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JavaScript & TypeScript SDK integrations

DVARA is an AI governance platform with an OpenAI-compatible data plane. Every JavaScript / TypeScript SDK below works by changing baseURL — Policy-as-Code, audit, PII redaction, and cost attribution all kick in on the first request. Examples assume DVARA is running at http://localhost:8080 and you've created a tenant + API key via the Quickstart.

OpenAI JavaScript​

Install:

npm install openai

Basic chat completion:

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://localhost:8080/v1",
  apiKey: "your-dvara-api-key",
});

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Explain microservices in one paragraph." },
  ],
});

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

Streaming:

const stream = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Write a poem about code." }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}

Structured output:

import { zodResponseFormat } from "openai/helpers/zod";
import { z } from "zod";

const Planet = z.object({
  name: z.string(),
  diameter_km: z.number(),
  has_rings: z.boolean(),
});

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Tell me about Saturn." }],
  response_format: zodResponseFormat(Planet, "planet"),
});

const planet = JSON.parse(response.choices[0].message.content!);
console.log(`${planet.name}: ${planet.diameter_km} km, rings: ${planet.has_rings}`);

Tool calls:

const response = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "What's the weather in London?" }],
  tools: [
    {
      type: "function",
      function: {
        name: "get_weather",
        description: "Get weather for a city",
        parameters: {
          type: "object",
          properties: { city: { type: "string" } },
          required: ["city"],
        },
      },
    },
  ],
});

const toolCall = response.choices[0].message.tool_calls?.[0];
if (toolCall) {
  console.log(`Call: ${toolCall.function.name}(${toolCall.function.arguments})`);
}

Environment variable approach:

export OPENAI_BASE_URL="http://localhost:8080/v1"
export OPENAI_API_KEY="your-dvara-api-key"

Anthropic JavaScript​

Same approach as Python — use the OpenAI SDK with Claude model names. DVARA translates internally.

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://localhost:8080/v1",
  apiKey: "your-dvara-api-key",
});

const response = await client.chat.completions.create({
  model: "claude-sonnet-4-20250514",
  messages: [{ role: "user", content: "Hello from TypeScript!" }],
});

Vercel AI SDK​

Install:

npm install ai @ai-sdk/openai

Usage:

import { createOpenAI } from "@ai-sdk/openai";
import { generateText, streamText } from "ai";

const dvara = createOpenAI({
  baseURL: "http://localhost:8080/v1",
  apiKey: "your-dvara-api-key",
});

// Non-streaming
const { text } = await generateText({
  model: dvara("gpt-4o"),
  prompt: "Explain REST APIs briefly.",
});

// Streaming
const result = streamText({
  model: dvara("gpt-4o"),
  prompt: "Write a short story about a robot.",
});

for await (const chunk of result.textStream) {
  process.stdout.write(chunk);
}

Using Claude via the Vercel AI SDK:

const response = await generateText({
  model: dvara("claude-sonnet-4-20250514"),
  prompt: "Compare TypeScript to Rust in three sentences.",
});

Next steps​

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