AI Infrastructure Together AI

Together AI SDK Patterns

Quick Guide: Use the together-ai npm package to access 200+ open-source models (Llama, Qwen, Mistral, DeepSeek) via Together AI's fast inference API. The SDK mirrors the OpenAI API shape -- client.chat.completions.create() for chat, client.images.generate() for images, client.embeddings.create() for embeddings. Use response_format: { type: "json_schema" } with Zod-generated schemas for structured output. Function calling uses the same tools parameter shape as OpenAI. You can also use the OpenAI SDK directly by pointing baseURL to https://api.together.xyz/v1.

CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST use the together-ai package (import Together from "together-ai") -- NOT the OpenAI SDK -- unless explicitly building an OpenAI-compatible integration)

(You MUST include the JSON schema in BOTH the response_format parameter AND the system prompt when using structured output -- the model needs both)

(You MUST handle errors using Together.APIError and its subclasses -- never use bare catch blocks without error type checking)

(You MUST never hardcode API keys -- always use environment variables via process.env.TOGETHER_API_KEY)

Auto-detection: Together AI, together-ai, together.ai, TOGETHER_API_KEY, client.chat.completions (together), client.images.generate, client.embeddings.create (together), Llama-3, Qwen3, Mistral, DeepSeek, FLUX, together.images, together.chat, together.embeddings, together.fineTuning, api.together.xyz

When to use:

• Running open-source LLMs (Llama, Qwen, Mistral, DeepSeek) via serverless inference
• Generating images with FLUX or Stable Diffusion models
• Creating embeddings for RAG pipelines with open-source embedding models
• Using function calling / tool use with open-source models
• Extracting structured JSON output from LLM responses
• Fine-tuning open-source models on custom data
• Migrating from OpenAI to open-source models with minimal code changes

Key patterns covered:

• Client initialization and configuration (retries, timeouts, logging)
• Chat completions with open-source models (Llama, Qwen, Mistral, DeepSeek)
• Streaming with stream: true and for await...of
• Structured output with response_format: { type: "json_schema" } and Zod
• Function calling / tool use with tools parameter
• Image generation with FLUX and Stable Diffusion models
• Embeddings API with open-source embedding models
• Fine-tuning API (file upload, job creation, monitoring)
• OpenAI SDK compatibility (base URL swap)
• Error handling, retries, timeouts

When NOT to use:

• You need OpenAI-specific features (Responses API, Batch API, Realtime API) -- use the OpenAI SDK directly
• You want framework-specific chat UI hooks -- use a framework-integrated AI SDK
• You only use OpenAI models and never plan to use open-source models

Examples Index

• Core: Setup & Configuration -- Client init, production config, error handling, OpenAI compatibility
• Chat Completions -- Basic chat, multi-turn, model selection, vision
• Streaming -- Async iteration, stream cancellation
• Tool/Function Calling -- Tool definitions, multi-step tool loops
• Structured Output -- JSON mode, Zod schemas, regex mode
• Images & Embeddings -- FLUX image generation, embedding models, semantic search
• Quick API Reference -- Model IDs, method signatures, error types

Philosophy

Together AI provides fast serverless inference for open-source models. The TypeScript SDK (together-ai) is auto-generated with Stainless and mirrors the OpenAI API shape, making migration straightforward.

Core principles:

1. OpenAI-compatible API shape -- Same client.chat.completions.create() pattern, same messages array, same tools parameter. Switching from OpenAI is often just changing the import and model name.
2. Open-source model access -- Run Llama, Qwen, Mistral, DeepSeek, and 200+ other models without managing infrastructure. Models are identified by their Hugging Face-style IDs (e.g., meta-llama/Llama-3.3-70B-Instruct-Turbo).
3. Multi-modal support -- Chat completions, image generation (FLUX, Stable Diffusion), embeddings, audio, and video -- all through one SDK.
4. Structured output via JSON Schema -- Pass a JSON schema in response_format and include it in the system prompt. Use Zod's z.toJSONSchema() to generate schemas from TypeScript types.
5. Fine-tuning open-source models -- Upload JSONL data, create LoRA or full fine-tuning jobs, and deploy custom models -- all via the API.

When to use Together AI:

• You want to use open-source models with fast serverless inference
• You need cost-effective inference (often cheaper than proprietary APIs)
• You want to fine-tune open-source models on your data
• You need image generation with FLUX models
• You want OpenAI API compatibility for easy migration

When NOT to use:

• You need OpenAI-specific features (Responses API, Batch API, Realtime) -- use the OpenAI SDK
• You need Anthropic or Google-specific features -- use their respective SDKs
• You want a provider-agnostic SDK -- use a unified provider framework

Core Patterns

Pattern 1: Client Setup

Initialize the Together client. It reads TOGETHER_API_KEY from the environment.

// lib/together.ts -- basic setup
import Together from "together-ai";
const client = new Together();
export { client };

// lib/together.ts -- production configuration
const TIMEOUT_MS = 30_000;
const MAX_RETRIES = 3;

const client = new Together({
  apiKey: process.env.TOGETHER_API_KEY,
  timeout: TIMEOUT_MS,
  maxRetries: MAX_RETRIES,
});
export { client };

Why good: Minimal setup, env var auto-detected, named constants for production settings

// BAD: Hardcoded API key
const client = new Together({
  apiKey: "sk-abc123...",
});

Why bad: Hardcoded keys get leaked in version control, security breach risk

See: examples/core.md for error handling, OpenAI compatibility, per-request overrides

Pattern 2: Chat Completions

Stateless text generation with open-source models.

const completion = await client.chat.completions.create({
  model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
  messages: [
    { role: "system", content: "You are a helpful coding assistant." },
    { role: "user", content: "Explain TypeScript generics." },
  ],
});
console.log(completion.choices[0].message.content);

Why good: Clear message roles, system message for behavior control, direct content access

// BAD: No system message, no model specified
const res = await client.chat.completions.create({
  messages: [{ role: "user", content: "do something" }],
});

Why bad: Missing model field will error, no system instruction means unpredictable behavior

See: examples/chat.md for multi-turn, vision models, model selection guide

Pattern 3: Streaming

Use streaming for user-facing responses.

const stream = await client.chat.completions.create({
  model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
  messages: [{ role: "user", content: "Explain async/await." }],
  stream: true,
});
for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) process.stdout.write(content);
}

Why good: Progressive output for better UX, standard async iterator pattern

// BAD: Not consuming the stream
const stream = await client.chat.completions.create({
  model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
  messages: [{ role: "user", content: "Hello" }],
  stream: true,
});
// Stream never consumed -- tokens are lost

Why bad: Stream must be consumed via iteration, otherwise tokens are silently lost

See: examples/streaming.md for stream cancellation, controller access

Pattern 4: Structured Output with JSON Schema

Use response_format: { type: "json_schema" } with Zod-generated schemas.

import Together from "together-ai";
import { z } from "zod";

const client = new Together();

const EventSchema = z.object({
  name: z.string(),
  date: z.string(),
  participants: z.array(z.string()),
});

const jsonSchema = z.toJSONSchema(EventSchema);

const completion = await client.chat.completions.create({
  model: "Qwen/Qwen3.5-9B",
  messages: [
    {
      role: "system",
      content: `Extract event details. Only answer in JSON. Follow this schema: ${JSON.stringify(jsonSchema)}`,
    },
    { role: "user", content: "Alice and Bob meet next Tuesday for lunch." },
  ],
  response_format: {
    type: "json_schema",
    json_schema: { name: "calendar_event", schema: jsonSchema },
  },
});

const event = JSON.parse(completion.choices[0].message.content ?? "{}");

Why good: Zod generates schema, schema included in both system prompt and response_format, named schema object

// BAD: Schema only in response_format, not in system prompt
const completion = await client.chat.completions.create({
  model: "Qwen/Qwen3.5-9B",
  messages: [{ role: "user", content: "Extract event details." }],
  response_format: {
    type: "json_schema",
    json_schema: { name: "event", schema: jsonSchema },
  },
});

Why bad: Model needs the schema in the system prompt AND response_format for reliable structured output -- omitting the prompt instruction degrades output quality

See: examples/structured-output.md for regex mode, vision with JSON, complex schemas

Pattern 5: Function Calling / Tool Use

Define functions the model can call. Same tools parameter shape as OpenAI.

const completion = await client.chat.completions.create({
  model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
  messages: [{ role: "user", content: "Weather in Paris?" }],
  tools: [
    {
      type: "function",
      function: {
        name: "get_weather",
        description: "Get current weather for a location",
        parameters: {
          type: "object",
          properties: {
            location: { type: "string", description: "City name" },
          },
          required: ["location"],
          additionalProperties: false,
        },
        strict: true,
      },
    },
  ],
});

const toolCall = completion.choices[0].message.tool_calls?.[0];
if (toolCall) {
  const args = JSON.parse(toolCall.function.arguments);
  console.log(`Call ${toolCall.function.name} with:`, args);
}

Why good: Standard OpenAI-compatible tool format, strict mode for reliable arguments, additionalProperties: false prevents hallucinated fields

See: examples/tools.md for multi-step tool loops, tool_choice, parallel calls, supported models

Pattern 6: Image Generation

Generate images with FLUX and Stable Diffusion models.

const response = await client.images.generate({
  model: "black-forest-labs/FLUX.1-schnell",
  prompt: "A serene mountain landscape at sunset with a lake reflection",
  steps: 4,
});
console.log(response.data[0].url);

Why good: Simple API, model-specific parameters, URL response by default

See: examples/images.md for FLUX variants, base64, reference images, multiple variations

Pattern 7: Embeddings

Create embeddings for semantic search and RAG pipelines.

const EMBEDDING_MODEL = "BAAI/bge-large-en-v1.5";

const response = await client.embeddings.create({
  model: EMBEDDING_MODEL,
  input: "TypeScript provides static type checking.",
});
console.log(response.data[0].embedding);

Why good: Named model constant, simple single-input embedding, array response

See: examples/images.md for batch embeddings, semantic search with cosine similarity

Pattern 8: Error Handling

Always catch Together.APIError and its subclasses.

try {
  const completion = await client.chat.completions.create({
    model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages: [{ role: "user", content: "Hello" }],
  });
} catch (error) {
  if (error instanceof Together.APIError) {
    console.error(`API Error [${error.status}]: ${error.message}`);
    if (error instanceof Together.RateLimitError) {
      console.error("Rate limited -- SDK will auto-retry.");
    }
    if (error instanceof Together.AuthenticationError) {
      throw new Error("Invalid API key. Check TOGETHER_API_KEY.");
    }
  } else {
    throw error; // Re-throw non-API errors
  }
}

Why good: Specific error types, re-throws unexpected errors, actionable error messages

See: examples/core.md for full production error handling, error type hierarchy

Performance Optimization

Model Selection for Cost/Speed

Fast + cheap              -> Llama 3.3 70B Turbo, Qwen3.5 9B
Most capable              -> DeepSeek V3.1, Qwen3.5 397B
Complex reasoning         -> DeepSeek R1
Function calling          -> Llama 3.3 70B, Qwen3.5 9B, DeepSeek V3
Structured output (JSON)  -> Qwen3.5 9B, Llama 3.3 70B
Embeddings                -> BAAI/bge-large-en-v1.5 (quality), UAE-Large-V1
Image generation (fast)   -> FLUX.1 schnell (4 steps)
Image generation (quality)-> FLUX.2 pro, FLUX.1.1 pro
Vision / multimodal       -> Qwen3-VL-8B-Instruct, Llama 3.2 Vision

Key Optimization Patterns

• Use Turbo variants for chat models -- they are optimized for Together's infrastructure
• Set temperature: 0 for deterministic output when possible
• Batch embedding inputs -- pass an array of strings to client.embeddings.create() instead of one at a time
• Use steps: 4 for FLUX.1 schnell images (higher steps have diminishing returns)
• Use streaming for user-facing responses to reduce perceived latency

Decision Framework

Which Model to Choose

What is your task?
+-- General chat / instruction following -> Llama 3.3 70B Turbo (fast, cheap)
+-- Most capable reasoning -> DeepSeek V3.1, Qwen3.5 397B
+-- Complex math / chain-of-thought -> DeepSeek R1
+-- Function calling / tool use -> Llama 3.3 70B, Qwen3.5 9B
+-- Structured JSON output -> Qwen3.5 9B (best JSON mode support)
+-- Vision / image understanding -> Qwen3-VL-8B-Instruct
+-- Code generation -> DeepSeek V3, Qwen Coder
+-- Embeddings -> BAAI/bge-large-en-v1.5 (default)
+-- Image generation (fast) -> FLUX.1 schnell
+-- Image generation (quality) -> FLUX.2 pro, FLUX.1.1 pro

Together AI SDK vs OpenAI SDK

Do you ONLY use Together AI models?
+-- YES -> Use together-ai package (purpose-built, full API coverage)
+-- NO -> Do you also use OpenAI models?
    +-- YES -> Two options:
    |   +-- Separate SDKs: together-ai for Together, openai for OpenAI
    |   +-- OpenAI SDK only: Point baseURL to api.together.xyz/v1
    +-- NO -> Use a provider-agnostic SDK

Streaming vs Non-Streaming

Is the response user-facing?
+-- YES -> Use streaming (stream: true)
+-- NO -> Use non-streaming
    +-- Background processing -> client.chat.completions.create()
    +-- Structured output -> Non-streaming with response_format

RED FLAGS

High Priority Issues:

• Hardcoding TOGETHER_API_KEY instead of using environment variables (security breach risk)
• Using bare catch blocks without checking Together.APIError (hides API errors)
• Not consuming streams returned by stream: true (tokens are silently lost)
• Using JSON.parse() on completion content without response_format (fragile, model may return non-JSON)
• Omitting the schema from the system prompt when using response_format: { type: "json_schema" } (degrades output quality)

Medium Priority Issues:

• Not setting maxRetries / timeout for production deployments (default timeout is 1 minute)
• Missing system role message (no system instruction means unpredictable behavior)
• Using a model that does not support function calling with tools parameter (will silently fail or error)
• Not checking if tool_calls is defined before accessing arguments
• Using width/height with FLUX schnell/Kontext models (use aspect_ratio instead)

Common Mistakes:

• Using OpenAI model names (e.g., gpt-4o) with the Together AI SDK -- Together uses Hugging Face-style IDs like meta-llama/Llama-3.3-70B-Instruct-Turbo
• Confusing client.images.generate() (Together) with client.images.create() (OpenAI) -- different method name
• Forgetting to use z.toJSONSchema() (Zod v4) or zodToJsonSchema() (Zod v3) to convert schemas before passing to response_format
• Using the developer role (OpenAI-specific) instead of system role with Together AI models
• Passing max_completion_tokens instead of max_tokens -- Together uses max_tokens

Gotchas & Edge Cases:

• The SDK auto-retries on 429 (rate limit), 408, 409, and 5xx errors -- 2 retries by default. Disable with maxRetries: 0.
• Model IDs are case-sensitive and follow the org/model-name format from Hugging Face.
• Not all models support function calling. See examples/tools.md for the current supported list, or check the official docs.
• FLUX.1 schnell and Kontext models use aspect_ratio parameter; FLUX.1 Pro and FLUX.1.1 Pro use width/height.
• Image generation returns URLs by default. Use response_format: "base64" for inline data.
• The response_format: { type: "json_schema" } requires telling the model to "only answer in JSON" in the system prompt -- the schema alone is not sufficient.
• Structured output uses z.toJSONSchema() (Zod v4) -- if using Zod v3, use zodToJsonSchema() from the zod-to-json-schema package.
• Together AI's client.images.generate() is the method name, not client.images.create() like OpenAI.
• Fine-tuning supports LoRA and full fine-tuning. File format is JSONL with messages array per line.
• The OpenAI compatibility endpoint (api.together.xyz/v1) supports chat, embeddings, images, vision, function calling, and structured output -- but not fine-tuning or model management.

CRITICAL REMINDERS

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST use the together-ai package (import Together from "together-ai") -- NOT the OpenAI SDK -- unless explicitly building an OpenAI-compatible integration)

(You MUST include the JSON schema in BOTH the response_format parameter AND the system prompt when using structured output -- the model needs both)

(You MUST handle errors using Together.APIError and its subclasses -- never use bare catch blocks without error type checking)

(You MUST never hardcode API keys -- always use environment variables via process.env.TOGETHER_API_KEY)

Failure to follow these rules will produce insecure, unreliable, or incorrectly structured AI integrations.