# Portkey Integration with CrewAI
 <img src="https://raw.githubusercontent.com/siddharthsambharia-portkey/Portkey-Product-Images/main/Portkey-CrewAI.png" alt="Portkey CrewAI Header Image" width=70% />

[Portkey](https://portkey.ai) is a 2-line upgrade to make your CrewAI agents reliable, cost-efficient, and fast.

Portkey adds 4 core production capabilities to any CrewAI 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:**

   ```bash
   pip install crewai portkey-ai langchain_openai
   ```

2. **Configure CrewAI with Portkey:**

   ```python
   from langchain_openai import ChatOpenAI
   from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL

   llm_gpt = ChatOpenAI(
        api_key="OpenAI_API_Key",
        base_url=PORTKEY_GATEWAY_URL,
        default_headers=createHeaders(
            provider="openai", #choose your provider
            api_key="PORTKEY_API_KEY"
        )
    )
   ```

   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 CrewAI requests and make them reliable.

3. **Let's Run your Crew**

``` python
from crewai import Agent, Task, Crew, Process

# Define your agents with roles and goals
product_manager = Agent(
    role='Product Manager',
    goal='Define requirements for a software product',
    backstory="You are an experienced Product Manager skilled in defining clear and concise requirements.",
    llm = llm_gpt
)

# Create tasks for your agents
task1 = Task(
    description="Based on the provided requirements, develop the code for the classic ping pong game. Focus on gameplay mechanics and a simple user interface.",
    expected_output="Complete code for the ping pong game",
    agent=coder
)

# Instantiate your crew with a sequential process
crew = Crew(
    agents=[coder],
    tasks=[task1],
    verbose=1,
)

# Get your crew to work!
result = crew.kickoff()
print(result)
     
```
<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 CrewAI 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 CrewAI, check out our notebook<br> <br>[![Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://git.new/PortkeyCrewAIdocs) .



## 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"
        )
    }
]
```

### Reliability

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

```python
portkey_config = {
  "retry": {
    "attempts": 5
  },
  "strategy": {
    "mode": "loadbalance"  # Options: "loadbalance" or "fallback"
  },
  "targets": [
    {
      "provider": "openai",
      "api_key": "OpenAI_API_Key"
    },
    {
      "provider": "anthropic",
      "api_key": "Anthropic_API_Key"
    }
  ]
}
```

### 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
CrewAI agents, while powerful, can sometimes produce unexpected or undesired outputs. Portkey's Guardrails feature helps enforce agent behavior in real-time, ensuring your CrewAI 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/DD7vgKK299)
- [📊 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).