Local Deep Research vs GPT Researcher
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
GPT Researcher
Research agent that writes cited reports from web and local documents
| What we compare | Local Deep Research | GPT Researcher |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 42, known | 69, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 75, fair | 99, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 0, none | 0, none |
| Facts from GitHub and the README | ||
| Stars | 9.2k | 30k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Aug 2026 | Sep 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
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