{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"cloudflare-workers-ai","slug":"cloudflare-workers-ai","name":"Cloudflare Workers AI","type":"platform","url":"https://ai.cloudflare.com","page_url":"https://unfragile.ai/cloudflare-workers-ai","categories":["deployment-infra","rag-knowledge"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"cloudflare-workers-ai__cap_0","uri":"capability://text.generation.language.edge.distributed.llm.inference.with.sub.100ms.latency","name":"edge-distributed llm inference with sub-100ms latency","description":"Executes LLM inference (Llama 3, Gemma 3, Mistral) across Cloudflare's 190+ global edge locations, routing requests to the nearest datacenter for sub-100ms response times. Uses Workers compute runtime paired with optimized model serving infrastructure, eliminating centralized API bottlenecks. Supports streaming responses via WebSocket for real-time token delivery.","intents":["I need to run LLM inference with minimal latency for end-users globally","I want to avoid sending user data to centralized cloud LLM APIs for privacy","I need to serve LLM responses in <100ms for interactive applications"],"best_for":["developers building low-latency AI applications with global user bases","teams requiring data residency compliance (inference stays on edge)","builders integrating LLMs into real-time chat or autocomplete features"],"limitations":["Model selection is limited to Cloudflare's curated catalog (Llama 3, Gemma 3, Mistral variants); no custom model deployment","Context window and max token limits not publicly documented; likely constrained vs cloud LLM APIs","Streaming latency depends on WebSocket connection stability; no fallback to polling documented"],"requires":["Cloudflare account with Workers enabled","TypeScript/Node.js SDK (@cloudflare/agents or direct Workers API)","API authentication credentials (format unspecified in documentation)"],"input_types":["text prompts","structured JSON (via function calling)","multi-turn conversation history"],"output_types":["text (streamed or buffered)","structured JSON (via tool calling)","token-level streaming events"],"categories":["text-generation-language","edge-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_1","uri":"capability://tool.use.integration.tool.calling.with.schema.based.function.registry.and.multi.provider.fallback","name":"tool-calling with schema-based function registry and multi-provider fallback","description":"Enables LLMs to invoke external tools and APIs through a declarative schema registry, with automatic model-specific formatting (OpenAI function_calling, Anthropic tool_use, etc.). Supports synchronous tool execution, multi-step reasoning chains, and model fallback via AI Gateway when primary model fails. Built on Workers compute for stateless execution and Durable Objects for multi-turn state persistence.","intents":["I want my LLM agent to call external APIs and databases dynamically based on user intent","I need reliable tool calling that works across multiple LLM providers without rewriting schemas","I want to build agents that can recover from tool failures by switching models automatically"],"best_for":["developers building autonomous agents that interact with external systems","teams deploying multi-model LLM applications with provider fallback requirements","builders creating chatbots that need real-time data access (weather, stock prices, databases)"],"limitations":["Tool execution is synchronous only; no built-in support for parallel tool invocation or async tool chains","Schema validation and formatting overhead adds latency per tool call (exact overhead not documented)","No built-in retry logic for failed tool calls; developers must implement custom retry handlers","Tool registry is in-memory per Workers instance; no persistent tool catalog across deployments"],"requires":["TypeScript SDK (@cloudflare/agents)","Tool schemas defined as JSON Schema or TypeScript interfaces","Cloudflare Workers environment with Durable Objects enabled for multi-turn state"],"input_types":["natural language prompts","structured tool schemas (JSON Schema)","conversation history with prior tool calls"],"output_types":["tool invocation requests (with arguments)","tool execution results","LLM responses incorporating tool outputs"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_10","uri":"capability://image.visual.image.generation.with.model.selection.and.parameter.control","name":"image generation with model selection and parameter control","description":"Enables agents to generate images using built-in image generation models (specific models not documented). Agents can specify generation parameters (style, size, quality, etc.) and receive generated images as outputs. Images are stored in R2 for persistence and can be returned to users via HTTP or embedded in agent responses.","intents":["I want my agent to generate images based on user descriptions","I need to create visual content programmatically without external image generation APIs","I want to build agents that combine text and image generation for multimedia responses"],"best_for":["developers building creative AI applications (design assistants, content generators)","teams deploying agents that need to generate visual content","builders creating multimedia chatbots with image and text responses"],"limitations":["Available image generation models are not documented; unclear which models are supported","Generation parameters (style, size, quality, guidance scale) are not documented","Image output formats are not specified; unclear if PNG, JPEG, WebP are supported","Generation latency is not documented; unclear if real-time generation is feasible","No documented content policy; unclear what image types are allowed or blocked"],"requires":["Cloudflare Workers with Agents SDK","R2 enabled for image storage (optional, for persistence)","Text prompt describing the desired image"],"input_types":["text prompts","generation parameters (style, size, etc.)","optional seed for reproducibility"],"output_types":["generated image files (format unspecified)","image URLs (if stored in R2)","image metadata (dimensions, generation time)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_11","uri":"capability://data.processing.analysis.embedding.generation.for.semantic.search.and.similarity.matching","name":"embedding generation for semantic search and similarity matching","description":"Provides built-in embedding generation that converts text into vector representations for semantic search and similarity matching. Embeddings are generated using a built-in model (specific model not documented) and can be stored in Vectorize for later retrieval. Supports batch embedding generation for processing multiple texts efficiently.","intents":["I want to generate embeddings for my documents without calling external embedding APIs","I need to find semantically similar documents or queries","I want to build recommendation systems based on text similarity"],"best_for":["developers building semantic search and RAG systems","teams deploying similarity-based recommendation engines","builders creating knowledge-base chatbots that need semantic matching"],"limitations":["Embedding model is not documented; unclear which model is used or its dimensionality","Batch size limits for embedding generation are not documented","Embedding latency is not documented; unclear if real-time embedding is feasible","No documented way to use custom embedding models; only built-in model is available","Similarity metric options are not documented; unclear if cosine, L2, or other metrics are supported"],"requires":["Cloudflare Workers with Agents SDK","Text input (single or batch)","Vectorize enabled for storage (optional)"],"input_types":["text strings","batch of texts (array)","optional metadata (for context)"],"output_types":["embedding vectors (float arrays)","vector IDs (if stored in Vectorize)","batch operation confirmations"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_12","uri":"capability://automation.workflow.serverless.deployment.with.automatic.scaling.and.global.distribution","name":"serverless deployment with automatic scaling and global distribution","description":"Deploys agents as serverless functions on Cloudflare Workers, automatically scaling to handle traffic spikes without manual provisioning. Agents are deployed to 190+ edge locations globally, ensuring low latency for users worldwide. Billing is based on actual usage (requests, compute time) with no minimum fees or reserved capacity. Deployment is triggered via Git push or API, with automatic rollback on errors.","intents":["I want to deploy my agent globally without managing servers or infrastructure","I need my agent to scale automatically during traffic spikes","I want to pay only for what I use without reserved capacity costs"],"best_for":["developers building cost-sensitive AI applications with variable traffic","teams deploying agents globally without infrastructure expertise","builders creating MVPs or prototypes that need rapid deployment"],"limitations":["Cold start latency is not documented; unclear if Workers have noticeable startup delays","Maximum execution time per request is not documented; long-running tasks may timeout","Memory limits per Worker instance are not documented","Pricing structure is not detailed; 'serverless pricing' is mentioned but no per-request or per-compute-unit cost is specified","Rollback mechanism is mentioned but not detailed; unclear how automatic rollback is triggered"],"requires":["Cloudflare account with Workers enabled","Git repository or API access for deployment","TypeScript/Node.js code for agent logic"],"input_types":["agent code (TypeScript)","configuration (wrangler.toml or environment variables)","deployment trigger (Git push or API call)"],"output_types":["deployed agent endpoint (HTTPS URL)","deployment logs and status","usage metrics (requests, compute time)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_13","uri":"capability://data.processing.analysis.object.storage.with.zero.egress.costs.r2","name":"object storage with zero-egress costs (r2)","description":"Provides integrated object storage (R2) for persisting agent outputs, training data, checkpoints, and user uploads. R2 is replicated globally and offers zero egress costs (no charges for downloading data), making it cost-effective for storing large files. Agents can read and write to R2 directly, and files can be served via HTTP or embedded in agent responses.","intents":["I want to store agent outputs and user uploads without egress charges","I need persistent storage for agent checkpoints and training data","I want to serve files from my agent without paying for bandwidth"],"best_for":["developers building agents that generate or process large files","teams deploying agents with high data egress requirements","builders creating file-sharing or document management agents"],"limitations":["R2 pricing structure is not detailed in the provided documentation; storage cost per GB is not specified","Maximum file size limits are not documented","Replication latency is not documented; unclear how quickly files propagate globally","No built-in versioning or lifecycle policies documented; unclear if old versions are automatically deleted","Access control options are not documented; unclear if fine-grained permissions are supported"],"requires":["Cloudflare account with R2 enabled","R2 bucket created and configured","API credentials for R2 (format unspecified)"],"input_types":["files (any format)","metadata (JSON)","access control policies (optional)"],"output_types":["file URLs (HTTPS)","file metadata (size, upload time, etc.)","storage usage metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_2","uri":"capability://memory.knowledge.agent.state.management.with.sql.database.and.client.sync","name":"agent state management with sql database and client sync","description":"Persists agent conversation state, memory, and execution context in a built-in SQL database per agent instance, with automatic client-side state synchronization via WebSocket. Uses Durable Objects as the state coordination layer, ensuring consistency across multiple Workers instances and preventing race conditions in multi-turn conversations. Supports both server-side state (agent reasoning, tool call history) and client-side state (UI context, user preferences).","intents":["I need my agent to remember conversation history and context across multiple requests","I want to build multi-turn agents that maintain state without external databases","I need to sync agent state between server and client for real-time UI updates"],"best_for":["developers building stateful chatbots and conversational agents","teams deploying agents that require conversation persistence without managing external databases","builders creating real-time agent interfaces with WebSocket-based state synchronization"],"limitations":["SQL database schema is opaque; no documented way to query or export agent state directly","State is scoped per agent instance; no built-in support for sharing state across multiple agents","Client state sync relies on WebSocket; no fallback to polling or HTTP long-polling documented","No documented retention policy; unclear how long state persists after agent termination"],"requires":["Cloudflare Workers with Durable Objects enabled","TypeScript SDK (@cloudflare/agents)","WebSocket connection for real-time state sync (or HTTP polling as fallback)"],"input_types":["conversation messages","tool call results","client-side state updates"],"output_types":["persisted conversation history","agent memory snapshots","state sync events (WebSocket)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_3","uri":"capability://tool.use.integration.multi.modal.agent.interfaces.websocket.email.voice","name":"multi-modal agent interfaces (websocket, email, voice)","description":"Enables agents to receive and respond to user input via multiple channels—WebSocket for real-time chat, email for asynchronous communication, and voice for audio-based interaction. Each interface is abstracted through a unified agent API, allowing the same agent logic to serve multiple input modalities without channel-specific code. Voice input is processed via Whisper speech-to-text, and responses can be delivered as text-to-speech audio.","intents":["I want my agent to handle user requests from chat, email, and voice without duplicating logic","I need to build agents that can respond to voice commands and deliver audio responses","I want to offer users multiple ways to interact with my agent (chat, email, voice)"],"best_for":["developers building omnichannel AI assistants","teams deploying agents that need to support voice and email in addition to chat","builders creating accessible AI interfaces for users with different interaction preferences"],"limitations":["Email interface requires polling or webhook integration; no documented push notification mechanism","Voice processing (Whisper) adds latency; no documented SLA for speech-to-text turnaround","Text-to-speech output format and quality not documented; unclear if streaming audio is supported","No built-in rate limiting per channel; developers must implement custom throttling for email/voice"],"requires":["Cloudflare Workers with Agents SDK","WebSocket support for real-time chat (or HTTP polling fallback)","Email integration (SMTP or webhook receiver for inbound email)","Microphone/audio input for voice (browser API or external voice service)"],"input_types":["text (WebSocket chat)","email messages (RFC 5322)","audio (WAV, MP3, or other formats supported by Whisper)"],"output_types":["text (WebSocket response)","email reply (SMTP)","audio (TTS output, format unspecified)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_4","uri":"capability://memory.knowledge.rag.with.automatic.indexing.and.fresh.data.support.ai.search","name":"rag with automatic indexing and fresh data support (ai search)","description":"Provides a built-in RAG pipeline (AI Search) that automatically indexes documents and web content, enabling agents to retrieve relevant context without manual embedding or vector database setup. Supports fresh data by re-indexing on-demand, and integrates with Vectorize for vector storage and semantic search. Agents query the index via natural language, and retrieved documents are injected into the LLM context window automatically.","intents":["I want my agent to answer questions based on my company's documentation without manual embedding setup","I need my agent to access fresh data (web content, recent documents) in real-time","I want to build a knowledge-base chatbot without managing a separate vector database"],"best_for":["developers building knowledge-base chatbots and Q&A agents","teams deploying agents that need to reference company documentation or web content","builders creating customer support agents that require up-to-date information"],"limitations":["Indexing latency not documented; unclear how quickly new documents become searchable","No control over chunking strategy or embedding model; both are opaque and non-configurable","Retrieval ranking algorithm not documented; no way to tune relevance or boost specific documents","No built-in deduplication; duplicate documents in the index may inflate retrieval results","Maximum index size and document count limits not documented"],"requires":["Cloudflare Workers with AI Search enabled","Documents in supported format (HTML, PDF, text, or web URLs)","Vectorize enabled for vector storage (included with Workers AI)"],"input_types":["natural language queries","document URLs or file uploads","structured metadata (optional)"],"output_types":["ranked document chunks with relevance scores","augmented LLM context (injected into prompt)","citation metadata (document source, page number)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_5","uri":"capability://memory.knowledge.vector.storage.with.global.replication.vectorize","name":"vector storage with global replication (vectorize)","description":"Provides a managed vector database (Vectorize) that stores and retrieves embeddings across Cloudflare's global network with automatic replication. Integrates natively with Workers AI for embedding generation and AI Search for RAG. Supports semantic search queries, filtering by metadata, and batch operations. Vectors are replicated globally for low-latency retrieval from any edge location.","intents":["I want to store embeddings globally without managing a separate vector database","I need semantic search on my data with low latency from any geographic region","I want to build recommendation engines or similarity search without external vector stores"],"best_for":["developers building semantic search and recommendation features","teams deploying globally distributed applications that need vector search","builders creating RAG systems that require persistent vector storage"],"limitations":["Vector dimension and similarity metric options not documented; unclear if cosine, L2, or other metrics are supported","Batch operation limits not specified; maximum vectors per batch unknown","Metadata filtering capabilities not documented; unclear if complex queries (AND, OR, NOT) are supported","No documented SLA for replication latency; unclear how quickly vectors propagate globally","Pricing per vector or per query not documented; billing model unclear"],"requires":["Cloudflare Workers with Vectorize enabled","Embeddings from Workers AI or external embedding service","API credentials for Vectorize (format unspecified)"],"input_types":["embedding vectors (float arrays)","metadata (JSON)","query vectors for similarity search"],"output_types":["ranked search results with similarity scores","vector IDs and metadata","batch operation confirmations"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_6","uri":"capability://automation.workflow.inference.caching.and.rate.limiting.via.ai.gateway","name":"inference caching and rate limiting via ai gateway","description":"Provides a proxy layer (AI Gateway) that sits between agents and LLM inference endpoints, implementing request caching, rate limiting, and model fallback. Caches identical prompts to avoid redundant inference calls, applies per-user or per-IP rate limits, and automatically routes requests to fallback models if the primary model is unavailable. Supports observability features (logging, metrics) for monitoring inference usage.","intents":["I want to reduce inference costs by caching repeated prompts","I need to rate-limit users to prevent abuse or control spending","I want my agents to automatically switch models if the primary model fails"],"best_for":["developers building cost-sensitive AI applications with repetitive queries","teams deploying multi-model LLM systems with failover requirements","builders creating public-facing agents that need abuse protection"],"limitations":["Cache key strategy not documented; unclear if caching is based on exact prompt match or semantic similarity","Cache TTL (time-to-live) not configurable or not documented; unclear how long cached results persist","Rate limit granularity options not specified; unclear if limits can be per-user, per-IP, per-API-key, or per-model","Fallback model selection is automatic; no documented way to specify fallback priority or custom fallback logic","No documented cache hit/miss metrics; observability features are mentioned but not detailed"],"requires":["Cloudflare Workers with AI Gateway enabled","Multiple LLM models configured for fallback (or single model with caching)","Rate limit configuration (format unspecified)"],"input_types":["inference requests (prompts, parameters)","rate limit policies (JSON or configuration format unspecified)"],"output_types":["cached or fresh inference responses","rate limit headers (X-RateLimit-Remaining, etc.)","observability metrics (cache hit rate, latency, model used)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_7","uri":"capability://automation.workflow.asynchronous.long.running.agent.workflows","name":"asynchronous long-running agent workflows","description":"Enables agents to execute long-running tasks (hours or days) asynchronously without blocking the user request. Uses Durable Objects to coordinate workflow state, Workers to execute tasks, and R2 for storing intermediate results and checkpoints. Agents can pause, resume, and checkpoint progress, allowing recovery from failures without restarting from the beginning. Supports email or webhook notifications when workflows complete.","intents":["I want my agent to process large datasets or complex tasks without timing out","I need agents to recover from failures and resume work from the last checkpoint","I want to notify users when long-running agent tasks complete"],"best_for":["developers building batch processing agents (data analysis, report generation)","teams deploying agents that perform complex multi-step reasoning over hours","builders creating agents that need fault tolerance and resumable execution"],"limitations":["Checkpoint frequency and granularity are not documented; unclear how often state is saved","Maximum workflow duration not specified; unclear if there are limits on task execution time","Resumption logic is opaque; no documented way to manually trigger workflow resumption","Notification delivery (email, webhook) is not guaranteed; no retry logic documented","No built-in monitoring or progress tracking UI; developers must implement custom dashboards"],"requires":["Cloudflare Workers with Durable Objects and R2 enabled","TypeScript SDK (@cloudflare/agents)","Email or webhook endpoint for completion notifications (optional)"],"input_types":["workflow definition (agent logic with checkpoints)","input data (files, parameters)","checkpoint state (serialized)"],"output_types":["workflow execution logs","checkpoint snapshots (stored in R2)","final results (stored in R2 or returned via webhook)","completion notifications (email or webhook)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_8","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.integration.with.oauth.2.1.scoping","name":"mcp (model context protocol) server integration with oauth 2.1 scoping","description":"Enables agents to connect to remote MCP servers (e.g., GitHub, Slack, databases) using the Model Context Protocol standard. Agents authenticate via OAuth 2.1 with granular permission scoping, allowing users to authorize specific capabilities (read-only, write, delete) without exposing full credentials. Includes an MCP playground for testing server connections and a built-in OAuth provider implementation for custom MCP servers.","intents":["I want my agent to access external services (GitHub, Slack, databases) via MCP without hardcoding credentials","I need to let users authorize my agent to access their accounts with granular permissions","I want to test MCP server connections before deploying agents"],"best_for":["developers building agents that integrate with multiple SaaS platforms","teams deploying agents that require user-authorized access to external services","builders creating MCP servers that need OAuth authentication"],"limitations":["MCP server catalog is not documented; unclear which services have pre-built integrations","OAuth 2.1 scope definitions are service-specific; no standardized scope format documented","Token refresh and expiration handling is not documented; unclear how long tokens persist","MCP playground is web-based; no CLI or programmatic testing tools documented","Error handling for failed MCP calls is not documented; no retry logic or fallback mechanisms"],"requires":["Cloudflare Workers with Agents SDK","MCP server endpoint (remote or local)","OAuth 2.1 credentials (client ID, client secret) for the service","User authorization (OAuth consent flow)"],"input_types":["MCP server URL","OAuth 2.1 credentials","requested scopes (permissions)","MCP method calls (JSON-RPC)"],"output_types":["OAuth authorization token","MCP method responses","error messages and logs"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__cap_9","uri":"capability://text.generation.language.speech.to.text.with.whisper.and.text.to.speech.synthesis","name":"speech-to-text with whisper and text-to-speech synthesis","description":"Provides built-in speech processing via OpenAI's Whisper model for converting audio to text, and text-to-speech (TTS) synthesis for converting text responses to audio. Both are integrated into the agent runtime, allowing agents to receive voice input and deliver audio responses without external speech services. Supports multiple audio formats (WAV, MP3, etc.) and languages for Whisper.","intents":["I want my agent to understand voice commands and respond with audio","I need to build voice-first interfaces for accessibility or hands-free interaction","I want to add voice capabilities to my agent without integrating external speech services"],"best_for":["developers building voice-enabled chatbots and assistants","teams creating accessible AI interfaces for users with visual impairments","builders deploying agents for voice-first devices (smart speakers, phones)"],"limitations":["Whisper language support is not documented; unclear which languages are supported","TTS voice options and quality levels are not documented; unclear if multiple voices are available","Audio format support is not fully specified; only WAV and MP3 mentioned in description","Latency for speech-to-text is not documented; unclear if real-time streaming is supported","TTS streaming capability is not documented; unclear if audio is streamed or buffered"],"requires":["Cloudflare Workers with Agents SDK","Audio input (microphone, file upload, or streaming audio)","Audio output capability (speaker, file download, or streaming)"],"input_types":["audio files (WAV, MP3, or other formats)","audio streams (real-time microphone input)","text (for TTS synthesis)"],"output_types":["transcribed text (from Whisper)","audio files (from TTS)","audio streams (real-time TTS output)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"cloudflare-workers-ai__headline","uri":"capability://deployment.infra.ai.model.deployment.platform.at.the.edge","name":"ai model deployment platform at the edge","description":"Cloudflare Workers AI enables developers to run AI models, including LLMs and multi-modal applications, on a serverless global network, optimizing for performance and scalability.","intents":["best AI model deployment platform","AI platform for edge computing","serverless AI model hosting","AI solutions for global scalability","deploying LLMs at the edge"],"best_for":["developers looking for edge deployment","teams needing scalable AI solutions"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["deployment-infra"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Cloudflare account with Workers enabled","TypeScript/Node.js SDK (@cloudflare/agents or direct Workers API)","API authentication credentials (format unspecified in documentation)","TypeScript SDK (@cloudflare/agents)","Tool schemas defined as JSON Schema or TypeScript interfaces","Cloudflare Workers environment with Durable Objects enabled for multi-turn state","Cloudflare Workers with Agents SDK","R2 enabled for image storage (optional, for persistence)","Text prompt describing the desired image","Text input (single or batch)"],"failure_modes":["Model selection is limited to Cloudflare's curated catalog (Llama 3, Gemma 3, Mistral variants); no custom model deployment","Context window and max token limits not publicly documented; likely constrained vs cloud LLM APIs","Streaming latency depends on WebSocket connection stability; no fallback to polling documented","Tool execution is synchronous only; no built-in support for parallel tool invocation or async tool chains","Schema validation and formatting overhead adds latency per tool call (exact overhead not documented)","No built-in retry logic for failed tool calls; developers must implement custom retry handlers","Tool registry is in-memory per Workers instance; no persistent tool catalog across deployments","Available image generation models are not documented; unclear which models are supported","Generation parameters (style, size, quality, guidance scale) are not documented","Image output formats are not specified; unclear if PNG, JPEG, WebP are supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.547Z","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=cloudflare-workers-ai","compare_url":"https://unfragile.ai/compare?artifact=cloudflare-workers-ai"}},"signature":"eY5FOB+jpfQhlXQd94Jhq1qtzt5OeB3+SQYoiivB3zAEucRqQ4FAk/EVEWxTGITN1SOHOGOkV7BJdugmyy59DA==","signedAt":"2026-06-22T03:55:43.412Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cloudflare-workers-ai","artifact":"https://unfragile.ai/cloudflare-workers-ai","verify":"https://unfragile.ai/api/v1/verify?slug=cloudflare-workers-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"}}