# Portkey Integration with AutoGen
 <img src="https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Autogen.png?raw=true" alt="Portkey Metrics Visualization" width=70% />

[Portkey](https://portkey.ai) 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. ```bash
   pip install -qU autogen-agentchat~=0.2 portkey-ai
   ```
   **Configure AutoGen with Portkey:**

   ```python
   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](https://app.portkey.ai/).

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**

``` python
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")
```
<br>
Here’s the output from your Agent’s run on Portkey's dashboard<br>
<img src=https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true width=70%" alt="Portkey Dashboard" />

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

| Feature | Description |
|---------|-------------|
| 🌐 [Multi-LLM Integration](#interoperability) | Access 200+ LLMs with simple configuration changes |
| 🛡️ [Enhanced Reliability](#reliability) | Implement fallbacks, load balancing, retries, and much more |
| 📊 [Advanced Metrics](#metrics) | Track costs, tokens, latency, and 40+ custom metrics effortlessly |
| 🔍 [Detailed Traces and Logs](#comprehensive-logging) | Gain insights into every agent action and decision |
| 🚧 [Guardrails](#guardrails) | Enforce agent behavior with real-time checks on inputs and outputs |
| 🔄 [Continuous Optimization](#continuous-improvement) | Capture user feedback for ongoing agent improvements |
| 💾 [Smart Caching](#caching) | Reduce costs and latency with built-in caching mechanisms |
| 🔐 [Enterprise-Grade Security](#security-and-compliance) | 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<br> <br>[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://git.new/Portkey-Autogen) .



## 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

```python
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.

```python
{
  "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](https://docs.portkey.ai/docs/product/ai-gateway-streamline-llm-integrations/configs).
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.

<details>
  <summary><b>Portkey's Observability Dashboard</b></summary>
<img src=https://github.com/siddharthsambharia-portkey/Portkey-Product-Images/blob/main/Portkey-Dashboard.png?raw=true width=70%" alt="Portkey Dashboard" />
</details>

### Comprehensive Logging

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

<details>
  <summary><b>Traces</b></summary>
  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Traces.png" alt="Portkey Logging Interface" width=70% />
</details>

<details>
  <summary><b>Logs</b></summary>
  <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-Logs.png" alt="Portkey Metrics Visualization" width=70% />
</details>

### 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](https://docs.portkey.ai/product/guardrails)

### 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.

```python
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](https://docs.portkey.ai)
- [🐦 Twitter](https://twitter.com/portkeyai)
- [💬 Discord Community](https://discord.gg/JHPt4C7r)
- [📊 Portkey App](https://app.portkey.ai)

For more information on using these features and setting up your Config, please refer to the [Portkey documentation](https://docs.portkey.ai).
