framework

name	framework
description	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.

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

client = instructor.from_openai(openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"]))

agent = AtomicAgent[BasicChatInputSchema, BasicChatOutputSchema](
    config=AgentConfig(
        client=client,
        model="gpt-5-mini",
        history=ChatHistory(),
    )
)

reply = agent.run(BasicChatInputSchema(chat_message="Hello"))
print(reply.chat_message)

AtomicAgent and BaseTool use PEP 695 generics — the type parameters carry runtime information, so write them explicitly and keep them accurate. Full runnable version: atomic-examples/quickstart/quickstart/1_0_basic_chatbot.py.

Targeted creation skills

For the four most common authoring tasks, dedicated atomic skills give a step-by-step workflow (clarify → write → verify → hand off) instead of just reference material. Prefer them when the user is actively building something specific.

User intent	Atomic skill
"create a schema" / "design the input/output schema"	atomic-agents:create-atomic-schema
"create an agent" / "add another agent" / "wire up an AtomicAgent"	atomic-agents:create-atomic-agent
"add a tool" / "wrap an API as a tool" / "build a BaseTool"	atomic-agents:create-atomic-tool
"add a context provider" / "inject X into the prompt" / "wire up RAG"	atomic-agents:create-atomic-context-provider

These skills auto-trigger on the matching phrasing. The reference files below are what they (and you) load for deeper material.

Decision routing

Pick the reference file that matches the task. Each is loaded only when read.

Task	Reference
Design or validate an input/output schema	references/schemas.md
Build, configure, or run an agent	references/agents.md
Write a tool the agent will invoke	references/tools.md
Inject dynamic data into the system prompt	references/context-providers.md
Structure the system prompt	references/prompts.md
Coordinate multiple agents	references/orchestration.md
Manage conversation state and multi-agent memory	references/memory.md
Register telemetry, retries, or logging	references/hooks.md
Swap LLM provider or configure roles	references/providers.md
Decide the project layout or pyproject.toml	references/project-structure.md
Write tests for agents and tools	references/testing.md

When a concept is unclear, start from the user's verb: create a schema → create-atomic-schema skill, hook up a weather API → create-atomic-tool skill, inject user name into prompt → create-atomic-context-provider skill, route between agents → orchestration reference.

Working style

Follow these defaults unless the project says otherwise. The reference files go deeper on each.

Schemas are the contract. Design the BaseIOSchema pair before writing the agent. Field descriptions flow into the LLM prompt via Instructor, so write them for the model, not just the developer. Every subclass needs a non-empty docstring — the framework enforces this at class-definition time.

System prompts have three sections. Use SystemPromptGenerator(background=..., steps=..., output_instructions=...). Put persona in background, the ordered procedure in steps, and output-format rules in output_instructions. The agent falls back to a sensible default when omitted.

Wrap the provider client with Instructor. Always. instructor.from_openai(...), instructor.from_anthropic(...), instructor.from_genai(...) — without this the agent cannot enforce output schemas.

Use model_api_parameters for provider knobs. temperature, max_tokens, reasoning_effort, etc. live in the model_api_parameters dict on AgentConfig, not on the agent itself.

Errors and retries flow through hooks. Register handlers for parse:error, completion:error, completion:last_attempt rather than wrapping run() in try/except. See references/hooks.md.

Tools return the output schema on success. Failure should surface as validation errors or typed result schemas the caller pattern-matches on — don't raise through run() unless the failure is truly unrecoverable.

When the user is starting from nothing

Scaffolding a brand-new project (fresh directory, pyproject.toml, first agent) is handled by the sibling skill new-app. Suggest it when the user says "new project", "start from scratch", or equivalent.

When the user wants to understand an existing codebase

Delegate to the atomic-explorer subagent when the project has more than a handful of atomic-agents files and the user asks to "explore", "map", "understand how X works", or similar. The subagent reads the relevant files in isolated context and returns a compact architecture map (agents, tools, schemas, context providers, orchestration, essential-reading list). Invoke via the Task tool with the scope (project root, module path, or feature) in the prompt.

For a small project (a single main.py + one or two agents), reading the files directly in the main thread is fine — the isolation upside is thin.

When the user wants a review

Delegate to the atomic-reviewer subagent — do not review in the main thread. The subagent runs in isolated context with read-only tools, keeping the review's file exploration out of the parent conversation. Invoke it via the Task tool with the scope (diff, paths, or module) in the prompt. Review findings return as a single structured report the parent thread can act on.

Versioning and compatibility

• Python 3.12+ (PEP 695 generics are used internally).
• Instructor 1.14+ with provider extras (instructor[openai], instructor[anthropic], etc.) — the workspace uses Instructor's extras to pull provider SDKs.
• Pydantic 2.
• For MCP interop, see atomic_agents.connectors.mcp — fetch_mcp_tools, MCPFactory, MCPTransportType are stable.

Anti-patterns (surface these in review)

• Plain BaseModel instead of BaseIOSchema.
• Missing docstrings on BaseIOSchema subclasses (framework raises at import).
• Field(..., description="...") missing — Instructor leans on descriptions for prompt generation.
• Raw provider client passed as AgentConfig.client (must be wrapped in Instructor). Raw SDK use for embeddings, image generation, audio, or moderation is fine — the framework only covers structured chat/completions.
• Hardcoded API keys instead of env vars.
• Unbounded ChatHistory on long-running sessions.
• Blocking I/O inside BaseDynamicContextProvider.get_info() — it runs on every agent.run().
• Catching ValidationError to hide schema problems instead of fixing descriptions or constraints.
• MCPTransportType.STREAMABLE_HTTP — the correct value is HTTP_STREAM.
• ChatHistory.load(...) called as a classmethod — it is an instance method that mutates self.

For deeper guidance load the relevant reference file above. For code-review runs, delegate to the atomic-reviewer subagent.