful applications. It leverages Pydantic for data validation and serialization. The framework follows the principles of Atomic Design, providing small and single-purpose components that can be combined. It integrates with Instructor for AI agent architecture and supports various APIs like Cohere, Anthropic, and Gemini. The tool includes documentation, examples, and testing features to ensure smooth development and usage.
README:
The Atomic Agents framework is designed to be modular, extensible, and easy to use. The main goal of the framework is to get rid of redundant complexity, unnecessary abstractions, and hidden assumptions while still providing a flexible and powerful framework for building AI applications through atomicity. The resulting framework provides a set of tools and agents that can be combined to create powerful applications. The framework is built on top of Instructor and leverages the power of Pydantic for data/schema validation and serialization.
To install Atomic Agents, you can use pip:
pip install atomic-agents
This also installs the CLI Atomic Assembler which can be used to download Tools (and soon also Agents and Pipelines).
A complete list of examples can be found in the examples directory.
We do our best to thoroughly document each example, but if something is unclear, please don't hesitate to open an issue or a pull request in order to improve the documentation.
Here's a quick snippet demonstrating how easy it is to create a powerful agent with Atomic Agents:
# Define a custom output schema
class CustomOutputSchema(BaseIOSchema):
chat_message: str = Field(..., description="The chat message from the agent.")
suggested_questions: List[str] = Field(..., description="Suggested follow-up questions.")
# Set up the system prompt
system_prompt_generator = SystemPromptGenerator(
background=["This assistant is knowledgeable, helpful, and suggests follow-up questions."],
steps=[
"Analyze the user's input to understand the context and intent.",
"Formulate a relevant and informative response.",
"Generate 3 suggested follow-up questions for the user."
],
output_instructions=[
"Provide clear and concise information in response to user queries.",
"Conclude each response with 3 relevant suggested questions for the user."
]
)
# Initialize the agent
agent = BaseAgent(
config=BaseAgentConfig(
client=your_openai_client, # Replace with your actual client
model="gpt-4",
system_prompt_generator=system_prompt_generator,
memory=AgentMemory(),
output_schema=CustomOutputSchema
)
)
# Use the agent
response = agent.run(user_input)
print(f"Agent: {response.chat_message}")
print("Suggested questions:")
for question in response.suggested_questions:
print(f"- {question}")
This snippet showcases how to create a customizable agent that responds to user queries and suggests follow-up questions. For full, runnable examples, please refer to the following files in the atomic-examples/quickstart/quickstart/
directory:
-
Basic Chatbot A minimal chatbot example to get you started.
-
Custom Chatbot A more advanced example with a custom system prompt.
-
Custom Chatbot with Schema An advanced example featuring a custom output schema.
-
Multi-Provider Chatbot Demonstrates how to use different providers like Ollama or Groq.
These examples provide a great starting point for understanding and using Atomic Agents.
To run the CLI simply run the following command:
atomic
After running this command you should be presented with a menu, allowing you to download Tools.
Atomic Agents depends on the Instructor package. This means that in all examples where OpenAI is used, any other API supported by Instructor can be used, such as Ollama, Groq, Mistral, Cohere, Anthropic, Gemini, and more. For a complete list please refer to the instructor documentation on its GitHub page.
API documentation can be found here.
We welcome contributions! Please follow these steps to contribute:
See the Developer Guide for more information on how to contribute to Atomic Agents.
This project is licensed under the MIT License - see the LICENSE file for details.
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