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Documentation Index

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Use this file to discover all available pages before exploring further.

Maxim AI provides comprehensive agent monitoring, evaluation, and observability for your Smolagents applications. With Maxim’s one-line integration, you can easily trace and analyze agent interactions, tool usage, and performance metrics.

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Getting Started

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Prerequisites

  • Python version >=3.10
  • A Maxim account (sign up here)
  • Generate Maxim API Key
  • A Smolagents project

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Installation

Install the required packages via pip: 
pip install maxim-py smolagents python-dotenv
Or add them to your requirements.txt: 
maxim-py
smolagents
python-dotenv

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Basic Setup

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1. Set up environment variables

Create a .env file in your project root: 
# Maxim API Configuration
MAXIM_API_KEY=your_api_key_here
MAXIM_LOG_REPO_ID=your_repo_id_here

# OpenAI API Configuration (for Smolagents)
OPENAI_API_KEY=your_openai_api_key_here

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2. Import the required packages

from maxim import Maxim
from maxim.logger.smolagents import instrument_smolagents
from smolagents import OpenAIServerModel, CodeAgent
from dotenv import load_dotenv
import os

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3. Initialize Maxim with your API key

# Load environment variables
load_dotenv()

# Instrument Smolagents with just one line
instrument_smolagents(Maxim().logger())

​
4. Create and run your Smolagents application

# Create your agent with tools
agent = CodeAgent(
    tools=[your_tools],  # Your custom tools
    model=OpenAIServerModel(
        model_id="gpt-4o-mini",
        api_key=os.environ["OPENAI_API_KEY"],
        api_base="https://api.openai.com/v1",
    ),
)

# Run your agent
result = agent.run("Your query here")
That’s it! All your Smolagents interactions will now be logged and available in your Maxim dashboard.

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Complete Example

Here’s a complete example showing how to integrate Smolagents with Maxim, including a custom SQL tool: 
from sqlalchemy import (
    Column,
    Float,
    Integer,
    MetaData,
    String,
    Table,
    create_engine,
    insert,
    inspect,
    text,
)
from maxim import Maxim
from maxim.logger.smolagents import instrument_smolagents
import os
from smolagents import OpenAIServerModel
from smolagents import CodeAgent
from smolagents import tool
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize Maxim
instrument_smolagents(Maxim().logger())

# Set up SQL database
engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()

# Create receipts SQL table
table_name = "receipts"
receipts = Table(
    table_name,
    metadata_obj,
    Column("receipt_id", Integer, primary_key=True),
    Column("customer_name", String(16), primary_key=True),
    Column("price", Float),
    Column("tip", Float),
)
metadata_obj.create_all(engine)

# Insert sample data
rows = [
    {"receipt_id": 1, "customer_name": "Alan Payne", "price": 12.06, "tip": 1.20},
    {"receipt_id": 2, "customer_name": "Alex Mason", "price": 23.86, "tip": 0.24},
    {"receipt_id": 3, "customer_name": "Woodrow Wilson", "price": 53.43, "tip": 5.43},
    {"receipt_id": 4, "customer_name": "Margaret James", "price": 21.11, "tip": 1.00},
]
for row in rows:
    stmt = insert(receipts).values(**row)
    with engine.begin() as connection:
        connection.execute(stmt)

# Create SQL tool
@tool
def sql_engine(query: str) -> str:
    """
    Allows you to perform SQL queries on the table. Returns a string representation of the result.
    The table is named 'receipts'. Its description is as follows:
        Columns:
        - receipt_id: INTEGER
        - customer_name: VARCHAR(16)
        - price: FLOAT
        - tip: FLOAT

    Args:
        query: The query to perform. This should be correct SQL.
    """
    output = ""
    with engine.connect() as con:
        rows = con.execute(text(query))
        for row in rows:
            output += "\n" + str(row)
    return output

# Create and run agent
agent = CodeAgent(
    tools=[sql_engine],
    model=OpenAIServerModel(
        model_id="gpt-4o-mini",
        api_key=os.environ["OPENAI_API_KEY"],
        api_base="https://api.openai.com/v1",
    ),
)

# Run the agent
result = agent.run("Can you give me the name of the client who got the most expensive receipt?")
print(result)

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Viewing Your Traces

After running your Smolagents application:
  1. Log in to your Maxim Dashboard
  2. Navigate to your repository
  3. View detailed agent traces, including:
    • Agent conversations
    • Tool usage patterns
    • Performance metrics
    • Cost analytics

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Advanced Usage

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Custom Tools with Maxim Integration

You can create custom tools and they will automatically be traced by Maxim: 
@tool
def custom_tool(input_data: str) -> str:
    """
    A custom tool that processes input data.
    
    Args:
        input_data: The input string to process
        
    Returns:
        Processed result
    """
    # Your tool logic here
    result = f"Processed: {input_data}"
    return result

# Add to your agent
agent = CodeAgent(
    tools=[custom_tool, sql_engine],
    model=OpenAIServerModel(
        model_id="gpt-4o-mini",
        api_key=os.environ["OPENAI_API_KEY"],
    ),
)

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Error Handling

Ensure proper cleanup even when errors occur: 
try:
    result = agent.run("Your query here")
    print(result)
except Exception as e:
    print(f"Error: {e}")
finally:
    # Ensure Maxim cleanup happens
    maxim.cleanup()

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Troubleshooting

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Common Issues

  • No traces appearing: Ensure your API key and repository ID are correct
  • Import errors: Make sure you’ve installed all required packages (maxim-py, smolagents, python-dotenv)
  • Tool not working: Ensure instrument_smolagents() is called before creating your agent
  • Environment variables: Verify your .env file is in the correct location and contains all required keys

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Debug Mode

Enable debug mode to surface any internal errors: 
instrument_smolagents(Maxim().logger(), debug=True)

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Resources

Smolagents Docs

Official Smolagents documentation

Github (smolagents cookbook)

Official Maxim GitHub repository