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atomic agents

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Basic Information

Atomic Agents is a lightweight, modular Python framework for building agentic AI pipelines and applications using the principle of atomicity. It is designed for developers who want to construct predictable, maintainable AI systems by composing single-purpose components such as agents, tools and context providers. The project enforces explicit input and output schemas and integrates with Instructor and Pydantic to enable schema validation, provider-agnostic model usage, and familiar software engineering practices. The repository includes a CLI (Atomic Assembler) and an Atomic Forge of reusable tools, example projects demonstrating chatbots, RAG, web search, multimodal extraction and orchestration, and documentation and tests to help developers adopt the framework.

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4699
App URL
https://github.com/BrainBlend-AI/atomic-agents
Github Repository
https://github.com/BrainBlend-AI/atomic-agents/blob/main/README.md

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App Details

Features
The repo emphasizes composability and predictability through single-purpose atomic components and explicit input/output schemas. Core features include System Prompt generation, Context Providers for runtime context injection, chat history management, and easy chaining of agents and tools by aligning schemas. Version 2.0 introduced cleaner imports, renamed classes (e.g. AtomicAgent, AgentConfig), stronger type safety with generics, and improved streaming methods. The project ships a CLI to install and manage tools, an Atomic Forge collection of tools (calculator, search, scrapers), many runnable examples, per-tool tests, provider compatibility via Instructor for multiple LLM backends, and comprehensive docs and guides for upgrading and contributing.
Use Cases
Atomic Agents helps teams build robust, maintainable AI applications by enforcing well-defined interfaces and encouraging small, reusable components. Schema validation and structured system prompts increase output reliability and control, reducing ambiguity in model responses. Context Providers and history management let agents incorporate dynamic, runtime information. Chaining and swapability of tools simplify experimentation and integration with different search or model providers. The included examples, CLI, and tool collection accelerate prototyping of chatbots, RAG systems, multimodal extractors and orchestration agents. Developer-focused tooling, tests, docs, and MIT licensing make it suitable for production-oriented development and collaboration.

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