Skip to main content

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:

uv tool install --managed-python --python 3.12 "lanternkb[embed,ui]"
lanternkb setup        # asks for folders and whether an assistant may see them, offers the models, indexes

--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.

lanternkb search "what did the plumber say about the boiler"
lanternkb search "boiler quote" --type pdf,docx --since 30d
lanternkb recent       # files changed lately
lanternkb ui           # the settings page, with a search box
lanternkb doctor       # checks the setup and says what to fix

Use it from your AI tools

Claude Code:

  1. lanternkb mcp-setup prints a claude mcp add command. Run it.
  2. The server it registers returns files from folders you marked open, and nothing else.
  3. Claude Code can also read files with its own tools, so mcp-setup prints deny rules too. Add them to ~/.claude/settings.json to 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:

[mcp_servers.lanternkb]
command = "lanternkb"
args = ["serve", "--tier", "open"]

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 open or local-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-exec on 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.

Metadata

Release files for lanternkb 0.1.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for lanternkb 0.1.2
File Size Uploaded
lanternkb-0.1.2.tar.gz 234.6 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for lanternkb 0.1.2
File Interpreter ABI Platform
lanternkb-0.1.2-py3-none-any.whl Python 3 none any Details

Total release size: 500.1 kB

Release files / lanternkb-0.1.2.tar.gz

Download URL lanternkb-0.1.2.tar.gz
Size 234.6 kB
Tags Source
SHA-256 checksum
How to use checksums
b68680ff7022052018faa222c54188add8825b67612a5ad84906fe45532b4ee1
BLAKE2b-256 checksum
How to use checksums
cdf90a6b7b960c413e5f36978e0cffb1e1eed645a96273762342728d54c0b7d6
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 13, 2026.

Transparency log

Release files / lanternkb-0.1.2-py3-none-any.whl

Download URL lanternkb-0.1.2-py3-none-any.whl
Size 265.6 kB
Tags Python 3
SHA-256 checksum
How to use checksums
72f6e7d7d6e9963bee1b5ab15c4e0a472b025440f265999338ecc3a0a14e1e1c
BLAKE2b-256 checksum
How to use checksums
d2665a7baeb2cd798c2659f1bc073c99ef4ad59ec499073aea256f05689eccf3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 13, 2026.

Transparency log

Release history Release notifications | RSS feed

0.1.3

2 release files

This release

0.1.2 This release

2 release files

0.1.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page