Setup
- Python
Usage
Once initialized, all Agno agent runs and tool invocations are captured automatically:What Gets Captured
Run the example
Clone the repo and run a complete agent end-to-end.Python
Run the Python example
Documentation Index
Fetch the complete documentation index at: /docs/llms.txt
Use this file to discover all available pages before exploring further.
Auto-instrument Agno agents, tools, and multi-step reasoning
import traceroot
from traceroot import Integration
traceroot.initialize(integrations=[Integration.AGNO])
import traceroot
from traceroot import Integration
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools
traceroot.initialize(integrations=[Integration.AGNO])
agent = Agent(
model=Claude(id="claude-sonnet-4-20250514"),
tools=[
YFinanceTools(),
DuckDuckGoTools(),
],
instructions=[
"Use YFinance for stock prices and fundamentals.",
"Use DuckDuckGo for general web searches.",
"Always cite your sources.",
],
markdown=True,
)
# The entire agent run, including every tool call, is traced
agent.print_response(
"What is the current stock price of NVDA and what are its fundamentals?",
stream=False,
)
traceroot.flush()
| Attribute | Description |
|---|---|
| Agent runs | Each agent.run() / agent.print_response() invocation |
| Reasoning steps | Individual ReAct-style steps within the agent loop |
| Tool calls | Each tool invocation with input arguments and results |
| LLM calls | Raw completion requests to the underlying provider |
| Tokens & Cost | Aggregated token usage and pricing |
| Latency | Duration per agent run and per span |