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The plugin translates Genkit's model configuration schema (including system prompts, temperature, max tokens, stop sequences) into OpenAI API parameters, handling request/response marshalling and error propagation through Genkit's middleware stack.","intents":["I want to use OpenAI models in my Genkit application without writing custom API integration code","I need to switch between OpenAI and other LLM providers without changing my application logic","I want to configure OpenAI models declaratively through Genkit's config system"],"best_for":["Teams building multi-model AI applications who want provider abstraction","Developers already invested in Firebase Genkit ecosystem","Projects requiring OpenAI models with standardized prompt/config management"],"limitations":["Abstracts away OpenAI-specific features (vision, function calling nuances) that don't map to Genkit's generic model interface","No built-in retry logic or exponential backoff — relies on Genkit's error handling","Latency overhead from abstraction layer adds ~5-15ms per request","Cannot access OpenAI-specific response metadata (usage tokens, finish_reason details) without extending the plugin"],"requires":["Firebase Genkit 0.3.0 or higher","Node.js 18+","OpenAI API key (environment variable OPENAI_API_KEY)","npm or yarn package manager"],"input_types":["text prompts","structured message arrays (role/content pairs)","system prompts","configuration objects (temperature, max_tokens, top_p)"],"output_types":["text completions","structured JSON (when using response_format)","streaming text chunks"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_1","uri":"capability://text.generation.language.streaming.text.generation.with.token.level.control","name":"streaming text generation with token-level control","description":"Enables real-time streaming of OpenAI completions through Genkit's async generator pattern, yielding individual tokens or chunks as they arrive from the API. Supports configuration of streaming behavior (chunk size, timeout) and integrates with Genkit's flow system to pipe streamed output to downstream processors or UI handlers.","intents":["I want to stream OpenAI responses to users in real-time without waiting for full completion","I need to process tokens as they arrive for analytics, filtering, or transformation","I want to build interactive chat interfaces that show live token generation"],"best_for":["Web/mobile applications requiring real-time LLM output","Chat applications and conversational interfaces","Streaming analytics pipelines that process tokens incrementally"],"limitations":["Streaming adds complexity to error handling — partial responses may be sent before failure","No built-in token counting during streaming — requires post-hoc calculation","Network interruptions during stream require client-side reconnection logic","Streaming responses cannot use OpenAI's response_format (JSON mode) reliably"],"requires":["OpenAI API key with streaming support enabled","Genkit flow context (streaming only works within Genkit's async execution model)","Client capable of handling Server-Sent Events (SSE) or similar streaming protocol"],"input_types":["text prompts","message arrays"],"output_types":["async generator yielding text chunks","streaming event objects with metadata"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_2","uri":"capability://data.processing.analysis.embedding.generation.with.vector.output.standardization","name":"embedding generation with vector output standardization","description":"Provides OpenAI embedding models (text-embedding-3-small, text-embedding-3-large) through Genkit's embedder interface, converting text input into dense vectors with standardized output format. The plugin handles batch embedding requests, normalizes vector dimensions, and integrates with Genkit's vector storage and RAG systems for semantic search and retrieval.","intents":["I want to generate embeddings for semantic search without managing OpenAI API details","I need to embed documents and queries in a standardized format compatible with Genkit's RAG system","I want to batch embed multiple texts efficiently while respecting OpenAI rate limits"],"best_for":["Teams building RAG systems with Genkit","Semantic search applications requiring OpenAI embeddings","Multi-model applications needing consistent embedding interfaces"],"limitations":["Batch embedding API not exposed — single requests only, requiring manual batching for large datasets","No built-in caching of embeddings — repeated identical inputs trigger new API calls","Embedding dimension is fixed per model (1536 for text-embedding-3-small, 3072 for large) — no dimension reduction","No support for OpenAI's latest embedding features (e.g., embedding truncation) without plugin extension"],"requires":["OpenAI API key with embedding model access","Genkit 0.3.0+","Text input (strings or arrays of strings)"],"input_types":["single text string","array of text strings"],"output_types":["single vector (Float32Array or number[])","array of vectors with metadata"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_3","uri":"capability://tool.use.integration.multi.model.orchestration.through.genkit.s.model.registry","name":"multi-model orchestration through genkit's model registry","description":"Registers OpenAI models in Genkit's global model registry, enabling dynamic model selection at runtime and composition with other providers' models in the same application. Supports model aliasing (e.g., 'default-gpt4' → 'gpt-4-turbo') and fallback chains where requests can be routed to alternative models if the primary fails.","intents":["I want to use multiple OpenAI models in the same application and switch between them dynamically","I need to implement fallback logic where requests route to GPT-4 first, then GPT-3.5 if unavailable","I want to mix OpenAI models with Anthropic/Gemini models in a single Genkit application"],"best_for":["Multi-model applications requiring provider diversity","Cost-optimization scenarios where model selection varies by request type","Teams building resilient AI systems with fallback strategies"],"limitations":["Model registry is global per Genkit instance — no per-request provider configuration","Fallback chains require explicit configuration; no automatic cost-based routing","Model aliases are static at initialization; dynamic alias remapping requires app restart","No built-in load balancing across model instances or API keys"],"requires":["Genkit 0.3.0+","Multiple OpenAI API keys if using multiple accounts","Declarative model configuration in Genkit config file or code"],"input_types":["model identifier strings","configuration objects specifying model aliases"],"output_types":["model instance references","routing decisions (which model to use)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_4","uri":"capability://automation.workflow.configuration.driven.model.parameter.management","name":"configuration-driven model parameter management","description":"Exposes OpenAI model parameters (temperature, max_tokens, top_p, frequency_penalty, presence_penalty, stop sequences) through Genkit's configuration schema, allowing declarative parameter management without code changes. Parameters can be set at plugin initialization, per-flow, or per-request, with validation and type coercion handled by Genkit's config system.","intents":["I want to tune OpenAI model parameters through configuration files instead of hardcoding them","I need different parameter sets for different flows (e.g., creative writing vs. code generation)","I want to A/B test different temperature/top_p combinations without code deployment"],"best_for":["Teams managing multiple AI workflows with different parameter requirements","Experimentation-heavy projects requiring frequent parameter tuning","Non-technical stakeholders who need to adjust model behavior via config"],"limitations":["Parameter validation is basic — no semantic validation (e.g., temperature + top_p conflicts)","No built-in parameter optimization or auto-tuning","Parameter changes require flow restart; no hot-reload during execution","No parameter history or audit trail for compliance/debugging"],"requires":["Genkit 0.3.0+","Configuration file (JSON, YAML, or TypeScript config object)","Understanding of OpenAI parameter semantics"],"input_types":["configuration objects","JSON/YAML config files"],"output_types":["validated parameter objects","OpenAI API request payloads"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_5","uri":"capability://safety.moderation.error.handling.and.api.failure.recovery","name":"error handling and api failure recovery","description":"Wraps OpenAI API calls with standardized error handling that translates OpenAI-specific errors (rate limits, authentication failures, model unavailability) into Genkit's error contract. Provides hooks for custom retry logic, error logging, and fallback behavior through Genkit's middleware system.","intents":["I want consistent error handling across OpenAI and other LLM providers in my Genkit app","I need to detect rate limits and implement exponential backoff without custom code","I want to log and monitor OpenAI API failures in a standardized way"],"best_for":["Production applications requiring robust error handling","Teams building multi-provider systems needing unified error semantics","Applications with strict SLA requirements"],"limitations":["Retry logic is not built-in — requires implementing Genkit middleware","Rate limit detection relies on HTTP status codes; no proactive rate limit tracking","Error messages are translated to generic Genkit errors, losing OpenAI-specific context","No automatic circuit breaker pattern — requires external implementation"],"requires":["Genkit 0.3.0+","Understanding of Genkit's error handling and middleware patterns"],"input_types":["OpenAI API responses (success and error)"],"output_types":["Genkit error objects","error event logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_6","uri":"capability://automation.workflow.request.response.logging.and.observability.hooks","name":"request/response logging and observability hooks","description":"Integrates with Genkit's observability system to log OpenAI API requests and responses (prompts, completions, token counts, latency) for debugging, monitoring, and cost tracking. Provides hooks for custom logging middleware and integrates with Genkit's tracing system for distributed tracing across multi-step flows.","intents":["I want to track OpenAI API usage and costs across my Genkit application","I need to debug what prompts and responses are being sent to OpenAI","I want to monitor latency and token consumption per flow for performance optimization"],"best_for":["Production applications requiring cost tracking and billing","Teams debugging complex multi-step AI workflows","Organizations with compliance requirements for LLM audit trails"],"limitations":["Logging is opt-in via Genkit's debug mode; no built-in persistent logging to external systems","Token counts are logged post-response; no real-time token counting during streaming","No built-in cost calculation — requires manual mapping of token counts to pricing","Sensitive data (prompts, responses) may be logged in plaintext without explicit redaction"],"requires":["Genkit 0.3.0+","Debug mode enabled or custom logging middleware","Optional: external logging service (e.g., Datadog, CloudLogging)"],"input_types":["OpenAI API requests and responses"],"output_types":["structured log entries","trace spans with metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_7","uri":"capability://code.generation.editing.type.safe.prompt.and.response.handling","name":"type-safe prompt and response handling","description":"Provides TypeScript types and runtime validation for OpenAI model inputs (prompts, message arrays, system prompts) and outputs (completions, structured JSON responses). Integrates with Genkit's schema system to enable compile-time type checking and runtime validation without manual serialization/deserialization.","intents":["I want TypeScript type safety for OpenAI prompts and responses in my Genkit flows","I need to validate that responses match expected JSON schemas before using them","I want IDE autocomplete for OpenAI model configuration and parameters"],"best_for":["TypeScript-first teams building type-safe AI applications","Projects requiring strict input/output validation","Teams using Genkit's schema system for structured data handling"],"limitations":["Type safety is TypeScript-only; JavaScript users get no compile-time checking","Runtime validation adds ~2-5ms overhead per request","Schema validation for complex nested structures can be verbose","No automatic type inference from OpenAI API documentation"],"requires":["TypeScript 4.7+","Genkit 0.3.0+","Understanding of Genkit's schema system (zod or similar)"],"input_types":["TypeScript types","Genkit schema objects"],"output_types":["typed prompt objects","validated response objects"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_8","uri":"capability://image.visual.vision.model.integration.for.image.understanding","name":"vision model integration for image understanding","description":"Exposes OpenAI's vision models (GPT-4V, GPT-4-turbo with vision) through Genkit's model interface, enabling image analysis, OCR, and visual question-answering. Handles image encoding (base64, URLs), multimodal prompt construction, and response parsing within Genkit's flow system.","intents":["I want to analyze images using OpenAI's vision models in my Genkit application","I need to extract text from images (OCR) or answer questions about image content","I want to build multimodal flows combining text and image inputs"],"best_for":["Applications requiring image analysis or OCR","Multimodal AI systems combining text and vision","Teams building document processing or visual search systems"],"limitations":["Image encoding must be handled by application code; plugin doesn't auto-fetch URLs","Large images are automatically downsampled by OpenAI; no control over resolution","Vision model pricing is significantly higher than text-only models","No built-in image preprocessing (resizing, format conversion) — requires external libraries"],"requires":["OpenAI API key with vision model access","Genkit 0.3.0+","Image input as base64 string or URL"],"input_types":["text prompts","image URLs","base64-encoded images"],"output_types":["text descriptions","extracted text (OCR)","structured JSON responses"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-genkitx-openai__cap_9","uri":"capability://tool.use.integration.function.calling.with.structured.tool.invocation","name":"function calling with structured tool invocation","description":"Exposes OpenAI's function calling API through Genkit's tool-use interface, enabling models to request execution of predefined functions with structured arguments. Handles function schema generation, argument validation, and result marshalling within Genkit's agentic flow system.","intents":["I want OpenAI models to call functions in my application with structured arguments","I need to build agents that can use tools (APIs, databases) to accomplish tasks","I want to enforce strict argument validation for function calls"],"best_for":["Agent-based applications requiring tool use","Systems where LLMs need to interact with APIs or databases","Teams building autonomous AI workflows"],"limitations":["Function schema must be manually defined; no automatic schema generation from TypeScript types","No built-in function execution — application must implement the actual function logic","Function calling is not guaranteed; model may refuse to call functions or hallucinate calls","No automatic retry if function execution fails; requires explicit error handling in agent loop"],"requires":["OpenAI API key with function calling support","Genkit 0.3.0+","Function definitions with JSON schema"],"input_types":["function schemas (JSON schema format)","function definitions with descriptions"],"output_types":["function call requests (name + arguments)","function execution results"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"low","permissions":["Firebase Genkit 0.3.0 or higher","Node.js 18+","OpenAI API key (environment variable OPENAI_API_KEY)","npm or yarn package manager","OpenAI API key with streaming support enabled","Genkit flow context (streaming only works within Genkit's async execution model)","Client capable of handling Server-Sent Events (SSE) or similar streaming protocol","OpenAI API key with embedding model access","Genkit 0.3.0+","Text input (strings or arrays of strings)"],"failure_modes":["Abstracts away OpenAI-specific features (vision, function calling nuances) that don't map to Genkit's generic model interface","No built-in retry logic or exponential backoff — relies on Genkit's error handling","Latency overhead from abstraction layer adds ~5-15ms per request","Cannot access OpenAI-specific response metadata (usage tokens, finish_reason details) without extending the plugin","Streaming adds complexity to error handling — partial responses may be sent before failure","No built-in token counting during streaming — requires post-hoc calculation","Network interruptions during stream require client-side reconnection logic","Streaming responses cannot use OpenAI's response_format (JSON mode) reliably","Batch embedding API not exposed — single requests only, requiring manual batching for large datasets","No built-in caching of embeddings — repeated identical inputs trigger new API calls","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2513537208434065,"quality":0.3,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-05-03T14:04:47.472Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":16312,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=genkitx-openai","compare_url":"https://unfragile.ai/compare?artifact=genkitx-openai"}},"signature":"uibsxURHQgOzgBIvT/ytvfmieEC4W14rygGFGG2iQv2qiJcfGtPID2+6IXtYyhcraqicE3trGhpTD1ZWniHlAQ==","signedAt":"2026-06-21T11:58:55.935Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/genkitx-openai","artifact":"https://unfragile.ai/genkitx-openai","verify":"https://unfragile.ai/api/v1/verify?slug=genkitx-openai","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"}}