RAGFlow vs Morphic
Two of the top projects in RAG, knowledge and search, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
RAGFlow
RAG engine with deep document parsing, template chunking and agentic retrieval
Morphic
AI answer engine with cited search results and generative UI components
Verdict
Both score 74; RAGFlow ranks higher on stars, 92,455 to 9,177. Of the 5 score parts we measure for both, RAGFlow leads on 1, Morphic on 3 and 1 is even.
- AdoptionRAGFlow leads by 81 points, 97 to 16: a wide gap.
- FreshnessEven at 100.
- MaintenanceMorphic leads by 4 points, 97 to 93: close to even.
- Easy to runMorphic leads by 50 points, 83 to 33: a wide gap.
- Agent-readyMorphic leads by 40 points, 70 to 30: a wide gap.
Pick RAGFlow if you are
Teams self-hosting document Q&A over messy PDFs and Office files
- Template-based chunking with a visual view of chunks so humans can correct them
- Answers include traceable citations back to source chunks
Pick Morphic if you are
Self-hosters wanting a private, cited AI search app with their own models
- Docker Compose bundles SearXNG, so no search API key is required
- Many model providers, including local Ollama and OpenAI-compatible endpoints
| What we compare | RAGFlow | Morphic |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 97, widely used | 16, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 93, healthy | 97, healthy |
| Easy to run | 33, some setup | 83, very easy |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 92.5k | 9.2k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Go | TypeScript |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Not stated | Mentioned |
RAGFlow
RAGFlow parses complex documents (PDF, Word, Excel, PowerPoint, scans, web pages) with its DeepDoc layout, OCR and table recognition, then chunks them by template and answers with traceable citations. It adds multi-step agentic retrieval with four thinking modes, plus knowledge compilation into wikis, graphs and mind maps. Services are written in Go and deployed with Docker Compose, with a web UI and HTTP API.
Who it is for: Teams self-hosting document Q&A over messy PDFs and Office files
Strengths
- Template-based chunking with a visual view of chunks so humans can correct them
- Answers include traceable citations back to source chunks
- Ingests many formats, plus sitemap crawling and Google BigQuery sync
- Configurable LLM, embedding and reranker models; multiple recall with fused re-ranking
Weaknesses
- Heavy stack: MySQL, MinIO, NATS, Kvrocks and a document engine; 16 GB RAM suggested
- macOS is not supported for the Docker deployment
- DeepDoc runs on CPU inference in the 1.0 release, which may slow parsing
- Current release is 1.0.0-rc1, a release candidate rather than a stable 1.0
- RAM ≥ 16 GB
- no GPU
- Docker + Compose
- Needs Docker, Docker Compose, MySQL, Elasticsearch or Infinity, MinIO, NATS, Kvrocks
- Models: Configurable LLM, embedding and reranker models via provider page
- port 80
Morphic
Morphic is a Next.js (TypeScript) AI search app that answers queries with cited sources and renders results as inline components streamed from a JSON spec. It supports OpenAI, Anthropic, Google, Ollama, Vercel AI Gateway and OpenAI-compatible models, with Tavily, SearXNG, Brave or Exa for search. Chat history lives in PostgreSQL, and Docker Compose bundles Postgres, Redis and SearXNG.
Who it is for: Self-hosters wanting a private, cited AI search app with their own models
Strengths
- Docker Compose bundles SearXNG, so no search API key is required
- Many model providers, including local Ollama and OpenAI-compatible endpoints
- Four search backends: Tavily, SearXNG, Brave, Exa
- Built-in accounts via Better Auth or Supabase Auth, plus guest mode and shareable result URLs
Weaknesses
- Compose stack needs PostgreSQL, Redis and SearXNG alongside the app
- Needs at least one model provider API key or a local Ollama setup
- Local development requires Bun; RAM and GPU requirements are not stated
- Chat history, auth and file upload need extra configuration beyond the quick start
- no GPU
- Docker + Compose
- Needs PostgreSQL, Redis, SearXNG, Tavily, Brave, Exa, Supabase
- Models: OpenAI, Anthropic, Google, Ollama, Vercel AI Gateway
- port 3000
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