Portkey Integration with AutoGen

Portkey is a 2-line upgrade to make your AutoGen agents reliable, cost-efficient, and fast.

Portkey adds 4 core production capabilities to any AutoGen agent:

1. Routing to 200+ LLMs
2. Making each LLM call more robust
3. Full-stack tracing & cost, performance analytics
4. Real-time guardrails to enforce behavior

Getting Started

1. Install Required Packages:

2. pip install -qU autogen-agentchat~=0.2 portkey-ai

   Configure AutoGen with Portkey:

   from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
   from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders
   
   config = [
       {
           "api_key": "OPENAI_API_KEY",
           "model": "gpt-3.5-turbo",
           "base_url": PORTKEY_GATEWAY_URL,
           "api_type": "openai",
           "default_headers": createHeaders(
               api_key="YOUR_PORTKEY_API_KEY",
               provider="openai",
           )
       }
   ]

   Generate your API key in the Portkey Dashboard.

And, that's it! With just this, you can start logging all of your AutoGen requests and make them reliable.

3. Let's Run your Agent

import autogen

# Create user proxy agent, coder, product manager


user_proxy = autogen.UserProxyAgent(
    name="User_proxy",
    system_message="A human admin who will give the idea and run the code provided by Coder.",
    code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
    human_input_mode="ALWAYS",
)


coder = autogen.AssistantAgent(
    name="Coder",
    system_message = "You are a Python developer who is good at developing games. You work with Product Manager.",
    llm_config={"config_list": config},
)

# Create groupchat
groupchat = autogen.GroupChat(
    agents=[user_proxy, coder], messages=[])
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config={"config_list": config})



# Start the conversation
user_proxy.initiate_chat(
    manager, message="Build a classic & basic pong game with 2 players in python")

Here’s the output from your Agent’s run on Portkey's dashboard

Key Features

Portkey offers a range of advanced features to enhance your AutoGen agents. Here’s an overview

Feature	Description
🌐 Multi-LLM Integration	Access 200+ LLMs with simple configuration changes
🛡️ Enhanced Reliability	Implement fallbacks, load balancing, retries, and much more
📊 Advanced Metrics	Track costs, tokens, latency, and 40+ custom metrics effortlessly
🔍 Detailed Traces and Logs	Gain insights into every agent action and decision
🚧 Guardrails	Enforce agent behavior with real-time checks on inputs and outputs
🔄 Continuous Optimization	Capture user feedback for ongoing agent improvements
💾 Smart Caching	Reduce costs and latency with built-in caching mechanisms
🔐 Enterprise-Grade Security	Set budget limits and implement fine-grained access controls

Colab Notebook

For a hands-on example of integrating Portkey with Autogen, check out our notebook

.

Advanced Features

Interoperability

Easily switch between 200+ LLMs by changing the provider and API key in your configuration.

Example: Switching from OpenAI to Azure OpenAI

config = [
    {
        "api_key": "api-key",
        "model": "gpt-3.5-turbo",
        "base_url": PORTKEY_GATEWAY_URL,
        "api_type": "openai",
        "default_headers": createHeaders(
            api_key="YOUR_PORTKEY_API_KEY",
            provider="azure-openai",
            virtual_key="AZURE_VIRTUAL_KEY"
        )
    }
]

Note: AutoGen messages will go through Portkey's AI Gateway following OpenAI's API signature. Some language models may not work properly because messages need to be in a specific role order.

Reliability

Implement fallbacks, load balancing, and automatic retries to make your agents more resilient.

{
  "strategy": {
    "mode": "fallback" # Options: "loadbalance" or "fallback"
  },
  "targets": [
    {
      "provider": "openai",
      "api_key": "openai-api-key",
      "override_params": {
        "top_k": "0.4",
        "max_tokens": "100"
      }
    },
    {
      "provider": "anthropic",
      "api_key": "anthropic-api-key",
      "override_params": {
        "top_p": "0.6",
        "model": "claude-3-5-sonnet-20240620"
      }
    }
  ]
}

Learn more about Portkey Config object here. Be Careful to Load-Balance/Fallback to providers that don't support tool calling when the request contains a function call.

Metrics

Agent runs are complex. Portkey automatically logs 40+ comprehensive metrics for your AI agents, including cost, tokens used, latency, etc. Whether you need a broad overview or granular insights into your agent runs, Portkey's customizable filters provide the metrics you need.

Portkey's Observability Dashboard

Comprehensive Logging

Access detailed logs and traces of agent activities, function calls, and errors. Filter logs based on multiple parameters for in-depth analysis.

Traces

Logs

Guardrails

AutoGen agents, while powerful, can sometimes produce unexpected or undesired outputs. Portkey's Guardrails feature helps enforce agent behavior in real-time, ensuring your AutoGen agents operate within specified parameters. Verify both the inputs to and outputs from your agents to ensure they adhere to specified formats and content guidelines. Learn more about Portkey's Guardrails here

Continuous Improvement

Capture qualitative and quantitative user feedback on your requests to continuously enhance your agent performance.

Caching

Reduce costs and latency with Portkey's built-in caching system.

portkey_config = {
 "cache": {
    "mode": "semantic"  # Options: "simple" or "semantic"
 }
}

Security and Compliance

Set budget limits on provider API keys and implement fine-grained user roles and permissions for both your application and the Portkey APIs.

Additional Resources

• 📘 Portkey Documentation
• 🐦 Twitter
• 💬 Discord Community
• 📊 Portkey App

For more information on using these features and setting up your Config, please refer to the Portkey documentation.