Skip to content

Quick Start

This guide will help you get up and running with Scoras quickly. We'll cover the basics of creating agents, using tools, and understanding complexity scores.

Creating Your First Agent

Let's start by creating a simple agent:

import scoras as sc

# Create a basic agent
agent = sc.Agent(
    model="openai:gpt-4o",
    system_prompt="You are a helpful assistant."
)

# Run the agent
response = agent.run_sync("What is the capital of France?")
print(response)

This creates an agent using OpenAI's GPT-4o model and runs it with a simple query.

Adding Tools to Your Agent

Agents become more powerful when they have tools to work with:

import scoras as sc

# Define a tool using the decorator
@sc.tool(name="calculator", description="Perform calculations", complexity="simple")
async def calculator(operation: str, a: float, b: float) -> float:
    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b == 0:
            raise ValueError("Cannot divide by zero")
        return a / b
    else:
        raise ValueError(f"Unknown operation: {operation}")

# Create an agent with the tool
agent = sc.Agent(
    model="openai:gpt-4o",
    system_prompt="You are a helpful assistant with calculation abilities.",
    tools=[calculator]
)

# Run the agent with a query that will use the tool
response = agent.run_sync("What is 25 multiplied by 16?")
print(response)

Building a Simple RAG System

Retrieval-Augmented Generation (RAG) enhances your agent with document knowledge:

import scoras as sc
from scoras.rag import Document, SimpleRAG

# Create documents
documents = [
    Document(content="The capital of France is Paris, known as the City of Light."),
    Document(content="Paris is famous for the Eiffel Tower, built in 1889."),
    Document(content="France has a population of about 67 million people.")
]

# Create a RAG system
rag = SimpleRAG(
    agent=sc.Agent(model="openai:gpt-4o"),
    documents=documents
)

# Run the RAG system
response = rag.run_sync("What is the capital of France and what is it known for?")
print(response)

Understanding Complexity Scores

One of Scoras' unique features is its ability to track and report on the complexity of your agent workflows:

# Create an agent with tools
agent = sc.Agent(
    model="openai:gpt-4o",
    system_prompt="You are a helpful assistant.",
    tools=[calculator],
    enable_scoring=True
)

# Run the agent
response = agent.run_sync("What is 42 divided by 6?")
print(response)

# Get the complexity score
score = agent.get_complexity_score()
print(f"Complexity: {score['complexity_rating']} (Score: {score['total_score']})")
print(f"Component breakdown: {score['breakdown']}")

The complexity score helps you understand how sophisticated your agent workflow is, which can be useful for optimization and resource planning.

Next Steps

Now that you've seen the basics, you can: