Best self-hosted AI search engine apps for desktop (2026)

An XDA-Developers writer recently described cancelling both a Perplexity and a ChatGPT subscription after pairing a local LLM with Perplexica and SearXNG, and the setup is easier to replicate than it sounds. A mid-tier open model like Qwen, Llama, or Mistral running through Ollama already handles reasoning and summarization well. What it lacks is fresh information from the open web, and that’s the piece a self-hosted metasearch layer supplies: a private retriever that fetches and ranks results, then hands them to the model to cite and synthesize. The result reads like Perplexity, minus the subscription and the query log.

This roundup covers the best apps for self-hosted AI search, from full Perplexity clones to the metasearch engine underneath most of them. Every pick runs on a home server or a spare desktop, connects to Ollama, LM Studio, or any OpenAI-compatible endpoint, and ships a Docker image so the whole stack comes up with one command.

What to look for in a self-hosted AI search app

Not every “AI search” project is actually built for this. A handful of traits separate the ones worth running from the ones that stall out at a demo:

• Local model support. It has to talk to Ollama, LM Studio, or a generic OpenAI-compatible endpoint, not just a hosted API key.
• Cited sources. Answers should link back to the pages they draw from, not present synthesized text as fact with no trail.
• A real ranking step. Raw search results need reranking or relevance scoring before they reach the model, or answers drift off-topic on ambiguous queries.
• One-command Docker setup. A docker compose up that brings its own metasearch backend beats a manual install with five moving parts.
• Multi-user support. Matters if more than one person in the house or team will query it.
• Tool calling and plugins. The stronger agents can chain a search, open a result, and search again before answering.

Quick comparison

The 8 best self-hosted AI search apps for desktop in 2026

1. Perplexica — best for the closest Perplexity clone

Perplexica is the project the XDA piece built its setup around: a Next.js frontend and API backend that bundle a private SearXNG instance, so one Docker container gives you a search box, an LLM answer with inline citations, and image and video panels. It was rebranded to Vane in March 2026, though the GitHub repository, Docker images, and community still go by the Perplexica name it launched under. It connects to Ollama for local models or to OpenAI, Anthropic, and other cloud providers when you want a stronger backend for harder queries.

Where it falls short: The rebrand has left documentation and search results split between the old and new names, which makes troubleshooting slightly more confusing than it should be.

Pricing: Free and open source, no paid tier.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: The single closest thing to running Perplexity on hardware you own, and the project this whole category takes its cues from.

2. SearXNG — best for the metasearch layer alone

SearXNG is the federated metasearch engine that powers Perplexica, Farfalle, and Morphic under the hood, and it’s worth running on its own if all you want is a private front end to Google, Bing, DuckDuckGo, Brave, and roughly 280 other search services without an AI layer attached. Queries reach those engines from your server, not your browser, so none of them see your IP or build a profile from your searches. Point any of the AI search apps below at an existing SearXNG instance instead of letting them spin up their own, and you get one shared search backend for several tools.

Where it falls short: No AI synthesis on its own. It returns ranked links, not answers, unless paired with an LLM front end.

Pricing: Free and open source, no paid tier.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: The metasearch foundation underneath most of this list, and the right starting point if the AI layer is a later addition, not day one.

3. Farfalle — best for choosing your own search backend

Farfalle is a Perplexity-style answer engine that leaves the search provider up to you: SearXNG, Tavily, Serper, or Bing, mixed with local models through Ollama or cloud models through LiteLLM. The Next.js and FastAPI stack ships a docker-compose.dev.yaml that gets a working instance running at localhost:3000 from a fresh clone in a few minutes, and swapping search or model providers is a config change, not a rebuild.

Where it falls short: The default setup expects at least one API key (a search provider or a cloud model) unless everything is routed through a local SearXNG and Ollama pair, so the zero-key path takes a little more configuration than Perplexica’s.

Pricing: Free and open source, no paid tier.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: The pick for anyone who wants a Perplexica-like experience but with more control over which search and model providers sit behind it.

4. Morphic — best for generative-UI answers

Morphic renders answers as more than plain text: source-credited image grids, structured headings, and inline components build alongside the written response instead of a wall of prose with links at the bottom. It supports Quick and Adaptive search modes, a model selector across OpenAI, Anthropic, Google, Ollama, and any OpenAI-compatible provider, and ships PostgreSQL, Redis, and SearXNG in its Docker Compose file, so no external search API key is required to get started.

Where it falls short: The richer UI means more moving parts (Postgres, Redis, SearXNG, the app itself) than a single-container setup, which is more to monitor if something breaks.

Pricing: Free and open source, no paid tier.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: The best-looking answer engine on this list, worth the extra containers if a polished, componentized UI matters as much as the answer quality.

5. Open WebUI — best for adding search to an existing Ollama setup

Open WebUI started as a chat interface for Ollama and OpenAI-compatible APIs, and its built-in web search toggle (backed by SearXNG or a handful of other providers) turns an existing local chat setup into an answer engine without standing up a separate app. Anyone already running Open WebUI for local chat gets AI search as a settings change rather than a new deployment.

Where it falls short: Search is a feature bolted onto a chat UI, not the primary design goal, so citation rendering and source ranking are less polished than in the dedicated answer engines above. Deployments serving 51 or more users in a rolling 30-day window must keep Open WebUI branding visible under its current license.

Pricing: Free and open source, no paid tier; a separate enterprise license covers white-labeled or large-scale deployments.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: The lowest-effort upgrade path for anyone who already has Open WebUI running for local chat and wants search added on top.

6. MindSearch — best for multi-agent deep research

MindSearch, built by InternLM, splits a query across two agent roles: a WebPlanner that decomposes the question into sub-queries and builds a reasoning graph, and one or more WebSearchers that execute those sub-queries and report findings back. That structure lets it work through more than 300 source pages on a complex research question, well past what a single-pass search-and-summarize app handles cleanly. It runs against InternLM’s own models or GPT-4, and supports DuckDuckGo, Bing, Brave, and Google Serper as search backends.

Where it falls short: Development has slowed since its late-2024 refactor, and setup leans toward users comfortable configuring an agent framework rather than a one-click appliance.

Pricing: Free and open source, no paid tier.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: The choice for genuinely deep, multi-step research questions where a single search pass would miss context.

7. LLocalSearch — best for a minimal fully local agent

LLocalSearch wraps a local LLM in a simple tool-use loop: the model decides when to search, reads what comes back, and decides whether to search again before answering, with the whole chain visible in a live log. It needs no API keys at all and was built to run on modest hardware, which made it a popular first stop for anyone testing local AI search on a budget GPU.

Where it falls short: GitHub archived the repository in June 2026 and the project has been without active development for over a year, so it still runs via Docker but won’t receive fixes or model-compatibility updates going forward.

Pricing: Free and open source, no paid tier.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: Still functional and worth trying for the simplicity of its design, but treat it as a reference implementation rather than a long-term daily driver.

8. AnythingLLM — best for search inside a broader RAG workspace

AnythingLLM is less a dedicated search engine and more a full workspace: document RAG, AI agents, and multi-user access control, with web search available as one tool an agent can call alongside document retrieval. Anyone who already wants a self-hosted chat platform for their own files gets AI search as one more capability rather than a separate deployment, with Admin, Manager, and Default roles for shared household or team use.

Where it falls short: Search is one tool among several, not the focus, so it won’t match a dedicated answer engine’s citation formatting or ranking quality on pure search queries.

Pricing: Free and open source, no paid tier; a hosted plan is available for those who don’t want to self-host.

Platforms: Windows, macOS, Linux (Docker), self-hosted.

Download: GitHub

Bottom line: The right pick when search needs to live alongside document chat and agents in one workspace, not stand alone.

How to pick

If you want the simplest Perplexity clone: Perplexica. One container, one command, citations and images out of the box.

If you want the metasearch layer alone, with no AI attached yet: SearXNG. Run it first, point an answer engine at it later.

If you want control over which search provider and model sit behind the answers: Farfalle.

If the interface matters as much as the answer: Morphic, for its generative UI and source-credited image grids.

If Open WebUI is already your local chat app: turn on its built-in web search rather than deploying something new.

If the question needs multi-step, multi-source research: MindSearch, for its planner-and-searcher agent design.

If budget hardware and zero API keys are the priority, and stability matters less than simplicity: LLocalSearch, with the caveat that it’s archived.

If search needs to sit inside a document-chat and agent workspace: AnythingLLM.

Frequently asked questions

Do these apps work without an internet connection?

No. The LLM itself can run fully offline once downloaded, but every app on this list still needs to reach the open web to fetch current search results. What stays local is the model inference and, depending on setup, the query itself, since a self-hosted SearXNG instance queries other search engines on your server’s behalf rather than sending your IP directly.

Which local models pair best with these search engines?

A mid-sized instruction-tuned model in the 7B to 30B range, such as a recent Qwen, Llama, or Mistral build, handles citation-heavy summarization well on consumer GPU hardware. Smaller models tend to hallucinate around sources rather than quoting them accurately, so it’s worth testing a query against a known answer before trusting a lightweight model’s output.

Do I need a GPU to run a self-hosted AI search stack?

Not strictly. The search and retrieval side (SearXNG, the app’s backend) runs fine on CPU. The LLM is the part that benefits from a GPU; without one, expect noticeably slower responses, especially on larger models, or point the app at a cloud model API as a fallback.

Is self-hosted AI search actually more private than Perplexity or ChatGPT?

Search queries stay on infrastructure you control rather than a vendor’s servers, and none of the apps on this list send conversation history to a third party by default. The tradeoff is that the underlying search engines (Google, Bing, and the rest) still see the individual queries SearXNG relays to them, just without your IP or account attached.

Can more than one person use the same self-hosted instance?

Some of these apps support it natively. AnythingLLM and Open WebUI both ship role-based multi-user accounts out of the box. Others, including Perplexica, Farfalle, and Morphic, were built with single-user or small-household use in mind and need additional setup (a reverse proxy with authentication, for example) before exposing them to a wider group safely.

What happens if a project like LLocalSearch stops being maintained?

An archived open-source repository keeps working exactly as it did on its last commit; Docker images already built continue to run. What stops is fixes for new bugs, compatibility updates for newer model APIs, and security patches, so an archived project is fine for testing or light use but is a poor long-term bet for anything depending on ongoing updates.