OpenAI

First, install the OpenAI adapter package:

npm install @hashbrownai/openai

Streaming Text Responses

Hashbrown’s OpenAI adapter lets you stream chat completions from OpenAI’s GPT models, including support for tool calling, response schemas, and request transforms.

API Reference

`HashbrownOpenAI.stream.text(options)`

Streams an OpenAI chat completion as a series of encoded frames. Handles content, tool calls, and errors, and yields each frame as a Uint8Array.

Options:

Name	Type	Description
`apiKey`	`string`	Your OpenAI API Key.
`request`	`Chat.Api.CompletionCreateParams`	The chat request: model, messages, tools, system, responseFormat, etc.
`transformRequestOptions`	`(params) => params \| Promise`	(Optional) Transform or override the final OpenAI request before it is sent.

Supported Features:

Roles: user, assistant, tool
Tools: Supports OpenAI tool calling, including toolCalls and strict function schemas.
Response Format: Optionally specify a JSON schema for structured output (uses OpenAI’s response_format parameter).
System Prompt: Included as the first message if provided.
Tool Calling: Handles OpenAI tool calling modes and emits tool call frames.
Streaming: Each chunk is encoded into a resilient streaming format

How It Works

Messages: Translated to OpenAI’s message format, supporting all roles and tool calls.
Tools/Functions: Tools are passed as OpenAI function definitions, using your JSON schemas as parameters.
Response Format: Pass a JSON schema in responseFormat for OpenAI to validate the model output.
Streaming: All data is sent as a stream of encoded frames (Uint8Array). Chunks may contain text, tool calls, errors, or finish signals.
Error Handling: Any thrown errors are sent as error frames before the stream ends.

Example: Node.js Server Integration

import { HashbrownOpenAI } from '@hashbrownai/openai';
import express from 'express';

const app = express();
app.use(express.json());

app.post('/chat', async (req, res) => {
  const stream = HashbrownOpenAI.stream.text({
    apiKey: process.env.OPENAI_API_KEY!,
    request: req.body, // must be Chat.Api.CompletionCreateParams
  });

  res.header('Content-Type', 'application/octet-stream');

  for await (const chunk of stream) {
    res.write(chunk); // Pipe each encoded frame as it arrives
  }

  res.end();
});

app.listen(3000);

import { HashbrownOpenAI } from '@hashbrownai/openai';
import Fastify from 'fastify';

const fastify = Fastify();

fastify.post('/chat', async (request, reply) => {
  const stream = HashbrownOpenAI.stream.text({
    apiKey: process.env.OPENAI_API_KEY!,
    request: request.body, // must be Chat.Api.CompletionCreateParams
  });

  reply.header('Content-Type', 'application/octet-stream');

  for await (const chunk of stream) {
    reply.raw.write(chunk); // Pipe each encoded frame as it arrives
  }

  reply.raw.end();
});

fastify.listen({ port: 3000 });

import { Controller, Post, Body, Res } from '@nestjs/common';
import { HashbrownOpenAI } from '@hashbrownai/openai';
import { Response } from 'express';

@Controller()
export class ChatController {
  @Post('chat')
  async chat(@Body() body: any, @Res() res: Response) {
    const stream = HashbrownOpenAI.stream.text({
      apiKey: process.env.OPENAI_API_KEY!,
      request: body, // must be Chat.Api.CompletionCreateParams
    });

    res.header('Content-Type', 'application/octet-stream');

    for await (const chunk of stream) {
      res.write(chunk); // Pipe each encoded frame as it arrives
    }

    res.end();
  }
}

import { HashbrownOpenAI } from '@hashbrownai/openai';
import { Hono } from 'hono';

const app = new Hono();

app.post('/chat', async (c) => {
  const body = await c.req.json();

  const stream = HashbrownOpenAI.stream.text({
    apiKey: process.env.OPENAI_API_KEY!,
    request: body, // must be Chat.Api.CompletionCreateParams
  });

  return new Response(
    new ReadableStream({
      async start(controller) {
        for await (const chunk of stream) {
          controller.enqueue(chunk); // Pipe each encoded frame as it arrives
        }
        controller.close();
      },
    }),
    {
      headers: {
        'Content-Type': 'application/octet-stream',
      },
    },
  );
});

export default app;

Transform Request Options

The transformRequestOptions parameter allows you to intercept and modify the request before it's sent to OpenAI. This is useful for server-side prompts, message filtering, logging, and dynamic configuration.

import { HashbrownOpenAI } from '@hashbrownai/openai';
import express from 'express';

const app = express();
app.use(express.json());

app.post('/chat', async (req, res) => {
  const stream = HashbrownOpenAI.stream.text({
    apiKey: process.env.OPENAI_API_KEY!,
    request: req.body,
    transformRequestOptions: (options) => {
      return {
        ...options,
        // Add server-side system prompt
        messages: [
          { role: 'system', content: 'You are a helpful assistant.' },
          ...options.messages,
        ],
        // Adjust temperature based on user preferences
        temperature: getUserPreferences(req.user.id).creativity,
      };
    },
  });

  res.header('Content-Type', 'application/octet-stream');

  for await (const chunk of stream) {
    res.write(chunk);
  }

  res.end();
});

import { HashbrownOpenAI } from '@hashbrownai/openai';
import Fastify from 'fastify';

const fastify = Fastify();

fastify.post('/chat', async (request, reply) => {
  const stream = HashbrownOpenAI.stream.text({
    apiKey: process.env.OPENAI_API_KEY!,
    request: request.body,
    transformRequestOptions: (options) => {
      return {
        ...options,
        // Add server-side system prompt
        messages: [
          { role: 'system', content: 'You are a helpful assistant.' },
          ...options.messages,
        ],
        // Adjust temperature based on user preferences
        temperature: getUserPreferences(request.user.id).creativity,
      };
    },
  });

  reply.header('Content-Type', 'application/octet-stream');

  for await (const chunk of stream) {
    reply.raw.write(chunk);
  }

  reply.raw.end();
});

import { Controller, Post, Body, Res, Req } from '@nestjs/common';
import { HashbrownOpenAI } from '@hashbrownai/openai';
import { Response, Request } from 'express';

@Controller()
export class ChatController {
  @Post('chat')
  async chat(@Body() body: any, @Req() req: Request, @Res() res: Response) {
    const stream = HashbrownOpenAI.stream.text({
      apiKey: process.env.OPENAI_API_KEY!,
      request: body,
      transformRequestOptions: (options) => {
        return {
          ...options,
          // Add server-side system prompt
          messages: [
            { role: 'system', content: 'You are a helpful assistant.' },
            ...options.messages,
          ],
          // Adjust temperature based on user preferences
          temperature: getUserPreferences(req.user.id).creativity,
        };
      },
    });

    res.header('Content-Type', 'application/octet-stream');

    for await (const chunk of stream) {
      res.write(chunk);
    }

    res.end();
  }
}

import { HashbrownOpenAI } from '@hashbrownai/openai';
import { Hono } from 'hono';

const app = new Hono();

app.post('/chat', async (c) => {
  const body = await c.req.json();

  const stream = HashbrownOpenAI.stream.text({
    apiKey: process.env.OPENAI_API_KEY!,
    request: body,
    transformRequestOptions: (options) => {
      return {
        ...options,
        // Add server-side system prompt
        messages: [
          { role: 'system', content: 'You are a helpful assistant.' },
          ...options.messages,
        ],
        // Adjust temperature based on user preferences
        temperature: getUserPreferences(c.req.user.id).creativity,
      };
    },
  });

  return new Response(
    new ReadableStream({
      async start(controller) {
        for await (const chunk of stream) {
          controller.enqueue(chunk);
        }
        controller.close();
      },
    }),
    {
      headers: {
        'Content-Type': 'application/octet-stream',
      },
    },
  );
});

Learn more about transformRequestOptions

Advanced: Tools and Response Schema

Tools: Add tools using OpenAI-style function specs (name, description, parameters).
Tool Calling: Supported via toolChoice (auto, required, none, etc.).
Response Format: Pass a JSON schema in responseFormat for OpenAI to return validated structured output.

Getting Started

Guide

Recipes

Platforms