GPT Researcher vs Vane
Two of the top search, side by side: score, setup, license, activity and what each review found.
GPT Researcher
Research agent that writes cited reports from web and local documents
Vane
Self-hosted answer engine with cited sources over SearXNG
| What we compare | GPT Researcher | Vane |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 69, popular | 86, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 99, healthy | 9, weak |
| Easy to run | 33, some setup | 33, some setup |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 30k | 37.2k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Sep 2026 | Apr 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
GPT Researcher
Planner and execution agents generate research questions, scrape 20+ sources, filter passages (Jev by default, BM25 fallback with no key) and write cited reports over 2,000 words, exportable to PDF and Word. Runs as a FastAPI server on port 8000 with a static or Next.js frontend, or as a pip package; local PDF, Office, CSV and Markdown files can be sources. For analysts automating long-form research.
Who it is for: Analysts and developers automating long-form research
Strengths
- Deep Research mode: tree-like exploration, about 5 minutes and $0.40 per run on o3-mini
- Hybrid retrievers: Tavily plus MCP servers such as GitHub as research sources
- Works with any OpenAI-compatible endpoint via OPENAI_BASE_URL
- Multi-agent LangGraph and AG2 variants produce 5-6 page PDF, DOCX and Markdown reports
Weaknesses
- Default setup needs OpenAI and Tavily API keys
- Jev context filtering needs a TYPESAFE_API_KEY; the fallback is keyword BM25
- Python 3.12 or later required
- Disclaimer labels the project experimental and for academic purposes
- no GPU
- Docker + Compose
- Needs OpenAI or OpenAI-compatible LLM API, Tavily API key (default retriever), TypeSafe API key (optional Jev filter)
- Models: OpenAI models, any OpenAI-compatible endpoint via OPENAI_BASE_URL, Gemini 2.5 Flash Image (inline images)
- port 8000
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