By Eigenwise
Build, scaffold, and audit applications using the Atomic Agents Python framework with guided skills for creating agents, tools, schemas, and context providers, plus subagents for codebase exploration and framework-specific code review.
Maps existing Atomic Agents Python codebases — catalogs agents, tools, schemas, context providers, and orchestration patterns; traces data flow between them; returns a compact architecture summary with file:line references. Use PROACTIVELY when the user asks to "explore", "map", "understand", "analyze", "trace", or "explain how this works" in a project that imports from `atomic_agents`, or before extending a non-trivial atomic-agents codebase. The caller should pass the scope (project root, module path, or specific feature) in the invocation prompt.
Reviews Atomic Agents Python code for framework-specific correctness — BaseIOSchema invariants, AtomicAgent/AgentConfig wiring, BaseTool generics, context-provider I/O hygiene, orchestration hazards, Instructor integration — using confidence-based filtering. Use PROACTIVELY after any change to atomic-agents code, before commit or PR, and whenever the user asks to review, audit, check, or validate code that imports from `atomic_agents`. Complements generic code review by focusing only on Atomic-Agents-specific concerns. The caller should pass the scope (diff, file paths, or module) in the invocation prompt.
Build and wire an `AtomicAgent[InSchema, OutSchema]` — schemas, `AgentConfig`, `SystemPromptGenerator`, provider client, history, hooks, optional context providers. Use when the user asks to "create an agent", "add another agent", "build an `AtomicAgent`", "wire up an agent", "make a planner/router/extractor agent", or runs `/atomic-agents:create-atomic-agent`.
Build a `BaseDynamicContextProvider` that injects a named, titled block into an agent's system prompt at every `run()` — current time, user identity, retrieved RAG docs, session state, cached DB schema. Use when the user asks to "add a context provider", "inject X into the prompt", "give the agent dynamic context", "wire up RAG", "make a `BaseDynamicContextProvider`", or runs `/atomic-agents:create-atomic-context-provider`.
Design and write a `BaseIOSchema` input/output pair for an Atomic Agents agent or tool — docstrings, field descriptions, validators, error variants. Use when the user asks to "create a schema", "design the input/output schema", "define an `IOSchema`", "write a `BaseIOSchema`", "model the agent's output", or runs `/atomic-agents:create-atomic-schema`.
Build a `BaseTool[InSchema, OutSchema]` subclass — input/output schemas, `BaseToolConfig`, `run()` (and optional `run_async()`), env-driven secrets, typed failure outputs. Use when the user asks to "add a tool", "create a tool", "wrap an API as a tool", "build a `BaseTool`", "make a calculator/search/weather tool", or runs `/atomic-agents:create-atomic-tool`.
Guide for the Atomic Agents Python framework — schemas, agents, tools, context providers, prompts, orchestration, and provider configuration. Use when code imports from `atomic_agents`, defines an `AtomicAgent`, `BaseTool`, or `BaseIOSchema`, or the user asks about multi-agent orchestration or LLM-provider wiring in an atomic-agents project.
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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:
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!
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
npx claudepluginhub joshuarweaver/cascade-ai-ml-agents-agent-framework --plugin brainblend-ai-atomic-agentsMCP gateway that lets any web app expose typed actions to Claude over WebSocket — no browser automation. Reverse-connection architecture: the gateway is a pure WebSocket client that discovers apps via `~/.tesseron/tabs/` — no fixed ports, no env vars. Plus a `framework` skill for the Tesseron mental model, a `tesseron-docs` skill that calls the published `@tesseron/docs-mcp` server for authoritative chapter-and-verse spec lookups, a `tesseron-dev` skill that picks the right consumer package (`@tesseron/react` for React, `@tesseron/svelte` for Svelte 5, `@tesseron/vue` for Vue 3, `/server` for Node, `/web` for any other browser context) and inserts the canonical API at the project's entry point, and `tesseron-explorer` / `tesseron-reviewer` skills for mapping and auditing Tesseron codebases.
Editorial "Agent Architect" bundle for Claude Code from Antigravity Awesome Skills.
Scaffold a Deep Agents (LangChain) agent or a dcode CLI agent from one command (/new-dcode-agent). The agents it builds work with any OpenAI-compatible provider.
HelloAGENTS — The orchestration kernel that makes any AI CLI smarter. Adds intelligent routing, unified QA gates, safety guards, and notifications.
A single-skill package for generating harness blueprints for agentic systems.
Make AI coding agents follow a repeatable engineering workflow with memory, verification, skills, and multi-agent setup
Skills, hooks, templates, and engineering guides for bootstrapping AI-agent-friendly projects, with a per-project self-improvement loop.