Atomic Agents Framework

Atomic Agents is a lightweight Python framework for building LLM applications with typed, structured input and output. It layers on top of Instructor and Pydantic so every interaction between user, agent, tool, and context is a validated schema.

This skill orients Claude on the framework and routes to focused reference files as the task requires.

Core abstractions

Concept	Class	Role
Schema	BaseIOSchema	Typed input/output contract, every agent/tool I/O is one
Agent	AtomicAgent[In, Out]	LLM-backed transformer from input schema to output schema
Config	AgentConfig	Wires client, model, history, prompt, roles, API params
Prompt	SystemPromptGenerator	Three-section prompt: background, steps, output_instructions
History	ChatHistory	Conversation state, serializable, token-counted
Tool	BaseTool[In, Out]	Deterministic capability the agent can invoke
Context	BaseDynamicContextProvider	Dynamic section injected into the system prompt at runtime

All communication between these uses BaseIOSchema subclasses with docstring-required descriptions.

Canonical imports

from atomic_agents import (
    AtomicAgent, AgentConfig,
    BasicChatInputSchema, BasicChatOutputSchema,
    BaseIOSchema, BaseTool, BaseToolConfig,
)
from atomic_agents.context import (
    ChatHistory, Message,
    SystemPromptGenerator, BaseDynamicContextProvider,
)
# Optional: MCP interop
from atomic_agents.connectors.mcp import fetch_mcp_tools, MCPTransportType

Do not use legacy paths like atomic_agents.lib.base.* or atomic_agents.agents.base_agent, those were retired. Import from the top-level package where possible.

Minimum viable agent

import os, instructor, openai
from atomic_agents import AtomicAgent, AgentConfig, BasicChatInputSchema, BasicChatOutputSchema
from atomic_agents.context import ChatHistory