{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fal-ai","slug":"fal-ai","name":"FAL.ai","type":"api","url":"https://fal.ai","page_url":"https://unfragile.ai/fal-ai","categories":["llm-apis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"fal-ai__cap_0","uri":"capability://tool.use.integration.unified.serverless.model.api.with.sub.second.cold.starts","name":"unified serverless model api with sub-second cold starts","description":"Provides a single API endpoint pattern (`fal_client.subscribe(\"fal-ai/{model-id}\", arguments={...})`) that abstracts away infrastructure provisioning and model deployment complexity. Requests are routed to globally distributed GPU runners with claimed sub-second cold start latency, eliminating the need to manage containers, scaling policies, or model loading overhead. The architecture uses a queue-based execution model supporting both synchronous blocking calls and asynchronous job submission with webhook callbacks.","intents":["Run inference on 1,000+ open-source and proprietary models without provisioning GPU infrastructure","Minimize latency for real-time inference by leveraging pre-warmed serverless runners","Scale inference workloads from single requests to batch processing without managing autoscaling policies","Access the latest community models (Flux, Stable Diffusion, Whisper, etc.) without maintaining model weights locally"],"best_for":["Startups and solo developers building AI applications without DevOps expertise","Teams needing rapid iteration on model selection without infrastructure lock-in","Applications with variable inference load that require pay-per-use pricing"],"limitations":["Cold start claims of 'sub-second' are unverified against specific baselines; actual latency depends on model size and GPU availability","No stated maximum concurrent requests per API key; rate limiting structure is undocumented","Synchronous calls block until completion; no streaming response support documented for most models","Model selection must be explicit per request; no default model or model auto-selection logic"],"requires":["FAL.ai API key (obtained from account dashboard)","Python 3.7+ with fal_client library OR JavaScript SDK OR cURL for HTTP calls","Network connectivity to FAL.ai global endpoints (specific regions undocumented)"],"input_types":["text prompts (no token limit specified)","image files (format types undocumented; pricing normalized to 1MP)","video files (max duration undocumented; pricing per second)","audio files (Whisper support mentioned; format details unknown)"],"output_types":["JSON with model-specific structure (e.g., {\"images\": [{\"url\": \"...\"}]} for image models)","Streaming responses (implementation details unknown)","Webhook callbacks for async jobs (payload structure undocumented)"],"categories":["tool-use-integration","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_1","uri":"capability://data.processing.analysis.output.based.pricing.for.image.and.video.generation","name":"output-based pricing for image and video generation","description":"Implements a granular, consumption-based billing model where image generation is priced per image (normalized to 1 megapixel, with proportional scaling for higher resolutions) and video generation is priced per second of output. Pricing is transparent and published per model (e.g., Seedream V4 at $0.03/image, Flux Kontext Pro at $0.04/image, Kling 2.5 Turbo Pro at $0.07/second). No minimum commitment, no lock-in, and no hidden fees are claimed. Billing is aggregated at the account level with usage visible in the dashboard.","intents":["Predict inference costs accurately before running large batches of image or video generation","Scale inference workloads without worrying about reserved capacity or minimum spend commitments","Compare cost-effectiveness across different models (e.g., Seedream V4 vs. Nanobanana) for the same task","Optimize budget by selecting lower-cost models for non-critical inference and premium models for high-quality output"],"best_for":["Bootstrapped startups and indie developers with variable inference budgets","Teams building image/video generation features who need predictable per-request costs","Enterprises with usage-based billing requirements for cost allocation to product lines"],"limitations":["Image pricing normalized to 1MP; actual resolution pricing formula for images >1MP is not explicitly documented","Video pricing per second assumes standard frame rates and resolutions; pricing for non-standard formats (e.g., 4K, 60fps) is undocumented","No volume discount tiers are published; enterprise discounts require manual negotiation","Free tier does not exist; all inference incurs charges from the first request","Data egress and storage costs (if applicable) are not documented"],"requires":["FAL.ai account with valid payment method","API key for authentication","Understanding of model-specific pricing (varies by model; no default pricing)"],"input_types":["text prompts for image/video generation","image files for image-to-image tasks (pricing impact unknown)"],"output_types":["Generated images (billed per image)","Generated video (billed per second of output)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_10","uri":"capability://tool.use.integration.javascript.typescript.sdk.for.browser.and.node.js","name":"javascript/typescript sdk for browser and node.js","description":"Provides a JavaScript client library for calling FAL.ai models from browser-based and Node.js applications. The SDK supports both synchronous and asynchronous calls, integrates with modern JavaScript tooling (TypeScript, bundlers), and handles authentication and response parsing. Implementation details (async patterns, error handling, connection pooling) are undocumented but implied by the architecture.","intents":["Call FAL.ai models from React, Vue, or other frontend frameworks without CORS issues","Build Node.js backend services that integrate FAL.ai inference","Use TypeScript for type-safe inference calls with auto-completion","Stream inference results to browsers in real-time via WebSocket"],"best_for":["Frontend developers building AI-powered web applications","Full-stack teams using Node.js for backend services","Teams using TypeScript for type safety and developer experience"],"limitations":["JavaScript SDK details (async patterns, error handling, connection pooling) are undocumented","No documented support for browser-specific features (service workers, IndexedDB caching)","CORS handling and authentication flow for browser-based calls are not documented","SDK version numbers and compatibility guarantees are not published","No built-in support for request deduplication or caching"],"requires":["Node.js 14+ (for Node.js applications) OR modern browser with ES6 support (for frontend)","JavaScript SDK (installable via npm)","FAL.ai API key"],"input_types":["JavaScript objects with model-specific arguments"],"output_types":["JavaScript objects with model-specific results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_11","uri":"capability://tool.use.integration.curl.and.http.api.for.language.agnostic.access","name":"curl and http api for language-agnostic access","description":"Exposes all FAL.ai models via standard HTTP endpoints (specific URLs and methods are undocumented) that can be called with cURL or any HTTP client. This enables integration with languages and tools not supported by official SDKs (Go, Rust, Java, shell scripts, etc.). Authentication is via API key (header format undocumented), and requests/responses are JSON-based.","intents":["Integrate FAL.ai inference into applications written in languages without official SDKs","Call FAL.ai models from shell scripts, Makefile targets, or CI/CD pipelines","Debug API behavior and test models without writing code","Build custom client libraries for unsupported languages"],"best_for":["Developers using languages without official FAL.ai SDKs (Go, Rust, Java, C#, etc.)","DevOps engineers integrating FAL.ai into CI/CD pipelines and automation scripts","Teams building custom client libraries for specific use cases"],"limitations":["HTTP endpoint URLs and request/response schemas are not documented; developers must infer from examples or reverse-engineer from SDK source code","Authentication header format is not documented; developers must infer from examples","Error response format and HTTP status codes are not documented","No built-in support for streaming or WebSocket via cURL; developers must use specialized tools (curl with --raw, jq, etc.)","Rate limiting and retry behavior are not documented"],"requires":["cURL or HTTP client library","FAL.ai API key","Knowledge of HTTP methods and JSON"],"input_types":["JSON request bodies with model-specific arguments"],"output_types":["JSON response bodies with model-specific results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_12","uri":"capability://data.processing.analysis.file.storage.and.signed.url.generation.for.outputs","name":"file storage and signed url generation for outputs","description":"Automatically stores inference outputs (generated images, videos, audio files) in FAL.ai's file storage and returns signed URLs for retrieval. Signed URLs are time-limited and can be shared with external parties without exposing API keys. This eliminates the need for developers to manage file storage infrastructure and enables efficient distribution of large outputs.","intents":["Store generated images and videos without managing S3 buckets or other cloud storage","Share inference results with users via time-limited signed URLs","Retrieve outputs from long-running async jobs without polling for results","Build user-facing features that display generated content without exposing API keys"],"best_for":["Applications generating images, videos, or audio that need to be stored and shared","Teams without dedicated DevOps expertise to manage cloud storage","Platforms with user-generated content that needs to be stored and distributed"],"limitations":["File retention period is undocumented; outputs may be deleted after a certain time","Signed URL expiration time is undocumented; developers must infer from examples","No documented support for custom storage backends (S3, GCS, etc.)","No built-in support for file encryption or access control beyond signed URLs","Storage pricing is not documented; it may be included in inference costs or billed separately"],"requires":["FAL.ai account with file storage enabled","Ability to handle signed URLs and time-limited access"],"input_types":["Inference outputs (images, videos, audio, etc.)"],"output_types":["Signed URLs with time-limited access","File metadata (size, format, etc.)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_2","uri":"capability://code.generation.editing.custom.model.deployment.with.fal.app.framework","name":"custom model deployment with fal.app framework","description":"Provides a Python class-based framework (`fal.App`) that allows developers to define custom inference endpoints by declaring a `setup()` method for initialization (runs once per runner) and `@fal.endpoint()` decorated request handlers. Hardware is declared inline (e.g., `machine_type = \"GPU-H100\"`) alongside code, and the framework automatically provisions, scales, and manages the underlying GPU infrastructure. Deployed models get auto-generated playground UIs and are accessible via the same unified API as pre-built models.","intents":["Deploy custom fine-tuned models or proprietary inference logic without writing Dockerfile or Kubernetes manifests","Declare hardware requirements (GPU type, VRAM) as code alongside the inference logic","Test custom models in an auto-generated web UI before integrating them into applications","Scale custom inference endpoints from zero to thousands of concurrent requests without manual infrastructure management"],"best_for":["ML teams with custom models (fine-tuned LLMs, domain-specific vision models) who want serverless deployment","Researchers prototyping new inference architectures without DevOps overhead","Startups building proprietary AI features that require custom inference logic"],"limitations":["Python-only framework; no support for Go, Rust, or other languages","Setup method runs once per runner, not per request; stateful initialization may cause issues if runners are recycled","Hardware declaration is limited to predefined machine types (H100, H200, A100, B200); custom GPU configurations are not supported","No documented support for multi-GPU inference or distributed training","Deployment pipeline details (build time, cold start for custom models) are undocumented"],"requires":["Python 3.7+","fal_client library with fal.App class","FAL.ai account with deployment permissions","Understanding of async/await patterns for request handlers"],"input_types":["Python function arguments (type hints supported but validation details unknown)","File uploads (format support undocumented)"],"output_types":["JSON-serializable Python objects","File URLs (stored in FAL.ai file storage)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_3","uri":"capability://automation.workflow.hourly.gpu.compute.rental.for.custom.workloads","name":"hourly gpu compute rental for custom workloads","description":"Offers direct access to GPU instances (H100, H200, A100, B200) billed hourly, enabling developers to run custom inference, training, or batch processing workloads without deploying through the fal.App framework. Instances are provisioned on-demand with SSH access, allowing arbitrary code execution. Pricing is transparent and published per GPU type (e.g., H100 at $1.89/hour, A100 at $0.99/hour), with no minimum commitment. This complements the serverless model API for use cases requiring long-running or stateful compute.","intents":["Run long-running inference jobs (e.g., batch processing 10,000 images) more cost-effectively than per-request serverless pricing","Fine-tune large language models or vision models on custom datasets","Execute arbitrary code (data preprocessing, model evaluation, hyperparameter tuning) on GPU hardware","Prototype inference architectures before packaging them as fal.App endpoints"],"best_for":["ML teams running batch inference or training jobs with predictable duration","Researchers needing GPU access without long-term cloud commitments","Startups optimizing costs for high-volume inference by switching from per-request to hourly billing"],"limitations":["Hourly billing means short jobs (e.g., 5-minute inference) are inefficient compared to serverless per-request pricing","No auto-scaling; developers must manually provision and manage instance count","SSH access requires managing SSH keys and security; no built-in isolation between workloads","No documented support for multi-node distributed training or inference","Idle time is billed at full hourly rate; no pause/resume functionality documented"],"requires":["FAL.ai account with compute access enabled","SSH client and SSH key pair for instance access","Understanding of Linux command line and GPU setup (CUDA, cuDNN, etc.)"],"input_types":["Custom code (Python, bash scripts, etc.)","Data files (uploaded via SSH or downloaded from external sources)"],"output_types":["Arbitrary files (logs, model checkpoints, results) accessible via SSH or downloaded to local machine"],"categories":["automation-workflow","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_4","uri":"capability://tool.use.integration.multi.model.marketplace.with.1.000.pre.built.models","name":"multi-model marketplace with 1,000+ pre-built models","description":"Aggregates 1,000+ open-source and proprietary models (Stable Diffusion, Flux, Whisper, Qwen, Kling, Veo, etc.) in a searchable marketplace accessible via a single unified API. Each model is pre-optimized for FAL.ai's infrastructure, with published pricing, input/output specifications, and example code. Models span image generation, video generation, audio processing, 3D generation, and language tasks. The marketplace is continuously updated with new community models, eliminating the need for developers to source, optimize, and host models independently.","intents":["Discover and compare models for a specific task (e.g., image generation) without evaluating dozens of GitHub repositories","Switch between models (e.g., Seedream V4 to Flux Kontext Pro) with a single parameter change, without re-implementing inference logic","Access cutting-edge community models (e.g., Kling 2.5 Turbo Pro, Veo) immediately after release without waiting for official API support","Evaluate model quality and cost trade-offs by running the same prompt across multiple models"],"best_for":["Developers building AI features who want to experiment with multiple models without infrastructure overhead","Non-technical founders prototyping AI products who need access to state-of-the-art models without ML expertise","Teams evaluating models for production use who need side-by-side comparison capabilities"],"limitations":["Model availability depends on FAL.ai's curation and licensing agreements; not all open-source models are available","Model versions are not explicitly versioned; updates to models may change output quality or behavior without notice","No model performance benchmarks (latency, quality) are published; developers must test models individually","Model documentation varies in quality; some models lack detailed input/output specifications","No built-in model recommendation system; developers must manually select models based on task description"],"requires":["FAL.ai account with API access","Knowledge of which model to use for a given task (marketplace search helps but is not AI-powered)"],"input_types":["Model ID (e.g., \"fal-ai/flux-pro\")","Model-specific arguments (text prompts, images, configuration parameters)"],"output_types":["Model-specific outputs (images, videos, text, audio, 3D models, etc.)"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_5","uri":"capability://automation.workflow.asynchronous.job.queue.with.webhook.callbacks","name":"asynchronous job queue with webhook callbacks","description":"Supports asynchronous inference via a queue-based execution model where requests are submitted without blocking, and results are delivered via webhook callbacks to a developer-specified URL. This enables long-running inference (e.g., video generation, batch processing) without maintaining persistent connections. Job status can be polled via the API, and results are stored in FAL.ai's file storage with signed URLs for retrieval.","intents":["Submit batch inference jobs (e.g., 1,000 image generations) without blocking the application","Integrate FAL.ai inference into event-driven architectures (e.g., trigger inference when a user uploads an image)","Build user-facing features that show inference progress and notify users when results are ready","Decouple inference latency from application response time by processing requests asynchronously"],"best_for":["Web applications and APIs that need to return responses quickly without waiting for inference completion","Batch processing pipelines that submit hundreds or thousands of inference jobs","Event-driven architectures (serverless functions, message queues) that trigger inference asynchronously"],"limitations":["Webhook callback payload structure and retry logic are undocumented; developers must infer behavior from examples","No built-in support for webhook signature verification; developers must implement security checks independently","Job retention period is undocumented; results may be deleted after a certain time","No built-in support for job cancellation or timeout configuration","Polling API for job status is not documented; developers must infer endpoint and response format"],"requires":["Public webhook URL accessible from FAL.ai infrastructure","Ability to handle HTTP POST requests with JSON payloads","Understanding of asynchronous job patterns and eventual consistency"],"input_types":["Same as synchronous API (text prompts, images, etc.)"],"output_types":["Webhook callback with job status and result URLs","Signed URLs for file retrieval (format and expiration undocumented)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_6","uri":"capability://tool.use.integration.real.time.streaming.inference.with.websocket.support","name":"real-time streaming inference with websocket support","description":"Supports streaming responses for models that generate output incrementally (e.g., text generation, audio synthesis) via WebSocket connections. Clients establish a persistent connection and receive partial results as they are generated, enabling real-time user interfaces and low-latency streaming applications. Implementation details (message format, frame structure, error handling) are undocumented but implied by the architecture.","intents":["Build chat interfaces that display LLM responses token-by-token as they are generated","Stream audio output from speech synthesis models in real-time","Implement real-time video generation or image-to-image transformation with progressive output","Reduce perceived latency in user-facing applications by showing partial results immediately"],"best_for":["Web and mobile applications requiring real-time user feedback during inference","Chat applications and conversational AI interfaces","Live streaming or real-time content generation features"],"limitations":["WebSocket implementation details are undocumented; developers must infer message format and error handling from examples","No documented support for connection pooling or multiplexing multiple requests over a single WebSocket","Streaming is not supported for all models; model-specific streaming support is undocumented","No documented backpressure or flow control mechanism; clients may receive data faster than they can process","Connection timeout and retry behavior are undocumented"],"requires":["WebSocket client library (e.g., ws for Node.js, websockets for Python)","Understanding of WebSocket protocol and message framing","Ability to handle partial/incomplete results and reassemble them into final output"],"input_types":["Same as synchronous API (text prompts, images, etc.)"],"output_types":["Streaming JSON messages with partial results","Final result message with complete output"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_7","uri":"capability://tool.use.integration.sandbox.ui.with.side.by.side.model.comparison","name":"sandbox ui with side-by-side model comparison","description":"Provides an auto-generated web interface for each deployed model (both pre-built and custom fal.App endpoints) that allows developers and non-technical users to test models interactively. The UI includes input fields for model parameters, output visualization (images, videos, text), and side-by-side comparison mode for running the same prompt across multiple models simultaneously. This enables rapid experimentation without writing code.","intents":["Test models interactively before integrating them into applications","Compare output quality and cost across different models for the same task","Share model demos with stakeholders and non-technical team members","Prototype AI features and gather feedback before building production integrations"],"best_for":["Developers and product managers evaluating models for production use","Non-technical founders and designers prototyping AI features","Teams gathering stakeholder feedback on model outputs before committing to a specific model"],"limitations":["Sandbox UI is auto-generated and may not expose all model parameters or advanced options","No built-in support for batch testing or parameterized experiments","Comparison results are not persisted; developers must manually document side-by-side comparisons","No built-in cost calculator; developers must manually calculate costs based on published pricing","UI customization is not supported; all models use the same generic interface"],"requires":["FAL.ai account with model access","Web browser with JavaScript enabled"],"input_types":["Model-specific parameters (text, images, configuration options)"],"output_types":["Model outputs rendered in the browser (images, videos, text, etc.)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_8","uri":"capability://safety.moderation.enterprise.security.and.compliance.features","name":"enterprise security and compliance features","description":"Provides SOC 2 Type II compliance certification, Single Sign-On (SSO) integration for team access control, private endpoints for custom models (isolated from public API), and enterprise procurement readiness (e.g., custom contracts, volume licensing). Data retention policies are documented but not publicly detailed. These features enable enterprise adoption and compliance with security and regulatory requirements.","intents":["Deploy AI inference in enterprise environments with SOC 2 compliance requirements","Manage team access to models and compute resources via SSO and role-based access control","Isolate custom models and sensitive inference workloads on private endpoints","Negotiate custom contracts and volume discounts for large-scale deployments"],"best_for":["Enterprise teams deploying AI in regulated industries (healthcare, finance, legal)","Organizations with existing SSO infrastructure (Okta, Azure AD, etc.)","Teams handling sensitive data that require isolated inference endpoints"],"limitations":["SOC 2 Type II certification is claimed but audit details and scope are not publicly documented","SSO integration details (supported providers, provisioning flow) are undocumented","Private endpoint isolation guarantees and multi-tenancy safeguards are not documented","Data retention policies exist but are not publicly detailed; developers must contact sales for specifics","No documented support for data residency requirements or geographic isolation"],"requires":["Enterprise FAL.ai account","SSO provider (Okta, Azure AD, Google Workspace, etc.) for team access","Contact with FAL.ai sales for custom contracts and private endpoints"],"input_types":["Same as standard API"],"output_types":["Same as standard API"],"categories":["safety-moderation","deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__cap_9","uri":"capability://tool.use.integration.python.sdk.with.async.await.support","name":"python sdk with async/await support","description":"Provides a Python client library (`fal_client`) with a simple `subscribe()` method for synchronous calls and async/await support for non-blocking inference. The SDK abstracts HTTP details and handles authentication, error handling, and response parsing. It integrates with Python's asyncio event loop, enabling efficient concurrent inference in async applications. The SDK is available on PyPI and can be installed via pip.","intents":["Call FAL.ai models from Python applications with minimal boilerplate","Run multiple inference requests concurrently without blocking the event loop","Integrate FAL.ai inference into async web frameworks (FastAPI, Starlette, aiohttp)","Handle errors and retries transparently without manual HTTP error handling"],"best_for":["Python developers building AI applications","Teams using async web frameworks (FastAPI, Starlette) that need non-blocking inference","Data science teams prototyping inference pipelines in Jupyter notebooks"],"limitations":["Python-only; no support for other languages (Go, Rust, Java, etc.) in the official SDK","Async support details (connection pooling, timeout handling, retry logic) are undocumented","Error handling and retry behavior are not documented; developers must infer behavior from examples","SDK version numbers and compatibility guarantees are not published","No built-in support for batch inference or request deduplication"],"requires":["Python 3.7+","fal_client library (installable via pip)","FAL.ai API key"],"input_types":["Python dictionaries with model-specific arguments"],"output_types":["Python dictionaries with model-specific results"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fal-ai__headline","uri":"capability://tool.use.integration.serverless.inference.api.for.ai.models","name":"serverless inference api for ai models","description":"FAL.ai is a serverless inference API that provides fast access to a wide range of open-source AI models, including Stable Diffusion and Whisper, with sub-second cold starts and pay-per-use pricing, making it ideal for developers seeking efficient AI integration.","intents":["best AI inference API","AI model API for image and text generation","serverless API for AI models","fast AI model access API","pay-per-use AI model service"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"moderate","permissions":["FAL.ai API key (obtained from account dashboard)","Python 3.7+ with fal_client library OR JavaScript SDK OR cURL for HTTP calls","Network connectivity to FAL.ai global endpoints (specific regions undocumented)","FAL.ai account with valid payment method","API key for authentication","Understanding of model-specific pricing (varies by model; 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all inference incurs charges from the first request","Data egress and storage costs (if applicable) are not documented","JavaScript SDK details (async patterns, error handling, connection pooling) are undocumented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"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=fal-ai","compare_url":"https://unfragile.ai/compare?artifact=fal-ai"}},"signature":"KRSzDavYesmIHHftzmOtVUikHrdPwKTCj7bgxrHbANdT0FGc5mEZMiSLCEuTMD4IRZRGEUvzYWwkrPtRKn4VAg==","signedAt":"2026-06-20T06:58:25.966Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/fal-ai","artifact":"https://unfragile.ai/fal-ai","verify":"https://unfragile.ai/api/v1/verify?slug=fal-ai","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"}}