{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-replicate","slug":"pypi-replicate","name":"replicate","type":"platform","url":"https://pypi.org/project/replicate/","page_url":"https://unfragile.ai/pypi-replicate","categories":["deployment-infra"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-replicate__cap_0","uri":"capability://tool.use.integration.remote.model.inference.via.rest.api.abstraction","name":"remote model inference via rest api abstraction","description":"Provides a Python wrapper that abstracts Replicate's REST API endpoints, handling HTTP request/response serialization, authentication via API tokens, and polling for asynchronous job completion. The client manages the full lifecycle of model invocations—from parameter validation to result retrieval—without requiring direct HTTP calls, using a request-response pattern with built-in retry logic and timeout handling for long-running predictions.","intents":["Run a machine learning model hosted on Replicate without managing HTTP requests directly","Execute image generation, text processing, or other ML tasks from Python code with minimal boilerplate","Handle asynchronous model execution and poll for results without implementing custom polling logic"],"best_for":["Python developers building applications that need access to hosted ML models","Teams integrating Replicate models into existing Python backends or scripts","Rapid prototyping of ML-powered features without local model infrastructure"],"limitations":["Synchronous polling for async jobs adds latency compared to webhook-based callbacks","No built-in streaming support for real-time model outputs","Rate limiting depends on Replicate account tier; client does not implement local rate limiting","Requires network connectivity; no offline mode or local fallback"],"requires":["Python 3.8+","Replicate API token (obtainable from replicate.com account)","Network access to api.replicate.com"],"input_types":["model identifier (string)","version hash (string)","input parameters (dict/JSON-serializable Python objects)"],"output_types":["prediction object with status and output data","structured output from model (URLs, text, arrays, etc.)","error details and status codes"],"categories":["tool-use-integration","api-client"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_1","uri":"capability://search.retrieval.model.discovery.and.metadata.retrieval","name":"model discovery and metadata retrieval","description":"Exposes methods to query Replicate's model registry, retrieving metadata about available models including descriptions, input/output schemas, version history, and pricing information. The client caches model metadata locally to reduce API calls and provides structured access to model versions, allowing developers to inspect model capabilities before invocation without hardcoding model identifiers.","intents":["Browse available models and their versions programmatically without visiting the web UI","Inspect input/output schemas to validate parameters before sending predictions","Discover model versions and select specific versions for reproducibility"],"best_for":["Developers building model selection UIs or dynamic model routing","Teams needing programmatic access to model capabilities and pricing","Applications that need to validate user inputs against model schemas before submission"],"limitations":["Metadata caching may be stale if models are updated frequently on Replicate","No full-text search across model descriptions; filtering is limited to model name/owner","Pricing information may not reflect real-time cost changes","No support for private/custom models unless explicitly shared with API token owner"],"requires":["Python 3.8+","Replicate API token for authenticated requests","Network access to api.replicate.com"],"input_types":["model identifier (string, e.g., 'owner/model-name')","version hash (optional, string)"],"output_types":["model metadata object (name, description, owner, created_at)","version list with timestamps and URLs","input/output schema (JSON schema format)","pricing information (per-second or per-prediction costs)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_2","uri":"capability://automation.workflow.batch.prediction.processing.with.result.aggregation","name":"batch prediction processing with result aggregation","description":"Supports submitting multiple predictions in sequence or parallel, aggregating results and handling partial failures gracefully. The client manages concurrent API calls (respecting rate limits), collects outputs, and provides unified error reporting across the batch, enabling efficient processing of multiple inputs without manual loop management or error handling boilerplate.","intents":["Process a list of images or text inputs through a model and collect all results","Run multiple model predictions in parallel while respecting API rate limits","Handle failures in batch jobs without losing successful results"],"best_for":["Data processing pipelines that need to apply models to large datasets","Applications processing user-submitted batches (e.g., bulk image generation)","Teams building ETL workflows that integrate Replicate models"],"limitations":["No built-in checkpointing; if batch fails mid-way, no automatic resume from last successful prediction","Parallelism is limited by Replicate account concurrency limits, not client-side","No streaming results; entire batch must complete before results are returned","Memory usage scales with batch size; large batches may exhaust client memory"],"requires":["Python 3.8+","Replicate API token with sufficient concurrency quota","Network access to api.replicate.com"],"input_types":["list of input dictionaries (one per prediction)","model identifier (string)","version hash (optional, string)"],"output_types":["list of prediction results","error list with indices and error messages","aggregated statistics (success count, failure count, total time)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_3","uri":"capability://automation.workflow.asynchronous.prediction.polling.with.timeout.management","name":"asynchronous prediction polling with timeout management","description":"Handles the asynchronous nature of Replicate's prediction API by automatically polling prediction status at configurable intervals until completion, with built-in timeout and cancellation support. The client abstracts away the complexity of managing prediction IDs, polling loops, and state transitions, providing a simple blocking interface that internally manages long-running jobs.","intents":["Wait for a model prediction to complete without manually polling the API","Set timeouts to prevent indefinite blocking on stuck predictions","Cancel in-flight predictions if needed"],"best_for":["Synchronous Python applications that need to wait for model results","Scripts and notebooks where blocking calls are acceptable","Developers who want simple, sequential prediction workflows"],"limitations":["Polling adds latency compared to webhook-based notifications (typically 1-5 second polling intervals)","Blocking calls prevent other async operations; not suitable for high-concurrency servers","No exponential backoff configuration; polling interval is fixed","Timeout is wall-clock time, not actual model execution time"],"requires":["Python 3.8+","Replicate API token","Network access to api.replicate.com"],"input_types":["prediction ID (string)","timeout in seconds (optional, float)","polling interval in seconds (optional, float)"],"output_types":["completed prediction object with output data","timeout exception if prediction exceeds timeout","cancellation confirmation if cancelled"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_4","uri":"capability://safety.moderation.input.validation.against.model.schemas","name":"input validation against model schemas","description":"Validates user-provided input parameters against the model's JSON schema before submitting predictions, catching schema violations early and providing detailed error messages about missing required fields, type mismatches, or invalid enum values. This prevents wasted API calls and provides immediate feedback to developers about parameter correctness.","intents":["Validate input parameters before submitting to Replicate to avoid failed predictions","Provide users with clear error messages about what inputs are required or invalid","Ensure type safety for model inputs (e.g., ensuring image URLs are strings, not objects)"],"best_for":["Applications with user-facing input forms that need real-time validation","Developers building model wrappers that need to enforce strict input contracts","Teams building APIs that expose Replicate models to end users"],"limitations":["Validation is schema-based only; does not validate semantic correctness (e.g., whether a URL actually points to a valid image)","Schema may be incomplete or outdated if model maintainers don't update it","No custom validation rules beyond JSON schema constraints","Validation errors may not match Replicate's server-side validation exactly"],"requires":["Python 3.8+","Model metadata with JSON schema (retrieved via model discovery)","jsonschema library (typically bundled with replicate package)"],"input_types":["input parameters (dict)","model schema (JSON schema object)"],"output_types":["validation success (boolean)","validation error details (list of error messages with field paths)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_5","uri":"capability://safety.moderation.api.authentication.and.token.management","name":"api authentication and token management","description":"Manages Replicate API authentication by accepting API tokens (via environment variables, constructor arguments, or config files) and automatically injecting them into all HTTP requests as Bearer tokens. The client handles token refresh logic if needed and provides clear error messages if authentication fails, abstracting away HTTP header management.","intents":["Authenticate with Replicate API without manually managing HTTP headers","Load API tokens from environment variables for secure credential management","Handle authentication errors gracefully with informative error messages"],"best_for":["Any Python application using the Replicate client","Teams following security best practices by storing tokens in environment variables","Developers deploying to cloud platforms (AWS, GCP, Heroku) that support env var injection"],"limitations":["No built-in token rotation or refresh; tokens must be manually updated if revoked","No support for OAuth2 or other advanced auth schemes; only API token authentication","Tokens are passed in memory; no hardware security module (HSM) integration","No audit logging of API calls; authentication is transparent"],"requires":["Python 3.8+","Valid Replicate API token (obtainable from replicate.com account)","Environment variable REPLICATE_API_TOKEN or explicit token in constructor"],"input_types":["API token (string)","environment variable name (string, default 'REPLICATE_API_TOKEN')"],"output_types":["authenticated HTTP client (internal)","authentication error if token is invalid or missing"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_6","uri":"capability://automation.workflow.error.handling.and.retry.logic.with.exponential.backoff","name":"error handling and retry logic with exponential backoff","description":"Implements automatic retry logic for transient failures (network timeouts, 5xx errors) using exponential backoff with jitter, while distinguishing between retryable errors (temporary service issues) and non-retryable errors (invalid inputs, authentication failures). The client provides detailed error objects with status codes, messages, and context, enabling developers to handle failures gracefully.","intents":["Automatically retry failed API calls without manual retry loop implementation","Distinguish between temporary failures (retry) and permanent failures (fail fast)","Get detailed error information for debugging and logging"],"best_for":["Production applications that need resilience to transient network failures","Batch processing jobs that should survive temporary API outages","Developers who want automatic retry without implementing custom logic"],"limitations":["Retry logic is fixed; no configuration for custom backoff strategies","Maximum retry attempts are hardcoded; not configurable per request","No circuit breaker pattern; client will continue retrying even if service is down","Retries increase total latency; not suitable for latency-sensitive applications"],"requires":["Python 3.8+","Network connectivity to api.replicate.com"],"input_types":["HTTP request (internal)","error response from API"],"output_types":["successful response after retry","final error object with status code and message if all retries exhausted","error classification (retryable vs non-retryable)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_7","uri":"capability://text.generation.language.streaming.prediction.output.handling","name":"streaming prediction output handling","description":"Supports consuming model outputs as they are generated in real-time via streaming, rather than waiting for the entire prediction to complete. The client provides an iterator interface that yields output chunks as they arrive from the model, enabling progressive rendering or processing of results without buffering the entire output in memory.","intents":["Display model outputs progressively as they are generated (e.g., text generation token-by-token)","Process large outputs without loading them entirely into memory","Provide real-time feedback to users while models are still generating results"],"best_for":["Chat and text generation applications that need token-by-token output","Web applications using Server-Sent Events (SSE) or WebSockets to stream results to clients","Applications processing very large model outputs that don't fit in memory"],"limitations":["Not all Replicate models support streaming; depends on model implementation","Streaming adds complexity to error handling; failures mid-stream may leave partial results","No built-in buffering or backpressure handling; client must consume chunks at model's pace","Streaming connections may timeout if chunks arrive too slowly"],"requires":["Python 3.8+","Model that supports streaming output (not all models do)","Replicate API token"],"input_types":["model identifier (string)","input parameters (dict)","streaming flag (boolean)"],"output_types":["iterator yielding output chunks (strings or objects)","final prediction object after stream completes"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-replicate__cap_8","uri":"capability://automation.workflow.webhook.based.prediction.notifications","name":"webhook-based prediction notifications","description":"Supports registering webhooks for prediction completion events, allowing Replicate to push results to a specified URL rather than requiring the client to poll. The client provides helpers to construct webhook URLs and validate incoming webhook payloads, enabling event-driven architectures where predictions trigger downstream actions automatically.","intents":["Receive notifications when predictions complete without polling","Build event-driven workflows where prediction completion triggers other actions","Scale to high-concurrency scenarios without blocking on prediction results"],"best_for":["Web applications and APIs that need to handle prediction results asynchronously","High-throughput systems processing many concurrent predictions","Teams building event-driven architectures with message queues or serverless functions"],"limitations":["Requires publicly accessible webhook endpoint; not suitable for local development without tunneling","Webhook delivery is not guaranteed; Replicate may retry failed deliveries but eventual consistency is not guaranteed","No built-in webhook signature verification; developers must implement HMAC validation","Webhook payloads may arrive out of order if multiple predictions complete simultaneously"],"requires":["Python 3.8+","Publicly accessible HTTP endpoint to receive webhooks","Replicate API token","HTTPS endpoint (Replicate requires secure webhooks)"],"input_types":["webhook URL (string)","prediction parameters (dict)","webhook payload (JSON from Replicate)"],"output_types":["prediction object with webhook URL registered","webhook payload containing prediction results","validation result for webhook signature"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","Replicate API token (obtainable from replicate.com account)","Network access to api.replicate.com","Replicate API token for authenticated requests","Replicate API token with sufficient concurrency quota","Replicate API token","Model metadata with JSON schema (retrieved via model discovery)","jsonschema library (typically bundled with replicate package)","Valid Replicate API token (obtainable from replicate.com account)","Environment variable REPLICATE_API_TOKEN or explicit token in constructor"],"failure_modes":["Synchronous polling for async jobs adds latency compared to webhook-based callbacks","No built-in streaming support for real-time model outputs","Rate limiting depends on Replicate account tier; client does not implement local rate limiting","Requires network connectivity; no offline mode or local fallback","Metadata caching may be stale if models are updated frequently on Replicate","No full-text search across model descriptions; filtering is limited to model name/owner","Pricing information may not reflect real-time cost changes","No support for private/custom models unless explicitly shared with API token owner","No built-in checkpointing; if batch fails mid-way, no automatic resume from last successful prediction","Parallelism is limited by Replicate account concurrency limits, not client-side","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.3,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:10.823Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-replicate","compare_url":"https://unfragile.ai/compare?artifact=pypi-replicate"}},"signature":"UDSqC75XSKyG3yrcVYpPj0V3u/kleJmS6VkGVa9aJbcnQTFF8jQ9w3j6kOhQZ8IlcXJC3/1rqQ6roQd5yZl5DQ==","signedAt":"2026-06-17T02:10:31.352Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-replicate","artifact":"https://unfragile.ai/pypi-replicate","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-replicate","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}