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Creating a Simple Agent

This guide demonstrates how to create and use a basic QuantaLogic agent. We'll walk through setting up the agent and solving a coding task.

Prerequisites

  • Python 3.12 or later
  • QuantaLogic library installed
  • API key for your chosen LLM provider

Environment Setup

Before creating an agent, ensure you have the necessary API keys set:

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import os

# Set API keys for different LLM providers
os.environ["DEEPSEEK_API_KEY"] = "your-deepseek-key"
os.environ["OPENAI_API_KEY"] = "your-openai-key"  # Optional
os.environ["MISTRAL_API_KEY"] = "your-mistral-key"  # Optional

API Key Configuration

  • Always set API keys as environment variables
  • Never hardcode sensitive credentials in your script
  • Supports multiple LLM providers for flexibility

Creating an Agent

Initialize an agent with your preferred language model:

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from quantalogic import Agent

# DeepSeek model (default)
agent = Agent(model_name="deepseek/deepseek-chat")

# Alternative model configurations
# agent = Agent(model_name="openai/gpt-4")
# agent = Agent(model_name="mistral/mistral-large-2411")
# agent = Agent(model_name="bedrock/amazon.nova-pro-v1:0")  # Requires AWS credentials

Solving a Task

Use the agent to generate code or solve programming challenges:

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# Generate a Fibonacci sequence function
result = agent.solve_task("Create a Python function that calculates the Fibonacci sequence")
print(result)

Example Output

Here's what you might see:

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def fibonacci(n: int) -> list[int]:
    """Generate Fibonacci sequence up to n numbers.

    Args:
        n: Number of Fibonacci numbers to generate

    Returns:
        List of Fibonacci numbers
    """
    if n <= 0:
        return []
    elif n == 1:
        return [0]

    sequence = [0, 1]
    while len(sequence) < n:
        sequence.append(sequence[-1] + sequence[-2])

    return sequence

# Example usage
print(fibonacci(10))  # [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

Best Practices

  • Choose models based on your specific requirements
  • Validate API keys before initializing the agent
  • Handle potential errors gracefully
  • Experiment with different models to find the best fit

Supported LLM Providers

  • DeepSeek
  • OpenAI
  • Mistral AI
  • AWS Bedrock (enterprise)

Flexibility

The QuantaLogic Agent supports multiple language models, giving you the freedom to choose the best AI for your task.

Complete Code

Here's the complete example:

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import os
from quantalogic import Agent

# Set API keys for different LLM providers
if not os.environ.get("DEEPSEEK_API_KEY"):
    raise ValueError("DEEPSEEK_API_KEY environment variable is not set")

# Initialize agent
agent = Agent(model_name="deepseek/deepseek-chat")

# Solve a task
result = agent.solve_task("Create a Python function that calculates the Fibonacci sequence")
print(result)

Best Practice

Always verify API keys are set before creating the agent to avoid runtime errors.