Vane — Privacy-Focused AI Answer Engine
Self-hosted AI search and Q&A engine with multi-LLM support and cited answers.
Evidence shows: README emphasizes privacy and local data storage, but lacks specific mechanisms for least privilege, user confirmation, data flow transparency. Many dependencies but no security audit or vulnerability scanning evidence. External effects (web search) exist but no user confirmation or revocation mechanism. Rollback not mentioned. Source attribution mentioned but not detailed. Deductions: lack of implementation details and evidence.
Evidence shows: README and package.json are consistent, feature descriptions match dependencies. Many dependencies but no availability guarantee or mirror. Failure messages partially present (e.g., Ollama connection errors) but not comprehensive. Deductions: dependency availability unverified, failure messages incomplete.
Evidence shows: README describes multiple use cases (local, cloud, search modes), capability boundaries described (multiple providers and sources), but trigger precision (e.g., search mode selection) not detailed. Environment fit has Docker and non-Docker installation instructions. Deductions: trigger precision and boundary descriptions not precise.
Evidence shows: README structure clear, installation notes detailed, examples provided (e.g., Docker commands), but missing known limitations and version changelog. License is MIT, but maintenance responsibility not clear. Deductions: missing known limitations, version changelog, and maintenance responsibility.
Evidence shows: Output usability (cited sources, multiple search modes) described, marginal value (privacy, local running) evident, but cost-benefit (resource consumption, API costs) not discussed. Deductions: cost-benefit not addressed.
Evidence shows: README claims features but no testing or verification evidence. Source citation mentioned but fact/inference separation not clear. Deductions: lack of verifiable evidence and fact/inference separation.
- No security audit or vulnerability scanning evidence; many dependencies, supply chain risk needs assessment.
- No user confirmation mechanism; external searches may execute automatically, privacy and compliance concerns.
- No rollback mechanism; upgrades or misconfigurations may be hard to recover.
- No version changelog; upgrade risks are opaque.
What does this agent do, and when should you use it?
Vane is a fully self-hostable AI answering engine designed with privacy at its core, handling all search processing on your own hardware. It combines internet knowledge with support for local LLMs (via Ollama) and cloud providers (OpenAI, Anthropic Claude, Google Gemini, Groq) to deliver accurate, cited answers while keeping searches private. It includes a built-in SearxNG aggregator for private web search, multiple search modes, source selection (web, discussions, academic), widgets (weather, calculations), image/video search, file uploads, and domain-specific search. All search history is stored locally. Installation is Docker-first (one command) with a manual fallback. Vane also offers an API for developers.
Accepts user queries, dispatches based on chosen mode (Speed, Balanced, Quality), gathers results from SearxNG (requires JSON format and Wolfram Alpha enabled), and generates cited answers via configured LLM providers (Ollama, OpenAI, Claude, Gemini, Groq). Supports file uploads (PDF, text, images) for document Q&A. Features domain-limited search, smart suggestions, Discover page, and local search history. Deployment via Docker (docker run ... itzcrazykns1337/vane:latest) or manual (npm i && npm run build && npm run start), serving on port 3000 with an HTTP API.
- Privacy-conscious users: use local LLMs and SearxNG to avoid sending data to cloud services.
- Researchers: leverage academic sources and Quality mode for well-cited answers.
- Technical documentation: restrict search to specific domains for quick API lookup.
- Offline environments: run without internet using local models and SearxNG.
- Productivity: set Vane as default search engine in browser.
- Developers: integrate Vane's API into custom applications.
What are this agent's strengths and limitations?
- Completely self-hosted, privacy is under your control, all data stays local
- Supports multiple LLM providers, including local Ollama and major cloud APIs, for flexible combinations
- Bundled SearxNG aggregator obfuscates search queries
- Offers Speed/Balanced/Quality modes and source selection
- Supports file uploads (PDF, text, images) for document Q&A
- Active community and Discord support
- Requires Docker or Node.js environment, technical setup barrier
- Common configuration issues with Ollama or SearxNG connections
- Local models demand significant hardware resources
- Depends on SearxNG instance; JSON interface must be enabled
- Some features (e.g., Tavily/Exa) are incomplete and on roadmap
- No authentication mechanism, exposing publicly is risky
How do you install or deploy this agent?
Quick start with Docker: run docker run -d -p 3000:3000 -v vane-data:/home/vane/data --name vane itzcrazykns1337/vane:latest and open http://localhost:3000 to configure API keys and models. For external SearxNG, use the slim image and set SEARXNG_API_URL environment variable. Manual install: clone repo, set up SearxNG (JSON format enabled, Wolfram Alpha enabled), then npm i, npm run build, npm run start, and access localhost:3000.
How do you use this agent?
After installation, open http://localhost:3000, enter API keys (for Ollama, put any value in the key field) and select models. Type a query, choose mode (Speed/Balanced/Quality) and source (web/discussions/academic). Use file upload for document Q&A and domain filter for site-specific searches. To use as a search engine, add http://localhost:3000/?q=%s in browser settings. For API integration, refer to docs/API/SEARCH.md.
How does this agent compare with similar options?
Vane is functionally similar to Perplexica (both are AI search tools), but Vane emphasizes privacy and self-hosting, whereas Perplexica may focus more on immediate usability. Compared to traditional search engines, Vane provides AI-generated answers with citations. Specific benchmark comparisons are not provided.
FAQ
Does Vane support Chinese language queries?
What hardware is required to run Vane?
Can Vane be integrated into existing applications?
How to resolve Ollama connection errors?
http://host.docker.internal:11434, Linux use host's private IP; ensure Ollama listens on 0.0.0.0.