Atomic Agents
Building AI agents, atomically. Contribute to BrainBlend-AI/atomic-agents development by creating an account on GitHub.
There was an error while loading. Please reload this page. 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. Think of it like building AI applications with LEGO blocks - each component (agent, tool, context provider) is: To install Atomic Agents, you can use pip: Make sure you also install the provider you want to use. Provider SDKs are available as instructor extras: OpenAI is included by default. For a full list of supported providers, see the Instructor docs. This also installs the CLI Atomic Assembler, which can be used to download Tools (and soon also Agents and Pipelines). Here's a quick snippet demonstrating how easy it is to create a powerful agent with 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: 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. In Atomic Agents, an agent is composed of several key components: Here's a high-level architecture diagram: Atomic Agents allows you to enhance your agents with dynamic context using Context Providers. Context Providers enable you to inject additional information into the agent's system prompt at runtime, making your agents more flexible and context-aware. You can then register your Context Provider with the agent: This allows your agent to include the search results (or any other context) in its system prompt, enhancing its responses based on the latest information. Atomic Agents makes it easy to chain agents and tools together by aligning their input and output schemas. This design allows you to swap out components effortlessly, promoting modularity and reusability in your AI applications. Suppose you have an agent that generates search queries and you want to use these queries with different search tools. By aligning the agent's output schema with the input schema of the search tool, you can easily chain them together or switch between different search providers. Here's how you can achieve this: For instance, to switch to another search service: This design pattern simplifies the process of chaining agents and tools, making your AI applications more adaptable and easier to maintain. A complete list of examples can be found in the examples directory. We strive to thoroughly document each example, but if something is unclear, please don't hesitate to open an issue or pull request to improve the documentation. For full, runnable examples, pleas
Instructor
I don't see any actual user reviews provided in your request - only a single social media mention from dev.to about someone who used "Instructor" to teach AI/LLM development to engineers. Based on this limited information, I can only note that the tool appears to be used in professional training contexts for AI/machine learning education, with at least one case of successfully scaling to nearly 500 engineers. However, without actual user reviews, pricing information, or detailed feedback, I cannot provide a meaningful summary of user sentiment, strengths, complaints, or overall reputation. Could you provide the actual user reviews and additional social mentions for a proper analysis?
Atomic Agents
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