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Atomic Agents

Atomic Agents is a Python framework for building AI agents, LLM applications and pipelines from reusable components such as agents, tools and context providers. It uses Instructor and Pydantic and supports structured input and output schemas, configurable system prompts, conversation history and dynamic context.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Atomic Agents — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Choose Atomic Agents when building a Python LLM application and you want to keep prompts, schemas, history and components separately configurable. Its quickstarts include basic chatbots, custom prompts, custom output schemas and a multi-provider example.

Who it is for

  • Python developers building LLM-powered applications
  • Teams that want reusable agent components and structured input or output
  • Developers integrating OpenAI or another supported model provider

Documented uses

  • Compose small, reusable components into an agent or AI pipeline
  • Define and validate the structure of agent inputs and outputs
  • Configure an agent’s system prompt, conversation history and dynamic context
  • Create a chat application that returns structured responses, such as a message with suggested follow-up questions
  • Explore provider-specific chatbot examples
Before you choose

Requirements & limits

The framework is distributed as a pip package and its public GitHub repository specifies the MIT license.

  • Python is the framework’s implementation language, and its documented foundations are Instructor and Pydantic.
  • OpenAI is included by default; other provider SDKs may require Instructor extras.
  • Atomic Assembler is installed with the package and is documented for downloading tools, while downloading agents and pipelines is described as planned.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Atomic Agents and configure a provider

Set up the Python package and the provider SDK needed for an agent project.

Before you start

  • Python environment with pip
  • Select an LLM provider; OpenAI is included by default

Steps

  1. Install the framework with the command below.

    Documented command
    pip install atomic-agents

  2. For Groq, Anthropic or Gemini, install the corresponding Instructor extra: instructor[groq], instructor[anthropic] or instructor[google-genai].

Expected outcome

The package and, where needed, the selected provider SDK are installed; Atomic Assembler CLI is installed with the package.

Guide limitations

  • The instructions do not cover provider credentials or model-specific configuration.
  • The CLI is described as downloading tools; agent and pipeline downloads are noted as planned.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
13 October 2026
Original website title
Eigenwise/atomic-agents · Official homepage title · retained in its source language
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