Open Source App Cloud Marketplace

A self-hosted "second brain": Khoj indexes your own files and answers questions from them, parsing Markdown (whole Obsidian vaults included), org-mode, PDF, Word, plain text, Notion pages, GitHub repositories, and images described by a vision model, then embedding everything with sentence-transformers into a vector index for semantic search and RAG with cited sources. Any LLM backend works: local models like Llama, Qwen, or Mistral via Ollama, or cloud models like GPT, Claude, and Gemini. You can build custom agents, each with its own persona, scoped knowledge base, chat model, and tools such as web search and code execution. Scheduled automations run recurring research and deliver newsletters or notifications to your inbox, and research mode performs multi-hop web searches with inline citations. Access it from a browser, the Obsidian plugin, Emacs, desktop, or WhatsApp - all clients connect to the same self-hosted instance, making Khoj one of the few AI assistants Emacs users can point at decades of org files. Semantic search means recall works without exact keywords: "that paper about forecasting with transformers" surfaces the right PDF even when you cannot remember its title. Switching LLM backends never requires re-indexing your documents, and with a local model via Ollama, even inference stays on hardware you control - journals, research, and private notes are never sent anywhere. Python/FastAPI stack, AGPL-licensed, with PostgreSQL storage.

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.

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.

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.

Zapier's job, on your own server: Activepieces is an open-source workflow automation platform built to be exactly that replacement. Flows are built in a visual no-code editor with triggers, actions, loops, conditional branches, auto-retries, raw HTTP steps, and code steps that run JavaScript or TypeScript with full npm package support. Integrations are "pieces" - type-safe TypeScript npm packages with hot reloading for local development - and the catalog spans 600+ services, with the large majority contributed by the community. The platform is AI-first in two directions: native AI pieces call OpenAI, Anthropic, Google, and Azure models inside flows, and every piece automatically doubles as an MCP server, so assistants like Claude Desktop and Cursor can invoke your integrations and workflows through natural language. A built-in MCP server also exposes 30 tools for building flows, managing tables, and running tests agentically. Flows are fully versioned with draft and locked states. The core is MIT-licensed and runs on TypeScript with PostgreSQL and Redis.

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.

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.

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.

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.

Describe what you want in plain language and get real SQL against your real schema: SQL Chat is an open-source, chat-based SQL client from the Bytebase team. Instead of writing queries in a traditional editor, you connect a database and describe what you want in plain language; the AI reads your schema automatically, generates SQL that references real table and column names, executes it, and returns tabular results in the conversation. Follow-up messages refine the query, so exploration becomes a dialogue - narrow a result set, add a join, change an aggregation - without retyping statements. It supports MySQL, PostgreSQL, SQL Server, TiDB Cloud, and OceanBase from one interface, and covers modification as well as reads: insert, update, and delete operations phrased conversationally. Built with Next.js and TypeScript, it deploys as a single stateless Docker container in single-user mode - connection profiles live in the browser, so there is nothing server-side to maintain. A custom AI endpoint setting routes inference through any OpenAI-compatible API, including self-hosted models, and an optional database-backed mode adds accounts and quotas for offering the tool to a team. MIT-licensed.

Any SSH-accessible Linux box - VPS, bare metal, Raspberry Pi, EC2 - becomes a Heroku-like deployment environment under Coolify, an open-source, self-hostable platform-as-a-service. Connect a GitHub, GitLab, Bitbucket, or Gitea repository and every push builds and deploys automatically via Nixpacks, a Dockerfile, or Docker Compose, with Traefik reverse proxying, automatic Let's Encrypt certificates, and per-branch preview deployments with their own URLs. Databases - PostgreSQL, MySQL, MariaDB, MongoDB, Redis - provision in a few clicks, and a catalog of 280+ one-click service templates covers WordPress, n8n, Grafana, MinIO, Plausible, and more, replacing an afternoon of Compose YAML with a two-minute operation. One dashboard manages multiple servers, with Docker Swarm available for clustering. Backups go to any S3-compatible storage with one-click restore, a browser terminal gives real-time server access, and a full API supports CI/CD integration. All configuration lives on your own servers, so resources keep running even without Coolify. Apache 2.0 licensed.

The de facto dashboard of observability: Grafana is the open-source frontend that turns the data stores you already run into interactive graphs. It does not store metrics itself; it connects to the data stores you already run and turns their contents into interactive dashboards. Supported sources number over 150 via plugins: Prometheus, Loki, Tempo, InfluxDB, Elasticsearch, MySQL, PostgreSQL, Microsoft SQL Server, AWS CloudWatch, Azure Monitor, Google Cloud Monitoring, and many more. Dashboards are built from a large library of panel types (time series, heatmaps, tables, gauges, logs) with template variables for reusable, parameterized views. Unified alerting evaluates rules against any connected data source, not just Prometheus, and routes notifications to Slack, PagerDuty, email, and other channels with grouping and silencing - unlike Prometheus Alertmanager, a single rule can combine a Loki log pattern, a PostgreSQL query result, and a CloudWatch metric. Dashboards serialize to JSON and data sources configure via provisioning files, so the entire observability setup can live in Git and deploy repeatably across environments. Explore mode adds ad-hoc querying outside dashboards, with split view for correlating a metric spike against the matching log lines, and access control spans organizations, teams, folder permissions, and OAuth, LDAP, and SAML integration. Written in Go and TypeScript, AGPL-licensed. Self-hosting gives you unlimited users, dashboards, and queries at flat hosting cost, without Grafana Cloud's usage-based pricing.

CRMs, project trackers, inventory tools - NocoBase is an open-source no-code/low-code platform for building business systems like these. Its architecture is data-model driven: you define collections and relationships first, then compose any number of interface blocks (tables, forms, kanban, charts) on top of the same model, so data structure is never coupled to a particular view. The core is a microkernel where every feature is a plugin, WordPress-style; you enable official plugins, install marketplace ones, or write your own as npm packages with server and client parts. Data sources include the main PostgreSQL or MySQL database, external databases, and third-party APIs - so you can build admin panels over existing production data instead of migrating it. Built-in infrastructure covers role-based permissions down to collection, record, and field level, workflow automation with approval steps and scheduled triggers, and audit logs; a one-click switch flips between usage and configuration modes. Because custom features live in isolated plugins with a documented lifecycle, core upgrades do not overwrite your customizations, and swapping UIs never requires data migrations since interfaces sit on independent models. Written in TypeScript on Node.js, Koa, and React under the AGPL license, it is light enough for one person to run and extend - and where no-code SaaS platforms charge per seat and per app, a self-hosted instance runs unlimited applications for unlimited users at hosting cost alone.

Perplexity's answer-engine experience, self-hostable and open-source: Morphic searches the web and writes cited answers. Instead of returning a list of links, it searches the web, reads the sources, and generates a complete answer with inline numbered citations. The generative UI streams rich components, source cards with thumbnails, image grids, syntax-highlighted code, and LaTeX math, rather than plain markdown. Quick mode answers fast; Adaptive mode runs deeper multi-step research. Search backends are pluggable: the Docker Compose bundle ships with a private SearXNG instance so no search API key is required, and Tavily, Brave, and Exa are supported alternatives. LLM providers include OpenAI, Anthropic, Google, Ollama, and any OpenAI-compatible endpoint, with per-mode model mapping - fast, cheap models for quick searches, stronger models for adaptive research, tuning the cost-quality trade-off per query type. An inspector panel exposes tool execution during multi-step research, and AI-suggested follow-up questions keep an investigation moving. Chat history persists in PostgreSQL, results are shareable by URL, file uploads feed context into queries, and optional Supabase authentication adds multi-user or guest access. Because the default search path is your private SearXNG instance, research topics never hit a commercial search API - and with local Ollama models the marginal cost of a query approaches zero. Built with Next.js, TypeScript, and the Vercel AI SDK under Apache 2.0.

Every major model provider behind one ChatGPT-style interface: LibreChat spans OpenAI, Anthropic, Google, Azure, AWS Bedrock, Vertex AI, Groq, Mistral, OpenRouter, DeepSeek, and any OpenAI-compatible endpoint including local Ollama. You can switch models mid-conversation and compare providers without changing tools. Its Agents framework builds no-code custom assistants with tool access via Model Context Protocol servers, file search over uploaded documents through an optional pgvector-backed RAG service, and a sandboxed Code Interpreter that executes Python, JavaScript, Go, C++, Java, PHP, and Rust. Artifacts render React components, HTML, and Mermaid diagrams directly in chat, and image generation works through DALL-E and other configured providers. Multi-user support is enterprise-grade, with OAuth, SAML, LDAP, and two-factor authentication, per-user conversation history in MongoDB, and Meilisearch-powered search across all messages and files, plus reusable presets, forkable threads, and persistent memory across conversations. The economics favor teams: instead of a ChatGPT Plus seat per person, everyone shares one instance billed per API token, with access to every provider rather than one - and providers see individual API calls, not your accumulated organizational knowledge. Deployment is Docker Compose; API keys and endpoints are configured through .env and librechat.yaml.

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.

Website analytics, uptime monitoring, and server status - three tools most teams run separately - combined in Tianji, an open-source observability platform. The analytics layer tracks page views, unique visitors, referrers, and UTM parameters with a lightweight cookie-less script, which keeps collection GDPR and CCPA friendly. The uptime monitor checks availability and latency on configurable intervals, accepts passively reported results, and publishes public status pages for incident communication. Server status agents report CPU, memory, disk, and network metrics with threshold-based alerts, and notifications route through webhooks, Slack, Telegram, and other channels with noise control. It also includes anonymous telemetry for tracking deployments of your own open-source projects, surveys, waitlists, team collaboration, and an OpenAPI interface for integrations and exports. The consolidation is the point: traffic analytics, uptime checks, and server metrics share one interface and one alerting layer, so diagnosing an incident does not mean hopping between Google Analytics, Uptime Kuma, and Prometheus - and the built-in public status pages replace a separate paid Statuspage-style subscription. Because collection uses no cookies with IP truncation and aggregation by default, basic traffic measurement requires no consent banner. Built in TypeScript under the Apache 2.0 license and inspired by Umami and Uptime Kuma, it is deliberately right-sized for independent developers and small SaaS teams whose monitoring needs are real but lightweight.

Airtable's spreadsheet-database model, self-hostable and open-source: that is Baserow. It presents data in a spreadsheet-style grid, but underneath each table is a real relational structure with typed fields, links between tables, filters, sorts, and multiple views (grid, gallery, form, kanban, calendar). Beyond the database core, it includes an application builder for composing pages and portals on your data, workflow automations, and dashboards. Everything is API-first: each table exposes a REST endpoint with token auth and webhooks, so it plugs directly into n8n, Zapier, or custom scripts. The stack is Django (Python) on the backend, Vue.js on the frontend, PostgreSQL for storage, with Redis for async tasks. Core features are MIT-licensed; premium features are a paid add-on. The self-hosted version has no row, storage, or API request limits - Airtable's per-base record caps and monthly API quotas simply don't exist here, and capacity is bounded only by your PostgreSQL database and disk. Existing Airtable bases, CSVs, and Excel files import directly with structure preserved, so migration doesn't start from a blank slate, and both the backend and frontend support plugins for custom field types and integrations without forking the core. For non-technical teammates the interface behaves like a spreadsheet; for engineers, the data model is the API.