Agno

Agno is a lightweight library for building multi-modal agents, designed with simplicity, speed, and flexibility in mind. It avoids complex graphs and chains, offering a pure Python experience for effortless development. Optimized for performance, Agno enables blazing-fast agents with minimal memory usage. With Langtrace, you can gain deep visibility into your agents’ operations, including tool calls, memory operations, reasoning steps, and model interactions.

Setup

Install the Langtrace’s SDK and initialize the SDK in your code.

pip install langtrace-python-sdk

Install the Agno Library.

pip install agno

Setup environment variables:

export LANGTRACE_API_KEY=YOUR_LANGTRACE_API_KEY
export OPENAI_API_KEY=YOUR_OPENAI_API_KEY # this is assuming you're using OPENAI for inference

Usage

Initialize Langtrace before creating your Phidata agent:

Python

from langtrace_python_sdk import langtrace  # Must precede other imports
from langtrace_python_sdk.utils.with_root_span import with_langtrace_root_span

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.yfinance import YFinanceTools

langtrace.init()

Create and run the agent:

def analyze_stock(symbol: str):
    finance_agent = Agent(
        name="Finance Agent",
        model=OpenAIChat(id="gpt-4o"),
        tools=[
            YFinanceTools(
                stock_price=True,
                analyst_recommendations=True,
                company_info=True,
                company_news=True
            )
        ],
        instructions=["Use tables to display data"],
        show_tool_calls=True,
        markdown=True,
    )

    finance_agent.print_response(
        f"Analyze {symbol}. Show me:\n" +
        "1. Current stock price\n" +
        "2. Analyst recommendations\n" +
        "3. Latest company news\n" +
        "Present the information in a clear, organized format.",
        stream=True
    )

# Run the analysis
analyze_stock("NVDA")

What’s being traced?

With Langtrace, the following operations are automatically traced:

Agent Run Operations:
- Input/output content
- Run duration and timestamps
- Stream events for streaming responses
Tool Calls:
- Function name and parameters
- Execution time
- Return values and errors
Memory Operations:
- Memory updates
- Chat history retrievals
- User memory creation

View all these trace details in the Langtrace dashboard:

Getting Started

Tracing

Prompting

Evaluations & Testing

Supported Integrations

API Reference

Hosting

Contact Us

Setup

Usage

What’s being traced?

Resources

​Setup

​Usage

​What’s being traced?

​Resources

Setup

Usage

What’s being traced?

Resources