While tools like NotebookLM and Perplexity are impressive and highly effective for conducting research on any topic, SurfSense elevates this capability by integrating with your personal knowledge base. It is a highly customizable AI research agent, connected to external sources such as search engines (Tavily), Slack, Notion, and more to come.

Have your own highly customizable private NotebookLM and Perplexity integrated with external sources.

Save content from your own personal files (Documents, images and supports 27 file extensions) to your own personal knowledge base .

Quickly research or find anything in your saved content .

Interact in Natural Language and get cited answers.

Get Cited answers just like Perplexity.

Works Flawlessly with Ollama local LLMs.

Open source and easy to deploy locally.

• Supports 150+ LLM's
• Supports 6000+ Embedding Models.
• Supports all major Rerankers (Pinecode, Cohere, Flashrank etc)
• Uses Hierarchical Indices (2 tiered RAG setup).
• Utilizes Hybrid Search (Semantic + Full Text Search combined with Reciprocal Rank Fusion).
• RAG as a Service API Backend.

• Search Engines (Tavily)
• Slack
• Notion
• and more to come.....

• The SurfSense extension is currently being reworked for better UI and stability. Expect it soon.

• The SurfSense Podcast feature is currently being reworked for better UI and stability. Expect it soon.

For File uploading you need Unstructured.io API key. You can get it at http://platform.unstructured.io/

SurfSense now only works with Google OAuth. Make sure to set your OAuth Client at https://developers.google.com/identity/protocols/oauth2 . We need client id and client secret for backend.

SurfSense currently uses Firecrawl.py right now. Playwright crawler support will be added soon.

This is the core of SurfSense. Before we begin let's look at .env variables' that we need to successfully setup SurfSense.

IMPORTANT: Since LLM calls are routed through LiteLLM make sure to include API keys of LLM models you are using. For example if you used litellm:openai/gpt-4o make sure to include OpenAI API Key OPENAI_API_KEY or if you use litellm:gemini/gemini-2.0-flash then you include GEMINI_API_KEY.

You can also integrate any LLM just follow this https://docs.litellm.ai/docs/providers

Now once you have everything let's proceed to run SurfSense.

1. Install uv : https://docs.astral.sh/uv/getting-started/installation/
2. Now just run this command to install dependencies i.e uv sync
3. That's it. Now just run the main.py file using uv run main.py.
4. If everything worked fine you should see screen like this.

For local frontend setup just fill out the .env file of frontend.

1. Now install dependencies using pnpm install
2. Run it using pnpm run dev

You should see your Next.js frontend running at localhost:3000

Extension is in plasmo framework which is a cross browser extension framework.

For building extension just fill out the .env file of frontend.

Build the extension for your favorite browser using this guide: https://docs.plasmo.com/framework/workflows/build#with-a-specific-target

When you load and start the extension you should see a Login page like this

After logging in you should be able to use extension now.

• FastAPI: Modern, fast web framework for building APIs with Python

• PostgreSQL with pgvector: Database with vector search capabilities for similarity searches

• SQLAlchemy: SQL toolkit and ORM (Object-Relational Mapping) for database interactions

• FastAPI Users: Authentication and user management with JWT and OAuth support

• LangChain: Framework for developing AI-powered applications

• GPT Integration: Integration with LLM models through LiteLLM

• Rerankers: Advanced result ranking for improved search relevance

• GPT-Researcher: Advanced research capabilities

• Hybrid Search: Combines vector similarity and full-text search for optimal results using Reciprocal Rank Fusion (RRF)

• Vector Embeddings: Document and text embeddings for semantic search

• pgvector: PostgreSQL extension for efficient vector similarity operations

• Chonkie: Advanced document chunking and embedding library

• Uses AutoEmbeddings for flexible embedding model selection

• LateChunker for optimized document chunking based on embedding model's max sequence length

• Next.js 15.2.0: React framework featuring App Router, server components, automatic code-splitting, and optimized rendering.

• React 19.0.0: JavaScript library for building user interfaces.

• TypeScript: Static type-checking for JavaScript, enhancing code quality and developer experience.

• Vercel AI SDK Kit UI Stream Protocol: To create scalable chat UI.

• Tailwind CSS 4.x: Utility-first CSS framework for building custom UI designs.

• Shadcn: Headless components library.

• Lucide React: Icon set implemented as React components.

• Framer Motion: Animation library for React.

• Sonner: Toast notification library.

• Geist: Font family from Vercel.

• React Hook Form: Form state management and validation.

• Zod: TypeScript-first schema validation with static type inference.

• @hookform/resolvers: Resolvers for using validation libraries with React Hook Form.

• @tanstack/react-table: Headless UI for building powerful tables & datagrids.

Manifest v3 on Plasmo

• Add More Connectors.
• Patch minor bugs.
• Implement Canvas.
• Complete Hybrid Search. [Done]
• Add support for file uploads QA. [Done]
• Shift to WebSockets for Streaming responses. [Deprecated in favor of AI SDK Stream Protocol]
• Based on feedback, I will work on making it compatible with local models. [Done]
• Cross Browser Extension [Done]
• Critical Notifications [Done | PAUSED]
• Saving Chats [Done]
• Basic keyword search page for saved sessions [Done]
• Multi & Single Document Chat [Done]

https://github.com/MODSetter/SurfSense/blob/main/CHANGELOG.md

Contributions are very welcome! A contribution can be as small as a ⭐ or even finding and creating issues. Fine-tuning the Backend is always desired.