168 lines
5.2 KiB
Plaintext
168 lines
5.2 KiB
Plaintext
---
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title: Agno
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description: "Track your Agno agents, teams, and workflows with AgentOps"
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---
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## Video Tutorial
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<iframe
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width="560"
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height="315"
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src="https://www.youtube.com/embed/M4e1Ybkn_K0"
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title="AgentOps + Agno Integration Tutorial"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
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allowfullscreen
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></iframe>
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[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.
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## Installation
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Install AgentOps and Agno:
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<CodeGroup>
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```bash pip
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pip install agentops agno
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```
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```bash poetry
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poetry add agentops agno
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```
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```bash uv
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uv pip install agentops agno
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```
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</CodeGroup>
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## Setting Up API Keys
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You'll need API keys for AgentOps and your chosen LLM provider:
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- **AGENTOPS_API_KEY**: From your [AgentOps Dashboard](https://app.agentops.ai/)
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- **OPENAI_API_KEY**: From the [OpenAI Platform](https://platform.openai.com/api-keys) (if using OpenAI)
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- **ANTHROPIC_API_KEY**: From [Anthropic Console](https://console.anthropic.com/) (if using Claude)
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Set these as environment variables or in a `.env` file.
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<CodeGroup>
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```bash Export to CLI
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export AGENTOPS_API_KEY="your_agentops_api_key_here"
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export OPENAI_API_KEY="your_openai_api_key_here"
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export ANTHROPIC_API_KEY="your_anthropic_api_key_here" # Optional
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```
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```txt Set in .env file
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AGENTOPS_API_KEY="your_agentops_api_key_here"
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OPENAI_API_KEY="your_openai_api_key_here"
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ANTHROPIC_API_KEY="your_anthropic_api_key_here" # Optional
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```
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</CodeGroup>
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## Quick Start
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```python
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import os
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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from agno.agent import Agent
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from agno.team import Team
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from agno.models.openai import OpenAIChat
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# Initialize AgentOps
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import agentops
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agentops.init(api_key=os.getenv("AGENTOPS_API_KEY"))
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# Create and run an agent
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agent = Agent(
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name="Assistant",
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role="Helpful AI assistant",
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model=OpenAIChat(id="gpt-4o-mini")
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)
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response = agent.run("What are the key benefits of AI agents?")
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print(response.content)
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```
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## AgentOps Integration
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### Basic Agent Tracking
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AgentOps automatically instruments Agno agents and teams:
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```python
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import agentops
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from agno.agent import Agent
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from agno.team import Team
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from agno.models.openai import OpenAIChat
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# Initialize AgentOps - this enables automatic tracking
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agentops.init(api_key=os.getenv("AGENTOPS_API_KEY"))
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# Create agents - automatically tracked by AgentOps
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agent = Agent(
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name="Assistant",
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role="Helpful AI assistant",
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model=OpenAIChat(id="gpt-4o-mini")
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)
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# Create teams - coordination automatically tracked
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team = Team(
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name="Research Team",
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mode="coordinate",
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members=[agent]
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)
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# All operations are automatically logged to AgentOps
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response = team.run("Analyze the current AI market trends")
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print(response.content)
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```
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## What Gets Tracked
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AgentOps automatically captures:
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- **Agent Interactions**: All agent inputs, outputs, and configurations
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- **Team Coordination**: Multi-agent collaboration patterns and results
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- **Tool Executions**: Function calls, parameters, and return values
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- **Workflow Steps**: Session states, caching, and performance metrics
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- **Token Usage**: Costs and resource consumption across all operations
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- **Timing Metrics**: Response times and concurrent operation performance
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- **Error Tracking**: Failures and debugging information
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## Dashboard and Monitoring
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Once your Agno agents are running with AgentOps, you can monitor them in the [AgentOps Dashboard](https://app.agentops.ai/):
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- **Real-time Monitoring**: Live agent status and performance
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- **Execution Traces**: Detailed logs of agent interactions
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- **Performance Analytics**: Token usage, costs, and timing metrics
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- **Team Collaboration**: Visual representation of multi-agent workflows
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- **Error Tracking**: Comprehensive error logs and debugging information
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## Examples
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<CardGroup cols={2}>
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<Card title="Basic Agents and Teams" icon="users" href="/v2/examples/agno">
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Learn the fundamentals of creating AI agents and organizing them into collaborative teams
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</Card>
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<Card title="Async Operations" icon="bolt" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_async_operations.ipynb">
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Execute multiple AI tasks concurrently for improved performance using asyncio
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</Card>
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<Card title="Research Team Collaboration" icon="magnifying-glass" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_research_team.ipynb">
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Build sophisticated multi-agent teams with specialized tools for comprehensive research
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</Card>
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<Card title="RAG Tool Integration" icon="database" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_tool_integrations.ipynb">
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Implement Retrieval-Augmented Generation with vector databases and knowledge bases
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</Card>
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<Card title="Workflow Setup with Caching" icon="diagram-project" href="https://github.com/AgentOps-AI/agentops/blob/main/examples/agno/agno_workflow_setup.ipynb">
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Create custom workflows with intelligent caching for optimized agent performance
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</Card>
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</CardGroup>
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