Get Started

Quick Start

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Install the SDK, write your first agent, and test it — locally or deployed to the cloud.

Prerequisites

OpenAI API Key required. Set it as an environment variable before running your agent:

$export OPENAI_API_KEY="your-key-here"

Installation

$pip install smallestai

Write Your First Agent

Create two files: one for the agent logic, and one to run the application.

1

Create my_agent.py

Subclass OutputAgentNode and implement generate_response() to stream LLM output.

my_agent.py
1import os
2from smallestai.atoms.agent.nodes import OutputAgentNode
3from smallestai.atoms.agent.clients.openai import OpenAIClient
4
5class MyAgent(OutputAgentNode):
6    def __init__(self):
7        super().__init__(name="my-agent")
8        self.llm = OpenAIClient(
9            model="gpt-4o-mini",
10            api_key=os.getenv("OPENAI_API_KEY")
11        )
12
13    async def generate_response(self):
14        response = await self.llm.chat(
15            messages=self.context.messages,
16            stream=True
17        )
18        async for chunk in response:
19            if chunk.content:
20                yield chunk.content
2

Create main.py

Wire up AtomsApp with a setup_handler that adds your agent to the session.

main.py
1from smallestai.atoms.agent.server import AtomsApp
2from smallestai.atoms.agent.session import AgentSession
3from my_agent import MyAgent
4
5async def on_start(session: AgentSession):
6    session.add_node(MyAgent())
7    await session.start()
8    await session.wait_until_complete()
9
10if __name__ == "__main__":
11    app = AtomsApp(setup_handler=on_start)
12    app.run()

Your entry point can be named anything (app.py, run.py, etc.). When deploying, specify it with --entry-point your_file.py.

Want a greeting? Use @session.on_event to speak when the user joins:

1@session.on_event("on_event_received")
2async def on_event(_, event):
3    if isinstance(event, SDKSystemUserJoinedEvent):
4        agent.context.add_message({"role": "assistant", "content": "Hello!"})
5        await agent.speak("Hello! How can I help?")

Adding the greeting to context ensures the LLM knows the conversation has started.

Run Your Agent

Once your files are ready, you have two options:

For development and testing, run the file directly:

$python main.py

This starts a WebSocket server on localhost:8080. In a separate terminal, connect to it:

$smallestai agent chat

No account or deployment needed.

What’s Next?

Tool Calling

Give your agent calculators, search, and APIs.

Orchestration

Connect multiple agents for complex workflows.

Need Help?

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Ask questions, share what you’re building, and get help from other developers.