LightRAG 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.
LightRAG
Graph-based RAG framework with a REST API server and web UI
Morphic
AI answer engine with cited search results and generative UI components
Verdict
LightRAG ranks higher, 76 to 74. Of the 5 score parts we measure for both, LightRAG leads on 1, Morphic on 2 and 2 are even.
- AdoptionLightRAG leads by 65 points, 81 to 16: a wide gap.
- FreshnessEven at 100.
- MaintenanceMorphic leads by 11 points, 97 to 86.
- Easy to runMorphic leads by 33 points, 83 to 50: a wide gap.
- Agent-readyEven at 70.
Pick LightRAG if you are
Engineers building graph-based document Q&A over large corpora
- Document deletion rebuilds affected graph entries from the LLM cache; inserts are incremental
- Storage backends include PostgreSQL, Neo4j, MongoDB and OpenSearch, plus local file stores
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 | LightRAG | Morphic |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 81, widely used | 16, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 86, healthy | 97, healthy |
| Easy to run | 50, easy | 83, very easy |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 40.2k | 9.2k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Sep 2026 | Oct 2026 |
| Language | Python | TypeScript |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
LightRAG
LightRAG indexes documents into a knowledge graph plus vector embeddings and answers queries with dual-level (entity and concept) retrieval. It ships as a Python library and as lightrag-server, which provides a REST API and a web UI for inserting, querying and visualizing the graph. It supports incremental updates, document deletion with graph rebuild, reranking, and several parsing and chunking options.
Who it is for: Engineers building graph-based document Q&A over large corpora
Strengths
- Document deletion rebuilds affected graph entries from the LLM cache; inserts are incremental
- Storage backends include PostgreSQL, Neo4j, MongoDB and OpenSearch, plus local file stores
- Parsers: MinerU, Docling and a native engine; four chunking strategies
- Setup wizard generates .env and compose files; RAGAS evaluation and Langfuse tracing supported
Weaknesses
- Default in-memory/file stores are stated as not for production
- Server binds to 0.0.0.0 and endpoints are public until auth is set in .env
- Graph extraction needs LLM calls at index time; RAM and GPU needs are unstated
- Optional features need extra steps: spaCy models for smart_heading, libcairo for SVG
- Docker + Compose
- Needs LLM provider, embedding model, PostgreSQL (recommended for production), Neo4j (optional), MongoDB (optional), OpenSearch (optional), MinerU or Docling (optional)
- Models: LLMs configured via .env, open-source LLMs such as Qwen3-30B-A3B, rerankers
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
More in RAG, knowledge and search
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.