Local Operator
An open-source AI agent that lives in your terminal and works on your machine
Plans, runs tools, browses, spawns subagents, and remembers — all from a fast terminal UI
The Local Operator TUI mid-task: streamed responses, expandable tool cards, and one-line receipts for everything the agent does.
Local Operator is a terminal-native AI agent: describe what you want done and it does the work on your machine, asking before anything writes or executes. It is MIT-licensed and built to be lived in — sessions persist and resume, context compacts itself before it overflows, and the agent can schedule its own follow-ups.
📚 Table of Contents
- ✨ Why Local Operator
- 🚀 Quickstart
- 🖥️ A Tour of the TUI
- 🔌 Providers
- 🧰 What the Agent Can Do
- ⚙️ Headless & Server Modes
- 📦 Installation Options
- 🔧 Configuration & Credentials
- 🌟 Radient Agent Hub
- 🔒 Safety Model
- 📝 Examples
- 👥 Contributing
- 📜 License
✨ Why Local Operator
- A real terminal UI, not a REPL. A full-screen Textual app with streamed responses, expandable tool cards, session resume, 20+ built-in themes with live preview, and a status line that tells you what the agent is doing and what it costs.
- Sign in with the account you already have. OAuth login for OpenAI (ChatGPT), Anthropic (Claude), Kimi, xAI, Z.AI, and Qwen — or bring an API key, or run entirely offline with Ollama.
- Approval-gated execution. Reads are automatic; writes and shell commands
ask first.
/approvals auto(or--yolo) opts out deliberately, per session or as a saved default. - An agent workforce, not just an agent. Fan work out to concurrent
subagents with tool-restricted roles (a
reviewerthat cannot edit what it reviews), author reusable agent profiles, and save whole teams — a manager plus a roster — you launch by name with/team. Peek at, steer, pause, or cancel any worker mid-flight. - A session you can leave and come back to. Transcripts persist,
/resumepicks up where you left off, and context compaction runs itself before the window fills, so long sessions don't fall off a cliff. - Skills, MCP, scheduled wakes, web search, a browser tool — the agent's toolbox is broad, and everything it does leaves a visible receipt in the transcript.
🚀 Quickstart
Requires Python 3.12+.
pip install local-operator # pipx install local-operator on Linux (PEP 668)
The install provides both local-operator and its short alias lop — the
rest of this page uses lop.
Sign in to a provider (or skip this — the app tells you what's missing on first run):
lop login # lists login-capable providers
lop login anthropic # OAuth sign-in in your browser
Then start it:
lop
That's it. Type what you want done. esc stops the agent, /help lists
commands, /exit quits.
Prefer a fully local model? Install Ollama, pull a model, and point the agent at it:
lop --hosting ollama --model qwen2.5:14b
🖥️ A Tour of the TUI
Everything the agent does shows up as a card or a one-line receipt. Tool
cards expand (enter/space) to show the full command and output; the
status line tracks the current step, token usage, and cost.
When a tool call needs your sign-off, the approval prompt shows exactly what is about to run before anything touches your system:
Switching models is a picker, not a config file — /model lists every model
your signed-in providers offer, with fuzzy filtering:
Ask for parallel work and the agent delegates: the subagent dock shows each
worker's status, spend, and progress live, and you can open any of them to
watch its transcript. (This shot also shows one of the 20+ built-in themes —
/theme previews them live as you arrow through the list.)
Coming back later is /resume — a picker over your recent sessions, each
with its title and age:
And /usage answers the question every agent user has: how much provider
quota is left, and what the account has spent (the status line tracks the
current session's cost live).
Slash commands
/help shows the full table in-app. The highlights:
| Command | What it does |
|---|---|
/model |
Switch model for this session; /model default saves it for new ones |
/effort |
Show or set reasoning effort (shift+tab cycles) |
/approvals |
Set whether tools ask first (ask/auto; add default to keep it) |
/resume |
Pick a past conversation and continue it |
/new, /clear, /reload |
Fresh conversation · wipe the screen · reboot the session in place |
/goal, /loop |
Set an objective, then iterate autonomously toward it |
/btw |
Ask a side question off the record — it never joins the conversation |
/compact |
Compact the context now (it also happens automatically) |
/usage, /context |
Provider quota and account spend · what's occupying the context window |
/provider, /login, /logout, /accounts, /credential |
Manage providers and stored credentials |
/search |
Configure web-search providers and load balancing |
/team |
Launch a saved team: /team <name> <request> puts a manager and roster on it |
/skills, /mcp |
List loaded skills · MCP servers |
/theme, /rename |
Pick from 20+ built-in themes (arrows preview live) · rename the session |
Keys worth knowing
- Type while the agent works — your message is delivered at the next step as steering, no need to wait.
esc— stop the agent without ending the session.ctrl+b— open an aside (side question) without losing what you were typing;ctrl+fpromotes the aside into the conversation.shift+tab— cycle reasoning effort.ctrl+l— clear the transcript (history is untouched).
🔌 Providers
One agent, your choice of brain. OAuth providers sign in through the browser and use your existing subscription; API-key providers prompt once and store the key locally; Ollama runs models on your own hardware.
| Provider | Access |
|---|---|
| OpenAI / ChatGPT | OAuth (browser or device code) or OPENAI_API_KEY |
| Anthropic / Claude | OAuth or ANTHROPIC_API_KEY |
| Kimi (Moonshot) | OAuth or KIMI_API_KEY |
| xAI / Grok | OAuth or XAI_API_KEY |
| Z.AI (GLM) | OAuth or ZAI_API_KEY |
| Qwen (Alibaba) | OAuth (token plan) or API key |
| Google Gemini | GOOGLE_AI_STUDIO_API_KEY |
| DeepSeek | DEEPSEEK_API_KEY |
| Mistral | MISTRAL_API_KEY |
| OpenRouter | OPENROUTER_API_KEY — one key, many models |
| Radient | RADIENT_API_KEY — automatic per-step model selection |
| Ollama | Local, no key, no network |
lop login # list login-capable providers
lop login openai # OAuth flow
lop login-status # what's signed in
lop logout kimi
Legacy --hosting <name> --model <name> flags keep working, and API keys can
be stored with lop credential update <KEY_NAME> (a masked prompt).
🧰 What the Agent Can Do
The agent's built-in tools, each with its own card in the transcript:
- Run things —
bash(shell commands),eval(a persistent Python kernel: variables survive across calls). - Work with files —
read,write,edit(surgical search/replace),glob,grep, pluslspfor Jedi-backed Python code intelligence. - Reach the web — load-balanced
web_searchacross seven providers and abrowsertool for pages that need rendering or interaction. - Stay organized — a visible
todolist for multi-step work,askto put real decisions back to you as a picker instead of a wall of text. - Work in the background —
taskspawns subagents,jobs/wait/hubmanage and talk to them, andwakeschedules future follow-ups ("check the build again in 30 minutes").
🔎 Web search
Search works out of the box — DuckDuckGo and Tavily's keyless endpoint are enabled by default, and requests rotate across providers with automatic fallback when one is rate-limited or down:
lop search list
lop search test "Python 3.13 release notes"
lop search enable perplexity
lop search setup brave --api-key
lop search setup tavily --oauth # official Tavily MCP server
lop search setup searxng --endpoint https://search.example.com
| Provider | Access | Default |
|---|---|---|
| DuckDuckGo | Credential-free | Enabled |
| Tavily | Keyless, TAVILY_API_KEY, or OAuth MCP |
Enabled |
| Perplexity | Anonymous or PERPLEXITY_API_KEY |
Disabled |
| Brave | BRAVE_API_KEY |
Disabled |
| Exa | EXA_API_KEY |
Disabled |
| SerpApi | SERPAPI_API_KEY |
Disabled |
| SearXNG | Self-hosted endpoint URL | Disabled |
The same controls are available in-app via /search.
🤝 Subagents, agent profiles, and teams
This is where Local Operator stops being a chatbot and starts being a staff.
Subagents. Ask for parallel work and the agent fans it out into concurrent background workers, then keeps working while they run. Each worker is addressable: peek at its transcript, send it a note, ask it a question, steer it onto a different course, pause it, or resume it later — all without burning its attention on status meetings.
Roles are capability boundaries, not just prompts. A subagent launched as
reviewer carries vetted review guidance and loses the tools to edit code
— it can read and run tests but cannot alter what it reviews, which is what
keeps a review honest. Packaged starters ship for reviewer, coder,
architect, manager, designer, and scout, and you can author your own
agent profiles: reusable roles and named specialists with their own
instruction sets, matched to tasks by semantic routing. When a profile gives
bad guidance, you fix the profile once — not every prompt that uses it.
Teams. A saved roster — a manager plus members with counts — layered with
two briefs the individual agents never hard-code: a collaboration brief (how
this group works together, who blocks a release) and a project brief (what
product this instance owns). Swap the project brief and the same roster staffs
a different product. /team lists your saved teams:
The team picker. Launch one with /team <name> <request> — the current agent becomes that roster's manager and delegates from there:
Sending a request to a team is one line — the manager breaks it down and puts the right roles on it.
Agents can also be managed from the CLI:
lop agents create "My Agent"
lop agents list
lop teams list
🧠 Skills and guides
Drop a SKILL.md (with optional reference files) into
~/.local-operator/skills/<name>/ and the agent indexes it semantically —
only the skills relevant to the current turn are surfaced, and their bodies
load on demand via skill://<name> reads, so your context isn't taxed by
knowledge you aren't using. /skills lists what's loaded.
🔗 MCP servers
Local Operator speaks MCP over the official SDK, with lazy tool loading: servers advertise a bounded summary, and individual tool schemas enter the context only when the agent actually enables them.
lop mcp add linear --url https://mcp.linear.app/mcp --oauth
lop mcp login linear # complete the OAuth flow
lop mcp list
Server configs are discovered from the project (.local-operator/mcp.json,
.mcp.json), your home directory, and best-effort imports of Claude Code,
Cursor, and VS Code configs — so servers you already configured
elsewhere just show up. See docs/mcp.md for the trust model
before enabling project-supplied servers.
⚙️ Headless & Server Modes
One-shot execution for scripts and automation:
lop exec "summarize the failures in ./test.log"
lop exec "long migration" --background # detach with a log file
lop exec "audit deps" --json # one JSON line per event
Exit code 0 on success — pipeline-friendly.
Server mode exposes the agent as a FastAPI service (used by the optional desktop UI):
pip install "local-operator[server]"
lop serve # http://localhost:1111, docs at /docs
Phone access — an optional mobile portal daemon lets you check on and steer sessions from your phone:
lop mobile install
lop mobile status
📦 Installation Options
The default install is deliberately small. Optional features live behind extras:
| Extra | Adds |
|---|---|
server |
The HTTP API server (lop serve) and background scheduler |
mcp |
Model Context Protocol client support |
images |
HEIC/HEIF image attachment decoding |
tokenizer |
Exact BPE token counting (estimated otherwise) |
lsp |
Jedi-backed symbol-aware Python navigation for the lsp tool |
all |
Everything above except lsp |
pip install "local-operator[all]" # quote it — shells glob the brackets
If you hit a feature whose extra is missing, the agent tells you which one to install instead of failing with an import error.
Nix: nix develop drops you into a reproducible dev shell via the
provided flake.nix.
Docker: docker compose up -d with the provided compose file.
🔧 Configuration & Credentials
Configuration lives at ~/.local-operator/config.yml:
lop config create # scaffold it
lop config list # every option, with descriptions
lop config edit <key> <value>
lop config open # open it in your editor
Commonly set values: hosting and model_name (skip the CLI flags),
conversation_length / detail_length (history kept verbatim vs
summarized), and tui.theme (any registered theme name — easier to set with
/theme, which previews live).
Credentials are stored in ~/.local-operator/credentials.env and never
echoed:
lop credential update TAVILY_API_KEY
lop credential delete TAVILY_API_KEY
OAuth tokens from lop login are stored separately and refresh
themselves.
🌟 Radient Agent Hub
Radient adds two optional capabilities:
- Automatic model selection —
lop --hosting radientpicks the best model per step to balance quality and cost, no--modelneeded. - Agent sharing — push your agents to the public hub, pull agents others published:
lop credential update RADIENT_API_KEY
lop agents push --name "My Agent"
lop agents pull --id "<agent_id>" # no key needed to pull
🔒 Safety Model
- Approval tiers. Read-only tools run automatically; anything that writes
files or executes commands prompts first, showing the exact command.
/approvals autoor--yolodisables prompts only when you say so. - Visible receipts. Every tool call leaves a card or one-line receipt in the transcript — there is no invisible action.
- Local-first options. Run Ollama models for closed-circuit operation where nothing leaves your machine.
- MCP trust model. Project-supplied MCP configs are treated as trusted input and warned about on first connect — see docs/mcp.md.
- Credential hygiene. Keys live in a local credential store, are entered through hidden prompts, and are kept out of transcripts.
📝 Examples
👉 The example notebooks show real tasks completed with Local Operator, saved from live sessions:
- 🔄 Automated commit message generation from git diffs
- 🔀 End-to-end pull request automation — creation, review, template completion
- 🔢 MNIST digit recognition — 99.3% accuracy on the Kaggle competition
- 🏠 House price prediction with XGBoost — top 5% Kaggle score
- 🚢 Titanic survival prediction with LightGBM
- 🌐 Web research and data extraction — scraping a sanctions list
- 📈 Business pricing analysis — optimal subscription pricing
👥 Contributing
We welcome contributions! See CONTRIBUTING.md for how to
submit bug reports and feature requests, set up a development environment,
and open pull requests. docs/ covers the architecture
(REWRITE.md), benchmarks, and verification evidence.
📜 License
MIT — see LICENSE for details.
1 maintainer
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