Agno AI - Portkey Docs

Introduction

Agno is a powerful framework for building autonomous AI agents that can reason, use tools, maintain memory, and access knowledge bases. Portkey enhances Agno agents with enterprise-grade capabilities for production deployments.

Portkey transforms your Agno agents into production-ready systems by providing:

Complete observability of agent reasoning, tool usage, and knowledge retrieval
Access to 1600+ LLMs through a unified interface
Built-in reliability with fallbacks, retries, and load balancing
Cost tracking and optimization across all agent operations
Advanced guardrails for safe and compliant agent behavior
Enterprise governance with budget controls and access management

Agno AI Official Documentation

Learn more about Agno’s agent framework and core concepts

Setting Up Portkey

If this is your first time using Portkey, you will need to connect your LLM Provider in the app to use them in your Agno AI agents.

Create an Integration

Navigate to the Integrations section on Portkey’s Sidebar. This is where you’ll connect your LLM providers.

Find your preferred provider (e.g., OpenAI, Anthropic, etc.)
Click Connect on the provider card
In the “Create New Integration” window:
- Enter a Name for reference
- Enter a Slug for the integration
- Enter your API Key and other provider specific details for the provider
Click Next Step

In your next step you’ll see workspace provisioning options. You can select the default “Shared Team Workspace” if this is your first time OR chose your current one.

Configure Models

On the model provisioning page:

Leave all models selected (or customize)
Toggle Automatically enable new models if desired

Click Create Integration to complete the integration

Copy the Provider Slug

Once your Integration is created:

Go to Model Catalog → AI Providers tab
Find your integration
Copy the slug (e.g., openai-dev)

This slug is your provider’s unique identifier - you’ll need it for the next step.

Quickstart

You will need

Portkey API Key & Portkey AI Provider slug from Step 1

Install required packages

pip install -U agno portkey-ai

Get your Portkey API Key

Once you are on the Portkey follow this guide to create your first AI provider and integration that will help us connect Agno to any LLM provider.

Configure Portkey with Agno

Agno makes integration incredibly simple - just use the OpenAILike model class with Portkey’s configuration:

from agno.models.openai.like import OpenAILike

portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",  # you need to use "@provider-slug/model-name" for any provider
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
)

Simple E2E example agent

Let’s create a simple Agno agent that uses Portkey for LLM calls:

simple_agent.py

from agno.agent import Agent
from agno.models.openai.like import OpenAILike

# Configure Portkey as the model provider
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",  # You can use any model available on Portkey
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
)

# Create an Agno agent with Portkey
agent = Agent(
    model=portkey_model,
    instructions="Be concise and informative.",
    markdown=True,
)

# Run the agent
agent.print_response("What is Portkey AI?", stream=True)

Visit your Portkey dashboard to see detailed logs of your agent’s execution, including tool calls and model responses!

Production Features

1. Enhanced Observability

Portkey provides comprehensive observability for your Agno agents, helping you understand exactly what’s happening during each execution.

Traces provide a hierarchical view of your agent’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

# Add tracing to your Agno agents
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        trace_id="unique_execution_trace_id",  # Add unique trace ID
    )
)

agent = Agent(
    model=portkey_model,
    instructions="You are a helpful assistant.",
    markdown=True
)

Traces provide a hierarchical view of your agent’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

# Add tracing to your Agno agents
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        trace_id="unique_execution_trace_id",  # Add unique trace ID
    )
)

agent = Agent(
    model=portkey_model,
    instructions="You are a helpful assistant.",
    markdown=True
)

Portkey logs every interaction with LLMs, including:

Complete request and response payloads
Latency and token usage metrics
Cost calculations
Tool calls and function executions

All logs can be filtered by metadata, trace IDs, models, and more, making it easy to debug specific agent runs.

Portkey provides built-in dashboards that help you:

Track cost and token usage across all agent runs
Analyze performance metrics like latency and success rates
Identify bottlenecks in your agent workflows
Compare different agent configurations and LLMs

You can filter and segment all metrics by custom metadata to analyze specific agent types, user groups, or use cases.

Add custom metadata to your Agno agent calls to enable powerful filtering and segmentation:

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

# Add metadata to your Agno agents
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        metadata={"agent_type": "research_agent"},  # Add custom metadata
    )
)

agent = Agent(
    model=portkey_model,
    name="Research Agent",
    instructions="You are a research assistant.",
    markdown=True
)

This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, allowing you to analyze specific agent runs, users, or environments.

2. Reliability - Keep Your Agents Running Smoothly

When running agents in production, things can go wrong - API rate limits, network issues, or provider outages. Portkey’s reliability features ensure your agents keep running smoothly even when problems occur.

It’s this simple to enable fallback in your Agno agents:

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

# Create a config with fallbacks
# It's recommended that you create the Config in Portkey App rather than hard-code the config JSON directly
config = {
  "strategy": {
    "mode": "fallback"
  },
  "targets": [
    {
      "override_params": {"model": "@opeani-provider-slug/gpt-4o"}
    },
    {
      "override_params": {"model": "claude-3-opus-20240229"}
    }
  ]
}

# Configure Portkey model with fallback config
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(config=config)
)

agent = Agent(
    model=portkey_model,
    instructions="You are a helpful assistant.",
    markdown=True
)

This configuration will automatically try Claude if the GPT-4o request fails, ensuring your agent can continue operating.

Automatic Retries

Handles temporary failures automatically. If an LLM call fails, Portkey will retry the same request for the specified number of times - perfect for rate limits or network blips.

Request Timeouts

Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.

Conditional Routing

Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs.

Fallbacks

Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.

Load Balancing

Spread requests across multiple API keys or providers. Great for high-volume agent operations and staying within rate limits.

3. Prompting in Agno Agents

Portkey’s Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your Agno agents. Instead of hardcoding prompts or instructions, use Portkey’s prompt rendering API to dynamically fetch and apply your versioned prompts.

Manage prompts in Portkey's Prompt Library

Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It’s where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:

Iteratively develop prompts before using them in your agents
Test prompts with different variables and models
Compare outputs between different prompt versions
Collaborate with team members on prompt development

This visual environment makes it easier to craft effective prompts for each step in your Agno agent’s workflow.

Iteratively develop prompts before using them in your agents
Test prompts with different variables and models
Compare outputs between different prompt versions
Collaborate with team members on prompt development

This visual environment makes it easier to craft effective prompts for each step in your Agno agent’s workflow.

The Prompt Render API retrieves your prompt templates with all parameters configured:

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import Portkey, createHeaders

# Initialize Portkey client
portkey_client = Portkey(api_key="PORTKEY_API_KEY")

# Retrieve prompt using the render API
prompt_data = portkey_client.prompts.render(
    prompt_id="YOUR_PROMPT_ID",
    variables={
        "user_input": "Tell me about artificial intelligence"
    }
)

# Configure Agno model with Portkey
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
)

# Use the rendered prompt in your Agno agent
agent = Agent(
    name="Assistant",
    model=portkey_model,
    instructions=prompt_data.data.messages[0]["content"],  # Use the rendered prompt as instructions
    markdown=True
)

agent.print_response("Tell me about artificial intelligence", stream=True)

You can:

Create multiple versions of the same prompt
Compare performance between versions
Roll back to previous versions if needed
Specify which version to use in your code:

# Use a specific prompt version
prompt_data = portkey_client.prompts.render(
    prompt_id="YOUR_PROMPT_ID@version_number",
    variables={
        "user_input": "Tell me about quantum computing"
    }
)

Portkey prompts use Mustache-style templating for easy variable substitution:

You are an AI assistant helping with {{task_type}}.

User question: {{user_input}}

Please respond in a {{tone}} tone and include {{required_elements}}.

When rendering, simply pass the variables:

prompt_data = portkey_client.prompts.render(
    prompt_id="YOUR_PROMPT_ID",
    variables={
        "task_type": "research",
        "user_input": "Tell me about quantum computing",
        "tone": "professional",
        "required_elements": "recent academic references"
    }
)

Prompt Engineering Studio

Learn more about Portkey’s prompt management features

4. Guardrails for Safe Agents

Guardrails ensure your Agno agents operate safely and respond appropriately in all situations.

Why Use Guardrails?

Agno agents can experience various failure modes:

Generating harmful or inappropriate content
Leaking sensitive information like PII
Hallucinating incorrect information
Generating outputs in incorrect formats

Portkey’s guardrails protect against these issues by validating both inputs and outputs.

Implementing Guardrails

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

# Create a config with input and output guardrails
# It's recommended you create Config in Portkey App and pass the config ID in the client
config = {
    "input_guardrails": ["guardrails-id-xxx", "guardrails-id-yyy"],
    "output_guardrails": ["guardrails-id-xxx"]
}

# Configure Agno model with guardrails
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        config=config,
    )
)

agent = Agent(
    model=portkey_model,
    instructions="You are a helpful assistant that provides safe responses.",
    markdown=True
)

Portkey’s guardrails can:

Detect and redact PII in both inputs and outputs
Filter harmful or inappropriate content
Validate response formats against schemas
Check for hallucinations against ground truth
Apply custom business logic and rules

Learn More About Guardrails

Explore Portkey’s guardrail features to enhance agent safety

5. User Tracking with Metadata

Track individual users through your Agno agents using Portkey’s metadata system.

What is Metadata in Portkey?

Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special _user field is specifically designed for user tracking.

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

# Configure model with user tracking
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        metadata={
            "_user": "user_123",  # Special _user field for user analytics
            "user_name": "John Doe",
            "user_tier": "premium",
            "user_company": "Acme Corp"
        }
    )
)

agent = Agent(
    model=portkey_model,
    user_id="user_123",  # Also use Agno's user_id for agent-level tracking
    instructions="You are a personalized assistant.",
    markdown=True
)

Filter Analytics by User

With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis:

Filter analytics by user

This enables:

Per-user cost tracking and budgeting
Personalized user analytics
Team or organization-level metrics
Environment-specific monitoring (staging vs. production)

Learn More About Metadata

Explore how to use custom metadata to enhance your analytics

6. Caching for Efficient Agents

Implement caching to make your Agno agents more efficient and cost-effective:

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

portkey_config = {
  "cache": {
    "mode": "simple"
  },
}

# Configure Agno model with caching
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(config=portkey_config)
)

agent = Agent(
    model=portkey_model,
    instructions="You are a helpful assistant.",
    markdown=True
)

Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

portkey_config = {
  "cache": {
    "mode": "simple"
  },
}

# Configure Agno model with caching
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(config=portkey_config)
)

agent = Agent(
    model=portkey_model,
    instructions="You are a helpful assistant.",
    markdown=True
)

Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from portkey_ai import createHeaders

portkey_config = {
  "cache": {
    "mode": "semantic"
  },
}

# Configure Agno model with semantic caching
portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(config=portkey_config)
)

agent = Agent(
    model=portkey_model,
    instructions="You are a helpful assistant.",
    markdown=True
)

Semantic caching considers the contextual similarity between input requests, caching responses for semantically similar inputs.

7. Model Interoperability: using different LLMs

One of Portkey’s key strengths is providing access to 1600+ LLMs through a unified interface. Here’s how to use different providers with Agno:

Supported Providers

See the full list of LLM providers supported by Portkey

portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
)

portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
)

portkey_model = OpenAILike(
    id="@your-anthropic-provider-slug/claude-3-7-sonnet-latest",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
)

portkey_model = OpenAILike(
    id="@your-google-provider-slug/gemini-2.5-pro",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
)

End-to-End Examples

Level 1: Agent with Tools and Observability

Let’s build an agent that uses tools while leveraging Portkey’s observability features:

agent_with_tools.py

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.tools.yfinance import YFinanceTools
from portkey_ai import createHeaders

# Configure Portkey with enhanced tracking
portkey_model = OpenAILike(
    id="@anthropic/claude-3-sonnet-20240320",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        trace_id="agno_finance_agent",
        metadata={
            "agent_type": "financial_analyst",
            "environment": "production"
        }
    )
)

agent = Agent(
    name="Financial Analyst",
    model=portkey_model,
    tools=[
        YFinanceTools(
            stock_price=True,
            company_info=True,
            analyst_recommendations=True,
            company_news=True
        )
    ],
    instructions=[
        "Provide comprehensive financial analysis",
        "Use tables for numerical data",
        "Include relevant news and recommendations",
        "Be concise but thorough"
    ],
    markdown=True,
    debug_mode=True  # See detailed agent reasoning
)

# Run analysis
agent.print_response(
    "Analyze Tesla's current financial position and recent performance",
    stream=True
)

Level 2: Agent with Knowledge Base and Caching

Enhance your Agno agent with knowledge retrieval while using Portkey’s caching to optimize costs:

agent_with_knowledge.py

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.knowledge.url import UrlKnowledge
from agno.vectordb.lancedb import LanceDb, SearchType
from agno.embedder.openai import OpenAIEmbedder
from agno.storage.sqlite import SqliteStorage
from portkey_ai import createHeaders

# Configure Portkey with caching
cache_config = {
    "cache": {
        "mode": "semantic",
        "max_age": 3600  # Cache for 1 hour
    }
}

portkey_model = OpenAILike(
    id="opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        config=cache_config,
        metadata={"agent_type": "knowledge_assistant"}
    )
)

# Set up knowledge base
knowledge = UrlKnowledge(
    urls=["https://docs.agno.com/introduction.md"],
    vector_db=LanceDb(
        uri="tmp/lancedb",
        table_name="agno_docs",
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small", dimensions=1536),
    ),
)

# Configure storage
storage = SqliteStorage(table_name="agent_sessions", db_file="tmp/agent.db")

agent = Agent(
    name="Agno Assistant",
    model=portkey_model,
    knowledge=knowledge,
    storage=storage,
    instructions=[
        "Always search the knowledge base before answering",
        "Provide accurate information from the documentation",
        "If information isn't found, clearly state that"
    ],
    add_history_to_messages=True,
    num_history_runs=5,
    markdown=True
)

if __name__ == "__main__":
    # Load knowledge base (only needed first time)
    agent.knowledge.load(recreate=False)

    # Ask questions - responses will be cached by Portkey
    agent.print_response("What are the key features of Agno?", stream=True)

Level 3: Agent with Memory, Reasoning, and Reliability

Build a sophisticated agent that leverages Agno’s advanced features with Portkey’s reliability configurations:

advanced_agent.py

from agno.agent import Agent
from agno.models.openai.like import OpenAILike
from agno.memory.v2.memory import Memory
from agno.memory.v2.db.sqlite import SqliteMemoryDb
from agno.tools.reasoning import ReasoningTools
from agno.tools.yfinance import YFinanceTools
from portkey_ai import createHeaders

# Configure Portkey with fallbacks and retries
reliability_config = {
    "strategy": {
        "mode": "fallback"
    },
    "targets": [
        {
            "override_params": {"model": "@opeani-provider-slug/gpt-4o"}
        },
        {
            "override_params": {"model": "@anthropic-provider-slug/@anthropic-provider-slug/claude-3-opus-20240229"}
        }
    ],
    "retry": {
        "attempts": 3,
        "delay": 2
    }
}

portkey_model = OpenAILike(
    id="@opeani-provider-slug/gpt-4o",
    api_key="YOUR_PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        config=reliability_config,
        trace_id="advanced_trading_agent"
    )
)

# Set up memory
memory = Memory(
    model=portkey_model,
    db=SqliteMemoryDb(table_name="user_memories", db_file="tmp/agent.db"),
    delete_memories=True,
    clear_memories=True,
)

agent = Agent(
    name="Trading Assistant",
    model=portkey_model,
    tools=[
        ReasoningTools(add_instructions=True),
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
            company_info=True,
            company_news=True
        ),
    ],
    user_id="trader_1",
    instructions=[
        "Remember user preferences and trading patterns",
        "Use reasoning to analyze market conditions",
        "Provide data-driven recommendations",
        "Display results in clear tables"
    ],
    memory=memory,
    enable_agentic_memory=True,
    markdown=True,
)

if __name__ == "__main__":
    # First interaction - agent learns preferences
    agent.print_response(
        "I'm interested in tech stocks, particularly AI companies like NVIDIA and Microsoft",
        stream=True,
        show_full_reasoning=True,
    )

    # Second interaction - agent uses memory
    agent.print_response(
        "Based on my interests, what stocks should I watch today?",
        stream=True,
        show_full_reasoning=True,
    )

Set Up Enterprise Governance for Agno AI agents

Why Enterprise Governance? If you are using Agno AI agents inside your orgnaization, you need to consider several governance aspects:

Cost Management: Controlling and tracking AI spending across teams
Access Control: Managing which teams can use specific models
Usage Analytics: Understanding how AI is being used across the organization
Security & Compliance: Maintaining enterprise security standards
Reliability: Ensuring consistent service across all users

Portkey adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step.

Enterprise Implementation Guide

Portkey allows you to use 1600+ LLMs with your Agno AI agents setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.

Step 1: Implement Budget Controls & Rate Limits

Step 2: Define Model Access Rules

Step 4: Set Routing Configuration

Portkey allows you to control your routing logic very simply with it’s Configs feature. Portkey Configs provide this control layer with things like:

Data Protection: Implement guardrails for sensitive code and data
Reliability Controls: Add fallbacks, load-balance, retry and smart conditional routing logic
Caching: Implement Simple and Semantic Caching. and more…

Example Configuration:

Here’s a basic configuration to load-balance requests to OpenAI and Anthropic:

{
	"strategy": {
		"mode": "load-balance"
	},
	"targets": [
		{
			"override_params": {
				"model": "@YOUR_OPENAI_PROVIDER-SLUG/MODEL_NAME"
			}
		},
		{
			"override_params": {
				"model": "@YOUR_ANTHROPIC_PROVIDER-SLUG/MODEL_NAME"
			}
		}
	]
}

Create your config on the Configs page in your Portkey dashboard. You’ll need the config ID for connecting to Cline’s setup.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 4: Implement Access Controls

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

Track usage per developer/team with the help of metadata
Apply appropriate configs to route requests
Collect relevant metadata to filter logs
Enforce access permissions

Create API keys through:

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="frontend-engineering",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {
            "environment": "development",
            "department": "engineering",
            "team": "frontend"
        }
    },
    scopes=["logs.view", "configs.read"]
)

For detailed key management instructions, see our API Keys documentation.

Step 5: Deploy & Monitor

Enterprise Features Now Available

Agno AI agents now has:

Departmental budget controls
Model access governance
Usage tracking & attribution
Security guardrails
Reliability features

Frequently Asked Questions

How does Portkey enhance my Agno agents?

Can I use any LLM with Agno through Portkey?

How do I track costs for different agents?

Does Portkey support all Agno features?

How do I debug agent failures?

Resources

Agno Documentation

Learn more about building agents with Agno

Portkey Features

Explore all Portkey capabilities

Get Support

Join our Discord community

Autogen Control Flow

On this page

Introduction
Setting Up Portkey
Quickstart
Simple E2E example agent
Production Features
1. Enhanced Observability
2. Reliability - Keep Your Agents Running Smoothly
3. Prompting in Agno Agents
4. Guardrails for Safe Agents
5. User Tracking with Metadata
6. Caching for Efficient Agents
7. Model Interoperability: using different LLMs
End-to-End Examples
Level 1: Agent with Tools and Observability
Level 2: Agent with Knowledge Base and Caching
Level 3: Agent with Memory, Reasoning, and Reliability
Set Up Enterprise Governance for Agno AI agents
Frequently Asked Questions
Resources

Ecosystem

LLM Integrations

Cloud Platforms

Guardrails

Plugins

Vector Databases

Agents

AI Apps

Libraries

Tracing Providers

​Introduction

Agno AI Official Documentation

​Setting Up Portkey

​Quickstart

​Simple E2E example agent

​Production Features

​1. Enhanced Observability

​2. Reliability - Keep Your Agents Running Smoothly

Automatic Retries

Request Timeouts

Conditional Routing

Fallbacks

Load Balancing

​3. Prompting in Agno Agents

Prompt Engineering Studio

​4. Guardrails for Safe Agents

Learn More About Guardrails

​5. User Tracking with Metadata

Learn More About Metadata

​6. Caching for Efficient Agents

​7. Model Interoperability: using different LLMs

Supported Providers

​End-to-End Examples

​Level 1: Agent with Tools and Observability

​Level 2: Agent with Knowledge Base and Caching

​Level 3: Agent with Memory, Reasoning, and Reliability

​Set Up Enterprise Governance for Agno AI agents

​Enterprise Features Now Available

​Frequently Asked Questions

​Resources

Agno Documentation

Portkey Features

Get Support

Introduction

Setting Up Portkey

Quickstart

Simple E2E example agent

Production Features

1. Enhanced Observability

2. Reliability - Keep Your Agents Running Smoothly

3. Prompting in Agno Agents

4. Guardrails for Safe Agents

5. User Tracking with Metadata

6. Caching for Efficient Agents

7. Model Interoperability: using different LLMs

End-to-End Examples

Level 1: Agent with Tools and Observability

Level 2: Agent with Knowledge Base and Caching

Level 3: Agent with Memory, Reasoning, and Reliability

Set Up Enterprise Governance for Agno AI agents

Enterprise Features Now Available

Frequently Asked Questions

Resources