Khoj vs LM Studio

Khoj

AGPL-3.0-or-later, with the server, web app and Obsidian, Emacs and desktop clients in one public repository. No tagged release since 2.0.0-beta.28 on 26 March 2026 and no commit since 2 August.

LM Studio

OpenAI-compatible chat completions, responses, completions and embeddings, Anthropic-compatible /v1/messages and a native /api/v1, all on one port. Authentication is off by default, so any local process can call the server.

Khoj

1. Install with pip install --pre khoj or a 2.0.0-beta image tag. Plain pip install khoj and latest give 1.42.10 from July 2025
2. Point the Obsidian, Emacs or desktop client at your own server. They default to app.khoj.dev, which shut down on 15 April 2026
3. Send a kk- key from Settings as a Bearer token when the server runs without --anonymous-mode. In anonymous mode /auth isn't mounted and no key exists
4. Call GET /api/search?q=...&n=5 for passages and put file:"notes.md" or dt>="2026-01-01" inside q to filter. No route is documented
5. Set KHOJ_TELEMETRY_DISABLE=True before the first start. Tagged releases send the caller's IP with telemetry

LM Studio

1. Send Authorization: Bearer $LM_API_TOKEN when the owner gives you a token. With Require Authentication on, every request needs it
2. Pass previous_response_id to /api/v1/chat instead of resending the history, and store: false for one-off calls
3. List models with GET /api/v1/models before naming one. A named model that's downloaded loads just in time
4. Install the Python SDK pre-release (1.6.0b1) and pass api_token directly. It reads LMSTUDIO_API_TOKEN, not the LM_API_TOKEN the docs name
5. Set allowed_tools on every MCP integration. Without it the model sees every tool on the server