GenSX ⚡️

GenSX is a simple TypeScript framework for building complex LLM applications. It's a workflow engine designed for building agents, chatbots, and long-running workflows.

Why GenSX?

• 🎯 Pure Functions: Components are pure TypeScript functions that are easily testable, reusable, and sharable
• 🌴 Natural Composition: Building workflows is as simple as composing functions together
• 🔒 Type-safe: Full TypeScript support with no DSLs or special syntax - just standard language features
• 🚀 Built for Scale: Start simple and evolve to complex patterns like agents and reflection without changing your programming model
• 📊 Automatic Tracing: Real-time tracing of all component inputs/outputs, tool calls, and LLM calls making debugging and observability easy
• ☁️ One-Click Deployment: Deploy workflows as REST APIs with a single command, optimized for long-running LLM workloads up to 60 minutes
• 💾 Built-in Storage: Zero-config blob storage, SQL databases, and vector search for building stateful agents and workflows

Check out the documentation to learn more about building LLM applications with GenSX.

Building a workflow

Most LLM frameworks are graph oriented--you express your workflow with nodes, edges, and a global state object. GenSX takes a different approach--you compose your workflow with components, and GenSX handles the execution for you.

Components in GenSX look a lot like functions. You create them by passing in a function and a name to gensx.Component(), a higher order function::

import * as gensx from "@gensx/core";
import { openai } from "@ai-sdk/openai";
import { generateText } from "@gensx/vercel-ai";

// input interface
interface WriteDraftInput {
  research: string[];
  prompt: string;
}

// components are pure functions that are reusable by default
const WriteDraft = gensx.Component(
  "WriteDraft",
  async ({ prompt, research }: WriteDraftInput) => {
    const systemMessage = `You're an expert technical writer.
    Use the information when responding to users: ${research}`;

    const result = await generateText({
      messages: [
        {
          role: "system",
          content: systemMessage,
        },
        {
          role: "user",
          content: `Write a blog post about ${prompt}`,
        },
      ],
      model: openai("gpt-4.1-mini"),
    });

    return result.text;
  },
);

Components can be composed together to create more complex agents and workflows:

import * as gensx from "@gensx/core";
import { OpenAIProvider } from "gensx/openai";
import { Research, WriteDraft, EditDraft } from "./writeBlog";

interface WriteBlogInput {
  title: string;
  description: string;
}

const WriteBlog = gensx.Workflow(
  "WriteBlog",
  async ({ title, description }: WriteBlogInput) => {
    const queries = await GenerateQueries({
      title,
      description,
    });
    const research = await ResearchBlog({ queries });
    const draft = await WriteDraft({ title, context: research });
    const final = await EditDraft({ title, content: draft });
    return final;
  },
);

const result = await WriteBlog({
  title: "How AI broke modern infra",
  description: "Long-running workflows require a new approach to infra",
});

Getting Started

Check out the Quickstart Guide to build your first workflow in just a few minutes.

Examples

This repo contains a number of examples to help you get up and running with GenSX.

To run an example:

cd examples/<example-name>

pnpm install

pnpm start

Basic Examples

| Example | Description | | ------------------------------------------------------ | -------------------------------------------------------------------------------------------------------- | | 🔄 Reflection | Shows how to use a self-reflection pattern with GenSX | | 🦾 Anthropic Examples | Examples showing how to use @gensx/anthropic | | 🧠 OpenAI Examples | Examples showing how to use @gensx/openai | | 🌊 Vercel AI SDK Examples | Examples showing how to use @gensx/vercel-ai |

Full Examples

| Example | Description | | ---------------------------------------------------------- | -------------------------------------------------------------------------------------------- | | 🔍 Hacker News Analyzer | Analyzes HN posts and generates summaries and trends using Paul Graham's writing style | | ✍️ Blog Writer | Generates blogs through an end-to-end workflow including topic research and content creation | | 🔬 Deep Research | Generates a report from a prompt after researching and summarizing a list of research papers | | 💻 Computer Use | Demonstrates how to use the OpenAI computer use tool with GenSX | | 🗄️ Text to SQL | Shows how to use database storage to translate natural language to SQL queries | | 🔎 RAG | Demonstrates retrieval augmented generation using vector search storage | | 💬 Chat Memory | Shows how to build a chat application with persistent chat history using blob storage |

Working with this repo

This monorepo contains GenSX, its related packages, examples, and documentation. You can find more detailed instructions in CONTRIBUTING.md.

Repository Structure

• packages/ - Published packages
• examples/ - Example applications and use cases
• website/ - GenSX website

License

Apache 2.0