
lanternkb
io.github.ankitb073Published by GitHub account ankitb073v0.1.3Updated Oct 24, 2026
Private, local search over your own files, by meaning and keywords, with secrets redacted.
Overview
Lanternkb is a local MCP server that indexes folders you choose and lets an assistant search them by meaning and keywords, with detected secrets redacted.
- What you can do
- Search your indexed folders by meaning and by exact words, filtered by file type or recency, and list recently changed files from an assistant session. Index notes, code, PDFs, Word and OpenDocument files, rich text, saved web pages and subtitles, with optional OCR and audio. Keep folders open or local-only so an assistant that sends results to the cloud only sees what you allow. Redact detected secrets before content is split or embedded, and skip credential folders and secret-shaped files. Extend supported file types, embedding models and folder rules through plugins.
- When to use it
- Useful when you want an assistant to answer questions about your own notes, code and documents without uploading them to a hosted search service, and when you want per-folder control over what a cloud-connected assistant can see.
- Requirements
- Runs as a local stdio process installed with uv from PyPI; macOS 14 or later on Apple silicon, or Linux, with Python 3.12 selected explicitly. Run lanternkb setup first to choose folders and index them. About 0.9 GB of install space plus about 1.2 GB for the embedding model, which is downloaded with your consent. No accounts, API keys or headers are declared.
Installation
In SourceWeft
- Open lanternkb in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
lanternkb
Private, local search over your own files, for you and the AI tools you choose.
lanternkb indexes the folders you pick and searches them by meaning and by exact words. It runs as a local MCP server for Claude Code and other assistants, with no daemon, no open port and no telemetry. Secrets it detects are redacted before anything is stored, and you decide which folders an assistant that sends results to the cloud may see.
Install
macOS 14 or later on Apple silicon, or Linux, with uv:
--python 3.12 matters: without it uv may pick an older Python already on the machine, and the install fails.
The install takes about 0.9 GB on macOS, plus about 1.2 GB for the embedding model. uv tool upgrade lanternkb
updates it.
Search
Run lanternkb setup before connecting an assistant: the server searches the folders you chose there.
Use it from your AI tools
Claude Code:
lanternkb mcp-setupprints aclaude mcp addcommand. Run it.- The server it registers returns files from folders you marked open, and nothing else.
- Claude Code can also read files with its own tools, so
mcp-setupprints deny rules too. Add them to~/.claude/settings.jsonto keep its tools away from your local-only folders and the index.
Codex, Cursor, VS Code, Gemini CLI and any other MCP client: run lanternkb serve --tier open as a stdio
server. For Codex, in ~/.codex/config.toml:
The user guide has the JSON form the other clients take, and what to watch for.
What it reads
Notes, code, PDFs, Word and OpenDocument files, rich text, saved web pages and subtitles; scanned pages and audio optionally. Plugins can add file types, embedding models and rules for which folder is open (plugins).
Privacy and safety
- Every folder is
openorlocal-only, and new folders are local-only. - Credential folders and secret-shaped files are skipped, and a secret scanner redacts what it finds before anything is split up or embedded. A file in which it finds a secret becomes local-only, even in an open folder.
- Files are read in sandboxed worker processes that deny by default:
sandbox-execon macOS, Landlock and seccomp on Linux. - Nothing goes over the network except the model downloads you agree to.
The threat model says what is protected and what is not.
How well it works
Tested on a synthetic set of files (three open-source code bases and 654 generated notes) with 330 held-out questions: nDCG@10 0.831, against 0.777 for grooveseek with its reranker and 0.760 for QMD, at about 30 ms a search (about 120 ms with reranking, on an M5 Max). On the hardest held-out test of planted fake secrets, the scanner kept 71.9% out of the index, so treat it as a second line of defence: keep credential folders out of indexed folders, or local-only.
Limits
- The secret scanner misses some secrets, and values that read as examples (
example,changeme, a<placeholder>, a${VARIABLE}) are left alone. - On Linux, reranking runs on the CPU at 0.4 to 1 s a search, so it is off until you enable it.
- Email, browser history, calendars and contact cards are not indexed.
- Scanned PDFs and images need OCR, which is off by default.
More
Licence
Apache-2.0: see LICENSE, NOTICE and THIRD_PARTY_NOTICES.md.
Source: README.md at commit b56630a
Tools
0Version history
1- v0.1.3LatestOct 13, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts
