The Atomic Agents framework is designed around the concept of atomicity to be an extremely lightweight and modular framework for building Agentic AI pipelines and applications without sacrificing developer experience and maintainability. The framework provides a set of tools and agents that can be combined to create powerful applications. It is built on top of Instructor and leverages the power of Pydantic for data and schema validation and serialization. All logic and control flows are written in Python, enabling developers to apply familiar best practices and workflows from traditional software development without compromising flexibility or clarity. NEW: We now also have an official subreddit at /r/AtomicAgents - Be sure to join! If you want to learn more about the motivation and philosophy behind Atomic Agents, I suggest reading this Medium article (no account needed) or check out the overview video on YouTube. If you just want to dive into the code straight away, I suggest checking out the quickstart video on YouTube. Why Atomic Agents? While existing frameworks for agentic AI focus on building autonomous multi-agent systems, they often lack the control and predictability required for real-world applications. Businesses need AI systems that produce consistent, reliable outputs aligned with their brand and objectives. Atomic Agents addresses this need by providing: Modularity: Build AI applications by combining small, reusable components. Predictability: Define clear input and output schemas to ensure consistent behavior. Extensibility: Easily swap out components or integrate new ones without disrupting the entire system. Control: Fine-tune each part of the system individually, from system prompts to tool integrations. Anatomy of an Agent In Atomic Agents, an agent is composed of several key components: System Prompt: Defines the agents behavior and purpose. Input Schema: Specifies the structure and validation rules for the agents input. Output Schema: Specifies the structure and validation rules for the agents output. Memory: Stores conversation history or other relevant data. Context Providers: Inject dynamic context into the agents system prompt at runtime. Installation To install Atomic Agents, you can use pip: pip install atomic-agents Make sure you also install the provider you want to use. For example, to use OpenAI and Groq, you can install the openai and groq packages. This also installs the CLI Atomic Assembler, which can be used to download Tools (and soon also Agents and Pipelines). For local development, you can install from the repository. Project Structure Atomic Agents uses a monorepo structure with the following main components: 1. atomic-agents/: The core Atomic Agents library containing the tools and agents. 2. tools/: Additional tools that can be used with Atomic Agents. 3. providers/: External providers for integrating with third-party services such as OpenAI and Groq. If youre looking to build sophisticated AI applications with control, predictability, and modularity, Atomic Agents is a framework worth exploring.
