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Integration Examples

Because GuardLayer exposes an OpenAI-compatible interface, you can integrate it into existing AI applications by changing only 2 lines of code (the base URL and the API key).

Python OpenAI SDK

Before

import openai

client = openai.OpenAI(
    api_key="your-openai-api-key"
)

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

After (GuardLayer Integrated)

import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1",  # Route through GuardLayer gateway
    api_key="your-guardlayer-api-key"      # Use API key created in GuardLayer dashboard
)

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

JavaScript OpenAI SDK

Before

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'your-openai-api-key',
});

const completion = await openai.chat.completions.create({
  model: 'gpt-3.5-turbo',
  messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(completion.choices[0].message.content);

After (GuardLayer Integrated)

import OpenAI from 'openai';

const openai = new OpenAI({
  baseURL: 'http://localhost:8080/v1',  // Route through GuardLayer gateway
  apiKey: 'your-guardlayer-api-key',      // GuardLayer API Key
});

const completion = await openai.chat.completions.create({
  model: 'gpt-3.5-turbo',
  messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(completion.choices[0].message.content);

Python LangChain

Configure the standard ChatOpenAI model instances to point to GuardLayer:

from langchain_openai import ChatOpenAI

chat = ChatOpenAI(
    openai_api_base="http://localhost:8080/v1",  # Route through GuardLayer gateway
    openai_api_key="your-guardlayer-api-key",      # GuardLayer API Key
    model_name="gpt-3.5-turbo"
)

response = chat.predict("Introduce yourself.")
print(response)

JavaScript LangChain

import { ChatOpenAI } from "@langchain/openai";

const chat = new ChatOpenAI({
  configuration: {
    baseURL: "http://localhost:8080/v1",
  },
  openAIApiKey: "your-guardlayer-api-key",
  modelName: "gpt-3.5-turbo",
});

const response = await chat.predict("Introduce yourself.");
console.log(response);

Python LlamaIndex

For LlamaIndex agents, configure the LLM module globally:

from llama_index.llms.openai import OpenAI
from llama_index.core import Settings

# Overwrite default OpenAI settings globally
Settings.llm = OpenAI(
    api_base="http://localhost:8080/v1",
    api_key="your-guardlayer-api-key",
    model="gpt-3.5-turbo"
)

# Application usage remains completely unchanged

Generic Compatibility Guide

Any application, service, or workflow platform (such as Flowise, Langflow, Dify, etc.) that supports custom API endpoints using the OpenAI Chat Completions format can integrate with GuardLayer by setting:

  1. Endpoint / Base URL: http://localhost:8080/v1
  2. API Key / Authorization: Bearer token created in the GuardLayer dashboard keys page.
  3. Model Selection: Any string (the target provider routing model mapping is resolved internally by GuardLayer based on active scope settings).