Quickstart

1. Sign in to a provider

   Every provider runs on the same harness and the same model picker. OAuth providers sign in through the browser and use your existing subscription; API-key providers prompt once and store the key locally.

   bash

   lop login              # list every login-capable provider
   lop login anthropic    # or openai, kimi, xai-oauth, zai-oauth, ...
   

   lop login <provider> also sets that provider as your default hosting and picks a default model, so the very next lop just works. Skip this step and an interactive lop opens in a setup state that walks you through /login instead.

2. Launch the terminal UI

   bash

   lop
   

   The welcome screen is ready for a first prompt. Give it something real: sort this folder by project and tell me what's misnamed or summarize this repo and list what's untested.

3. Let it work — and steer it

   Tool calls stream in as one-line receipts; expand a card with enter or space to see the full command and output. Type while the agent works and your message is delivered at the next step as steering — no need to wait. esc stops the agent without ending the session.

4. Approve a tool call

   Read-only tools run automatically; anything that writes files or executes commands prompts first, showing the exact command before it runs. Approve once, or set a mode with /approvals when you trust the task.

5. Come back later

   Sessions persist. /resume opens a picker over your recent conversations, each with its title and age, and continues the one you pick. Configuration and credentials live under ~/.local-operator/ (config.yml, auth.db).

A 7–14B model needs roughly 10–16 GB of RAM or VRAM. Start LM Studio, load a chat model, enable its server, then /login lmstudio inside Local Operator — /login also offers Ollama, vLLM, llama.cpp and a generic OpenAI-compatible server. The CLI form works too, for a model already installed in Ollama:

lop --hosting ollama --model qwen2.5:14b