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tinyflow

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Basic Information

Tinyflow is a lightweight AI agent solution intended as a developer component rather than an end-user product. It provides tools to embed AI agent orchestration into existing applications by allowing developers to design agent workflows in a front-end UI and execute those workflows on a backend. The project separates concerns: a Web Component based front end for visual workflow design and interaction, and backends for running the designed workflows. The README emphasizes integration into traditional apps, support for multiple frontend frameworks via Web Components, and backend support across languages. The repository includes examples and configuration for installing the frontend UI package and points to an open source Java runtime for executing workflows.

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App URL
https://github.com/tinyflow-ai/tinyflow
Github Repository
https://github.com/tinyflow-ai/tinyflow/blob/main/README.md

App Details

Features
Frontend implemented as a Web Component that supports React, Vue, Angular, Svelte and native HTML/CSS/JS. A distributable UI package is available as @tinyflow-ai/ui with a simple initialization API and methods tinyflow.getData() and tinyflow.getOptions(). Workflows are represented as JSON and the frontend accepts configuration parameters including element, data and provider. Provider configurations currently include large model (llm) and knowledge store options. Backend support is documented for Java (no framework restriction) with a published artifact dev.tinyflow:tinyflow-java-core version 1.0.4. Node.js and Python backends are mentioned as under development. The repo includes architecture and screenshot assets.
Use Cases
Tinyflow helps developers add AI agent orchestration capabilities to existing applications without building a full product from scratch. The Web Component UI enables embedding a visual workflow editor into diverse front-end stacks. Workflow export and runtime APIs let teams persist workflow JSON and retrieve initialization options for programmatic control. A Java runtime is provided to execute workflows on the server side, with Node.js and Python runtimes planned to broaden deployment choices. The separation of UI and backend reduces integration effort, and documentation and visual assets assist developers in getting started and connecting model and knowledge providers.

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