replicate

name	replicate
description	Discover, compare, and run AI models using Replicate's API

Docs

• Reference docs: https://replicate.com/docs/llms.txt
• HTTP API schema: https://api.replicate.com/openapi.json
• MCP server: https://mcp.replicate.com
• Set an Accept: text/markdown header when requesting docs pages to get a Markdown response.

Workflow

Here's a common workflow for using Replicate's API to run a model:

1. Choose the right model - Search with the API or ask the user
2. Get model metadata - Fetch model input and output schema via API
3. Create prediction - POST to /v1/predictions
4. Poll for results - GET prediction until status is "succeeded"
5. Return output - Usually URLs to generated content

Choosing models

• Use the search and collections APIs to find and compare the best models. Do not list all the models via API, as it's basically a firehose.
• Collections are curated by Replicate staff, so they're vetted.
• Official models are in the "official" collection.
• Use official models because they:
  • are always running
  • have stable API interfaces
  • have predictable output pricing
  • are maintained by Replicate staff
• If you must use a community model, be aware that it can take a long time to boot.
• You can create always-on deployments of community models, but you pay for model uptime.

Running models

Models take time to run. There are three ways to run a model via API and get its output:

1. Create a prediction, store its id from the response, and poll until completion.
2. Set a Prefer: wait header when creating a prediction for a blocking synchronous response. Only recommended for very fast models.
3. Set an HTTPS webhook URL when creating a prediction, and Replicate will POST to that URL when the prediction completes.

Follow these guideliness when running models:

• Use the "POST /v1/predictions" endpoint, as it supports both official and community models.
• Every model has its own OpenAPI schema. Always fetch and check model schemas to make sure you're setting valid inputs. Even popular models change their schemas.
• Validate input parameters against schema constraints (minimum, maximum, enum values). Don't generate values that violate them.
• When unsure about a parameter value, use the model's default example or omit the optional parameter.
• Don't set optional inputs unless you have a reason to. Stick to the required inputs and let the model's defaults do the work.
• Use HTTPS URLs for file inputs whenever possible. You can also send base64-encoded files, but they should be avoided.
• Fire off multiple predictions concurrently. Don't wait for one to finish before starting the next.
• Output file URLs expire after 1 hour, so back them up if you need to keep them, using durable object storage.
• Webhooks are a good mechanism for receiving and storing prediction output.