Overview
SurfSense exposes live data from Reddit, YouTube, Instagram, TikTok, Google Search/Maps, Indeed, Amazon, and other sources to agents through REST and MCP, while storing findings in a searchable RAG knowledge base. Its stack includes FastAPI/Python, Next.js/TypeScript, PostgreSQL/pgvector, and support for local models such as Ollama and vLLM. The latest release checked is v0.0.40.
Features and best fit
Based on official documentation; not hands-on tested · Content checked:
Expose platform-specific live data as typed REST endpoints and MCP tools
Connectors return structured items for social, search, reviews, jobs, products, and open-web pages through a common API. The same catalog is exposed by the MCP server so Claude, Cursor, and other agents can use retrieval primitives without driving a browser for every page.
Sources: [2]
Connect live retrieval to a RAG knowledge base and automations
Fetched data, uploaded files, cloud-drive sync, and local folders can feed a knowledge base with semantic plus full-text search and cited answers. Scheduled or event-triggered agent turns can also write results back into systems such as Notion, Slack, Linear, and Jira.
Sources: [2]
Self-host in 3 steps
1. Install Docker and run the official docker/scripts/install.sh on Linux/macOS or install.ps1 on Windows.
2. Configure LLM and embedding providers; choose Ollama/vLLM for a private model path and add only the connector credentials you need.
3. Call a connector through REST or MCP, save results into a workspace/knowledge base, and verify citation-backed research.
For teams that want live web data and private knowledge behind the same agent research layer
SurfSense fits workflows that go beyond static document Q&A and need current web, social, search, or review data inside agent automation. A self-hosted deployment can keep the model, database, and connector orchestration within controlled infrastructure while exposing REST/MCP to existing agents.
Sources: [2]
Account for pre-1.0 churn, scraping terms, automatic updates, and mixed licensing
At v0.0.40 the project is still pre-1.0, so connector and schema changes should be regression-tested. Users remain responsible for site terms, robots policies, privacy, and applicable law when scraping. The self-host installer enables daily Watchtower updates unless disabled, which may conflict with pinned production deployments. Most code is Apache-2.0, but surfsense_backend/app/proprietary/ is under BSL 1.1.
Official sources
- [1]MODSetter/SurfSense — GitHub repository metadata(2026-09-17)
- [2]SurfSense — README(2026-09-17)
- [3]SurfSense v0.0.40 release(2026-09-17)
- [4]SurfSense — LICENSE(2026-09-17)
- [5]SurfSense — Docker installation documentation(2026-09-17)
Supplemental curator note
Treat SurfSense as a research platform increasingly centered on live-web connector/API/MCP primitives for agents, not only as a NotebookLM clone.
Try it in 3 steps
- 1
Get the source
git clone --depth 1 https://github.com/MODSetter/SurfSense.git - 2
Enter the repository
cd SurfSense - 3
Check the official steps
Continue with the commands in the README Installation, Quick Start, or Getting Started section.
find . -maxdepth 1 -iname 'README*' -exec sed -n '1,220p' {} \; -quit
Growth
Growth trends · Last 30 days
16,160 Stars
Trend data is still being collected.
Development activity
Last 90 days · weekly
- Commits (last 30 days)
- 739
- Open PRs
- 4
Development activity is still being collected.
Built with
Categories and tags
GitHub data
GitHub dataView detailed GitHub data
GitHub Topics
- ai
- fastapi
- langchain
- nextjs
- rag
- python
- typescript
- notebooklm
- ollama
- agent
- agents
- langgraph
- Stars
- 16,160
- Forks
- 1,537
- Watchers
- 93
- Open issues
- 126
- Primary language
- Python
- License
- Not determined
- Repository last updated
- Sep 17, 2026
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