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Tracing Agno

Agno is an open source framework for building agentic applications. It focuses on composing tools and policies into task-oriented agents.

MLflow Tracing integrates with Agno to capture comprehensive traces of agentic workflows and tool compositions. Enable it with mlflow.agno.autolog. The integration provides end-to-end visibility into:

  • Prompts and completion responses
  • Latencies
  • Metadata about the different Agents, such as function names
  • Token usages and cost
  • Cache hit
  • Any exception if raised

Prerequisites​

To use MLflow Tracing with Agno, you need to install MLflow and the relevant Agno packages.

For development environments, install the full MLflow package with Databricks extras and Agno:

Bash
pip install --upgrade "mlflow[databricks]>=3.1" agno openai

The full mlflow[databricks] package includes all features for local development and experimentation on Databricks.

note

MLflow 3 is recommended for the best tracing experience.

Before running the examples, you'll need to configure your environment:

For users outside Databricks notebooks: Set your Databricks environment variables:

Bash
export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="your-personal-access-token"

For users inside Databricks notebooks: These credentials are automatically set for you.

Basic example​

Python
import mlflow

mlflow.agno.autolog()

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.yfinance import YFinanceTools

agent = Agent(
    model=Claude(id="claude-sonnet-4-20250514"),
    tools=[YFinanceTools(stock_price=True)],
    instructions="Use tables to display data. Don't include any other text.",
    markdown=True,
)
agent.print_response("What is the stock price of Apple?", stream=False)

Run your Agno agents as usual. Traces will appear in the experiment UI.

Multi-agent example​

Python
import mlflow

from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools

# Enable auto tracing for Agno
mlflow.agno.autolog()


web_agent = Agent(
    name="Web Search Agent",
    role="Handle web search requests and general research",
    model=OpenAIChat(id="gpt-4.1"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    add_datetime_to_instructions=True,
)

finance_agent = Agent(
    name="Finance Agent",
    role="Handle financial data requests and market analysis",
    model=OpenAIChat(id="gpt-4.1"),
    tools=[
        YFinanceTools(
            stock_price=True,
            stock_fundamentals=True,
            analyst_recommendations=True,
            company_info=True,
        )
    ],
    instructions=[
        "Use tables to display stock prices, fundamentals (P/E, Market Cap), and recommendations.",
        "Clearly state the company name and ticker symbol.",
        "Focus on delivering actionable financial insights.",
    ],
    add_datetime_to_instructions=True,
)

reasoning_finance_team = Team(
    name="Reasoning Finance Team",
    mode="coordinate",
    model=Claude(id="claude-sonnet-4-20250514"),
    members=[web_agent, finance_agent],
    tools=[ReasoningTools(add_instructions=True)],
    instructions=[
        "Collaborate to provide comprehensive financial and investment insights",
        "Consider both fundamental analysis and market sentiment",
        "Use tables and charts to display data clearly and professionally",
        "Present findings in a structured, easy-to-follow format",
        "Only output the final consolidated analysis, not individual agent responses",
    ],
    markdown=True,
    show_members_responses=True,
    enable_agentic_context=True,
    add_datetime_to_instructions=True,
    success_criteria="The team has provided a complete financial analysis with data, visualizations, risk assessment, and actionable investment recommendations supported by quantitative analysis and market research.",
)

reasoning_finance_team.print_response(
    """Compare the tech sector giants (AAPL, GOOGL, MSFT) performance:
    1. Get financial data for all three companies
    2. Analyze recent news affecting the tech sector
    3. Calculate comparative metrics and correlations
    4. Recommend portfolio allocation weights""",
    show_full_reasoning=True,
)

Token usage tracking​

MLflow logs token usage for each Agent callto the mlflow.chat.tokenUsage attribute. The total token usage throughout the trace is available in the token_usage field of the trace info object.

Python
# Get the trace object just created
last_trace_id = mlflow.get_last_active_trace_id()
trace = mlflow.get_trace(trace_id=last_trace_id)

# Print the token usage
total_usage = trace.info.token_usage
print("== Total token usage: ==")
print(f"  Input tokens: {total_usage['input_tokens']}")
print(f"  Output tokens: {total_usage['output_tokens']}")
print(f"  Total tokens: {total_usage['total_tokens']}")

# Print the token usage for each LLM call
print("\n== Detailed usage for each LLM call: ==")
for span in trace.data.spans:
    if usage := span.get_attribute("mlflow.chat.tokenUsage"):
        print(f"{span.name}:")
        print(f"  Input tokens: {usage['input_tokens']}")
        print(f"  Output tokens: {usage['output_tokens']}")
        print(f"  Total tokens: {usage['total_tokens']}")

Disable auto-tracing​

Disable with mlflow.agno.autolog(disable=True) or globally with mlflow.autolog(disable=True).

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