GenSX Cloud

Note: GenSX Cloud is currently in developer preview.

GenSX Cloud provides everything you need to ship production-grade agents and workflows:

• Serverless runtime: One command to deploy all of your workflows and agents as REST APIs running on serverless infrastructure optimized for long-running agents and workflows. Support for synchronous and background invocation, streaming, and intermediate status included.
• Cloud storage: build stateful agents and workflows with built-in blob storage, SQL databases, and full-text + vector search namespaces — all provisioned at runtime.
• Tracing and observability: Real-time tracing of all component inputs and outputs, tool calls, and LLM calls within your agents and workflows. Tools to visualize and debug all historic executions.
• Collaboration: Organize agents, workflows, and traces into projects and environments. Search and view traces and to debug historical executions.

Unlike traditional serverless offerings, GenSX Cloud is optimized for long-running workflows. Free tier workflows can run up to 5 minutes and Pro tier workflows can run for up to 60 minutes.

All of this is available on a free tier for individuals and with $20/developer pricing for teams.

Serverless deployments

Serverless deployments allow you to turn your GenSX workflows and agents into APIs with a single command:

• Generated REST APIs: gensx deploy generates a REST API complete with schema and validation for every workflow in your project.
• Long-running: GenSX Cloud is optimized for long-running LLM workloads. Workflows can run up to 5 minutes on the free tier and 60 minutes on the Pro tier.
• Fast cold starts: on the order of ~100ms.

Serverless deployments are billed per-second, with 50,000 seconds included per month in the free tier for individuals.

Projects are deployed with a single CLI command:

$ npx gensx deploy ./src/workflows.ts

✔ Building workflow using Docker
✔ Generating schema
 
✔ Successfully built project
 
ℹ Using project name from gensx.yaml: support-tools
✔ Deploying project to GenSX Cloud (Project: support-tools)
 
✔ Successfully deployed project to GenSX Cloud
 
Dashboard: https://app.gensx.com/gensx/support-tools/default/workflows
 
Available workflows:
- ChatAgent
- TextToSQLWorkflow
- RAGWorkflow
- AnalyzeDiscordWorkflow
 
Project: support-tools

Each workflow is available via both a synchronous and asynchronous API:

// For synchronous and streaming calls:
https://api.gensx.com/org/{orgName}/projects/{projectName}/environments/{environmentName}/workflows/{workflowName}
// For running workflows async in the background
https://api.gensx.com/org/{orgName}/projects/{projectName}/environments/{environmentName}/workflows/{workflowName}/start

For more details see the full serverless deployments reference.

Cloud storage

GenSX Cloud includes runtime-provisioned storage to build stateful agents and workflows:

• Blob storage: Store and retrieve JSON and binary data for things like conversation history, agent memory, and audio and image generation.
• SQL databases: Runtime provisioned databases for scenarios like text-to-SQL.
• Full-text + vector search: Store and query vector embeddings for semantic search and retrieval augmented generation (RAG).

State can be long-lived and shared across workflows and agents, or it can be provisioned ephemerally on a per-request basis.

Blob storage

GenSX Cloud provides blob storage for persisting unstructured data like JSON, text, and binary files. With the useBlob hook, you can easily store and retrieve data across workflow executions.

Common scenarios enabled by blob storage include:

• Persistent chat thread history.
• Simple memory implementations.
• Storing generated audio, video, and photo files.

import { useBlob } from "@gensx/storage";
 
// Store and retrieve data with the useBlob hook
const UpdateConversation = gensx.Component(
  "UpdateConversation",
  async ({ userInput, threadId }: UpdateConversationInput) => {
    // Get access to a blob at a specific path
    const blob = useBlob(`chats/${threadId}.json`);
 
    // Load existing data (returns null if it doesn't exist)
    const history = (await blob.getJSON()) ?? [];
 
    // Add new data
    history.push({ role: "user", content: userInput });
 
    // Save updated data
    await blob.putJSON(history);
 
    return "Data stored successfully";
  },
);

Blob storage automatically adapts between local development (using filesystem) and cloud deployment with zero configuration changes.

For more details see the full storage components reference.

SQL databases

GenSX Cloud provides SQLite-compatible databases powered by Turso , enabling structured data storage with several properties important to agentic workloads:

• Millisecond provisioning: Databases are created on-demand in milliseconds, making them perfect for ephemeral workloads like parsing and querying user-uploaded CSVs or creating per-agent structured data stores.
• Strong consistency: All operations are linearizable, maintaining an ordered history, with writes fully serialized and subsequent writes awaiting transaction completion.
• Zero configuration: Like all GenSX storage components, databases work identically in both development and production.
• Local development: Uses libsql locally to enable a fast, isolated development loop without external dependencies.

import { useDatabase } from "@gensx/storage";
 
// Access a database with the useDatabase hook
const QueryTeamStats = gensx.Component(
  "QueryTeamStats",
  async ({ team }: QueryTeamStatsInput) => {
    // Get access to a database (created on first use)
    const db = await useDatabase("baseball");
 
    // Execute SQL queries directly
    const result = await db.execute("SELECT * FROM players WHERE team = ?", [
      team,
    ]);
 
    return result.rows; // Returns the query results
  },
);

For more details see the full storage components reference.

Full-text and vector search

GenSX Cloud provides vector and full-text search capabilities powered by turbopuffer , enabling semantic search and retrieval augmented generation (RAG) with minimal setup:

• Vector search: Store and query high-dimensional vectors for semantic similarity search with millisecond-level latency, perfect for RAG applications and finding content based on meaning rather than exact matches.
• Full-text search: Built-in BM25 search engine for string and string array fields, enabling traditional keyword search with low latency.
• Hybrid search: Combine vector similarity with full-text BM25 search to get both semantically relevant results and exact keyword matches in a single query.
• Rich filtering: Apply metadata filters to narrow down search results based on categories, timestamps, or any custom attributes, enhancing precision and relevance.

import { useNamespace } from "@gensx/storage";
import { embed } from "@gensx/vercel-ai";
import { openai } from "@ai-sdk/openai";
 
// Perform semantic search with the useNamespace hook
const SearchDocuments = gensx.Component(
  "SearchDocuments",
  async ({ query }) => {
    // Get access to a vector search namespace
    const namespace = await useNamespace("documents");
 
    // Generate an embedding for the query
    const { embedding } = await embed({
      model: openai.embedding("text-embedding-3-small"),
      value: query,
    });
 
    // Search for similar documents
    const results = await namespace.query({
      vector: embedding,
      topK: 5,
    });
 
    return results.map((r) => r.attributes?.title);
  },
);

Note: Unlike blob storage and SQL databases, vector search doesn’t have a local development implementation. When using useSearch locally, you’ll connect to the cloud service.

For more details see the full storage components reference.

Observability

GenSX Cloud provides comprehensive tracing and observability for all your workflows and agents.

• Complete execution traces: Every workflow execution generates a detailed trace that captures the entire flow from start to finish, allowing you to understand exactly what happened during execution.

• Comprehensive component visibility: Each component in your workflow automatically records its inputs and outputs, including:

  • All LLM calls with full prompts, parameters, and responses
  • Every tool invocation with input arguments and return values
  • All intermediate steps and state changes in your agents and workflows

• Real-time monitoring: Watch your workflows execute step by step in real time, which is especially valuable for debugging long-running agents or complex multi-step workflows.

• Historical execution data: Access and search through all past executions to diagnose issues, analyze performance patterns, and understand user interactions over time.

• Project and environment organization: Traces are automatically organized by project (a collection of related workflows in a codebase) and environment (such as development, staging, or production), making it easy to find relevant executions.

// Traces are automatically captured when workflows are executed
// No additional instrumentation required
const result = await MyWorkflow({ input: "some query" });

The trace viewer provides multiple ways to analyze workflow execution:

• Timeline view: See how long each component took and their sequence of execution
• Component tree: Navigate the hierarchical structure of your workflow
• Input/output inspector: Examine the exact data flowing between components
• Error highlighting: Quickly identify where failures occurred

For more details see the full observability reference.

Local development

GenSX provides a seamless development experience that mirrors the cloud environment, allowing you to build and test your workflows locally before deployment:

Development server

The gensx start command launches a local development server that:

• Compiles your TypeScript workflows on the fly
• Automatically generates schemas for your workflows
• Creates local REST API endpoints identical to the cloud endpoints
• Hot-reloads your code when files change
• Provides the same API shape locally as in production

# Start the development server with a TypeScript file
npx gensx start ./src/workflows.ts

When you start the development server, you’ll see something like this:

🔍 Starting GenSX Dev Server...
ℹ Starting development server...
✔ Compilation completed
✔ Generating schema
 
🚀 GenSX Dev Server running at http://localhost:1337
🧪 Swagger UI available at http://localhost:1337/swagger-ui
 
📋 Available workflows:
- MyWorkflow: http://localhost:1337/workflows/MyWorkflow
 
✅ Server is running. Press Ctrl+C to stop.

Local storage providers

GenSX provides local implementations for most storage providers, enabling development without cloud dependencies:

• BlobProvider: Uses local filesystem storage (.gensx/blobs) for development
• DatabaseProvider: Uses local SQLite databases (.gensx/databases) for development
• SearchProvider: Connects to the cloud vector search service even in development mode

The local APIs mirror the cloud APIs exactly, so code that works locally will work identically when deployed:

// This component works the same locally and in the cloud
const SaveData = gensx.Component(
  "SaveData",
  async ({ key, data }: { key: string; data: any }) => {
    // Blob storage works the same locally (filesystem) and in cloud
    const blob = useBlob(`data/${key}.json`);
    await blob.putJSON(data);
    return null;
  },
);

For more details see the full local development reference.

Projects & environments

GenSX Cloud organizes your workflows and deployments using a flexible structure of projects and environments:

Projects are a collection of related workflows that are deployed together, typically corresponding to a codebase or application. Projects help you organize and manage your AI components as cohesive units.

Projects are defined by the projectName field in your gensx.yaml configuration file at the root of your codebase:

# gensx.yaml
projectName: my-chatbot-app

Environments are sub-groupings within a project that allow you to deploy multiple instances of the same workflows with different configuration. This supports the common development pattern of separating dev, staging, and production environments.

# Deploy to the default environment
npx gensx deploy ./src/workflows.ts
 
# Deploy to a specific environment
npx gensx deploy ./src/workflows.ts --env production

Each environment can have its own configuration and environment variables to match the rest of your deployed infrastructure.

Traces and observability data are also separated by project and environment, making it easier to:

• Distinguish between development testing and production traffic
• Isolate and debug issues specific to a particular environment
• Compare performance or behavior between environments

This organizational structure is designed to be flexible and adaptable, allowing you to customize it to fit with the rest of your development, testing, and deployment lifecycle.

For more details see the full projects and environments reference.

Pricing

GenSX Cloud offers simple, predictable pricing designed to scale with your needs, including a free tier for individuals:

For more details, visit our pricing page or contact us for enterprise needs.

Get started

Ready to build AI agents and workflows with GenSX Cloud? Follow our step-by-step quickstart guide to create and deploy your first project in minutes:

1. Install the GenSX CLI: npm install -g gensx
2. Create a new project: gensx new my-project
3. Run it locally: gensx start src/workflows.ts
4. Deploy to the cloud: gensx deploy src/workflows.ts