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.

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.

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.

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.

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.

Up to 280 search services - Google, Bing, DuckDuckGo, Brave, Qwant, Startpage - aggregated without tracking or profiling: SearXNG is a privacy-respecting metasearch engine (AGPL-3.0, successor to Searx). Your instance queries the upstream engines on your behalf: your IP address, cookies, and search history never reach them, tracker parameters are stripped from result URLs, and an optional image proxy fetches thumbnails server-side so result pages leak nothing. It can even route outbound queries through Tor for full anonymity. Search is organized into categories - general, images, videos, news, maps, music, IT, science, files - with bang shortcuts for targeting specific engines, and every source can be enabled, disabled, or weighted per category in settings.yml. A plugin system adds calculators, hash tools, tracker removal, and unit conversions inline, and preferences (themes, safe search, languages, engine selection) persist in cookies rather than server-side accounts. The real argument for running your own instance rather than trusting a public one is control: you decide the logging policy (none), the engine mix, rate limiting, and who gets access - making it the default search backend for browsers, families, and teams that want Google-quality results without the profile.

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.

One clean interface in front of Anthropic, OpenAI, Google Gemini, Cohere, and more: Chat Chat is a Next.js front door to the major AI providers, ending the juggling of separate subscriptions, tabs, and UIs per model. Bring your own API keys, pick a provider and model per conversation, and switch between them as the task demands: Claude for long-form reasoning, GPT for code, Gemini for multimodal work - the interface stays identical. Beyond configured presets, custom providers plug in with their own API endpoints and keys, which covers OpenAI-compatible gateways and local inference servers. The design splits into two dedicated modes: a chat interface for conversational work with customizable system prompts, and a search interface that pairs AI processing with query handling for research-style questions. The stack is modern and hackable - Next.js 14, Tailwind CSS, shadcn/ui on Radix primitives, Jotai for state - with full internationalization including English, Chinese, and Japanese. Self-hosting means your conversation history and API keys live on your instance rather than a third-party wrapper service, and pay-per-token API pricing typically beats stacking multiple monthly chat subscriptions. AGPL-licensed and deliberately simple to deploy: one container, environment variables for keys, done.