Open Source App Cloud Marketplace

Webhooks, cron schedules, and app events trigger chains of nodes that fetch, transform, and route data: n8n is a workflow automation platform built around a visual, node-based editor. It ships with 400+ built-in integrations covering databases like Postgres, SaaS tools like Slack and HubSpot, and every major AI provider. When a pre-built node does not exist, the HTTP Request node calls any REST API, and the Code node runs JavaScript or Python inline, so you are never blocked by a missing connector. Workflows execute as directed graphs with branching, loops, error handling, and sub-workflows, and every run is logged for inspection and replay during debugging. It also includes LangChain-based nodes for building AI agents with tool calling and memory. Self-hosting on RepoCloud gives you unlimited workflow executions with no per-task pricing, and all data stays on your instance. Runs on Node.js with SQLite by default; add Postgres and Redis queue mode when you need to scale workers horizontally.

Prototype multi-agent AI systems without writing orchestration code: AutoGen Studio is Microsoft's low-code interface over the AutoGen AgentChat framework. You compose teams of LLM-powered agents in a visual Team Builder, either by drag-and-drop from a component library or by editing the declarative JSON specification directly. Each agent gets a model, a prompt, tools (Python functions), and the team gets termination conditions and an orchestration pattern, sequential or LLM-driven. The Playground runs teams interactively with live message streaming between agents, a visual control-transition graph, tool-call and code-execution tracking, and pause/stop controls, which makes it a practical debugger for agent behavior. Finished teams export as JSON for use in any Python application via the TeamManager class, or serve as an API endpoint. Any OpenAI-compatible model endpoint works, including local servers like Ollama or vLLM. Microsoft labels it a research prototype: use it for prototyping and evaluation, and build production systems on the underlying AutoGen framework.

Drag nodes onto a canvas and ship an LLM app: Flowise is an open-source visual builder for AI agents and LLM applications, written in Node.js on LangChain.js and licensed Apache-2.0. You assemble flows by dragging nodes onto a canvas: models, prompts, memory, vector stores, retrievers, and tools, then wire them together and test in the built-in chat panel. Three builder types cover increasing complexity: Assistant for simple RAG chat over uploaded files, Chatflow for single-agent systems with techniques like rerankers and Graph RAG, and Agentflow for multi-agent orchestration with branching, looping, shared flow state, and human-in-the-loop checkpoints. Over 100 integrations connect data sources, vector databases, and both proprietary and open-source models, plus MCP client and server nodes for standard tool interop. Finished flows are exposed as REST APIs, embedded chat widgets, or via JS and Python SDKs - each flow gets an endpoint the moment it is saved, removing the deployment gap between a working prototype and something your application can call. Execution logs, visual step debugging, and external log streaming trace behavior, while input moderation and rate limiting act as guardrails; RBAC, SSO, and workspaces cover team deployments. Self-hosting keeps prompts, encrypted credentials, and conversation data on your own instance, which matters when flows handle internal documents or customer data - and wiring a model, prompt, memory, and vector store on the canvas replaces the boilerplate a hand-coded LangChain project would need.

Large language models get a polished front end that can run fully offline: Open WebUI is the self-hosted front end of choice. It talks to local model runners, primarily Ollama, and to any OpenAI-compatible API, so LM Studio, vLLM, Groq, Mistral, OpenRouter, and cloud providers all plug into the same chat interface and can be mixed per conversation. RAG is built in: upload files to knowledge bases or reference them in chat with the # command, backed by a choice of nine vector databases (ChromaDB and PGVector officially maintained) and multiple extraction engines including Tika and Docling, with hybrid BM25-plus-vector search and cross-encoder reranking. Web search results from providers like SearXNG, Brave, and Tavily inject directly into conversations. Extensibility comes from Python tools and functions that run inside the chat, a Pipelines plugin framework, and native MCP support. Multi-user features include RBAC, SSO, and group permissions, and the instance itself exposes an OpenAI-compatible API your own apps can call.

An Airtable-style spreadsheet interface directly on PostgreSQL: Teable is an open-source no-code database where every table is a real Postgres table. Unlike tools that store records in a metadata abstraction layer, every Teable table is a real Postgres table with standard column types, so filtering, sorting, and grouping run at database speed, million-row tables answer complex filters in roughly 200 milliseconds without index tuning, and any PostgreSQL-compatible tool - psql, BI dashboards, ETL pipelines - can query the same data directly. The interface offers Grid, Kanban, Gallery, Calendar, and Form views as non-destructive overlays with their own filters and hidden fields, plus 20+ field types, formulas, comments, attachments, batch editing, undo/redo, and edit history. Collaboration is real-time with live cursors and instant sync across views, backed by Redis, and a REST API is auto-generated per table, largely compatible with Airtable API clients - alongside native SQL access for BI tools, analytics pipelines, and your own applications to JOIN and query directly, with no exports, API rate limits, or sync jobs. Global search spans all records, chart plugins handle quick visualization, and CSV and Excel import/export cover migrations. Where Airtable caps paid plans at 100K-500K rows and charges roughly $20 per user per month, a self-hosted Teable instance has neither limit: the Postgres database itself is the export if you ever leave. Built in TypeScript with NestJS, deployed via Docker with PostgreSQL and Redis, and licensed AGPL-3.0.

Any existing relational database becomes a collaborative, Airtable-style smart spreadsheet under NocoDB. It connects to PostgreSQL, MySQL, MariaDB, SQL Server, or SQLite, introspects the schema - tables, relationships, indexes - and renders it as interactive Grid, Gallery, Kanban, Calendar, and Form views without migrating a single row. Your business data stays in your database; NocoDB keeps only its own metadata (view configs, permissions, webhooks) in a separate store. Every connected table automatically gets REST APIs with Swagger documentation, effectively turning legacy databases into modern backends. The spreadsheet layer adds 20+ field types including formulas, lookups, rollups, links, attachments, and currency, plus sorting, filtering, grouping, and multi-field editing. Views can be locked or shared publicly with password protection, role-based access control scopes permissions per user, and webhooks plus CSV, Excel, and Airtable import round out integration. An ERD view visualizes the schema. Built with Node.js and Vue, deployed via Docker, handling millions of rows.

Admin panels, database GUIs, dashboards, approval flows, customer support consoles - Appsmith builds the internal tools your team keeps postponing, on an open-source low-code platform. The UI assembles from 45+ drag-and-drop widgets - tables with server-side pagination and inline editing, charts, forms, lists, buttons - which bind to data through {{ }} JavaScript expressions anywhere in the editor. Datasources cover PostgreSQL, MySQL, MongoDB, MS SQL, Redis, Snowflake, and more, plus any REST or GraphQL API, with SaaS integrations and AI query support for prompt-based steps inside apps. When the widget library falls short, custom widgets are plain JavaScript, HTML, and CSS, and external JS libraries can be imported, which keeps the platform extensible where pure no-code tools hit walls. Git-based version control enables branch-based collaboration, review, and rollback of app definitions. Queries and JS objects hold the business logic layer between datasources and UI. Self-hosted via Docker or Kubernetes, with role-based access control for published apps.

A private ChatGPT built with Next.js: Lobe Chat is the open-source AI chat interface teams self-host instead. Its main advantage is provider breadth: one interface connects to 40+ model providers, including OpenAI, Anthropic Claude, Google Gemini, Mistral, Groq, AWS Bedrock, Azure, and local models served through Ollama, so you can switch models per conversation and compare outputs. It handles multi-modal work: image recognition, image generation, text-to-speech, and speech-to-text. A plugin system based on function calling and the Model Context Protocol (MCP) adds external tools like web search and code execution. Run it in standalone mode as a single container with settings in browser storage, or in database mode with PostgreSQL and S3-compatible storage for persistent history, multi-user auth, and RAG knowledge bases built from uploaded documents with pgvector retrieval. Because tools arrive through function calling and MCP rather than a proprietary plugin format, custom internal tools can be exposed to the assistant with a standard server over STDIO or HTTP. Hundreds of pre-configured assistant roles import from the community marketplace. For teams the cost model matters: provider API keys billed per token typically undercut a ChatGPT Plus seat per person, and self-hosting keeps API keys, uploaded files, embeddings, and conversation history entirely on your own server.

Perplexity's search experience without Perplexity: Vane deploys Perplexica, an open-source AI answer engine built as the self-hosted alternative. Instead of returning a page of links, it reads your question, searches the live web through the SearxNG metasearch engine, and composes a direct answer with cited sources. Retrieval quality comes from embeddings and similarity search: fetched pages are re-ranked against the query so the model answers from the most relevant passages rather than whatever ranked first. Two query modes cover different needs - Normal mode runs a straightforward web search, while Copilot mode generates multiple reformulated queries and actively pulls content from top matches for harder questions. Focus modes specialize retrieval for academic papers, YouTube, Reddit discussions, Wolfram Alpha calculations, or the general web. The answering model is your choice: OpenAI-compatible APIs or fully local LLMs such as Llama 3 and Mixtral through Ollama, which keeps queries entirely on your infrastructure. Because SearxNG pulls live results, answers reflect current information, and no search history is tracked.

Open the page, write a Markdown note, move on - Memos is a lightweight, self-hosted service built for quick capture. Instead of folders, notebooks, and titles, it presents a timeline: open the page, write a Markdown note, and move on. Notes support headings, code blocks with syntax highlighting, task lists, tables, and file attachments, with tags auto-extracted from #hashtags in the text. Each memo carries a visibility level, private, protected (logged-in users), or public, so one instance works as a personal log, a small team wiki, or a lightweight microblog. The backend is a single Go binary with a React frontend, around 50 MB of memory at runtime and a ~20 MB Docker image, so it fits comfortably on the smallest instance size with near-zero maintenance. SQLite is the default store, with MySQL and PostgreSQL supported for multi-user deployments needing more concurrency, and full REST and gRPC APIs - Connect RPC for browsers, gRPC-Gateway for external tools - make capture scriptable from CLIs, bots, and automation platforms. Fast full-text search spans all memos, pinned notes keep references handy, and a masonry view suits visual browsing. MIT-licensed with zero telemetry; content is stored as plain Markdown in a database you control, so notes remain readable, exportable, and free of proprietary formats.

In-app, website, link, and email surveys feed one open-source experience management platform: Formbricks. Its distinguishing strength is targeted in-app research: a JavaScript SDK triggers surveys on user events and attributes, with segmentation rules like "power users who have not seen a survey in 10 days," so questions reach the right cohort at the right moment instead of a mass email blast. The no-code editor offers 20+ question types including NPS, CSAT, CES, matrix, ranking, and file upload, with skip logic, conditional branching, best-practice templates, and full brand theming. Responses feed built-in analytics with summaries and CSV/JSON export, and integrations push data to Slack, Notion, Google Sheets, Airtable, Zapier, and n8n, with webhooks and an open API on every tier. Because self-hosted surveys load from your own domain rather than a blacklisted third-party script host, ad blockers do not suppress them - in-app surveys reach users that Hotjar-style tools silently miss, which measurably raises response rates. Self-hosting also removes the third-party sub-processor from your privacy policy entirely: survey responses often contain PII, and keeping them on your own server matters for GDPR-sensitive and regulated industries. The Community Edition has no response caps or tier-gated features, so core functionality and your data stay accessible regardless of any subscription. Next.js on PostgreSQL, AGPLv3.

Automatisch runs your Zapier workflows on your own hardware - an open-source, self-hosted automation platform built as a direct alternative. Flows are chains of steps: one trigger (a polling or webhook event such as a new GitHub issue, a Stripe payment, or a form submission) followed by action steps that pass data downstream (post to Slack, append a Google Sheets row, update Notion). The visual builder deliberately mirrors Zapier's trigger-action model, so migrating existing Zaps requires no retraining and no programming knowledge. Roughly 60 integrations cover common business services - Slack, GitHub, Google Sheets, Notion, Stripe, Discord - and connections store credentials per service, with multiple accounts per app supported. Every execution runs on your own server: execution history, logs, and payload data never touch a third-party processor, which matters for GDPR, healthcare, and finance workloads. Error handling with retry logic, a REST API for programmatic flow management, and Docker Compose deployment round out the platform. The AGPL-3.0 Community Edition has no feature limits or per-task billing; an Enterprise Edition adds SSO, roles, and audit logs.

Retool's job, self-hosted: ToolJet is an open-source low-code platform for building internal tools, dashboards, and admin panels. Apps are assembled in a drag-and-drop visual builder with 60+ responsive components, including tables, charts, forms, and lists, and connected to 80+ data sources: PostgreSQL, MySQL, MongoDB, REST and GraphQL APIs, cloud storage, and common SaaS tools. When visual configuration is not enough, you can run JavaScript or Python inline for queries and transformations. A built-in no-code database (ToolJet Database) covers apps that need their own tables without provisioning an external database, Workflows add node-based automation for background jobs with dedicated worker containers and a Redis-backed queue, and multi-page apps with multiplayer editing, inline comments, and mentions support team development. Security is designed for internal data: credentials are AES-256-GCM encrypted, data flows proxy-only through your server so database contents never reach a third-party cloud, and granular per-app access control plus SSO gate each tool. Where Retool-style platforms bill per builder and sometimes per end user, the self-hosted Community Edition serves unlimited builders and users at hosting cost, and full source availability means the platform itself can be forked, audited, and extended. The stack is Node.js and React on PostgreSQL, deployed via Docker.

Your own Heroku or Vercel on a single server - Dokploy is the open-source, self-hosted Platform-as-a-Service that makes the swap. You point it at a Git repository or a Docker image, and it builds and deploys the application using Dockerfiles, Nixpacks, or Heroku/Paketo buildpacks. Traefik is integrated as the reverse proxy, handling routing, load balancing, automatic Let's Encrypt SSL certificates, and HTTP/3. It also provisions and manages databases (MySQL, PostgreSQL, MongoDB, MariaDB, Redis) with automated backups to external storage. Complex multi-service applications deploy through native Docker Compose support, and multi-node scaling uses Docker Swarm. The web UI covers environment variables, volumes, resource limits, real-time CPU/memory/network monitoring, and deployment logs, with a CLI and API for automation. Deployment notifications go to Slack, Discord, Telegram, or email. One-click templates install common open-source tools, and a single Dokploy control plane can manage deployments across multiple remote servers. Because everything is standard Docker under the hood, there is no lock-in: your Dockerfiles, Compose files, and data volumes work anywhere else Docker runs. You get the Heroku-style push-to-deploy workflow without operating a Kubernetes cluster, and the total cost is the server it runs on - no per-app, per-environment, or per-seat platform fees regardless of how many applications you deploy.

A question goes in; a cited, long-form report comes out - GPT Researcher is an open-source autonomous research agent. A planner agent decomposes the query into sub-questions, execution agents crawl 20+ web sources in parallel with JavaScript-enabled scraping, and a publisher aggregates findings into a 2,000+ word report with inline citations, exportable to PDF, Word, and Markdown. The Deep Research mode extends this recursively: each result yields follow-up questions that are explored to configurable breadth and depth in a tree pattern, while accumulated learnings, citations, and visited URLs are shared across branches. It also researches local documents (PDF, CSV, Word) alongside the web. LLM and search providers are pluggable, including OpenAI, Anthropic, Google, DeepSeek, and Ollama for models, and Tavily, Google, Bing, DuckDuckGo, and SearXNG for retrieval. It ships as a Python package, a FastAPI server with web frontend, a Docker image, and an MCP server for use inside Claude or Cursor. MIT-licensed.

Run a Stack Overflow of your own: Apache Answer brings the question-and-answer format in-house, maintained under the Apache Software Foundation with an Apache-2.0 license. You use it to run a community forum, product help center, or internal knowledge base where content lives as questions and answers rather than wiki pages. It ships the mechanics that make that format work: voting, accepted answers, a reputation system with privilege levels, tagging, full-text search with filters, revision history on every edit, and admin/moderator/user roles. Content is written in Markdown with real-time preview and code syntax highlighting. A plugin system covers OAuth login (Google, GitHub), S3 storage, external search backends like Algolia, and Akismet anti-spam, and a REST API exposes platform data for integration. The backend is Go, the frontend React, and it runs against SQLite, MySQL, or PostgreSQL. Self-hosting replaces per-seat tools like Stack Overflow for Teams with a flat-cost instance where you own all the content.

Thirteen-plus LLM providers, one unified client: NextChat (formerly ChatGPT-Next-Web) is an open-source AI chat interface built on Next.js that spans OpenAI GPT-4, Anthropic Claude, Google Gemini, DeepSeek, Groq, Azure endpoints, and self-hosted backends like Ollama, LocalAI, and RWKV-Runner. Its defining trait is minimalism - the first screen loads in about 100 KB, the desktop client is roughly 5 MB, and there is no database or user system to operate; chat history lives locally in the browser with optional WebDAV or UpStash Redis sync. The Mask system saves reusable prompt-template personas you can share and debug, long conversations auto-compress to fit context windows, and Markdown rendering covers LaTeX, Mermaid diagrams, and code highlighting with streaming responses. Plugins add web search and calculators, MCP support enables external tool calling, and Artifacts previews generated content in a separate pane. Ships as a web app, Docker image, and Tauri desktop builds for Windows, macOS, and Linux, translated into 20+ languages. MIT-licensed.

Data, AI, and infrastructure workflows, orchestrated from declarative YAML: Kestra is an open-source, event-driven orchestration platform. Flows are declared in YAML - no DSL rewrites or Python decorators - and the definition stays the single source of truth even when edited through the UI, API, CI/CD, or Terraform, which makes pull-request review, versioning, and rollback natural. Tasks run in any language: Python, Node.js, Go, Rust, R, SQL, or Bash scripts executed in containers, and a plugin ecosystem of 1,000+ integrations covers ingestion, dbt, Airbyte, Spark, cloud storage, databases, and messaging systems. Scheduling supports cron triggers, event triggers, backfills, and conditional branching, with retries, timeouts, error handling, and typed inputs and outputs that surface artifacts in the UI. Namespaces, labels, and subflows organize workflows at scale, and the embedded code editor includes Git integration. Common uses span ETL/ELT pipelines, dbt runs, microservice coordination, infrastructure provisioning, and human-in-the-loop approvals. Java-based, Apache 2.0 licensed, deployed via Docker or Kubernetes.