Eigenwise/atomic-agents

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:

• Single-purpose: Does one thing well
• Reusable: Can be used in multiple pipelines
• Composable: Easily combines with other components
• Predictable: Produces consistent, reliable outputs

Built on Instructor and Pydantic, it enables you to create AI applications with the same software engineering principles you already know and love.

NEW: Join our community on Discord at discord.gg/J3W9b5AZJR and our official subreddit at /r/AtomicAgents!

• Atomic Agents
  • What is Atomic Agents?
  • Table of Contents
  • Getting Started
    • Installation
    • Quick Example
  • Why Atomic Agents?
  • Core Concepts
    • Anatomy of an Agent
    • Context Providers
    • Chaining Schemas and Agents
  • Examples & Documentation
    • Quickstart Examples
    • Complete Examples
  • AI-Assisted Development
    • Project instructions (Cursor, Windsurf, Cline, Continue, and Aider)
    • Agent skills (Claude Code, Cursor, Copilot, Codex, Windsurf, Gemini CLI, ...)
    • Docs for LLMs
  • 🚀 Version 2.0 Released!
    • Key Changes in v2.0:
    • ⚠️ Upgrading from v1.x
  • Atomic Forge & CLI
    • Running the CLI
  • Project Structure
  • Provider & Model Compatibility
  • Support
  • Contributing
  • License
  • Additional Resources
  • Star History

To install Atomic Agents, you can use pip:

pip install atomic-agents

Make sure you also install the provider you want to use. Provider SDKs are available as instructor extras:

pip install instructor[groq]        # for Groq
pip install instructor[anthropic]   # for Anthropic
pip install instructor[google-genai] # for Gemini

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:

from pydantic import Field
from openai import OpenAI
import instructor
from atomic_agents import AtomicAgent, AgentConfig, BasicChatInputSchema, BaseIOSchema
from atomic_agents.context import SystemPromptGenerator, ChatHistory

# Define a custom output schema
class CustomOutputSchema(BaseIOSchema):
    """
    docstring for the custom output schema
    """
    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 OpenAI client
client = instructor.from_openai(OpenAI())

# Initialize the agent
agent = AtomicAgent[BasicChatInputSchema, CustomOutputSchema](
    config=AgentConfig(
        client=client,
        model="gpt-5-mini",
        system_prompt_generator=system_prompt_generator,
        history=ChatHistory(),
    )
)

# Example usage
if __name__ == "__main__":
    user_input = "Tell me about atomic agents framework"
    response = agent.run(BasicChatInputSchema(chat_message=user_input))
    print(f"Agent: {response.chat_message}")
    print("Suggested questions:")
    for question in response.suggested_questions:
        print(f"- {question}")

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.

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:

• System Prompt: Defines the agent's behavior and purpose.
• Input Schema: Specifies the structure and validation rules for the agent's input.
• Output Schema: Specifies the structure and validation rules for the agent's output.
• History: Stores conversation history or other relevant data.
• Context Providers: Inject dynamic context into the agent's system prompt at runtime.

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.

To use a Context Provider, create a class that inherits from BaseDynamicContextProvider and implements the get_info() method, which returns the context string to be added to the system prompt.

Here's a simple example:

from atomic_agents.context import BaseDynamicContextProvider

class SearchResultsProvider(BaseDynamicContextProvider):
    def __init__(self, title: str, search_results: List[str]):
        super().__init__(title=title)
        self.search_results = search_results

    def get_info(self) -> str:
        return "\n".join(self.search_results)

You can then register your Context Provider with the agent:

# Initialize your context provider with dynamic data
search_results_provider = SearchResultsProvider(
    title="Search Results",
    search_results=["Result 1", "Result 2", "Result 3"]
)

# Register the context provider with the agent
agent.register_context_provider("search_results", search_results_provider)

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:

import instructor
import openai
from pydantic import Field
from atomic_agents import BaseIOSchema, AtomicAgent, AgentConfig
from atomic_agents.context import SystemPromptGenerator

# Import the search tool you want to use
from web_search_agent.tools.searxng_search import SearXNGSearchTool

# Define the input schema for the query agent
class QueryAgentInputSchema(BaseIOSchema):
    """Input schema for the QueryAgent."""
    instruction: str = Field(..., description="Instruction to generate search queries for.")
    num_queries: int = Field(..., description="Number of queries to generate.")

# Initialize the query agent
query_agent = AtomicAgent[QueryAgentInputSchema, SearXNGSearchTool.input_schema](
    config=AgentConfig(
        client=instructor.from_openai(openai.OpenAI()),
        model="gpt-5-mini",
        system_prompt_generator=SystemPromptGenerator(
            background=[
                "You are an intelligent query generation expert.",
                "Your task is to generate a specified number of diverse and highly relevant queries based on a given instruction."
            ],
            steps=[
                "Receive the instruction and the number of queries to generate.",
                "Generate the queries in JSON format."
            ],
            output_instructions=[
                "Ensure each query is unique and relevant.",
                "Provide the queries in the expected schema."
            ],
        ),
    )
)

In this example:

• Modularity: By setting the output_schema of the query_agent to match the input_schema of SearXNGSearchTool, you can directly use the output of the agent as input to the tool.
• Swapability: If you decide to switch to a different search provider, you can import a different search tool and update the output_schema accordingly.

For instance, to switch to another search service:

# Import a different search tool
from web_search_agent.tools.another_search import AnotherSearchTool

# Update the output schema
query_agent.config.output_schema = AnotherSearchTool.input_schema

This design pattern simplifies the process of chaining agents and tools, making your AI applications more adaptable and easier to maintain.

Visit the Documentation Site »

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, 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 such as Ollama or Groq.

In addition to the quickstart examples, we have more complex examples demonstrating the power of Atomic Agents:

• Hooks System: Comprehensive demonstration of the AtomicAgent hook system for monitoring, error handling, and performance metrics with intelligent retry mechanisms.
• Phoenix Tracing: Traces agents and tools in Arize Phoenix through the optional OpenInference instrumentor (pip install "atomic-agents[observability]").
• Basic Multimodal: Demonstrates how to analyze images with text, focusing on extracting structured information from nutrition labels using GPT-4 Vision capabilities.
• Deep Research: An advanced example showing how to perform deep research tasks.
• Orchestration Agent: Shows how to create an Orchestrator Agent that intelligently decides between using different tools (search or calculator) based on user input.
• RAG Chatbot: A chatbot implementation using Retrieval-Augmented Generation (RAG) to provide context-aware responses.
• Web Search Agent: An intelligent agent that performs web searches and answers questions based on the results.
• YouTube Summarizer: An agent that extracts and summarizes knowledge from YouTube videos.
• YouTube to Recipe: An example that extracts structured recipe information from cooking videos, demonstrating complex information extraction and structuring.

For a complete list of examples, see the examples directory.

Building with an AI coding assistant? Atomic Agents ships first-class support so your assistant knows the framework's current API and conventions instead of guessing.

The root AGENTS.md is the canonical source of repository guidance. Each supported assistant receives that guidance through its native project-instruction mechanism:

These files configure assistants working on the Atomic Agents repository itself. To add Atomic Agents framework knowledge to another project, install the agent skills below.

Six agent skills cover the framework: an auto-triggering framework guide with progressive-disclosure references, four creation workflows (create-atomic-schema, create-atomic-agent, create-atomic-tool, create-atomic-context-provider), and a new-app project scaffolder.

Claude Code (also gets the atomic-explorer and atomic-reviewer subagents):

/plugin marketplace add eigenwise/atomic-agents
/plugin install atomic-agents@eigenwise

Any other skills-compatible assistant:

npx skills add eigenwise/atomic-agents

See claude-plugin/atomic-agents for details.

The documentation site publishes LLM-ready bundles: llms-full.txt (docs + source + examples), plus separate docs, source, and examples bundles. Point your assistant at one of these when it needs the full picture.

Atomic Agents is also indexed on Context7 for assistants with the Context7 MCP server.

Atomic Forge is a collection of tools that can be used with Atomic Agents to extend its functionality. Current tools include:

• arXiv Search
• BoCha Search
• Calculator
• DateTime
• Fía Signals
• Firecrawl Search
• FXMacroData
• Hacker News Search
• PDF Reader
• SearXNG Search
• Serply Search
• Tavily Search
• Webpage Scraper
• Weather
• Wikipedia Search
• YouTube Transcript Scraper

For more information on using and creating tools, see the Atomic Forge README.

To run the CLI, simply run the following command:

atomic

Or if you're running from a cloned repository with uv:

uv run atomic

After running this command, you will be presented with a menu allowing you to download tools.

Each tool's has its own:

• Input schema
• Output schema
• Usage example
• Dependencies
• Installation instructions

The atomic-assembler CLI gives you complete control over your tools, avoiding the clutter of unnecessary dependencies. It makes modifying tools straightforward additionally, each tool comes with its own set of tests for reliability.

But you're not limited to the CLI! If you prefer, you can directly access the tool folders and manage them manually by simply copying and pasting as needed.

Atomic Agents uses a monorepo structure with the following main components:

1. atomic-agents/: The core Atomic Agents library
2. atomic-assembler/: The CLI tool for managing Atomic Agents components
3. atomic-examples/: Example projects showcasing Atomic Agents usage
4. atomic-forge/: A collection of tools that can be used with Atomic Agents

For local development, you can install from the repository:

git clone https://github.com/eigenwise/atomic-agents.git
cd atomic-agents
uv sync

To install all workspace packages (examples and tools):

uv sync --all-packages

Atomic Agents depends on the Instructor package. This means that in all examples where OpenAI is used, any other API supported by Instructor can also be used, such as Ollama, Groq, Mistral, Cohere, Anthropic, Gemini, OpenAI and any OpenAI-compatible API such as OpenRouter, MiniMax, EdenAI, and more. For a complete list, please refer to the Instructor documentation on its GitHub page.

Atomic Agents is free and MIT-licensed. If it saves you time, a coffee or a GitHub sponsorship genuinely helps me keep building and maintaining it.

Ko-fi	GitHub Sponsors

We welcome contributions! Please see the Contributing Guide for detailed information on how to contribute to Atomic Agents. Here are some quick steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes
4. Run tests (uv run pytest --cov=atomic_agents atomic-agents)
5. Format your code (uv run black atomic-agents atomic-assembler atomic-examples atomic-forge)
6. Lint your code (uv run flake8 --extend-exclude=.venv atomic-agents atomic-assembler atomic-examples atomic-forge)
7. Commit your changes (git commit -m 'Add some feature')
8. Push to the branch (git push origin feature-branch)
9. Open a pull request

For full development setup and guidelines, please refer to the Developer Guide.

This project is licensed under the MIT License. See the LICENSE file for details.

If you want to learn more about the motivation and philosophy behind Atomic Agents, I suggest reading this Medium article (no account needed).

Video Resources:

• Watch the Overview Video - Learn about the framework's philosophy and design principles
• Watch the Quickstart Video - Get started with code examples