Ai infrastructure replicate

Replicate SDK Patterns

Quick Guide: Use the replicate npm package to run open-source ML models on serverless GPUs. Use replicate.run() for synchronous execution that returns output directly, replicate.stream() for SSE-based streaming, or replicate.predictions.create() for async background jobs with webhook notifications. Models are referenced as owner/model (uses latest version) or owner/model:version (pinned). File outputs are FileOutput objects implementing ReadableStream. Cold starts are expected for infrequently-used models -- use deployments with min_instances to keep models warm.

---

<critical_requirements>

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 never hardcode API tokens -- always use environment variables via process.env.REPLICATE_API_TOKEN)

(You MUST handle FileOutput objects for models that return files -- do not assume outputs are plain strings or URLs)

(You MUST validate webhooks using validateWebhook() from the replicate package -- never trust unverified webhook payloads)

(You MUST account for cold starts when running infrequently-used models -- use deployments for latency-sensitive applications)

(You MUST specify model versions (owner/model:version) in production to ensure reproducible results -- unversioned references use the latest, which can change)

</critical_requirements>

---

Auto-detection: Replicate, replicate, replicate.run, replicate.stream, replicate.predictions, replicate.deployments, replicate.trainings, replicate.models, FileOutput, validateWebhook, REPLICATE_API_TOKEN, serverless GPU, cold start, webhook_events_filter

When to use:

• Running open-source ML models (Llama, Stable Diffusion, Whisper, etc.) without managing GPU infrastructure
• Generating images, transcribing audio, running LLMs, or any ML inference via API
• Streaming LLM output in real-time with server-sent events
• Processing predictions asynchronously with webhook notifications
• Fine-tuning models with custom training data
• Running models on dedicated hardware with custom scaling via deployments

Key patterns covered:

• Client initialization and configuration (auth, user agent, file encoding)
• Running predictions (replicate.run(), replicate.predictions.create(), replicate.wait())
• Streaming output (replicate.stream() with SSE events)
• Model versioning (owner/model vs owner/model:version)
• File input/output handling (FileOutput, file uploads, Buffer inputs)
• Webhooks (setup, event filtering, signature validation)
• Deployments (custom hardware, scaling, keeping models warm)
• Training / fine-tuning

When NOT to use:

• You need a unified multi-provider LLM SDK (OpenAI, Anthropic, Google) -- use a provider-agnostic SDK
• You want to run models locally -- Replicate is a cloud-only serverless platform
• You need sub-second latency guarantees without deployments -- cold starts can take minutes

---

Examples Index

• Core: Setup, Predictions & Files -- Client init, run(), predictions.create(), wait(), file I/O, error handling
• Streaming & Webhooks -- stream(), SSE events, webhook setup, signature validation
• Deployments & Training -- Custom hardware, scaling, fine-tuning, model management
• Quick API Reference -- Method signatures, constructor options, error types, model reference format

---

<philosophy>

Philosophy

Replicate provides serverless GPU infrastructure for running open-source ML models. You send inputs, Replicate allocates GPU hardware, runs the model, and returns outputs. No Docker, no CUDA drivers, no GPU provisioning.

Core principles:

1. Serverless execution -- Models run on-demand on Replicate's infrastructure. You pay only for compute time. Cold starts are a trade-off for not maintaining always-on GPUs.
2. Model marketplace -- Thousands of community and official models available at replicate.com/explore. Run any public model with just its identifier.
3. Version pinning for reproducibility -- Models are versioned with SHA-256 hashes. Pin to a version in production (owner/model:abc123...) to guarantee identical behavior across deploys.
4. Three execution modes -- replicate.run() for synchronous wait, replicate.stream() for real-time SSE output, replicate.predictions.create() for fire-and-forget with webhooks.
5. File-first I/O -- Many models accept and produce files (images, audio, video). The SDK handles file uploads automatically and returns FileOutput objects for file outputs.

</philosophy>

---

<patterns>

Core Patterns

Pattern 1: Client Setup

Initialize the Replicate client. It auto-reads REPLICATE_API_TOKEN from the environment.

// lib/replicate.ts -- basic setup
import Replicate from "replicate";

const replicate = new Replicate();

export { replicate };

// lib/replicate.ts -- explicit auth + custom user agent
import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN, // Auto-reads from env if omitted
  userAgent: "my-app/1.0.0",
});

export { replicate };

Why good: Minimal setup, env var auto-detected, explicit auth optional but useful for clarity

// BAD: Hardcoded token
const replicate = new Replicate({
  auth: "r8_abc123...",
});

Why bad: Hardcoded API token is a security risk, will leak in version control

See: examples/core.md for full constructor options, error handling patterns

---

Pattern 2: Running Predictions

Use replicate.run() for synchronous execution. Returns the model output directly.

// Run an image generation model
const [output] = await replicate.run("black-forest-labs/flux-schnell", {
  input: {
    prompt: "a serene mountain landscape at sunset",
  },
});

// output is a FileOutput object for image models
console.log(output.url()); // URL of generated image

// Run an LLM -- output is a string for text models
const output = await replicate.run("meta/meta-llama-3-70b-instruct", {
  input: {
    prompt: "Explain TypeScript generics in 3 sentences.",
    max_tokens: 512,
  },
});

console.log(output); // Text response

Why good: Simple API, returns output directly, destructuring works for array outputs (images)

// BAD: Not pinning version in production
const output = await replicate.run("community-user/experimental-model", {
  input: { prompt: "hello" },
});

Why bad: Community models without version pinning can change behavior unexpectedly when authors push updates

See: examples/core.md for version pinning, predictions.create() + wait(), and progress callbacks

---

Pattern 3: Streaming

Use replicate.stream() for real-time SSE output from language models.

const stream = replicate.stream("meta/meta-llama-3-70b-instruct", {
  input: {
    prompt: "Write a short poem about TypeScript.",
    max_tokens: 512,
  },
});

for await (const event of stream) {
  if (event.event === "output") {
    process.stdout.write(event.data);
  }
}

Why good: Progressive output for better UX, event-based with typed event and data fields

// BAD: Using replicate.run() for user-facing LLM output
const output = await replicate.run("meta/meta-llama-3-70b-instruct", {
  input: { prompt: "Write a long essay..." },
});
// User waits for entire generation to complete before seeing anything

Why bad: No progressive feedback, user sees a blank screen for seconds

See: examples/streaming-webhooks.md for event types, error handling, cancellation

---

Pattern 4: Model Versioning

Models are referenced as owner/model (latest version) or owner/model:sha256hash (pinned version).

// Development: use latest version for convenience
const output = await replicate.run("stability-ai/sdxl", {
  input: { prompt: "a cat" },
});

// Production: pin to a specific version for reproducibility
const VERSION_HASH =
  "39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b";
const output = await replicate.run(`stability-ai/sdxl:${VERSION_HASH}`, {
  input: { prompt: "a cat" },
});

Why good: Pinned version guarantees identical behavior, hash is immutable

See: examples/core.md for listing model versions, getting version details

---

Pattern 5: File Handling

Models that output files return FileOutput objects implementing ReadableStream.

import { writeFile } from "node:fs/promises";

const [output] = await replicate.run("black-forest-labs/flux-schnell", {
  input: { prompt: "a sunset over mountains" },
});

// FileOutput has .url() and .blob() methods
console.log(output.url()); // Underlying URL

// Save to disk
const blob = await output.blob();
const buffer = Buffer.from(await blob.arrayBuffer());
await writeFile("./output.png", buffer);

// File inputs: pass URLs, Buffers, or ReadStreams
import { readFile } from "node:fs/promises";

const imageBuffer = await readFile("./input.png");

const output = await replicate.run("some-user/image-model", {
  input: {
    image: imageBuffer, // Auto-uploaded (max 100 MiB)
  },
});

Why good: FileOutput is a ReadableStream, works with Node.js stream APIs, .url() for the underlying URL

// BAD: Treating file output as a plain URL string
const [output] = await replicate.run("black-forest-labs/flux-schnell", {
  input: { prompt: "hello" },
});
const url = output; // WRONG: output is a FileOutput object, not a string

Why bad: FileOutput is an object, not a string -- use .url() to get the URL

See: examples/core.md for file uploads, large file handling, encoding strategies

---

Pattern 6: Async Predictions with Webhooks

Use replicate.predictions.create() for background jobs with webhook notifications.

const prediction = await replicate.predictions.create({
  model: "owner/model", // OR version: "sha256hash" for pinned version
  input: { prompt: "a painting of a cat" },
  webhook: "https://my.app/webhooks/replicate",
  webhook_events_filter: ["completed"],
});

console.log(prediction.id); // Use to track status
console.log(prediction.status); // "starting"

// Webhook signature validation (CRITICAL for security)
import { validateWebhook } from "replicate";

async function handleWebhook(request: Request): Promise<Response> {
  const secret = process.env.REPLICATE_WEBHOOK_SIGNING_SECRET;
  const isValid = await validateWebhook(request, secret);

  if (!isValid) {
    return new Response("Invalid signature", { status: 401 });
  }

  const prediction = await request.json();
  // Process prediction.output safely
  return new Response("OK", { status: 200 });
}

Why good: Decoupled processing, secure signature validation, filtered events reduce noise

See: examples/streaming-webhooks.md for webhook event types, polling alternative

---

Pattern 7: Deployments

Deployments give you a private, fixed endpoint with custom hardware and scaling.

// Create a prediction on a deployment (no cold start if min_instances > 0)
const prediction = await replicate.deployments.predictions.create(
  "my-org/my-deployment",
  {
    input: { prompt: "hello world" },
  },
);

const result = await replicate.wait(prediction);
console.log(result.output);

Why good: Predictable latency with min_instances, private endpoint, custom hardware selection

See: examples/deployments-training.md for creating/managing deployments, training API

---

Pattern 8: Error Handling

Catch API errors with status codes. The SDK auto-retries on 429 and 5xx errors (5 retries by default with exponential backoff).

try {
  const output = await replicate.run("owner/model", {
    input: { prompt: "hello" },
  });
} catch (error) {
  if (error instanceof Error) {
    console.error(`Replicate error: ${error.message}`);

    // Check for specific HTTP status codes in the error
    if ("status" in error) {
      const status = (error as { status: number }).status;
      if (status === 401) {
        throw new Error("Invalid API token. Check REPLICATE_API_TOKEN.");
      }
      if (status === 422) {
        console.error("Invalid input parameters");
      }
      if (status === 429) {
        console.error(
          "Rate limited -- SDK auto-retries (5 attempts) exhausted",
        );
      }
    }
  }
  throw error;
}

Why good: Checks error type, handles specific status codes, re-throws unexpected errors

See: examples/core.md for full error handling example with status code handling

</patterns>

---

<performance>

Performance Optimization

Cold Start Mitigation

Frequent model with varying load   -> Use deployments with min_instances >= 1
One-off batch jobs                  -> Use predictions.create() with webhooks (no waiting)
Popular public models               -> Usually warm, replicate.run() is fine
Custom/niche models                 -> Expect 30s-5min cold start on first run

Key Optimization Patterns

• Use deployments for latency-sensitive applications -- set min_instances: 1 to eliminate cold starts
• Use webhooks instead of polling for async jobs -- reduces API calls and latency
• Batch file inputs as URLs instead of uploading buffers -- avoids 100 MiB upload limit and is faster
• Pin model versions in production -- avoids unexpected behavior changes and enables caching
• Use replicate.stream() for LLMs -- progressive output feels faster than waiting for full completion
• Cancel unneeded predictions with replicate.predictions.cancel() -- stops billing immediately

</performance>

---

<decision_framework>

Decision Framework

Which Execution Method to Use

Is this a user-facing LLM response?
+-- YES -> Use replicate.stream() for real-time SSE output
+-- NO -> Do you need the result immediately?
    +-- YES -> Use replicate.run() (blocks until complete)
    +-- NO -> Use replicate.predictions.create() + webhook
        +-- Need to poll instead? -> Use replicate.wait(prediction)

Model Reference Format

Are you in development/prototyping?
+-- YES -> Use owner/model (latest version, convenient)
+-- NO -> Are you in production?
    +-- YES -> Use owner/model:version_hash (pinned, reproducible)
    +-- Does the model change frequently?
        +-- YES -> Pin version, test updates explicitly
        +-- NO -> Either format works, prefer pinned

Deployments vs Direct API

Do you need consistent low latency?
+-- YES -> Create a deployment with min_instances >= 1
+-- NO -> Do you need custom hardware (A100, H100)?
    +-- YES -> Create a deployment with specific hardware
    +-- NO -> Use replicate.run() / replicate.stream() directly
        (Replicate auto-allocates hardware)

When to Use This SDK vs Other AI SDKs

Are you running open-source models on serverless GPUs?
+-- YES -> Use Replicate SDK
+-- NO -> Are you calling proprietary APIs (OpenAI, Anthropic)?
    +-- YES -> Not this skill's scope -- use provider-specific SDKs
    +-- NO -> Do you need to switch between multiple providers?
        +-- YES -> Not this skill's scope -- use a unified provider SDK
        +-- NO -> Do you want to self-host models?
            +-- YES -> Not this skill's scope -- consider Cog or vLLM
            +-- NO -> Replicate SDK is appropriate

</decision_framework>

---

<red_flags>

RED FLAGS

High Priority Issues:

• Hardcoding REPLICATE_API_TOKEN in source code (security breach risk)
• Treating FileOutput as a string (it is a ReadableStream object -- use .url() or .blob())
• Not validating webhook signatures with validateWebhook() (allows forged webhook payloads)
• Using replicate.run() for long-running models in request handlers (blocks the response, can timeout)

Medium Priority Issues:

• Not pinning model versions in production (owner/model uses latest, which can change without notice)
• Relying solely on default retry behavior for production (5 retries with exponential backoff may be too aggressive for some use cases)
• Uploading large files as Buffer instead of hosting them at a URL (100 MiB limit on uploads)
• Ignoring cold start latency for infrequently-used models (first request can take minutes)

Common Mistakes:

• Confusing replicate.run() (returns output directly) with replicate.predictions.create() (returns a prediction object with status/id)
• Destructuring image output incorrectly: const output = await replicate.run(...) instead of const [output] = await replicate.run(...) (image models return arrays)
• Using replicate.stream() with models that do not support streaming (only language models with SSE support)
• Forgetting that replicate.predictions.create() accepts either a version hash or a model string (owner/model) -- use version for pinned reproducibility, model for latest-version convenience
• Not consuming the async iterator from replicate.stream() (events are lost)

Gotchas & Edge Cases:

• Prediction inputs and outputs are automatically deleted after one hour -- persist outputs via webhooks or download immediately
• The SDK auto-retries on 429 (rate limit) and 5xx errors -- 5 retries by default with exponential backoff. GET requests retry on 429 and 5xx; non-GET requests retry only on 429
• replicate.stream() returns ServerSentEvent objects with .event ("output", "error", "done") and .data (string) properties
• File uploads are limited to 100 MiB -- for larger files, host them at a URL and pass the URL as input
• Browser usage is not supported -- the SDK requires a server-side environment (Node.js 18+, Bun, Deno, Cloudflare Workers)
• webhook_events_filter accepts ["start", "output", "logs", "completed"] -- use ["completed"] unless you need intermediate status updates
• The Prefer: wait header enables sync mode on the HTTP API (up to 60s), but replicate.run() already handles this automatically
• Community models may disappear or change without warning -- pin versions and maintain fallbacks for critical workflows
• replicate.wait() polls the API until the prediction completes -- use webhooks for production to avoid polling overhead
• FileOutput.url() returns the underlying URL, but these URLs are temporary -- download or persist the file before it expires

</red_flags>

---

<critical_reminders>

CRITICAL REMINDERS

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

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

(You MUST handle FileOutput objects for models that return files -- do not assume outputs are plain strings or URLs)

(You MUST validate webhooks using validateWebhook() from the replicate package -- never trust unverified webhook payloads)

(You MUST account for cold starts when running infrequently-used models -- use deployments for latency-sensitive applications)

(You MUST specify model versions (owner/model:version) in production to ensure reproducible results -- unversioned references use the latest, which can change)

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

</critical_reminders>