ArchestackBest of Open-Source AI: List on GitHub ↗ (opens in a new tab)

LightRAG vs SurfSense

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.

33rd of 60 in RAG, knowledge and search

LightRAG

Graph-based RAG framework with a REST server and WebUI

90 out of 100 GitHub ↗ (opens in a new tab)
#66th of 60 in RAG, knowledge and search

SurfSense

Desktop NotebookLM-style agent that researches and edits local documents

87 out of 100 GitHub ↗ (opens in a new tab)

Verdict

LightRAG ranks higher, 90 to 87. Of the 4 score parts we measure for both, LightRAG leads on 1, SurfSense on 2 and 1 is even.

  • AdoptionLightRAG leads by 23 points, 91 to 68: a wide gap.
  • FreshnessEven at 100.
  • MaintenanceSurfSense leads by 8 points, 94 to 86.
  • Agent-readySurfSense leads by 15 points, 85 to 70: a wide gap.

Pick LightRAG if you are

Engineers building graph-based RAG over private documents with their own LLM

  • Runs with local file storage by default; no database required
  • Pluggable backends: PostgreSQL, Neo4j, MongoDB, OpenSearch, any OpenAI-compatible LLM

Pick SurfSense if you are

Users who need cited Q&A and edits over private documents, offline

  • Index stays on your disk; no account required
  • Edits PDF, Word and Excel files as copies, never altering originals
LightRAG vs SurfSense: score parts and facts
What we compareLightRAGSurfSense
Score parts, out of 100
Adoption91, widely used68, popular
Freshness100, active100, active
Maintenance86, healthy94, healthy
Agent-ready70, partly85, ready
Facts from GitHub and the README
Stars40.7k16.9k
LicenseMIT (permissive)Apache-2.0 (permissive)
Last commitNov 2026Nov 2026
Last releaseSep 2026Nov 2026
LanguagePythonPython
DockerYesYes
GPUNot neededOptional
arm64 or Apple SiliconMentionedMentioned

LightRAG

LightRAG indexes documents into both a knowledge graph and vector embeddings, then answers queries with dual-level retrieval over entities and concepts. It ships as a Python library and as lightrag-server, which provides a REST API and a WebUI for inserting, querying and visualizing the graph. Storage defaults to local files, with PostgreSQL, Neo4j, MongoDB and OpenSearch as options.

Who it is for: Engineers building graph-based RAG over private documents with their own LLM

Strengths

  • Runs with local file storage by default; no database required
  • Pluggable backends: PostgreSQL, Neo4j, MongoDB, OpenSearch, any OpenAI-compatible LLM
  • Optional reranker, citations, document deletion with KG regeneration, RAGAS and Langfuse
  • Separate LLM settings for extraction, query, keywords and VLM roles

Weaknesses

  • Entity extraction makes one LLM call per chunk, raising indexing time and cost
  • Server binds 0.0.0.0 with authentication off by default
  • Changing the embedding model requires re-indexing every document
  • Source install needs bun to build the WebUI; PyPI package avoids this
  • no GPU
  • Docker + Compose
  • Needs OpenAI-compatible LLM and embedding API, Optional: PostgreSQL, Neo4j, MongoDB, OpenSearch, Milvus, Optional: MinerU or Docling for multimodal parsing
  • Models: OpenAI-compatible APIs, Open-source LLMs such as Qwen3-30B-A3B
  • port 9621
Documentation ↗ (opens in a new tab)Website ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)

SurfSense

SurfSense is a desktop app for Windows, macOS (Apple Silicon) and Linux that answers questions over your files with citations and can also create, edit and convert PDF, Word, Excel and PowerPoint documents. The index stays on disk and there is no account. You choose the model: any OpenAI-compatible API or a local one. Edits always produce a new copy.

Who it is for: Users who need cited Q&A and edits over private documents, offline

Strengths

  • Index stays on your disk; no account required
  • Edits PDF, Word and Excel files as copies, never altering originals
  • Transcribes audio locally and reads scanned pages via OCR
  • Works with any OpenAI-compatible API or a local model

Weaknesses

  • Built-in plugins (Google Search, Web Crawl, etc.) need a SurfSense license; trial is 30 days
  • Word and PowerPoint to PDF needs LibreOffice, not included in the installer
  • Image generation needs a model you configure; none is chosen by default
  • Hosted web app is being sunset; only the desktop app remains
  • GPU optional
  • Docker + Compose
  • Needs LibreOffice (optional, for Office conversion)
  • Models: OpenAI-compatible APIs, local models
  • README: alternative to NotebookLM
Website ↗ (opens in a new tab)Repository on GitHub ↗ (opens in a new tab)

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.

AI app development →Send a project brief