Quickstart

Build your first Elsai agent in under 5 minutes.

Step 1 — Install

pip install --extra-index-url https://elsai-agents.elsai.ai/root/ elsai-agents==0.1.0 elsai-agents-tools

Step 2 — Create your first agent

from elsai import Agent
from elsai_tools import calculator

agent = Agent(tools=[calculator])
result = agent("What is the square root of 1764?")
print(result)

The agent will:

1. Receive your question
2. Decide to call the calculator tool
3. Execute the tool and get the answer
4. Return a natural-language response

Step 3 — Build a custom tool

Add your own Python function as a tool using the @tool decorator:

from elsai import Agent, tool

@tool
def get_weather(city: str) -> str:
    """Get current weather for a city.

    Args:
        city: The name of the city to get weather for.

    Returns:
        A description of current weather conditions.
    """
    # In a real app, call a weather API here
    return f"It's sunny and 72°F in {city}."

agent = Agent(tools=[get_weather])
result = agent("What's the weather like in Tokyo?")
print(result)

Step 4 — Add a system prompt

Guide the agent's behavior with a system prompt:

from elsai import Agent

agent = Agent(
    system_prompt="You are a helpful Python coding assistant. "
                  "Always provide complete, runnable code examples."
)
result = agent("Write a function that reverses a string")
print(result)

Step 5 — Choose a model provider

Configure a model provider using the standalone elsai-model package (e.g., Anthropic Claude on AWS Bedrock):

import os
from elsai import Agent
from elsai_model.bedrock import BedrockConnector

model = BedrockConnector(
    aws_access_key=os.environ.get("AWS_ACCESS_KEY_ID"),
    aws_secret_key=os.environ.get("AWS_SECRET_ACCESS_KEY"),
    aws_region="us-east-1",
    model_id="anthropic.claude-3-5-sonnet-20241022-v2:0"
)

agent = Agent(model=model, system_prompt="You are a concise assistant.")
result = agent("Explain machine learning in one sentence")
print(result)

Step 6 — Stream responses

Get tokens as they're generated:

import asyncio
from elsai import Agent

agent = Agent()

async def stream():
    async for event in agent.stream_async("Tell me a short story"):
        if "data" in event:
            print(event["data"], end="", flush=True)

asyncio.run(stream())

Step 7 — Multi-turn conversation

Agents maintain conversation history automatically:

from elsai import Agent

agent = Agent()

agent("My name is Alice and I'm a software engineer.")
agent("What do you know about me?")
# → "Your name is Alice and you're a software engineer."

What's next?

• Core Concepts — understand how agents work
• Tools — build powerful tools
• Model Providers — configure your preferred LLM
• Multi-Agent — orchestrate multiple agents