> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agentops.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Agno
> Track your Agno agents, teams, and workflows with AgentOps
## Video Tutorial
[Agno](https://docs.agno.com) is a modern AI agent framework for building intelligent agents, teams, and workflows. AgentOps provides automatic instrumentation to track all Agno operations including agent interactions, team coordination, tool usage, and workflow execution.
## Installation
Install AgentOps and Agno:
```bash pip theme={null}
pip install agentops agno
```
```bash poetry theme={null}
poetry add agentops agno
```
```bash uv theme={null}
uv pip install agentops agno
```
## Setting Up API Keys
You'll need API keys for AgentOps and your chosen LLM provider:
* **AGENTOPS\_API\_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)
* **OPENAI\_API\_KEY**: From the [OpenAI Platform](https://platform.openai.com/api-keys) (if using OpenAI)
* **ANTHROPIC\_API\_KEY**: From [Anthropic Console](https://console.anthropic.com/) (if using Claude)
Set these as environment variables or in a `.env` file.
```bash Export to CLI theme={null}
export AGENTOPS_API_KEY="your_agentops_api_key_here"
export OPENAI_API_KEY="your_openai_api_key_here"
export ANTHROPIC_API_KEY="your_anthropic_api_key_here" # Optional
```
```txt Set in .env file theme={null}
AGENTOPS_API_KEY="your_agentops_api_key_here"
OPENAI_API_KEY="your_openai_api_key_here"
ANTHROPIC_API_KEY="your_anthropic_api_key_here" # Optional
```
## Quick Start
```python theme={null}
import os
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
from agno.agent import Agent
from agno.team import Team
from agno.models.openai import OpenAIChat
# Initialize AgentOps
import agentops
agentops.init(api_key=os.getenv("AGENTOPS_API_KEY"))
# Create and run an agent
agent = Agent(
name="Assistant",
role="Helpful AI assistant",
model=OpenAIChat(id="gpt-4o-mini")
)
response = agent.run("What are the key benefits of AI agents?")
print(response.content)
```
## AgentOps Integration
### Basic Agent Tracking
AgentOps automatically instruments Agno agents and teams:
```python theme={null}
import agentops
from agno.agent import Agent
from agno.team import Team
from agno.models.openai import OpenAIChat
# Initialize AgentOps - this enables automatic tracking
agentops.init(api_key=os.getenv("AGENTOPS_API_KEY"))
# Create agents - automatically tracked by AgentOps
agent = Agent(
name="Assistant",
role="Helpful AI assistant",
model=OpenAIChat(id="gpt-4o-mini")
)
# Create teams - coordination automatically tracked
team = Team(
name="Research Team",
mode="coordinate",
members=[agent]
)
# All operations are automatically logged to AgentOps
response = team.run("Analyze the current AI market trends")
print(response.content)
```
## What Gets Tracked
AgentOps automatically captures:
* **Agent Interactions**: All agent inputs, outputs, and configurations
* **Team Coordination**: Multi-agent collaboration patterns and results
* **Tool Executions**: Function calls, parameters, and return values
* **Workflow Steps**: Session states, caching, and performance metrics
* **Token Usage**: Costs and resource consumption across all operations
* **Timing Metrics**: Response times and concurrent operation performance
* **Error Tracking**: Failures and debugging information
## Dashboard and Monitoring
Once your Agno agents are running with AgentOps, you can monitor them in the [AgentOps Dashboard](https://app.agentops.ai/):
* **Real-time Monitoring**: Live agent status and performance
* **Execution Traces**: Detailed logs of agent interactions
* **Performance Analytics**: Token usage, costs, and timing metrics
* **Team Collaboration**: Visual representation of multi-agent workflows
* **Error Tracking**: Comprehensive error logs and debugging information
## Examples
Learn the fundamentals of creating AI agents and organizing them into collaborative teams
Execute multiple AI tasks concurrently for improved performance using asyncio
Build sophisticated multi-agent teams with specialized tools for comprehensive research
Implement Retrieval-Augmented Generation with vector databases and knowledge bases
Create custom workflows with intelligent caching for optimized agent performance