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OpenAI

KaibanJS seamlessly integrates with OpenAI's powerful language models, allowing you to leverage state-of-the-art AI capabilities in your applications. This integration supports various OpenAI models, including GPT-4o and GPT-4o-mini.

Supported Models​

KaibanJS supports all of OpenAI's chat models available through the OpenAI API. These chat models are designed for natural language conversations and are ideal for a wide range of applications. The list of supported models is dynamic and may change as OpenAI introduces new chat models or retires older ones.

Here are some examples of popular OpenAI chat models:

  • GPT-4o
  • GPT-4o-mini
  • GPT-4
  • gpt-3.5-turbo
  • etc

For a comprehensive list of available models and their capabilities, please refer to the official OpenAI documentation.

Configuration​

To use an OpenAI model in your KaibanJS agent, configure the llmConfig property as follows:

const agent = new Agent({
    name: 'OpenAI Agent',
    role: 'Assistant',
    llmConfig: {
        provider: 'openai',
        model: 'gpt-4o',  // or 'gpt-4o-mini', etc.
    }
});

API Key Setup​

To use OpenAI models, you need to provide an API key. There are two recommended ways to do this:

  1. Agent Configuration: Specify the API key in the llmConfig when creating an agent:
const agent = new Agent({
    name: 'OpenAI Agent',
    role: 'Assistant',
    llmConfig: {
        provider: 'openai',
        model: 'gpt-4o', // or 'gpt-4o-mini', etc.
        apiKey: 'your-api-key-here'
    }
});
  1. Team Configuration: Provide the API key in the env property when creating a team:
const team = new Team({
    name: 'OpenAI Team',
    agents: [agent],
    env: {
        OPENAI_API_KEY: 'your-api-key-here'
    }
});
API Key Security

Always use environment variables for API keys instead of hardcoding them. This enhances security and simplifies key management across different environments.

Example:

apiKey: process.env.YOUR_API_KEY

Never commit API keys to version control. Use a .env file or a secure secrets management system for sensitive information.

Advanced Configuration and Langchain Compatibility​

KaibanJS uses Langchain under the hood, which means we're compatible with all the parameters that Langchain's OpenAI integration supports. This provides you with extensive flexibility in configuring your language models.

For more control over the model's behavior, you can pass additional parameters in the llmConfig. These parameters correspond to those supported by Langchain's OpenAI integration.

Here's an example of how to use advanced configuration options:

const agent = new Agent({
    name: 'Advanced OpenAI Agent',
    role: 'Assistant',
    llmConfig: {
        provider: 'openai',
        model: 'gpt-4',
        temperature: 0.7,
        // Any other Langchain-supported parameters...
    }
});

For a comprehensive list of available parameters and advanced configuration options, please refer to the official Langchain documentation:

Langchain OpenAI Integration Documentation

Best Practices​

  1. Model Selection: Choose the appropriate model based on your task complexity and required capabilities.
  2. Cost Management: Be mindful of token usage, especially with more powerful models like GPT-4.
  3. Error Handling: Implement proper error handling to manage API rate limits and other potential issues.

Limitations​

  • Token limits vary by model. Ensure your inputs don't exceed these limits.
  • Costs can accumulate quickly with heavy usage. Monitor your usage closely.

Further Resources​

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