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smolagents is a minimalist AI agent framework developed by Hugging Face, designed to simplify the creation and deployment of powerful agents with just a few lines of code. It focuses on simplicity and efficiency, making it easy for developers to leverage large language models (LLMs) for various applications. Phoenix provides auto-instrumentation, allowing you to track and visualize every step and call made by your agent.
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Google Colab

colab.research.google.com

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Install

pip install openinference-instrumentation-smolagents smolagents

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Setup

Add your HF_TOKEN as an environment variable: 
os.environ["HF_TOKEN"] = "<your_hf_token_value>"
Connect to your Phoenix instance using the register function. 
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

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Create & Run an Agent

Create your Hugging Face Model, and at every run, traces will be sent to Phoenix. 
from smolagents import (
    CodeAgent,
    InferenceClientModel,
    ToolCallingAgent,
    VisitWebpageTool,
    WebSearchTool,
)

model = InferenceClientModel()

managed_agent = ToolCallingAgent(
    tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
    model=model,
    name="managed_agent",
    description="This is an agent that can do web search.",
)
manager_agent.run("Based on the latest news, what is happening in extraterrestrial life?")

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Observe

Now that you have tracing setup, all invocations and steps of your Agent will be streamed to your running Phoenix for observability and evaluation.

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Resources