Open Source Perplexity Alternatives

Meant to extend what one person can do, Khoj runs as a personal AI app that scales from a laptop up to a deployment serving a whole company. The gap it closes is the one between what your documents know and what the internet knows.

It talks to whichever model you point it at, local or hosted, naming llama3, qwen, gemma, mistral, gpt, claude, gemini and deepseek among them. Access is not tied to one window either: the same assistant is reachable from a browser, Obsidian, Emacs, a desktop app, a phone or WhatsApp.

The features cluster around finding, asking and automating.

• Document grounding: answers drawn from images, PDFs, Markdown, org mode, Word and Notion files.
• Semantic search: relevant documents surfaced quickly rather than by exact keyword.
• Custom agents: agents given their own knowledge, persona, chat model and tools.
• Automations: repetitive research handled on a schedule, delivering personal newsletters and notifications to your inbox.
• Generation: images created from prompts, messages played back, replies spoken aloud.

Khoj is open source and self hostable, with setup documented for running it privately on your own computer, and a hosted app is available for anyone who would rather skip installation entirely. For organizations, the project is offered as a cloud service, on premises or as a hybrid arrangement.

SurfSense began as an open-source alternative to NotebookLM and now aims at a second problem: giving agents somewhere trustworthy to look when the answer lives on Reddit, in a video transcript or under a product review. Official platform APIs are rate-limited or priced for enterprises, scraping plumbing is brittle, and driving a browser with a model burns minutes per page.

Every source is a REST endpoint returning structured JSON, and the same set is published as an MCP server so Claude, Cursor or any agent framework can call them as native tools. Retries, structured output and credit metering come with the harness.

Behind the connectors, the research workspace that made the project popular is still in place.

• Connector catalog: Reddit, YouTube, Instagram, TikTok, Amazon, Walmart, Google Maps, Google Search, Indeed and open web crawling.
• Knowledge base: upload documents or sync Google Drive, OneDrive and Dropbox, then search with cited answers.
• Deliverables: reports, spreadsheets, slide decks, narrated video overviews and two-host podcasts built from your material.
• Automations: agent runs on a schedule or on events, writing results back to Notion, Slack, Linear and Jira.
• Team access: real-time collaborative chats with comments, plus owner, admin, editor and viewer roles.

Self-hosting needs Docker Desktop and ships with billing switched off, so crawls and agent runs are bounded only by your hardware and the model keys you bring. Over a hundred models are reachable through the OpenAI spec and LiteLLM, local inference included.

Scira answers a question by researching it. You type a question, upload a PDF or paste a URL, and the agent breaks the request into sub-tasks, searches live sources, cross-checks the evidence and returns an answer whose claims each carry an inline citation you can click and audit.

The application is built on Next.js with Tailwind CSS and shadcn/ui, and routes model calls through the Vercel AI SDK, so it runs against xAI, OpenAI, Anthropic, Google, Mistral, DeepSeek and many other providers. Retrieval comes from Exa, Firecrawl, Parallel and Tavily, with Upstash for rate limiting and Drizzle ORM over the database.

Seventeen search modes and twenty-eight tools cover most of what a research session needs.

• Agentic planning: breaks a complex question into steps, picks the models and tools for each, then runs the workflow end to end.
• Grounded retrieval: every answer ships with citations, so the evidence stays auditable rather than implied.
• Specialist modes: academic papers, X posts, Reddit threads, GitHub repositories, stock and crypto data, YouTube transcripts and more.
• Code interpreter: writes and executes Python in a sandboxed Daytona environment and generates charts from the result.
• Lookouts: schedules recurring agents that watch a topic, track changes and email you updates.

Self-hosting runs through Docker Compose or a plain Docker build, and needs API keys for whichever providers you want. The AGPL-3.0 license means bringing your own models and wiring in custom tools is expected rather than tolerated.

Morphic is an AI-powered search engine with a generative user interface. Rather than returning a wall of markdown, it streams a JSON spec that renders inline components, source-credited images, grids and headings, alongside grounded answers that cite where they came from.

It runs on whichever models and search backends you point it at. A model selector detects available providers, covering OpenAI, Anthropic, Google, Ollama, the Vercel AI Gateway and any OpenAI-compatible endpoint, while search can come from Tavily, SearXNG, Brave or Exa.

The application around the answer engine handles what a search tool needs to be usable day to day.

• Search modes: Quick and Adaptive, chosen per query depending on how much work an answer deserves.
• Chat history: conversations stored in PostgreSQL rather than kept only in the browser.
• Sharing: results given a unique link so a colleague can open the same answer.
• Authentication: Supabase Auth for accounts, with a guest mode for anonymous use.
• File upload: documents attached to a search once the feature is configured.

The quickest path is Docker Compose, which starts PostgreSQL, Redis, SearXNG and Morphic together, so no separate search API key is required to get going. Local development uses Bun, and a one-click deploy to Vercel is available.

It suits anyone who wants a private answer engine pointed at their own models and search providers, running on a laptop or a single server under the Apache 2.0 license.

Lumo is the confidential AI chat assistant from Proton, the company behind Proton Mail and Proton VPN. Mainstream assistants keep chat logs that can be read by the provider, used for training or handed to a third party. Lumo removes that exposure rather than promising not to look.

All conversations with Lumo are stored with zero-access encryption, so no one, not even Proton, can access them, and they are only accessible on your devices. Proton does not log chats, never uses the data to train AI models and cannot share it with third parties. The service is European and GDPR compliant, the company is based in Geneva, and Lumo's code is fully open source, so anyone can verify that it is private and secure.

The assistant itself does the ordinary work you would expect.

• Web search: current information retrieved and folded into an answer.
• Image generation: descriptions turned into images in seconds.
• Proton Drive files: files and folders linked from Drive and worked with in a chat.
• Projects and custom Lumos: saved contexts and tailored assistants on the paid tier.
• Guest access over Tor: anonymous use with no account at all.

Lumo runs as a web app and as iOS and Android apps. The free tier limits messages, history, image generation and use of the strongest model to one project, and Lumo Plus lifts those caps. The source lives in Proton's WebClients monorepo, beside Mail, Calendar, Drive and Pass, under version 3 of the GNU General Public License.