Local Deep Research vs Vane
Two of the top search, side by side: score, setup, license, activity and what each review found.
Local Deep Research
Agentic research assistant with local LLMs, SearXNG and encrypted libraries
Vane
Self-hosted answer engine with cited sources over SearXNG
| What we compare | Local Deep Research | Vane |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 42, known | 86, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 75, fair | 9, weak |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 9.2k | 37.2k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Aug 2026 | Apr 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Not stated |
Local Deep Research
Runs multi-step research across the web, academic engines and your own documents using Ollama or any OpenAI-compatible endpoint, with a LangGraph agent that picks engines adaptively and writes cited reports. Each user gets an AES-256 SQLCipher database, and egress scopes limit which engines and providers a run may use. Web UI on port 5000 via Docker, Compose or pip, for privacy-focused researchers.
Who it is for: Privacy-focused researchers running everything locally
Strengths
- Reports about 95% SimpleQA fully local on one RTX 3090 with Qwen3.6-27B
- Per-user SQLCipher databases; keys derived from the password, never stored
- No telemetry; Docker images signed with Cosign, with SLSA provenance and SBOMs
- Downloaded sources build a searchable, embedded personal library
Weaknesses
- Needs Ollama (or an LLM endpoint) and SearXNG running separately
- Private or localhost engine URLs are blocked unless an operator env var allows them
- Requires an AVX-capable x86-64 CPU; older CPUs crash with Illegal instruction
- docker run --network host only works on native Linux; Docker Desktop needs Compose
- GPU optional
- Docker + Compose
- Needs Ollama or OpenAI-compatible LLM endpoint, SearXNG, SQLCipher (bundled wheels)
- Models: Ollama models (e.g. gpt-oss:20b, Qwen3.6-27B), any OpenAI-compatible endpoint
- port 5000
Vane
Next.js answer engine (formerly Perplexica) that runs searches through a bundled SearXNG instance, then answers with citations using Ollama, OpenAI-compatible servers, OpenAI, Anthropic, Gemini or Groq models. Offers Speed, Balanced and Quality modes, web, discussion and academic sources, image and video search, file uploads and a search API. One container on port 3000; a slim image uses your own SearXNG.
Who it is for: Self-hosters replacing Perplexity with local models
Strengths
- Single Docker image bundles SearXNG; no search API key needed
- Local models via Ollama or any OpenAI-compatible server, plus cloud providers
- Browser search-engine shortcut via /?q=%s and a REST search API
- Slim image works with an existing SearXNG (JSON format and Wolfram Alpha enabled)
Weaknesses
- No authentication yet; listed as an upcoming feature
- Own-SearXNG setups must enable JSON output and Wolfram Alpha or searches fail
- Tavily and Exa search backends are marked coming soon
- Ollama on Linux must listen on 0.0.0.0 for the container to reach it
- no GPU
- Docker + Compose
- Needs SearXNG (bundled in the default image), LLM provider (Ollama, OpenAI-compatible server or cloud API)
- Models: Ollama, OpenAI, Anthropic Claude, Google Gemini, Groq
- port 3000