AnythingLLM vs LM Studio

AnythingLLM

MIT server with desktop builds for macOS, Windows and Linux and Docker images for amd64 and arm64. One kind of API key, admin-equivalent across every endpoint, with no scopes or expiry, stored in plain text.

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.

AnythingLLM

1. Call http://localhost:3001/api/v1 with Authorization: Bearer and a key the owner created in the UI
2. Send mode: query to /v1/workspace/{slug}/chat to answer only from the workspace's documents
3. Treat the key as admin. It can delete workspaces, users and documents
4. Read /api/docs on the instance for the endpoint list. Request bodies there are examples, not schemas
5. Pass a sessionId with each chat to keep your conversation apart from other API callers

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