Show HN: I built ThreadShelf to reuse hard-to-export AI chats like OpenRouter

A developer released ThreadShelf, a local-first tool that archives and semantically searches AI chat exports from six providers: ChatGPT, Claude, Google AI Studio, OpenRouter, LM Studio, and Grok. The archive pipeline — parsing, embeddings, LanceDB storage, search, HTTP API, and MCP — runs locally, while conversation generation is labeled an Experimental Beta that keeps llama.cpp loopback-only and sends context off-device only through the explicitly marked OpenRouter external provider. ThreadShelf is tested against snapshots of specific versions, such as LM Studio 0.4.x, and its developer warns that Google AI Studio, OpenRouter, LM Studio, and Grok have no documented, stable export schema.

Local-first archive, semantic search, and continuation for your AI conversations. One private workspace across ChatGPT, Claude, Google AI Studio, OpenRouter, LM Studio, and Grok . Search old conversations by meaning, reopen the complete thread, and continue it with a local GGUF model through llama.cpp or the explicitly external OpenRouter provider. The archive pipeline—parsing, embeddings, LanceDB storage, search, HTTP API, and MCP—runs locally. Conversation generation is an Experimental Beta : llama.cpp stays loopback-only; switching to the clearly marked OpenRouter · external provider sends the selected user/assistant context and new prompt off-device. | Capability | What ThreadShelf provides | |---|---| | Archive & retrieval | Multi-provider normalization, local multilingual embeddings, semantic and exact search, complete thread reconstruction | | Continue & create | Local GGUF inference through managed llama.cpp , plus optional OpenRouter streaming | | Use it anywhere | React UI, command-line ingest/search, HTTP API, and an MCP stdio server over the same index | | Keep control | Loopback defaults, isolated local storage, explicit off-device labeling, synthetic test data | 1. You export/copy your chats as JSON see Get your data get-your-data . 2. ThreadShelf parses them into a common format, embeds them locally , and stores them in a local vector database LanceDB . 3. You search by meaning in the web UI, open the full original thread, and export any conversation to Markdown — or query the same index from an MCP client. 4. Optionally start a new chat or continue an archived thread through local llama.cpp or explicitly external OpenRouter. php flowchart LR exports "AI chat exports" -- parser "Provider parsers" parser -- turns "Normalized turns" turns -- chunks "Chunks" chunks -- embed "Local embeddings" embed -- db "LanceDB + thread store" db -- ui "React UI" db -- api "HTTP API" db -- mcp "MCP stdio" db -- generation "Generation registry" generation -- llama "llama.cpp<br/ local GGUF" generation -. "explicit off-device provider" .- openrouter "OpenRouter" - One normalized archive across six providers. It includes sources whose history is otherwise split between exports, Drive files, browser pages, and local application data. - Semantic, not just keyword. Find a conversation by topic even when you don't remember the exact words — plus an exact-match mode for when you do error strings, identifiers, code . - A local archive with an optional generation layer. Search remains useful without configuring any LLM. When generation is wanted, the primary engine is a loopback-only llama.cpp server; OpenRouter is a separately marked external choice. - One index, three front-ends. The same archive is searchable from the web UI, the HTTP API, and any MCP-capable tool. | Source | How export works | Format stability | |---|---|---| | Google AI Studio | Download your Drive "Google AI Studio" folder | | | OpenRouter | Browser-console export script this repo | | | LM Studio | Copy local conversation files | 0.4.x | | Grok / xAI | Account data export prod-grok-backend.json | | | ChatGPT / OpenAI | Official data export conversations.json | semi-stable | | Claude / Anthropic | Official data export | semi-stable | Format note. Google AI Studio, OpenRouter, LM Studio, and Grok have no documented, stable export schema — their vendors can change it without warning. ThreadShelf is tested against snapshots of specific versions e.g. LM Studio 0.4.x . If a newer app version changes the shape, parsing may break; please open an issue with an anonymized sample. ⚠️ You only need the providers you actually use. Put exported files anywhere, then point ThreadShelf at that folder. Your prompts are saved to Google Drive in a folder named Google AI Studio . 1. Open Google Drive https://drive.google.com , find the Google AI Studio folder. 2. Right-click → Download Drive zips it . Unzip somewhere local. 3. Index that folder. Files have no .json extension — that's fine, they're detected by content. Text chats and Imagen prompt histories from the July 2026 Drive export shape are supported. The format is undocumented and may change. OpenRouter has no bulk export, so this repo ships two browser-console scripts: - All chats: scripts/openrouter-export-all.js /ChrystianSchutz/ThreadShelf/blob/main/scripts/openrouter-export-all.js — walks every chat in your sidebar and downloads one JSON per chat. - Single chat: scripts/openrouter-export-browser.js /ChrystianSchutz/ThreadShelf/blob/main/scripts/openrouter-export-browser.js — exports just the chat currently open. To export everything: 1. Open openrouter.ai https://openrouter.ai/ signed in, with your chat list sidebar visible. 2. Open DevTools → Console F12 → Console tab . 3. Copy the entire contents of scripts/openrouter-export-all.js , paste into the console, press Enter. 4. The script clicks each chat, scrolls to load full history, and downloads one JSON per chat. Allow " multiple downloads " if the browser asks. 5. Move the downloaded files into a folder and index that folder. The selectors are pinned to OpenRouter's current chat UI and covered by test/playwright/openrouter-export.spec.js . If OpenRouter changes its markup and the script finds no chats, that test is where to update the contract. You'll get a file shaped like this this is what the parser reads : { "platform": "openrouter", "exportedAt": "2026-05-24T12:00:00.000Z", "pageTitle": "OpenRouter chat title", "sourceUrl": "https://openrouter.ai/chat/...", "turns": { "role": "user", "content": "User message text" }, { "role": "assistant", "content": "Assistant reply", "model": "optional/model-name" } } Verify a download before indexing: npm run parse -- path/to/openrouter-export.json See docs/OPENROUTER.md /ChrystianSchutz/ThreadShelf/blob/main/docs/OPENROUTER.md for details and limitations. LM Studio stores one JSON file per conversation locally — no export step needed, just point ThreadShelf at the folder paths below : | OS | Path | |---|---| | Windows | %USERPROFILE%\.lmstudio\conversations\ | | macOS / Linux | ~/.lmstudio/conversations/ | | Older builds | ~/.cache/lm-studio/conversations/ check here too | Paste that path straight into the manual folder path box and index it — or copy it somewhere first: macOS / Linux cp -r ~/.lmstudio/conversations ~/lmstudio-export Windows PowerShell Copy-Item "$env:USERPROFILE\.lmstudio\conversations" "$env:USERPROFILE\lmstudio-export" -Recurse Files are named <id .conversation.json and look like this trimmed — the parser reads messages .versions currentlySelected , splitting thinking steps from the answer : { "name": "My chat", "createdAt": 1700000000000, "lastUsedModel": { "identifier": "gpt-oss-20b" }, "messages": { "versions": { "type": "singleStep", "role": "user", "content": { "type": "text", "text": "Hi" } } , "currentlySelected": 0 }, { "versions": { "type": "multiStep", "role": "assistant", "steps": { "type": "contentBlock", "style": { "type": "thinking" }, "content": { "type": "text", "text": "reasoning…" } }, { "type": "contentBlock", "content": { "type": "text", "text": "Hello " } } } , "currentlySelected": 0 } } Conversation folders/subfolders are walked recursively. Tested on LM Studio 0.4.x 0.4.16 ; the format is undocumented and may change. Note — LM Studio's local server "router" . LM Studio can also run an OpenAI-compatible local server Developer tab, default http://localhost:1234 that routes requests across loaded models. That's an inference endpoint, not an export — ThreadShelf indexes the on-disk conversations/ files above, so you don't need the server running to import or search your history. ChatGPT → Settings → Data controls → Export data . You'll get an email with a zip; index the conversations.json inside it. Claude → Settings → export your data . Index the exported conversation JSON. Grok → request your account data export xAI account/privacy settings . You'll get a download containing a prod-grok-backend.json somewhere under an export data/.../ folder. Index the folder that contains it — ThreadShelf detects the file by content, so the surrounding directory names don't matter. The file holds every conversation in one document, shaped roughly like this the parser reads conversations .responses .response , splitting the model's agent thinking traces reasoning from its message answer : { "conversations": { "conversation": { "id": "…", "title": "My chat", "create time": { "$date": { "$numberLong": "1771000000000" } } }, "responses": { "response": { "sender": "human", "message": "Hi", "create time": { "$date": { "$numberLong": "1771000000000" } } } }, { "response": { "sender": "assistant", "message": "Hello ", "model": "grok-3", "agent thinking traces": { "thinking trace": "reasoning…" } } } } } Timestamps are MongoDB extended JSON { "$date": { "$numberLong": … } } . The format is undocumented and may change. Requires Node.js 20.19+ and npm. npm install installs server + client npm workspaces npm run build:client builds the React UI into public/ npm start serves on http://localhost:3000 Then in the browser: 1. Create or select a collection think of it as a folder/project, e.g. ai studio , chatgpt , work 2026 . Start with a throwaway one. 2. Select a folder of exports, or paste an absolute folder path. 3. Click Index folder and watch live progress. 4. Search in natural language; use role filters user / thinking / response . Switch Semantic → Exact for case-insensitive substring matches — best for identifiers, error messages, and code fragments the embedding model blurs. 5. Click a result to open the full source thread; copy or export to Markdown or JSON. Use Stop indexing to cancel an active run. The current embedding batch is allowed to finish safely; completed files stay indexed and an uncommitted file keeps its previous index rows. First indexing is slow while the local embedding model downloads once, then it's cached. Working on the UI? npm run dev backend + npm run dev:client Vite hot reload on :5173, API proxied to :3000 . For scheduled local backups or headless indexing: npm run ingest -- path/to/exports work 2026 Omit the collection to use chunks . Add --clear only when you want to wipe the collection before indexing. To keep a folder indexed as it changes LM Studio rewrites its conversation files as you chat; AI Studio folders grow , add --watch : npm run ingest -- ~/.lmstudio/conversations lmstudio -- --watch After the initial pass the process stays running, watches the folder recursively, and re-indexes just the files that changed debounced; tune with --debounce <ms . Deleting a source file never removes it from the index — ThreadShelf is an archive, and indexed conversations outlive their files. You can also search straight from the terminal: npm run search -- "that regex for polish postal codes" npm run search -- "ECONNREFUSED 127.0.0.1" -- --mode keyword --n 5 npm run search -- "prompt injection" -- --collection work 2026 --json The second -- before CLI options is intentional: current npm versions can otherwise consume option names that appear after positional arguments. Full walkthrough: docs/GETTING STARTED.md /ChrystianSchutz/ThreadShelf/blob/main/docs/GETTING STARTED.md . Click any result to open the full source thread, jump to the matched turn, filter user/reasoning/response roles, copy text, or export the conversation to Markdown or JSON. With no query, the search page lists all indexed conversations with provider badges — sort them by recency, length, or title, and narrow the list with the filter box. - More like this — one click on any search result or thread turn runs a semantic search seeded with that passage, for "I know I discussed this somewhere else" moments. - Saved searches & pins — star a query with its filters and mode to rerun it later, and pin conversations to keep them at the top of the browse list. Both are stored in your browser's localStorage; nothing leaves the machine. Experimental Beta. The archive/search path is the stable release scope; generation interfaces and model compatibility may still change. Original export files are never modified. Use New chat to start a locally saved ThreadShelf conversation, or open an imported thread and choose Continue this conversation : - llama.cpp · local — the primary engine. ThreadShelf discovers GGUF files, launches a loopback-only llama-server , streams tokens, reports CPU/GPU/hybrid placement, and can eject the model from memory without deleting it. - OpenRouter · external — an optional provider with a live model catalog. Selecting this tab is the off-device choice: the model button carries an off-device chip and the composer says that the request is sent through OpenRouter. Archived thinking turns are excluded. Optional ZDR-only and data-collection-denial routing can reduce eligible providers. Completed chats and archive continuations are stored locally, embedded, and searchable. A ghost-icon Private conversation is tab-scoped and is never written to the thread store or semantic index. Failed or stopped streams remain in a clearly marked, unsaved recovery card for the current tab. The model menu discovers common LM Studio, llama.cpp, and Hugging Face model directories plus custom roots. It separates model selection from context/output settings, supports favorites, and exposes detailed runtime logs only on demand. Active streams hold a model lease so concurrent eject or configuration changes cannot unload a model mid-response. Settings → Conversation generation → Set up local generation resolves one plan for the whole first run: the official llama.cpp build for this machine, plus a GGUF model sized for the detected accelerator. The plan is shown before anything is fetched — every URL, SHA-256 digest, size, and destination — and one confirmation runs it. Nothing downloads until you confirm, and Cancel stops an in-flight transfer immediately. The plan is re-resolved on the server when you confirm; a client can never hand the server a URL to fetch and execute. If the resolved plan no longer matches the one you approved — a nightly build moved, the catalog changed, free VRAM shifted the recommendation — the run stops and shows the new plan for a fresh confirmation instead of downloading something you never agreed to. Settings → Conversation generation → Download a model… — or the same action in the chat's model menu — opens a read-only browser over the public Hugging Face API. It needs no account or token: search or sort by downloads, likes, trending, or recency, inspect a repository's quantizations, and see a per-quantization verdict for your hardware — fits inside the accelerator budget, tight partly on CPU , or too large . Builds from known publishers unsloth , lmstudio-community , bartowski , ggml-org , Qwen , google , mistralai are marked. Downloads land in .threadshelf/models override with THREADSHELF MODELS PATH , which is gitignored and always searched for models, so a downloaded model appears in the model menu without further configuration and is selected automatically when the download finishes. Every file is verified against the repository's LFS oid its SHA-256 before it is moved into place. A cancelled transfer keeps its .part file and the next attempt resumes from there; any other failure deletes it. Gated repositories are detected up front and marked in the UI. To use one, accept its licence on Hugging Face and set HF TOKEN in the gitignored root .env . A configured token is sent with every Hugging Face request; without one, public repositories still work. No conversation content is ever sent to Hugging Face — only catalog metadata requests and file downloads. The guided setup above covers the common case. The CLI installer remains the scripted, offline, and custom-build path, and stays the only way to install an archive ThreadShelf did not resolve itself. The setup command is cross-platform Windows x64/ARM64, macOS x64/Apple Silicon, Linux x64/ARM64 where official release assets exist . With no arguments it only looks for llama-server ; it does not make a network request, download, install, or execute it: npm run setup:llama Inspect the newest compatible official release without downloading an archive: npm run setup:llama -- -- --check Install the current official ggml-org/llama.cpp release. The interactive form requires typing install ; automation requires the explicit --yes flag. The official GitHub SHA-256 digest is verified and the MIT license/source metadata is kept beside the installed files: npm run setup:llama -- -- --install npm run setup:llama -- -- --install --yes Upstream's /releases/latest points at a semver release that carries no binaries, so ThreadShelf follows its nightly-tag.txt pointer to the real bNNNNN build. Pin an exact upstream build with --release , and set GITHUB TOKEN if you hit the anonymous API rate limit: npm run setup:llama -- -- --install --release b10088 Default builds are portable CPU builds Metal is automatic on macOS . Accelerated official variants may be selected with --variant vulkan|cuda|rocm|sycl ; their driver/runtime requirements still apply. Official variants are installed side-by-side under release-and-variant folders, so adding a GPU build never overwrites a working CPU build. Paste the desired variant's printed llama-server path into Settings when more than one is present. Autodiscovery prefers the newest managed release and an accelerator variant over CPU for the same release; an explicitly configured executable still wins. Official Windows CUDA releases split the server and CUDA runtime DLLs across two archives. The CUDA install command downloads and SHA-256 verifies both. Re-running the same explicitly approved command repairs an older ThreadShelf CUDA directory that is missing the companion runtime without touching GGUF models: npm run setup:llama -- -- --install --variant cuda For Alpine, an unsupported architecture, or a custom build, provide your own archive URL. Supplying --url is explicit download consent; provide a trusted SHA-256 whenever possible: npm run setup:llama -- -- --url https://example.invalid/llama-build.tar.gz --sha256 64 HEX DIGEST --tag custom The default destination is .threadshelf/tools/ , which is gitignored. No model is downloaded by this installer. Add existing GGUF directories and an optional llama-server path in Settings → Conversation generation . OpenRouter keys should preferably be supplied as OPENROUTER API KEY in the gitignored root .env file copy .env.example , then restart the server . A key entered in the UI exists only in server memory for the current process and is never written to .threadshelf/generation.json or returned by the API. Generation configuration, created-chat storage, eject, and chat endpoints are loopback-only even when the read/search UI is exposed with HOST and ALLOWED HOSTS . Full setup, persistence semantics, routing controls, runtime diagnostics, and API examples are documented in Experimental Generation /ChrystianSchutz/ThreadShelf/blob/main/docs/GENERATION BETA.md . The Insights view charts your whole archive from data captured at ingest time: activity over time, top models, turns per provider, and your longest conversations — scoped to one collection or all of them. ThreadShelf exposes your local index to MCP clients e.g. Claude Desktop, or any MCP-capable agent over stdio — so a model can search your past chats as a tool. npm run mcp starts the stdio MCP server Example Claude Desktop config claude desktop config.json : { "mcpServers": { "threadshelf": { "command": "npm", "args": "run", "mcp" , "cwd": "/absolute/path/to/this/repo" } } } It reads the same local LanceDB the UI uses — no extra setup. See docs/MCP.md /ChrystianSchutz/ThreadShelf/blob/main/docs/MCP.md for the exposed tools. - Collections are local LanceDB tables use them like folders/projects . - Threads are full conversations reconstructed from the parsed export; search returns matching chunks, opening a result shows the whole thread. - Stored thread snapshots keep indexed conversations readable after source files are moved, rewritten, or deleted. - Generation providers implement one streaming contract over managed llama.cpp and OpenRouter, while per-thread leases and write locks protect concurrent saves and model transitions. Internals: docs/ARCHITECTURE.md /ChrystianSchutz/ThreadShelf/blob/main/docs/ARCHITECTURE.md . ThreadShelf is an npm-workspaces TypeScript/ESM project with a React 19 client and an Express 5 server. The full gate is deliberately broader than unit tests: | Layer | What is exercised | |---|---| | Unit/regression | Provider parsing, Unicode, chunking, validation, generation config/runtime helpers, thread persistence, client utilities | | API + MCP E2E | A real server, isolated temporary LanceDB/uploads/collections, ingest/search/thread/generation routes, and MCP stdio | | Browser E2E | The built production UI in Chromium: search, routing, collections, uploads, generation streams, privacy labels, responsive layouts | | Repository gate | Git/privacy hygiene, Markdown links, ESLint, TypeScript, client build, and all tests above | Every provider has small synthetic fixtures. Real exports are used only to learn the JSON shape; private conversation content is never copied into the repository. CI runs the full gate on Linux and lightweight core checks on Windows. - Conversations indexed with the current version keep working search, listing, and full thread view even if the original export file is later moved, rewritten, or deleted — normalized turns are stored alongside the vectors at ingest time. Collections indexed with older versions still read threads from the original file path until you re-index them. - Very large archives are supported in normal use, but 100k chunk collections should still be validated against your own data before relying on exact stats. - Undocumented provider formats can change without notice; keep small anonymized fixtures for any real export shape that breaks parsing. - Conversation generation is Experimental Beta ; archive indexing and search do not depend on it. - ThreadShelf is a single-user local application. The HTTP API has no user accounts or authentication and should remain bound to loopback unless it is placed on a trusted network with deliberate host configuration. | Command | Description | |---|---| | npm start | Start server on port 3000 npm start -- 3001 for another port . | | npm run dev | Server with file watch. | | npm run dev:client | Vite dev server UI hot reload on :5173 . | | npm run build:client | Build the UI into public/ . | | npm run check | Full gate: repo hygiene, lint, typecheck, unit + API/MCP E2E, build, Playwright. | | npm run check:repo | Reject tracked private artifacts/secrets before commit. | | npm test | Fast unit/regression tests. | | npm run test:e2e | API + MCP E2E with a temporary server and LanceDB. | | npm run test:playwright | Browser E2E needs npm run build:client + Playwright . | | npm run mcp | Start the MCP stdio server. | | npm run parse -- <file -- flags | Parse one export file --no-user , --no-thinking , --no-ai . | | npm run ingest -- <folder collection -- flags | Ingest a folder --clear , --watch , --debounce <ms . | | npm run search -- "<query " -- flags | Search from the CLI --mode keyword , --collection , --roles , --n , --json . | | npm run setup:llama | Discover local llama-server ; add -- -- --check or explicit install flags. | Missing Playwright browsers? npx playwright install chromium . | Variable | Default | Purpose | |---|---|---| | PORT | 3000 | Server port. | | HOST | 127.0.0.1 | Server host. Set explicitly only when trusted LAN access is required. | | ALLOWED HOSTS | empty | Comma-separated extra Host/Origin names for trusted LAN access. | | LANCEDB PATH | .lancedb | LanceDB directory. | | UPLOADS DIR | .uploads | Uploaded source files. | | COLLECTIONS PATH | .collections.json | Manual-collections registry file. | | CHUNK MAX CHARS | 2000 | Max characters per embedded chunk. | | CHUNK OVERLAP CHARS | 100 | Overlap between long chunks. | | EMBED BATCH SIZE | 25 | Embedding batch size during ingest. | | GENERATION CONFIG PATH | .threadshelf/generation.json | Non-secret Experimental Beta generation settings. | | MASTER PROMPTS PATH | .threadshelf/master-prompts.json | Saved master system prompts. | | LLAMA CPP SERVER | auto | Absolute path to an existing llama-server executable. | | LLAMA CPP BASE URL | empty | Existing loopback-only llama.cpp server URL. | | LLAMA CPP CONTEXT SIZE | 8192 | Managed local server context size. | | LLAMA CPP ACCELERATION | auto | auto , cpu , gpu , hybrid , or multi-gpu . | | LLAMA CPP GPU LAYERS | 20 | Exact layer offload for the hybrid profile. | | LLAMA CPP SPLIT MODE | layer | Multi-GPU split: layer or row . | | LLAMA CPP MAIN GPU | 0 | Main GPU index for applicable profiles. | | LLAMA CPP TENSOR SPLIT | empty | Optional multi-GPU proportions, e.g. 3,1 . | | LLAMA CPP THREADS | -1 | CPU generation threads; -1 lets llama.cpp choose. | | LLAMA CPP FLASH ATTENTION | auto | Flash Attention: auto , on , or off . | | LLAMA MODEL PATHS | defaults | Extra model roots ; on Windows, : on macOS/Linux . | | THREADSHELF TOOLS PATH | .threadshelf/tools | llama.cpp discovery/installer root. | | THREADSHELF MODELS PATH | .threadshelf/models | Catalog download root; always searched for models. | | THREADSHELF DISABLE DEFAULT MODEL PATHS | 0 | Set 1 to scan only explicitly configured roots. | | HF TOKEN | empty | Hugging Face token; required only for gated repositories. | | HUGGING FACE HUB TOKEN | empty | Alternative name for HF TOKEN . | | GITHUB TOKEN | empty | Raises the GitHub API rate limit for llama.cpp releases. | | GH TOKEN | empty | Alternative name for GITHUB TOKEN . | | OPENROUTER API KEY | empty | OpenRouter key; may be set in .env , never exposed to the browser. | | OPENROUTER BASE URL | https://openrouter.ai/api/v1 | Override primarily intended for testing. | For LAN access, bind to the interface you need and allow the exact browser host, for example HOST=0.0.0.0 ALLOWED HOSTS=192.168.1.50,my-pc.local . Without ALLOWED HOSTS , API requests from other machines are rejected by Host/Origin checks. - Getting Started /ChrystianSchutz/ThreadShelf/blob/main/docs/GETTING STARTED.md — install, run, index, search. - Architecture /ChrystianSchutz/ThreadShelf/blob/main/docs/ARCHITECTURE.md — data flow, modules, storage, API. - MCP Setup /ChrystianSchutz/ThreadShelf/blob/main/docs/MCP.md — run the stdio MCP server and what it exposes. - OpenRouter Export /ChrystianSchutz/ThreadShelf/blob/main/docs/OPENROUTER.md — the browser export flow + limitations. - Experimental Generation /ChrystianSchutz/ThreadShelf/blob/main/docs/GENERATION BETA.md — llama.cpp/OpenRouter setup, privacy, and API. - Real Data Testing /ChrystianSchutz/ThreadShelf/blob/main/docs/REAL DATA TESTING.md — validate private exports safely. - FAQ /ChrystianSchutz/ThreadShelf/blob/main/docs/FAQ.md — common questions. - Changelog /ChrystianSchutz/ThreadShelf/blob/main/CHANGELOG.md — release highlights. - AGENTS.md /ChrystianSchutz/ThreadShelf/blob/main/AGENTS.md — guidance for AI coding agents and contributors. Indexing, embeddings, storage, search, MCP, and llama.cpp inference are local. The explicitly selected Experimental Beta OpenRouter generation is not local : it sends the selected archive or ThreadShelf chat's user/assistant history and prompt to OpenRouter and the routed provider. Two other surfaces reach the network but never carry conversation content: GitHub Releases for llama.cpp builds, and Hugging Face for model catalog metadata and GGUF downloads — both only after you ask for them. Do not commit real chat exports, uploaded files, local databases, .threadshelf/ , logs, or temp folders — .gitignore excludes them. npm run check:repo also rejects these paths if they become commit candidates. Fixtures in test/fixtures/ are synthetic and anonymized. See SECURITY.md /ChrystianSchutz/ThreadShelf/blob/main/SECURITY.md . See CONTRIBUTING.md /ChrystianSchutz/ThreadShelf/blob/main/CONTRIBUTING.md : add tests for parser/ingest/search changes, keep real exports out of git, and run npm run check before opening a PR. MIT — see LICENSE /ChrystianSchutz/ThreadShelf/blob/main/LICENSE .