AI features for Genkit including models, embedders, tools, retrievers, and prompts

Core functionality for Genkit including actions, flows, tracing, and registry

Genkit main module with Genkit class and reflection server

Jetty HTTP server plugin for Genkit

Spring Boot HTTP server plugin for Genkit

OpenAI-compatible API implementation for Genkit that can be used by multiple providers

Google GenAI (Gemini) model plugin for Genkit using the official google-genai SDK

OpenAI model plugin for Genkit

Weaviate vector database integration for Genkit - indexer and retriever for RAG workflows

Groq model plugin for Genkit

Anthropic Claude model plugin for Genkit

Ollama local model plugin for Genkit

XAI (x.ai / Grok) model plugin for Genkit

DeepSeek model plugin for Genkit

Mistral AI model plugin for Genkit

Cohere model plugin for Genkit

Model Context Protocol (MCP) plugin for Genkit - enables integration with MCP servers and tools

Local file-based vector store for development and testing

Firebase integration for Genkit - Firestore vector search, Cloud Functions, and telemetry

Azure AI Foundry model plugin for Genkit

PostgreSQL with pgvector extension integration for Genkit - indexer and retriever for RAG workflows

Pinecone vector database integration for Genkit - indexer and retriever for RAG workflows

Pre-defined evaluators plugin for Genkit, providing RAGAS-style evaluators for assessing LLM output quality

AWS Bedrock model plugin for Genkit