Replicate SDKs & Libraries: Python, JavaScript, Go (2026)

SDKs overview

Replicate offers a suite of Software Development Kits (SDKs) and client libraries designed to facilitate interaction with its platform for running machine learning models. These SDKs abstract the underlying RESTful API, providing language-specific methods for common operations such as making predictions, managing model versions, and handling asynchronous inference. The primary goal of these libraries is to streamline the integration of AI models into diverse applications, allowing developers to focus on application logic rather than low-level API communication details. Replicate's approach emphasizes ease of deployment for open-source models and serverless GPU inference, with SDKs reflecting this design philosophy by simplifying model invocation and result retrieval.

The official SDKs are maintained by Replicate and provide idiomatic interfaces for popular programming languages. In addition to these, a community of developers contributes unofficial libraries and tools, extending Replicate's reach into other environments and use cases. The platform's documentation provides comprehensive guides for using these SDKs, including detailed API references and example code snippets for various tasks. Developers can find extensive resources on the Replicate API reference documentation, which details endpoints and data structures.

Official SDKs by language

Replicate provides official SDKs for several programming languages, each designed to offer a native development experience. These libraries are typically the recommended way to interact with the Replicate API due to their direct support and maintenance by the platform. They encapsulate the complexities of HTTP requests, authentication, and response parsing, presenting developers with straightforward functions and objects.

The following table outlines the officially supported SDKs, their typical package names, and common installation methods:

Each SDK is designed to align with the conventions and best practices of its respective language, ensuring a familiar and efficient development experience. For instance, the Python SDK integrates well with asynchronous programming patterns, while the JavaScript SDK is suitable for both Node.js server-side applications and client-side web development through bundlers. Details on specific methods and object structures for each SDK are available in the Replicate documentation portal.

Installation

Installing Replicate SDKs typically involves using the standard package managers for each programming language. Before installation, developers need to ensure they have the correct language runtime and package manager set up in their development environment. For example, Python requires pip, JavaScript/Node.js uses npm or yarn, Go uses go get, and Ruby uses gem. After installation, an API token is required for authentication, which can be obtained from the Replicate account settings.

Python

pip install replicate

After installation, authentication involves setting the REPLICATE_API_TOKEN environment variable. For example:

export REPLICATE_API_TOKEN="r8_YOUR_API_TOKEN_HERE"

This token allows the SDK to authenticate requests against the Replicate API securely.

JavaScript/Node.js

npm install replicate
# or
yarn add replicate

Similar to Python, the API token is typically set as an environment variable:

export REPLICATE_API_TOKEN="r8_YOUR_API_TOKEN_HERE"

Alternatively, the token can be passed directly when initializing the client, though environment variables are generally preferred for security and ease of management. For browser-based applications, care must be taken to avoid exposing the API token publicly, often requiring a backend proxy or serverless function.

Go

go get github.com/replicate/replicate-go

In Go, the API token is often read from environment variables or explicitly passed to the client constructor:

import (
	"context"
	"os"

	replicate "github.com/replicate/replicate-go"
)

func main() {
	ctx := context.Background()
	client := replicate.NewClient(os.Getenv("REPLICATE_API_TOKEN"))

	// ... use client
}

The Go client provides methods for creating predictions, listing models, and managing various aspects of the Replicate platform. Developers can explore the Replicate Go SDK package documentation for more examples.

Quickstart example

This example demonstrates how to use the Replicate Python SDK to run a simple text-to-image model. The process involves initializing the client with an API token and then calling the predictions.create method with the model identifier and input parameters. This quickstart illustrates how to generate an image from a text prompt.

Python Quickstart: Running a Text-to-Image Model

First, ensure the replicate library is installed and your REPLICATE_API_TOKEN is set as an environment variable.

import replicate
import os

# Ensure your API token is set as an environment variable
# os.environ["REPLICATE_API_TOKEN"] = "r8_YOUR_API_TOKEN_HERE"

# Initialize the Replicate client
client = replicate.Client(api_token=os.environ.get("REPLICATE_API_TOKEN"))

# Define the model and input parameters
model_version = "stability-ai/stable-diffusion:ac732df83cea7fff18b47247d0d2feb6ad43e756757931eee09a7806573fe730"
input_data = {
    "prompt": "a photo of an astronaut riding a horse on mars, hdr, cinematic, high detail",
    "width": 512,
    "height": 512,
    "num_inference_steps": 50,
}

try:
    print(f"Starting prediction for model: {model_version}")
    # Create a prediction
    prediction = client.predictions.create(
        version=model_version,
        input=input_data
    )

    print(f"Prediction ID: {prediction.id}")
    print(f"Prediction status: {prediction.status}")

    # Wait for the prediction to complete and get the result
    # The wait() method polls the API until the prediction is done
    prediction.wait()

    if prediction.status == "succeeded":
        print("Prediction succeeded!")
        # The output is typically a list of URLs to generated images
        if prediction.output:
            print("Generated image URLs:")
            for url in prediction.output:
                print(url)
        else:
            print("No output found.")
    else:
        print(f"Prediction failed with status: {prediction.status}")
        if prediction.error:
            print(f"Error: {prediction.error}")
	except replicate.exceptions.ReplicateError as e:
    print(f"An error occurred during prediction: {e}")

This script first initializes the Replicate client. It then specifies a Stable Diffusion model version and input parameters, including the text prompt, image dimensions, and inference steps. The client.predictions.create call initiates the asynchronous prediction. The prediction.wait() method is then used to block until the prediction completes, polling the API for status updates. Once completed, the script checks the status and prints the URLs of the generated images, or any error messages if the prediction failed. This demonstrates a common pattern for interacting with asynchronous AI model inference services, as also seen in practices described by the Google Cloud AI inference patterns guide.

Community libraries

Beyond the official SDKs, the Replicate community has developed various libraries and tools to extend the platform's functionality and integrate it into different ecosystems. These community-contributed projects often fill gaps for less common languages, provide specialized utilities, or offer alternative interfaces to the Replicate API. While not officially supported by Replicate, many of these libraries are actively maintained by their creators and can be valuable resources for developers.

Examples of community contributions include:

• Client libraries for other languages: Developers have created wrappers and clients for languages not officially supported, allowing broader integration. These might include niche languages or specific framework integrations.
• Framework integrations: Libraries that integrate Replicate models directly into web frameworks (e.g., Flask, Django, Next.js) or data science platforms, simplifying deployment within existing application architectures.
• CLI tools: Command-line interface tools that offer quick interaction with Replicate models without writing extensive code, useful for scripting and rapid prototyping.
• Specialized utilities: Tools for specific tasks, such as managing large sets of predictions, batch processing, or integrating with specific data pipelines.

When using community libraries, it is advisable to check their documentation, GitHub repositories, and community support channels for activity, maintenance status, and compatibility with the latest Replicate API versions. The Replicate community documentation sometimes highlights popular third-party tools, but direct exploration of GitHub and other open-source platforms is often necessary to discover the full range of community contributions.

The open nature of Replicate's API encourages such development, fostering a vibrant ecosystem around AI model deployment. Developers interested in contributing can often find opportunities to build new tools or improve existing community-maintained projects, further enhancing the platform's utility across various development environments.