Quick Start

Overview

Lectr is a proxy that sits between your application and your AI provider. Change one line — the baseURL — and Lectr begins capturing metadata for every AI request your product makes: latency, tokens, cost, and errors. No SDK to install. No agent to run. No infrastructure changes.

Step 1: Get your org key

lc_key_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

Your org key controls access to your Lectr data. Treat it like a password — store it in an environment variable and never hardcode it.

Step 2: Point your client at Lectr

Replace your provider’s base URL with https://proxy.lectr.ai/v1 and add your org key as a header.

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://proxy.lectr.ai/v1",
  defaultHeaders: {
    "X-Lectr-Key": process.env.LECTR_KEY,
  },
});

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    base_url="https://proxy.lectr.ai/v1",
    default_headers={
        "X-Lectr-Key": os.environ["LECTR_KEY"],
    },
)

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
  baseURL: "https://proxy.lectr.ai/v1",
  defaultHeaders: {
    "X-Lectr-Key": process.env.LECTR_KEY,
  },
});

from anthropic import Anthropic

client = Anthropic(
    api_key=os.environ["ANTHROPIC_API_KEY"],
    base_url="https://proxy.lectr.ai/v1",
    default_headers={
        "X-Lectr-Key": os.environ["LECTR_KEY"],
    },
)

Your application now sends AI requests through Lectr.

Step 3: Send a request

Make any AI request as you normally would:

const response = await client.chat.completions.create({
  model: "gpt-4o-mini",
  messages: [{ role: "user", content: "Hello" }],
});

console.log(response.choices[0].message.content);

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello"}],
)

print(response.choices[0].message.content)

Open your dashboard at app.lectr.ai Within a few seconds you should see the request appear with:

model used
latency
token usage
cost

Your first request is now flowing through Lectr.

Optional: Test with `curl`

curl https://proxy.lectr.ai/v1/chat/completions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "X-Lectr-Key: $LECTR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [{"role":"user","content":"Hello"}]
  }'

Step 4: Tag your features (recommended)

Add the X-Lectr-Feature header to tell Lectr which part of your product made each request. This unlocks per-feature cost tracking, latency breakdowns, and model recommendations.

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://proxy.lectr.ai/v1",
  defaultHeaders: {
    "X-Lectr-Key": process.env.LECTR_KEY,
    "X-Lectr-Feature": "chat", // name of the feature making this request
  },
});

client = OpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    base_url="https://proxy.lectr.ai/v1",
    default_headers={
        "X-Lectr-Key": os.environ["LECTR_KEY"],
        "X-Lectr-Feature": "chat",  # name of the feature making this request
    },
)

Use a different client instance per feature, or override the header per request:

// Per-request override
const response = await client.chat.completions.create(
  {
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: "Summarise this document." }],
  },
  {
    headers: { "X-Lectr-Feature": "summariser" },
  },
);

Good feature names are short and match what you’d call the feature in conversation — chat, summariser, classifier, onboarding-assistant. Avoid generic names like api or backend.

Environment variables

We recommend storing your keys as environment variables:

.env

OPENAI_API_KEY=sk-...
LECTR_KEY=lc_key_...

What happens to your API key?

Your provider API key passes through Lectr in memory only and is forwarded directly to the provider. Lectr never stores, logs, or persists your API keys. See Security & Trust for the full details.

Supported providers

Lectr supports any OpenAI-compatible provider through the same endpoint:

Provider	Detection	Notes
OpenAI	Automatic	All models supported
Anthropic	Automatic	Via OpenAI-compatible endpoint
Groq	Automatic	Via OpenAI-compatible endpoint
Google Gemini	Automatic	Via OpenAI-compatible endpoint
Azure OpenAI	`X-Lectr-Provider: azure` header	Requires endpoint config in dashboard

Lectr detects the provider automatically from the model name. Use X-Lectr-Provider to override when needed.

Next steps

Feature Tagging

Break down cost and latency by feature across your product.

Task Types

Declare task type to unlock smarter model recommendations.

Routing Rules

Automatically route requests to the right model based on feature or task.

Multi-Provider

Route traffic across OpenAI, Anthropic, Groq, and Gemini from one proxy.

Getting started

Concepts

Guides

Reference

Security

Overview

Step 1: Get your org key

Step 2: Point your client at Lectr

Step 3: Send a request

Optional: Test with `curl`

Step 4: Tag your features (recommended)

Environment variables

What happens to your API key?

Supported providers

Next steps

Feature Tagging

Task Types

Routing Rules

Multi-Provider

​Overview

​Step 1: Get your org key

​Step 2: Point your client at Lectr

​Step 3: Send a request

​Optional: Test with curl

​Step 4: Tag your features (recommended)

​Environment variables

​What happens to your API key?

​Supported providers

​Next steps

Feature Tagging

Task Types

Routing Rules

Multi-Provider

Overview

Step 1: Get your org key

Step 2: Point your client at Lectr

Step 3: Send a request

Optional: Test with `curl`

Step 4: Tag your features (recommended)

Environment variables

What happens to your API key?

Supported providers

Next steps