{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-genkit-ai--genkit","slug":"genkit-ai--genkit","name":"genkit","type":"framework","url":"https://genkit.dev","page_url":"https://unfragile.ai/genkit-ai--genkit","categories":["frameworks-sdks","deployment-infra"],"tags":["agents","ai","embedders","genkit","llm","multimodal","rag","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-genkit-ai--genkit__cap_0","uri":"capability://tool.use.integration.multi.language.unified.generation.api.with.provider.abstraction","name":"multi-language unified generation api with provider abstraction","description":"Provides a consistent generate() interface across JavaScript/TypeScript, Go, and Python that abstracts away provider-specific APIs (OpenAI, Anthropic, Vertex AI, Ollama, etc.). Uses a Registry pattern to register model providers as plugins, enabling zero-code switching between LLM backends by changing configuration. Each language SDK implements the same semantic interface with native type systems (Zod for JS, native generics for Go/Python) for structured output validation.","intents":["I want to build an AI app that can swap between OpenAI and Vertex AI without rewriting code","I need consistent LLM calling semantics across my JavaScript frontend and Go backend services","I want to test my AI workflow with a local Ollama model before deploying to production Vertex AI"],"best_for":["teams building multi-language AI systems who need provider portability","developers migrating between cloud AI providers (AWS Bedrock, Azure OpenAI, GCP Vertex)","organizations standardizing on a single AI framework across polyglot codebases"],"limitations":["Provider-specific features (vision, function calling nuances) require adapter code in some cases","Streaming responses have different latency profiles across providers; no automatic optimization","Schema validation overhead adds ~5-15ms per generation call due to Zod parsing in JS"],"requires":["Node.js 18+ (JavaScript/TypeScript)","Go 1.19+ (Go SDK)","Python 3.9+ (Python SDK)","API credentials for at least one model provider (OpenAI, Vertex AI, Anthropic, etc.)"],"input_types":["text prompts","multimodal content (text + images for vision models)","structured schemas (Zod/TypeScript types, Go structs, Python dataclasses)"],"output_types":["text responses","structured JSON validated against schema","streaming token sequences","multimodal responses (text + generated images where supported)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_1","uri":"capability://automation.workflow.flow.based.orchestration.for.multi.step.ai.workflows","name":"flow-based orchestration for multi-step ai workflows","description":"Defines a Flow system that chains multiple AI operations (generation, retrieval, tool calls) into observable, deployable workflows using a declarative syntax. Flows are registered in the global Registry and can be invoked as HTTP endpoints, CLI commands, or from other flows. Each flow step is automatically instrumented with OpenTelemetry tracing, capturing inputs, outputs, latency, and errors for debugging and monitoring. Flows support branching, looping, and error handling through native language constructs (async/await in JS, goroutines in Go).","intents":["I want to define a multi-step RAG pipeline (retrieve → rerank → generate) that I can test locally and deploy as an API","I need to see detailed traces of where my AI workflow is spending time and where it's failing","I want to reuse a complex AI workflow across multiple applications without duplicating code"],"best_for":["teams building agent-like systems with multiple sequential AI steps","developers who need production observability for AI pipelines without adding instrumentation code","organizations deploying AI workflows as microservices or serverless functions"],"limitations":["Flows are synchronous by default; parallel execution requires explicit async patterns (Promise.all in JS, goroutine coordination in Go)","No built-in state persistence between flow invocations; requires external database for long-running workflows","Tracing overhead adds ~50-100ms per flow execution due to OpenTelemetry span creation and serialization","Flow composition across language boundaries requires HTTP/gRPC bridges; no direct cross-language flow calls"],"requires":["Genkit SDK for your language (JS/TS, Go, or Python)","OpenTelemetry-compatible trace backend (local dev server included, or external like Jaeger/Datadog)","HTTP server runtime for deploying flows as endpoints (Express.js for JS, net/http for Go, Flask for Python)"],"input_types":["structured input objects matching flow schema","text/string parameters","file paths or binary data (for document processing flows)"],"output_types":["structured JSON responses","streaming text (for long-running generations)","trace metadata (timing, token counts, model calls)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_10","uri":"capability://tool.use.integration.function.calling.with.schema.based.tool.integration","name":"function calling with schema-based tool integration","description":"Enables LLMs to call external tools (functions, APIs, custom actions) through a schema-based function calling mechanism. Developers define tool schemas (input/output types) and register them as actions in the Registry. When a model supports function calling, Genkit automatically converts action schemas to the model's function calling format (OpenAI functions, Anthropic tools, Vertex AI function calling). The framework handles tool invocation, result parsing, and re-prompting the model with results. Supports both single-turn tool calls and multi-turn agentic loops.","intents":["I want my LLM to call external APIs (weather, search, database) to answer user questions","I need to build an agent that can use multiple tools in sequence to solve problems","I want to ensure tool calls are type-safe and validated before execution"],"best_for":["teams building AI agents with tool use capabilities","developers integrating LLMs with external APIs and services","organizations building domain-specific AI assistants with custom tools"],"limitations":["Function calling adds latency (extra model round-trip for tool calls); not suitable for latency-critical applications","Models can hallucinate tool calls (calling non-existent tools or with wrong parameters); requires validation","Tool calling is not supported by all models; fallback to prompt-based tool use required for older models","Complex tool schemas can exceed token budgets when serialized into prompts","No built-in rate limiting or cost control for tool calls; can lead to expensive loops"],"requires":["Genkit SDK for your language","Model provider supporting function calling (OpenAI, Anthropic, Vertex AI, etc.)","Tool implementations (functions, API clients, database queries, etc.)","Schema definitions for tools (Zod for JS, Go structs, Python dataclasses)"],"input_types":["action/tool schemas (Zod, Go structs, Python dataclasses)","tool implementations (functions or API clients)","user prompts requesting tool use"],"output_types":["tool call requests from the model","tool execution results","final model response after tool calls","execution traces with tool call details"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_11","uri":"capability://automation.workflow.deployment.to.serverless.and.containerized.environments","name":"deployment to serverless and containerized environments","description":"Genkit flows can be deployed as HTTP endpoints to serverless platforms (Google Cloud Functions, AWS Lambda, Firebase Functions) or containerized services (Docker, Kubernetes). The framework provides deployment helpers and examples for each platform. Flows are automatically exposed as REST endpoints with OpenAPI documentation. Environment-specific configuration (API keys, model selection) is handled through environment variables or configuration files. Observability (tracing, metrics) is integrated with cloud provider observability services (Google Cloud Trace, CloudWatch, etc.).","intents":["I want to deploy my Genkit flow as a serverless function without writing deployment code","I need to expose my AI workflow as a REST API for frontend consumption","I want my deployed flows to integrate with my cloud provider's observability stack"],"best_for":["teams deploying AI workflows to cloud platforms (GCP, AWS, Azure)","developers building API-driven AI applications","organizations needing scalable, managed AI services"],"limitations":["Cold start latency for serverless (1-5 seconds) can be problematic for latency-sensitive applications","Serverless pricing can be expensive for high-volume AI workloads (per-invocation + compute time)","State management is difficult in serverless; long-running workflows require external state stores","Deployment configuration is platform-specific; moving between cloud providers requires code changes","Observability integration requires cloud provider-specific setup (not automatic)"],"requires":["Genkit SDK for your language","Cloud platform account (GCP, AWS, Azure, etc.)","Deployment tools (gcloud CLI, AWS SAM, Docker, etc.)","Container registry for Docker deployments (optional)"],"input_types":["Genkit flows and actions","deployment configuration (platform, region, resources)","environment variables for API credentials"],"output_types":["deployed HTTP endpoints","OpenAPI documentation","cloud platform integration (logging, tracing, metrics)","deployment artifacts (Docker images, Cloud Functions, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_12","uri":"capability://tool.use.integration.cross.language.interoperability.with.http.and.grpc.bridges","name":"cross-language interoperability with http and grpc bridges","description":"Enables flows and actions defined in one language (e.g., Go) to be called from another language (e.g., JavaScript) through HTTP or gRPC bridges. Flows are exposed as HTTP endpoints with JSON request/response bodies, and schemas are shared via JSON schema format. gRPC support (in development) will provide typed, efficient cross-language calls. This enables polyglot architectures where different services use different languages but share AI workflows.","intents":["I have a Go backend and JavaScript frontend; I want to call the same AI flow from both","I need to share an AI workflow across multiple microservices written in different languages","I want to build a polyglot system where each service uses its optimal language"],"best_for":["organizations with polyglot codebases needing shared AI workflows","teams building microservices architectures with AI components","developers integrating Genkit across multiple language ecosystems"],"limitations":["HTTP bridges add latency (network round-trip, serialization); not suitable for latency-critical calls","gRPC support is in development; HTTP is the current standard","Schema synchronization is manual (JSON schema format); breaking changes require coordination","No built-in authentication for cross-language calls; requires external auth layer","Debugging cross-language calls is harder than in-process calls; requires distributed tracing"],"requires":["Genkit SDKs for both languages (e.g., Go and JavaScript)","HTTP server runtime for exposing flows as endpoints","Network connectivity between services","Optional: gRPC support (in development)"],"input_types":["flow names and input JSON","HTTP requests with JSON bodies","gRPC requests (future)"],"output_types":["HTTP responses with JSON bodies","gRPC responses (future)","OpenAPI documentation for HTTP endpoints"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_2","uri":"capability://data.processing.analysis.schema.based.structured.output.with.cross.language.type.validation","name":"schema-based structured output with cross-language type validation","description":"Enforces strict typing and validation on LLM outputs using language-native schema systems: Zod for JavaScript/TypeScript, Go structs with reflection, and Python dataclasses. Schemas are registered in the Registry and used to validate model responses before returning to the caller. Supports JSON schema generation for OpenAI/Anthropic function calling, enabling models to produce structured outputs that are automatically parsed and validated. Schemas are shared across language boundaries via JSON schema interchange format.","intents":["I want to ensure my LLM always returns valid JSON matching my expected data structure, not arbitrary text","I need to use OpenAI's function calling with strict schema validation that works across my JS frontend and Go backend","I want to define a schema once and use it for validation in multiple languages without rewriting type definitions"],"best_for":["developers building production AI systems that require data integrity (e.g., structured extraction, form filling)","teams using function calling APIs and needing runtime validation beyond the model's schema awareness","organizations with polyglot codebases needing type-safe AI integrations"],"limitations":["Schema validation failures require retry logic; Genkit doesn't auto-retry with schema constraints","Complex nested schemas can exceed token budgets when serialized into prompts for function calling","Zod schema parsing adds ~10-20ms latency per response in JavaScript; Go/Python reflection is faster","Custom validation rules (e.g., 'email must be from company domain') require post-validation code outside the schema system"],"requires":["Zod library (JavaScript/TypeScript) or native type system (Go/Python)","Model provider supporting structured output or function calling (OpenAI, Anthropic, Vertex AI)","Schema definition in language-native format (Zod schema, Go struct, Python dataclass)"],"input_types":["Zod schemas (JS/TS)","Go struct definitions with JSON tags","Python dataclass definitions","JSON schema format for cross-language interchange"],"output_types":["validated JavaScript objects/TypeScript types","Go structs with populated fields","Python dataclass instances","JSON schema representations for API documentation"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_3","uri":"capability://tool.use.integration.plugin.based.extensibility.with.registry.pattern","name":"plugin-based extensibility with registry pattern","description":"Implements a global Registry that acts as a service locator for models, embedders, retrievers, evaluators, and custom actions. Plugins register implementations at startup, and the framework resolves them by name at runtime. Plugins can be first-party (Google AI, Vertex AI, Firebase) or third-party (OpenAI, Anthropic, Ollama, Pinecone, Chroma). Each plugin exports a standard interface (e.g., ModelProvider, EmbedderProvider) that the core framework calls. Plugins can depend on other plugins (e.g., a RAG plugin depends on embedders and retrievers).","intents":["I want to add support for a new LLM provider without modifying Genkit's core code","I need to integrate a custom vector database as a retriever in my RAG pipeline","I want to use Genkit with my company's proprietary AI model served via a custom API"],"best_for":["framework maintainers and ecosystem contributors building new integrations","enterprises with custom AI infrastructure needing Genkit integration","developers building domain-specific AI tools on top of Genkit"],"limitations":["Plugin initialization order matters; circular dependencies between plugins can cause startup failures","No built-in plugin versioning; breaking changes in a plugin can break dependent code","Plugin discovery is manual (explicit imports); no automatic plugin loading from package.json or requirements.txt","Cross-language plugin calls require HTTP/gRPC bridges; no native inter-language plugin communication"],"requires":["Genkit SDK for your language (JS/TS, Go, or Python)","Understanding of the plugin interface for your use case (ModelProvider, EmbedderProvider, etc.)","Package manager for your language (npm for JS, go.mod for Go, pip for Python)"],"input_types":["plugin configuration objects (API keys, endpoints, model names)","provider-specific options (temperature, max_tokens, etc.)"],"output_types":["registered plugin instances available in the global Registry","model/embedder/retriever implementations callable via the standard Genkit APIs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_4","uri":"capability://memory.knowledge.rag.pipeline.with.embedders.retrievers.and.rerankers","name":"rag pipeline with embedders, retrievers, and rerankers","description":"Provides a complete RAG (Retrieval-Augmented Generation) system with pluggable components: embedders (convert text to vectors), retrievers (query vector stores), and rerankers (re-score retrieved documents). Embedders are registered plugins that support multiple providers (Google Vertex AI, OpenAI, Ollama). Retrievers query vector stores (Pinecone, Chroma, Firebase Vector Store, custom implementations) and return ranked documents. Rerankers use cross-encoder models to improve retrieval quality. The framework handles chunking, embedding, storage, and retrieval orchestration; developers compose these into RAG flows.","intents":["I want to build a chatbot that answers questions about my company's documentation using semantic search","I need to index a large document corpus and retrieve relevant passages to augment LLM prompts","I want to improve retrieval quality by reranking results with a cross-encoder before passing to the LLM"],"best_for":["teams building knowledge-based chatbots and Q&A systems","organizations with large document repositories needing semantic search","developers implementing production RAG systems with observability requirements"],"limitations":["Embedding and retrieval latency adds 200-500ms per query depending on vector store size and network","No built-in document chunking strategies; developers must implement or use external libraries","Reranking adds additional latency (50-200ms) and API costs; not always worth the quality improvement","Vector store consistency issues (eventual consistency in cloud stores) can cause stale results","No built-in handling of document updates; requires manual re-indexing when source documents change"],"requires":["Genkit SDK for your language","Embedder provider (Google Vertex AI, OpenAI, Ollama, or custom)","Vector store (Pinecone, Chroma, Firebase Vector Store, or custom retriever implementation)","Optional: reranker model (Vertex AI Reranker, Cohere, or custom)"],"input_types":["text documents (strings or file paths)","query strings for retrieval","document metadata (titles, URLs, timestamps)"],"output_types":["vector embeddings (float arrays)","retrieved documents with similarity scores","reranked documents with cross-encoder scores","augmented prompts combining query + retrieved context"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_5","uri":"capability://text.generation.language.dotprompt.file.based.prompt.management.and.versioning","name":"dotprompt file-based prompt management and versioning","description":"Introduces a .prompt file format (YAML-like syntax) for defining prompts with metadata, input schemas, and output schemas in a version-controllable text format. Dotprompt files are compiled into Flow-like objects that can be invoked via the CLI or SDK. Supports prompt templating with variable substitution, conditional sections, and multi-turn conversation templates. Prompts are registered in the Registry and can be referenced by name, enabling prompt reuse across applications and easy A/B testing by swapping prompt files.","intents":["I want to version control my prompts in Git and track changes over time","I need to quickly iterate on prompt wording without redeploying code","I want to run the same prompt against multiple models to compare outputs"],"best_for":["teams managing many prompts and needing version control / collaboration","prompt engineers who want to iterate without touching code","organizations running prompt experiments and A/B tests"],"limitations":["Dotprompt syntax is Genkit-specific; not compatible with other prompt management tools","No built-in prompt evaluation or quality metrics; requires external evaluation framework","Complex conditional logic in prompts becomes hard to read; better to implement in code","Prompt versioning is manual (Git-based); no built-in rollback or version management"],"requires":["Genkit SDK for your language","Text editor or IDE with YAML syntax highlighting (optional but helpful)","Git or version control system for tracking prompt changes"],"input_types":["dotprompt file definitions (YAML syntax)","input variables matching the prompt's input schema","model selection (which model to use for this prompt)"],"output_types":["compiled Flow objects callable from code","CLI commands for testing prompts","rendered prompts with variables substituted","model responses with metadata (model used, tokens, latency)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_6","uri":"capability://automation.workflow.built.in.observability.with.opentelemetry.tracing.and.metrics","name":"built-in observability with opentelemetry tracing and metrics","description":"Automatically instruments all Genkit operations (generation, retrieval, flow execution) with OpenTelemetry spans, capturing inputs, outputs, latency, token counts, and errors. Traces are sent to a local Developer UI (included in Genkit CLI) or external backends (Jaeger, Datadog, Google Cloud Trace). Metrics include token usage, latency percentiles, and error rates. No manual instrumentation required; tracing is transparent to application code. Traces are queryable and filterable in the Developer UI, enabling debugging and performance analysis.","intents":["I want to see where my AI workflow is spending time and identify bottlenecks","I need to track token usage and costs across my AI operations","I want to debug why a specific flow execution failed without adding logging code"],"best_for":["teams running production AI systems needing observability without instrumentation overhead","developers debugging complex AI workflows with multiple steps","organizations tracking AI costs and optimizing token usage"],"limitations":["Tracing overhead adds ~50-100ms per flow execution; not negligible for latency-sensitive applications","Local Developer UI is for development only; production requires external trace backend (Jaeger, Datadog, etc.)","Trace retention is limited; local storage fills up quickly with high-volume applications","No built-in cost tracking; token counts are captured but cost calculation requires external mapping","Sensitive data (prompts, responses) is captured in traces; requires careful handling in production"],"requires":["Genkit SDK for your language","OpenTelemetry-compatible trace backend (local dev server included, or external like Jaeger/Datadog for production)","Optional: external trace storage for long-term retention"],"input_types":["any Genkit operation (generation, retrieval, flow execution)","custom span attributes (application-specific metadata)"],"output_types":["OpenTelemetry spans with timing, inputs, outputs, errors","metrics (latency, token counts, error rates)","trace visualizations in Developer UI","exportable traces in standard formats (OTLP, Jaeger, Datadog)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_7","uri":"capability://safety.moderation.evaluation.framework.with.built.in.metrics.and.custom.evaluators","name":"evaluation framework with built-in metrics and custom evaluators","description":"Provides a framework for evaluating AI workflows using built-in metrics (BLEU, ROUGE, exact match, semantic similarity) and custom evaluators. Evaluators are registered plugins that take a flow output and return a score or judgment. Supports batch evaluation of flows against test datasets, with results aggregated and visualized. Evaluation runs are traced and stored, enabling comparison across prompt/model/parameter changes. Custom evaluators can use LLMs (e.g., 'does this response answer the question?') or deterministic logic.","intents":["I want to measure the quality of my AI workflow outputs against a test dataset","I need to compare two prompt versions and see which one performs better","I want to set up automated quality checks that run on every prompt change"],"best_for":["teams iterating on prompts and models, needing quantitative feedback","organizations with quality requirements for AI outputs (e.g., customer-facing chatbots)","developers building evaluation pipelines for AI system validation"],"limitations":["Built-in metrics are generic; domain-specific metrics require custom evaluators","LLM-based evaluators add latency and cost; not suitable for real-time evaluation","Evaluation results are only as good as the metric; gaming metrics is possible (e.g., optimizing for BLEU without semantic correctness)","No built-in statistical significance testing; requires external analysis for small datasets","Evaluation datasets must be manually created or sourced; no automatic test data generation"],"requires":["Genkit SDK for your language","Test dataset with expected outputs or evaluation criteria","Optional: LLM provider for LLM-based evaluators"],"input_types":["flow outputs (text, structured data)","expected outputs or reference answers","evaluation criteria (for custom evaluators)"],"output_types":["evaluation scores (0-1 or custom ranges)","aggregated metrics (mean, median, percentiles)","evaluation reports with per-sample results","comparison visualizations (e.g., prompt A vs prompt B)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_8","uri":"capability://automation.workflow.developer.cli.with.local.telemetry.server.and.flow.testing","name":"developer cli with local telemetry server and flow testing","description":"Provides a command-line interface (genkit CLI) for local development, including: starting a local telemetry server that captures traces and metrics, running flows from the command line with JSON input, testing prompts with different models, and generating boilerplate code. The telemetry server exposes a web UI for visualizing traces, metrics, and flow execution history. CLI commands are auto-generated from registered flows and actions, enabling zero-code testing of AI workflows.","intents":["I want to test my flow locally without writing test code","I need to see detailed traces of my flow execution to debug issues","I want to quickly test a prompt against multiple models from the command line"],"best_for":["developers building and debugging AI workflows locally","prompt engineers iterating on prompts without touching code","teams onboarding to Genkit who need quick feedback loops"],"limitations":["CLI is for local development only; not suitable for production automation","Telemetry server is in-memory; traces are lost on restart","No built-in authentication; telemetry UI is accessible to anyone on the local network","CLI commands are auto-generated but not customizable; complex workflows require code"],"requires":["Genkit SDK installed for your language","Node.js 18+ (for the CLI itself, even if using Go/Python SDKs)","Local development environment with API credentials for models"],"input_types":["flow names and input JSON","prompt file paths and model selections","CLI flags for configuration (port, verbosity, etc.)"],"output_types":["flow execution results (JSON)","trace visualizations in web UI","metrics and performance data","generated boilerplate code"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-genkit-ai--genkit__cap_9","uri":"capability://image.visual.multimodal.content.support.with.image.and.video.handling","name":"multimodal content support with image and video handling","description":"Supports multimodal inputs and outputs including text, images, and video. The generation API accepts image inputs (base64, URLs, or file paths) for vision models (GPT-4V, Gemini, Claude 3 Vision). Responses can include generated images where supported by the model. Content is abstracted through a unified Content type that handles serialization across language boundaries. Image processing utilities (resizing, format conversion) are available through plugins.","intents":["I want to build a chatbot that can analyze images and answer questions about them","I need to generate images using DALL-E or Imagen and include them in my AI workflow","I want to process video frames and extract information using a vision model"],"best_for":["teams building vision-based AI applications (image analysis, document processing)","developers creating multimodal chatbots and assistants","organizations processing visual content at scale"],"limitations":["Image encoding/decoding adds latency (~50-200ms depending on image size and format)","Vision model costs are higher than text-only models; image tokens are expensive","Video support is limited; requires frame extraction and processing as separate images","Image quality and size affect model performance; no automatic optimization","Cross-language image serialization requires base64 encoding, increasing payload size"],"requires":["Genkit SDK for your language","Vision model provider (OpenAI GPT-4V, Google Gemini, Anthropic Claude 3 Vision, etc.)","Image input (file path, URL, or base64-encoded data)"],"input_types":["text prompts","images (JPEG, PNG, WebP, GIF)","video frames (as separate images)","mixed text + image content"],"output_types":["text responses analyzing images","generated images (where supported by model)","structured data extracted from images","multimodal responses combining text and images"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+ (JavaScript/TypeScript)","Go 1.19+ (Go SDK)","Python 3.9+ (Python SDK)","API credentials for at least one model provider (OpenAI, Vertex AI, Anthropic, etc.)","Genkit SDK for your language (JS/TS, Go, or Python)","OpenTelemetry-compatible trace backend (local dev server included, or external like Jaeger/Datadog)","HTTP server runtime for deploying flows as endpoints (Express.js for JS, net/http for Go, Flask for Python)","Genkit SDK for your language","Model provider supporting function calling (OpenAI, Anthropic, Vertex AI, etc.)","Tool implementations (functions, API clients, database queries, etc.)"],"failure_modes":["Provider-specific features (vision, function calling nuances) require adapter code in some cases","Streaming responses have different latency profiles across providers; no automatic optimization","Schema validation overhead adds ~5-15ms per generation call due to Zod parsing in JS","Flows are synchronous by default; parallel execution requires explicit async patterns (Promise.all in JS, goroutine coordination in Go)","No built-in state persistence between flow invocations; requires external database for long-running workflows","Tracing overhead adds ~50-100ms per flow execution due to OpenTelemetry span creation and serialization","Flow composition across language boundaries requires HTTP/gRPC bridges; no direct cross-language flow calls","Function calling adds latency (extra model round-trip for tool calls); not suitable for latency-critical applications","Models can hallucinate tool calls (calling non-existent tools or with wrong parameters); requires validation","Tool calling is not supported by all models; fallback to prompt-based tool use required for older models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6125199520552744,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-02T03:23:16Z"},"community":{"stars":5866,"forks":722,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=genkit-ai--genkit","compare_url":"https://unfragile.ai/compare?artifact=genkit-ai--genkit"}},"signature":"nHjhBu/RN0kt6lXhsQ4xDbNN//td/DMgzilEjgnKByg6g1DLNTmQOXWtE/Yp1SXsh35ussdtDkD9WP5q3kkxDQ==","signedAt":"2026-06-20T10:43:47.411Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/genkit-ai--genkit","artifact":"https://unfragile.ai/genkit-ai--genkit","verify":"https://unfragile.ai/api/v1/verify?slug=genkit-ai--genkit","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"}}