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

# Agno

> Auto-track LLM calls in Agno (formerly Phidata) agents

## Installation

```bash theme={null}
pip install meshai-sdk[agno]
```

## Usage

```python theme={null}
from meshai import MeshAI
from meshai.integrations.agno import track_agno
from agno.agent import Agent
from agno.models.openai import OpenAIChat

client = MeshAI(api_key="msh_...", agent_name="my-agno-agent")
client.register(framework="agno")

# Enable global tracking
track_agno(client)

# Run your agents as normal
agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    instructions=["You are a helpful assistant"],
)
response = agent.run("What is AI governance?")
# Model and tokens tracked automatically
```

## How It Works

`track_agno` patches `Agent.run()` to intercept LLM calls. After each agent run, it:

1. Extracts the **model name** from the agent's model configuration
2. Extracts **token counts** from the response usage metrics
3. Infers the **provider** from the model name
4. Sends the usage event to MeshAI (buffered, non-blocking)

Works with all Agno-supported model providers: OpenAI, Anthropic, Google, Groq, and others.

## Phidata Compatibility

If you are migrating from Phidata, the same integration works. Agno is Phidata's successor and shares the same agent interface.

## Alternative: Proxy (Zero-Code)

If your Agno agents use OpenAI or Anthropic, route through the proxy:

```bash theme={null}
export OPENAI_BASE_URL=https://proxy.meshai.dev/v1/openai/k/msh_YOUR_PROXY_KEY
```