Integration Examples#

Ready-to-use snippets to start caching your LLM requests.

Bring Your Own Key (BYOK) Examples#

When you configure your own API keys, you can use native provider model names directly.

Node.js with BYOK#

import OpenAI from "openai";
 
const client = new OpenAI({
  apiKey: "lgw_proj_your_watchllm_key",
  baseURL: "https://proxy.watchllm.dev/v1"
});
 
// Direct OpenAI models (requires OpenAI key in dashboard)
const gptResponse = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Analyze this code." }],
});
 
// Direct Anthropic models (requires Anthropic key in dashboard)
const claudeResponse = await client.chat.completions.create({
  model: "claude-3-sonnet-20240229",
  messages: [{ role: "user", content: "Explain semantic caching." }],
});
 
// Direct Groq models (requires Groq key in dashboard)
const groqResponse = await client.chat.completions.create({
  model: "llama2-70b-4096",
  messages: [{ role: "user", content: "Fast inference example." }],
});
 
console.log(gptResponse.choices[0].message.content);

Python with BYOK#

from openai import OpenAI
 
client = OpenAI(
    api_key="lgw_proj_your_watchllm_key",
    base_url="https://proxy.watchllm.dev/v1"
)
 
# OpenAI models
completion = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain semantic caching."}]
)
 
# Anthropic models
claude_completion = client.chat.completions.create(
    model="claude-3-opus-20240229",
    messages=[{"role": "user", "content": "Analyze this document."}]
)
 
# Groq models
groq_completion = client.chat.completions.create(
    model="mixtral-8x7b-32768",
    messages=[{"role": "user", "content": "Fast inference example."}]
)
 
print(completion.choices[0].message.content)

cURL with BYOK#

# OpenAI GPT-4
curl https://proxy.watchllm.dev/v1/chat/completions \
  -H "Authorization: Bearer lgw_proj_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello from GPT-4!"}]
  }'
 
# Anthropic Claude
curl https://proxy.watchllm.dev/v1/chat/completions \
  -H "Authorization: Bearer lgw_proj_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-sonnet-20240229",
    "messages": [{"role": "user", "content": "Hello from Claude!"}]
  }'
 
# Groq Llama
curl https://proxy.watchllm.dev/v1/chat/completions \
  -H "Authorization: Bearer lgw_proj_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama2-70b-4096",
    "messages": [{"role": "user", "content": "Hello from Groq!"}]
  }'

OpenRouter Examples#

For broader model access, use OpenRouter format (works without BYOK setup).

Node.js#

Using the official openai package.

import OpenAI from "openai";
 
const client = new OpenAI({
  apiKey: "lgw_proj_your_key",
  baseURL: "https://proxy.watchllm.dev/v1"
});
 
const response = await client.chat.completions.create({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Analyze this code." }],
});
 
console.log(response.choices[0].message.content);

Python#

Compatible with openai-python v1.0+.

from openai import OpenAI
 
client = OpenAI(
    api_key="lgw_proj_your_key",
    base_url="https://proxy.watchllm.dev/v1"
)
 
completion = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Explain semantic caching."}]
)
 
print(completion.choices[0].message.content)

cURL#

Quick test from your terminal.

curl https://proxy.watchllm.dev/v1/chat/completions \
  -H "Authorization: Bearer lgw_proj_your_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-4o",
    "messages": [{"role": "user", "content": "Ping"}]
  }'

Go#

Using simple HTTP requests.

// Example using standard library
req, _ := http.NewRequest("POST", "https://proxy.watchllm.dev/v1/chat/completions", payload)
req.Header.Set("Authorization", "Bearer lgw_proj_your_key")
req.Header.Set("Content-Type", "application/json")
// ... execute request

Integration Examples