A campaign engine wrapped around a contact database: Mautic, the largest open-source marketing automation platform, replaces HubSpot or Marketo without per-contact pricing. Contacts arrive through forms, landing pages, imports, or the REST API and flow into segments: dynamic filters that update automatically from behavior, custom profile fields, or point scores. Segments decide who qualifies; campaigns decide what happens. The drag-and-drop Campaign Builder composes multi-step workflows from actions, positive/negative decision trees, and conditions (field values, tags, device type, segment membership, point thresholds), with static or relative delays, a Jump-to-Step action for moving contacts between branches, and handoffs that push contacts into CRMs or entirely different campaigns. Messaging covers email, SMS, and web/app push out of the box, with A/B testing and a drag-and-drop email builder; dynamic website content swaps page sections per known contact. Lead scoring assigns points for clicks and visits with decay for inactivity, while stages track funnel position. Native integrations cover Salesforce, HubSpot, Zoho, and Dynamics, plus a full REST API for custom sync. It runs on PHP and MySQL with cron jobs processing campaigns and segment rebuilds - self-hosting keeps your entire contact database and behavioral history under your control.

Google's search results without Google's surveillance: Whoogle is a self-hosted proxy that strips the tracking and keeps the results. Your query goes from browser to your Whoogle instance, which fetches results from Google with a randomly generated User Agent and strips everything hostile before returning them: no ads or sponsored content, no third-party JavaScript or cookies, no AMP links, no URL tracking tags like utm_source, no referrer header - and Google sees your server's IP, never yours. Unlike metasearch engines that blend sources, Whoogle proxies Google exclusively, so result quality is exactly what you'd get logged out and incognito, minus the noise. A lightweight Flask app configured entirely through environment variables, it supports DuckDuckGo-style bang shortcuts, autocomplete suggestions, safe search, per-country and per-language filtering, site blocklists, and automatic rewriting of social links to privacy front-ends like Nitter and Invidious. Privacy hardening goes further: built-in Tor routing makes Google see an exit node instead of your server, HTTP/SOCKS proxy support covers other setups, and POST-based queries keep search terms out of logs. Light, dark, and fully custom CSS themes plus browser search-engine registration make it a drop-in default on desktop and mobile. Stateless, tiny, and trivial to run.

Most design tools stop at prototyping; Quant-UX also measures how real users actually perform with the prototype. The visual editor creates prototypes that behave like real apps - functional input widgets, animations, form validation, data binding across screens, and business logic modeled with REST requests and decision elements. Design systems are first-class, with components, design tokens, and master screens; if you design elsewhere, drop in image files or import from Figma. Testing is a shared link or QR code - no installs on the tester's side. Define user tasks up front, and Quant-UX records every session: click heatmaps show where users found (or missed) actionable elements, user journey graphs expose lost users, drop-off charts reveal where tasks stall, and success rates and task KPIs are extracted automatically into a dashboard. An A/B test operator wires two design variants into one prototype and compares task duration, success rate, and interaction counts. In-prototype surveys collect qualitative feedback alongside the numbers, and an AI assistant generates prototype fragments like styled forms on request. The RepoCloud deployment runs the full stack - frontend, backend, and WebSocket server containers over MongoDB - so all test recordings and research data stay on your infrastructure.

Web analytics tools ignore native mobile, desktop, and game apps; Aptabase was built for exactly those. If Firebase Analytics would force a privacy-policy footnote you don't want to write, this is the alternative - session-based metrics with no cookies, no IDFA or GAID, no device fingerprinting, and a daily-rotated salt that makes cross-day re-identification mathematically impossible. That design means GDPR, CCPA, and PECR compliance out of the box and "Data Not Collected" App Store privacy labels without ATT prompts. The SDK coverage is the widest in its category: eleven first-party libraries spanning Swift, Kotlin, Flutter, React Native, Tauri, Electron, .NET MAUI, NativeScript, Unity, Unreal Engine, and JavaScript for web - each MIT-licensed, following platform conventions, and accepting a custom host parameter that points at your instance. Integration is minutes: initialize with an app key, call trackEvent with optional properties, and the dashboard shows sessions, events, app versions, OS breakdowns, and country-level geography. The self-hosted stack is a .NET server over PostgreSQL for metadata and ClickHouse for high-volume event ingestion, giving cloud-parity features under an AGPL license. For indie iOS/Android apps, Electron and Tauri tools, and Unity or Unreal games, it replaces Firebase without the Google entanglement.

Audio lectures become structured, Markdown-formatted notes in about a minute with ScribeWizard (also known as GroqNotes). Upload an MP3, WAV, or M4A file - or paste a YouTube link - and the app runs a three-stage pipeline on Groq's LPU inference hardware: Whisper Large v3 transcribes the audio, a larger Llama model drafts a comprehensive outline of the material, and a faster Llama model fills each section with detailed content. This scaffolded prompting strategy is the core idea: the strong model handles structure where quality matters most, the fast model handles volume, and Groq's 1200+ tokens-per-second inference keeps the whole process near real time. Output renders as clean Markdown with support for tables and code blocks, and finished notes download as text or PDF. Model selection is configurable - swap in other Groq-hosted open models like Mixtral or Gemma to trade speed against quality or work around rate limits. Built as a single Streamlit app by Benjamin Klieger at Groq, it needs only a Groq API key to run, making it one of the simplest self-hosted AI tools to operate.

Quote to cash in one web application - create quotes, convert them to invoices, record payments, track customers: iDURAR is an open-source ERP and CRM platform for small and medium-sized businesses. Built on the MERN stack (MongoDB, Express, React, Node.js) with Ant Design components and Redux state management, it presents a clean SaaS-style interface that needs little onboarding. Core modules cover invoice management with PDF generation and email delivery, payment recording against invoices, quote and proforma handling, customer records, and accounting views over the resulting data. Multi-currency support and localization make it usable for internationally operating teams. Because the whole stack is JavaScript with an API-first backend, extending it - custom fields, new modules, integrations - is approachable for any Node/React developer rather than requiring a specialist ERP skill set. Deployment is straightforward via Docker with a MongoDB instance. Licensed under AGPL-3.0 with free commercial use; a hosted enterprise version exists but the self-hosted edition is fully functional.

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.

Retrieval-augmented chatbots on your own knowledge base - that is the whole mission of Dialoqbase, an open-source bot-building platform. Feed it content through a broad set of data loaders - web pages and full crawls, sitemaps, PDFs, DOCX, CSV, plain text, GitHub repositories, YouTube videos, and MP3/MP4 audio - and it handles the whole RAG pipeline in one self-contained app: chunking, embedding, vector storage, and LLM querying. The distinguishing architecture choice is PostgreSQL with pgvector for embedding storage and similarity search, which removes the separate vector-database dependency, and Redis-backed Bull queues for ingesting large documents without blocking the API. Model choice is wide open: OpenAI, Anthropic Claude, Google Gemini, Cohere, Fireworks, Hugging Face, local models via Ollama, and any OpenAI-compatible endpoint, with an equally broad list of embedding providers. Finished bots embed on any website with customizable styling or deploy to Telegram, Discord, and WhatsApp, and an API creates and manages bots programmatically. Multi-user support adds registration limits and per-user bot quotas. MIT-licensed and free for commercial use.

A fair-source chatbot and conversational-form builder: Typebot assembles conversations in a visual graph editor. In a visual graph editor you chain blocks from four categories: bubbles display text, images, video, audio, and embeds; inputs collect data through text fields, email, phone, buttons, picture choices, date pickers, file uploads, and Stripe payments; logic blocks handle conditional branching, variables, URL redirects, A/B testing, and custom JavaScript; integration blocks call webhooks, OpenAI, Google Sheets, Google Analytics, Meta Pixel, Zapier, Make, and Chatwoot. Build once, deploy anywhere: custom domains, WhatsApp, or embedded in any site as a container, popup, or chat bubble through a fast native JS library with no iframe and no external dependencies - plus an HTTP API for executing bots programmatically from any language. Theming covers fonts, colors, roundness, and shadows with custom CSS and reusable templates, and results arrive in real time with drop-off and completion analytics plus CSV export. Two Next.js apps (builder and viewer) self-host via Docker under the Functional Source License, which converts to Apache 2.0 after two years.

Magento's extensibility without PHP, Shopify's polish without the platform tax: EverShop is the TypeScript-first e-commerce platform built on that promise. Architected as a modular monolith on Node.js, it organizes every piece of business logic - catalog, checkout, customers, your custom extensions - into modules that plug in without touching core code, extended through a disciplined set of mechanisms: registry processors for transforming data across modules, hooks that wrap function calls, async event subscribers (product created, order placed), and route middleware. The storefront and the fully-featured admin panel are both React with server-side rendering and hydration, giving fast first paint and SEO-friendly pages, while a typed GraphQL API (plus REST endpoints) serves exactly the data each view needs - the same API that powers headless and PWA builds. Standard commerce is covered: product management with variants and attributes, category navigation, cart and checkout, order and customer management, coupons, and a theme system built on React components and Tailwind for deep storefront customization. PostgreSQL is the default database, deployment is Docker-friendly with near-zero configuration, and the GPL-3.0 license means the entire stack - types, resolvers, and checkout flow included - is yours to read and modify.

Character-based chat and storywriting with large language models: TavernAI is the open-source frontend that leaves model choice to you. It generates no text itself; it connects to the backend of your choice - OpenAI (including GPT-4), Anthropic Claude, KoboldAI and KoboldCpp, Oobabooga's Text Generation Web UI, NovelAI, Ollama, and the crowdsourced Horde - so cost, model quality, and content policy are decided by your backend, not the interface. Characters are defined by portable card files in PNG or JSON format with personality, scenario, and example dialogue, and tens of thousands of community-made cards from sites like Chub.ai import directly. Conversations support group chats with multiple characters, a story mode for long-form writing, message swiping to branch between alternative responses, and full editing of any message. World Info injects lore into context when keywords trigger, keeping long roleplays consistent. Themes, custom backgrounds, and configurable generation settings round out the interface. It runs on Node.js, and the SillyTavern project began as a fork of it.

Roughly $1M of development work, open-sourced: Maybe Finance began as a $249/year commercial personal finance product before the company released it all. It aggregates bank accounts, credit cards, loans, investments, crypto, and real estate into a single net worth dashboard with historical trend charts - replacing the spreadsheet that usually glues a whole portfolio together. Transactions are categorized and tagged with rules, with merchant tracking and search across imported or synced activity; budgets track spending by category against plan; and the investment view follows holdings, cost basis, and returns across brokerage accounts. Multi-currency support converts accounts held in different currencies into a single reporting currency, bank synchronization works through Plaid where supported, and manual CSV import covers any institution. An optional AI assistant answers questions grounded in your own financial data. Because the app was built as a paid product with professional design before being open-sourced, its interface quality exceeds most community finance tools - and self-hosting means your balances and transactions are not monetized by a free app or gated behind an annual subscription. The stack is Ruby on Rails with Hotwire on PostgreSQL, licensed AGPL-3.0 and deployed via Docker. The original repository is archived; development continues in the community fork Sure, compatible with the same self-hosted setup.

Typeform's conversational format, self-hosted: HeyForm is the open-source form builder that presents one question at a time. Forms present one question at a time, which measurably improves completion rates compared to long static pages. It supports 40+ field types, from text, email, and phone inputs to picture choices, date pickers, star ratings, signatures, and file uploads. Conditional logic shows or hides questions based on earlier answers, routes respondents to different endings, and redirects to URLs, so a single form can serve multiple flows. Completed submissions land in a results dashboard with drop-off and completion analytics, and connect outward through webhooks or integrations with Zapier, Make, Google Sheets, Notion, Airtable, and Slack. Theming covers fonts, colors, backgrounds, and custom CSS, so embedded forms look native to your site rather than like a third-party widget; the JavaScript embed library renders them inline, as popups, or full-page, with shareable standalone links as the default. Team workspaces and projects with member management let multiple teams share one instance without mixing data. Self-hosting removes per-response pricing entirely - unlimited forms and submissions for flat hosting cost - and keeps lead data, feedback, and quiz answers in your own MongoDB, simplifying GDPR compliance. The stack is a NestJS server and React webapp backed by MongoDB and KeyDB, distributed under GPLv3 as a Docker image.

Sketch a wireframe, get working code: Draw a UI turns hand-drawn layouts into web interfaces. It pairs the open-source tldraw canvas with an OpenAI vision model: you sketch a layout - boxes, labels, buttons, arrows, whatever communicates the idea - select the drawing, and click Make Real. The app snapshots your selection as a PNG, sends it to the vision API with instructions to return a single HTML file styled with Tailwind CSS, and renders the result in an iframe directly on the canvas next to your sketch. The loop is iterative: annotate the generated prototype or redraw parts of it, select both the sketch and the previous result, and generate again - the model receives the earlier HTML as context and produces an updated version. Built by Figma engineer Sawyer Hood as one of the first viral GPT-4 Vision demos and the basis for tldraw's "Make Real", it is a Next.js app that runs against your own OpenAI API key. Self-hosting matters here: the upstream demo ships without authentication, so a private deployment keeps your API key from being drained by strangers. MIT-licensed.

Live web search plus an LLM of your choice: Farfalle is an open-source, self-hosted answer engine in the Perplexity mold. Queries route through one of several search providers - self-hosted SearXNG for a fully independent stack, or Tavily, Serper, and Bing APIs - and the model composes a cited answer from the retrieved results. Model flexibility is the core design: run llama3, mistral, gemma, or phi3 locally through Ollama for zero per-query cost and full privacy, use cloud models like GPT-4o or Groq-hosted Llama 3 for speed, or route to any provider via LiteLLM. An Expert Search mode uses an agent that plans a multi-step search strategy and executes it for harder questions, and chat history keeps prior research sessions available. The stack is a Next.js and shadcn/ui frontend over a FastAPI backend with Redis rate limiting, shipped as a pre-built Docker image. A browser search-engine entry pointing at your instance makes it the default search from the address bar. Paired with SearXNG and Ollama, the whole pipeline runs with no external API at all.

Chat with your own documents: AnythingLLM, from Mintplex Labs, wraps retrieval-augmented generation (RAG) in an open-source application anyone can run. You organize content into workspaces, each an isolated namespace with its own documents, vector embeddings, chat history, and settings, so one instance can hold several separate knowledge bases. Upload PDFs, DOCX, TXT, and other formats, or scrape web pages; the built-in collector parses and chunks them into a vector database (LanceDB by default, with Pinecone, Chroma, Qdrant, and others supported). Answers cite their source documents. It works with both cloud LLMs (OpenAI, Anthropic, Gemini) and local ones via Ollama or LM Studio, and the embedding model is separately configurable. Beyond RAG chat, it includes AI agents that can browse the web and run tools, an embeddable chat widget for your website, a developer API, and multi-user mode with admin, manager, and default roles plus per-workspace access control. Context assembly is smarter than naive RAG: pinned documents, attached files, vector search hits, and recent chat history are combined under a token budget so the model's context window is filled efficiently, and each workspace supports multiple independent conversation threads against the same knowledge base. Because the embedding model, vector store, and chat LLM are all independently swappable, you can move between providers without re-ingesting a single document. The stack is Node.js with a React frontend, MIT-licensed.

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.

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.