> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langtrace.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Agno

> Learn how to use Langtrace with Agno for building multi-modal agents and tracing

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

1. Install the Langtrace's SDK and [initialize](/quickstart) the SDK in your code.

```bash theme={null}
pip install langtrace-python-sdk
```

2. Install the [Agno](https://github.com/agno-agi/agno) Library.

```bash theme={null}
pip install agno
```

3. Setup environment variables:

```bash theme={null}
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 Python theme={null}
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:

<CodeGroup>
  ```python Python theme={null}

  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")
  ```
</CodeGroup>

## What's being traced?

With Langtrace, the following operations are automatically traced:

1. **Agent Run Operations**:
   * Input/output content
   * Run duration and timestamps
   * Stream events for streaming responses

2. **Tool Calls**:
   * Function name and parameters
   * Execution time
   * Return values and errors

3. **Memory Operations**:
   * Memory updates
   * Chat history retrievals
   * User memory creation

View all these trace details in the Langtrace dashboard:

<img src="https://mintcdn.com/langtraceai-2/hZpPXosaHdgAfpXF/images/agno1.jpg?fit=max&auto=format&n=hZpPXosaHdgAfpXF&q=85&s=fab07af40a394a0db4093f36b026f91b" alt="traces" width="1788" height="850" data-path="images/agno1.jpg" />

<img src="https://mintcdn.com/langtraceai-2/hZpPXosaHdgAfpXF/images/agno2.jpg?fit=max&auto=format&n=hZpPXosaHdgAfpXF&q=85&s=dd2fe654922b515bccf9f5b89134048c" alt="traces" width="1788" height="850" data-path="images/agno2.jpg" />

## Resources

* [Getting started with Langtrace](/introduction)
* [Agno Documentation](https://github.com/agno-agi/agno)
* [Join our Discord Community](https://discord.langtrace.ai/)