Replicate Automation

Automate your Replicate AI model workflows -- run predictions on any public model (image generation, LLMs, audio, video), upload input files, inspect model schemas and documentation, list model versions, and track prediction history.

Toolkit docs: composio.dev/toolkits/replicate

Setup

1. Add the Composio MCP server to your client: https://rube.app/mcp
2. Connect your Replicate account when prompted (API token authentication)
3. Start using the workflows below

Core Workflows

1. Get Model Details and Schema

Use REPLICATE_MODELS_GET to inspect a model's input/output schema before running predictions.

Tool: REPLICATE_MODELS_GET
Inputs:
  - model_owner: string (required) -- e.g., "meta", "black-forest-labs", "stability-ai"
  - model_name: string (required) -- e.g., "meta-llama-3-8b-instruct", "flux-1.1-pro"

Important: Each model has unique input keys and types. Always check the openapi_schema from this response before constructing prediction inputs.

2. Run a Prediction

Use REPLICATE_MODELS_PREDICTIONS_CREATE to run inference on any model with optional synchronous waiting and webhooks.

Tool: REPLICATE_MODELS_PREDICTIONS_CREATE
Inputs:
  - model_owner: string (required) -- e.g., "meta", "black-forest-labs"
  - model_name: string (required) -- e.g., "flux-1.1-pro", "sdxl"
  - input: object (required) -- model-specific inputs, e.g., { "prompt": "A sunset over mountains" }
  - wait_for: integer (1-60 seconds, optional) -- synchronous wait for completion
  - cancel_after: string (optional) -- max execution time, e.g., "300s", "5m"
  - webhook: string (optional) -- HTTPS URL for async completion notifications
  - webhook_events_filter: array (optional) -- ["start", "output", "logs", "completed"]

Sync vs Async: Use wait_for (1-60s) for fast models. For long-running jobs, omit it and use webhooks or poll via REPLICATE_PREDICTIONS_LIST.

3. Upload Files for Model Input

Use REPLICATE_CREATE_FILE to upload images, documents, or other binary inputs that models need.

Tool: REPLICATE_CREATE_FILE
Inputs:
  - content: string (required) -- base64-encoded file content
  - filename: string (required) -- e.g., "input.png", "audio.wav" (max 255 bytes UTF-8)
  - content_type: string (default "application/octet-stream") -- MIME type
  - metadata: object (optional) -- custom JSON metadata

4. Read Model Documentation

Use REPLICATE_MODELS_README_GET to access a model's README in Markdown format for detailed usage instructions.

Tool: REPLICATE_MODELS_README_GET
Inputs:
  - model_owner: string (required)
  - model_name: string (required)

5. List Model Versions

Use REPLICATE_MODELS_VERSIONS_LIST to see all available versions of a model, sorted newest first.

Tool: REPLICATE_MODELS_VERSIONS_LIST
Inputs:
  - model_owner: string (required)
  - model_name: string (required)

6. Track Prediction History and Files

Use REPLICATE_PREDICTIONS_LIST to retrieve prediction history, and REPLICATE_FILES_GET/REPLICATE_FILES_LIST to manage uploaded files.

Tool: REPLICATE_PREDICTIONS_LIST
  - Lists all predictions for the authenticated user with pagination

Tool: REPLICATE_FILES_LIST
  - Lists uploaded files, most recent first

Tool: REPLICATE_FILES_GET
  - Get details of a specific file by ID

Known Pitfalls

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