8000 GitHub - jhonroxton/genkit: An open source framework for building AI-powered apps with familiar code-centric patterns. Genkit makes it easy to develop, integrate, and test AI features with observability and evaluations. Genkit works with various models and platforms.
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An open source framework for building AI-powered apps with familiar code-centric patterns. Genkit makes it easy to develop, integrate, and test AI features with observability and evaluations. Genkit works with various models and platforms.

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Genkit is a framework for building AI-powered applications. It provides open source libraries for Node.js and Go, along with tools to help you debug and iterate quickly.

Learn more in our documentation for Node.js and Go.

What can you build with Genkit?

Genkit is a versatile framework, which you can use to build many different types of AI applications. Common use cases include:

  • Intelligent agents: Create agents that understand user requests and perform tasks autonomously, such as personalized travel planning or itinerary generation.

  • Data transformation: Convert unstructured data, like natural language, into structured formats (e.g., objects, SQL queries, tables) for integration into your app or data pipeline.

  • Retrieval-augmented generation: Create apps that provide accurate and contextually relevant responses by grounding generation with your own data sources, such as chatbots or question answering systems.

Who should use Genkit?

Genkit is built for developers seeking to add generative AI to their apps with Node.js or Go, and can run anywhere these runtimes are supported. It's designed around a plugin architecture that can work with any generative model API or vector database, with many integrations already available.

While developed by the Firebase team, Genkit can be used independently of Firebase or Google Cloud services.

Get started

Note

Genkit for Go is in alpha, so we only recommend it for prototyping.

Library key features

  • Unified generation API: Generate text, media, structured objects, and tool calls from any generative model using a single, adaptable API.

  • Vector database support: Add retrieval-augmented generation (RAG) to your apps with simple indexing and retrieval APIs that work across vector database providers.

  • Enhanced prompt engineering: Define rich prompt templates, model configurations, input/output schemas, and tools all within a single, runnable .prompt file.

  • AI workflows: Organize your AI app logic into Flows - functions designed for observability, streaming, integration with Genkit devtools, and easy deployment as API endpoints.

  • Built-in streaming: Stream content from your Genkit API endpoints to your client app to create snappy user experiences.

Development tools

Genkit provides a CLI and a local UI to streamline your AI development workflow.

CLI