AgentOps provides seamless integration with Smolagents, HuggingFace’s lightweight framework for building AI agents. Monitor your agent workflows, tool usage, and execution traces automatically.

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Core Concepts

Smolagents is designed around several key concepts:

• Agents: AI assistants that can use tools and reason through problems
• Tools: Functions that agents can call to interact with external systems
• Models: LLM backends that power agent reasoning (supports various providers via LiteLLM)
• Code Execution: Agents can write and execute Python code in sandboxed environments
• Multi-Agent Systems: Orchestrate multiple specialized agents working together

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Installation

Install AgentOps and Smolagents, along with any additional dependencies:

pip install agentops smolagents python-dotenv

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Setting Up API Keys

Before using Smolagents with AgentOps, you need to set up your API keys:

• AGENTOPS_API_KEY: From your AgentOps Dashboard
• LLM API Keys: Depending on your chosen model provider (e.g., OPENAI_API_KEY, ANTHROPIC_API_KEY)

Set these as environment variables or in a .env file.

export AGENTOPS_API_KEY="your_agentops_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"

Then load them in your Python code:

from dotenv import load_dotenv
import os

load_dotenv()

AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

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Usage

Initialize AgentOps before creating your Smolagents to automatically track all agent interactions:

import agentops
from smolagents import LiteLLMModel, ToolCallingAgent, DuckDuckGoSearchTool

# Initialize AgentOps
agentops.init()

# Create a model (supports various providers via LiteLLM)
model = LiteLLMModel("openai/gpt-4o-mini")

# Create an agent with tools
agent = ToolCallingAgent(
    tools=[DuckDuckGoSearchTool()],
    model=model,
)

# Run the agent
result = agent.run("What are the latest developments in AI safety research?")
print(result)

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Examples

import agentops
from smolagents import LiteLLMModel, CodeAgent

# Initialize AgentOps
agentops.init()

# Create a model
model = LiteLLMModel("openai/gpt-4o-mini")

# Create a code agent that can perform calculations
agent = CodeAgent(
    tools=[],  # No external tools needed for math
    model=model,
    additional_authorized_imports=["math", "numpy"],
)

# Ask the agent to solve a math problem
result = agent.run(
    "Calculate the compound interest on $10,000 invested at 5% annual rate "
    "for 10 years, compounded monthly. Show your work."
)

print(result)

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More Examples

Visit your AgentOps Dashboard to see detailed traces of your Smolagents executions, tool usage, and agent reasoning steps.