Morphic vs GPT Researcher
Two of the top search, side by side: score, setup, license, activity and what each review found.
Morphic
Search engine that answers with citations and renders rich inline components
GPT Researcher
Research agent that writes cited reports from web and local documents
| What we compare | Morphic | GPT Researcher |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 29, niche | 69, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 99, healthy |
| Easy to run | 67, easy | 33, some setup |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 9.2k | 30k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Sep 2026 |
| Language | TypeScript | Not stated |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Not stated | Not stated |
Morphic
Morphic runs web searches and returns cited answers, rendering inline components such as images, grids and headings from a streamed JSON spec instead of plain markdown. It offers Quick and Adaptive search modes and works with OpenAI, Anthropic, Google, Ollama, Vercel AI Gateway and OpenAI-compatible models. Docker Compose brings up PostgreSQL, Redis, SearXNG and the app, with chat history stored in PostgreSQL.
Who it is for: Self-hosters wanting a cited AI search UI with their own models
Strengths
- Compose file bundles PostgreSQL, Redis and SearXNG, so no search API key is required
- Supports Tavily, SearXNG, Brave and Exa as search providers
- Model selector detects providers, including local Ollama and OpenAI-compatible endpoints
- Auth is switchable between Supabase, better-auth and none
Weaknesses
- Full stack needs four containers: PostgreSQL, Redis, SearXNG and the app
- Supabase is the default auth provider unless ENABLE_AUTH=false or AUTH_PROVIDER is set
- Needs at least one AI provider API key or a local Ollama setup
- README gives no RAM or hardware requirements
- Docker + Compose
- Needs PostgreSQL, Redis, SearXNG, Supabase Auth (optional)
- Models: OpenAI, Anthropic, Google, Ollama, Vercel AI Gateway
- port 3000
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