genkit

Flows are the core primitive: type-safe, observable, deployable AI functions. Manage prompts as versioned .prompt files: The Developer UI runs at and provides: - AI workflow orchestration : Building multi-step AI pipelines with type-safe inputs/outputs - Flow-based APIs : Wrapping LLM calls into deployable HTTP endpoints - Tool calling / agents : Equipping models with custom tools and implementing agentic loops - RAG pipelines : Retrieval-augmented generation with vector databases (Pinecone, pgvector, Firestore, Chroma, etc.) - Multi-agent systems : Coordinating multiple specialized AI agents - Streaming responses : Real-time token-by-token output for chat or long-form content - Firebase/Cloud Run deployment : Deploying AI functions to Google Cloud - Prompt management : Managing prompts as versioned .prompt files with Dotprompt - Flow runner : Execute flows with custom JSON inputs - Trace inspector : Visualize each step (generate, embed, retrieve, tool calls) - Prompt playground : Test prompts interactively - Model tester : Compare outputs across different models - Evaluator : Run evaluation datasets against flows - Always define input/output schemas — Use Zod objects for Dev UI labeled fields and API safety - Use flows for all AI logic — Even simple calls; flows give you tracing and deployment for free - Store API keys in environment variables — Never hardcode; use Firebase...