outo-llms 0.1.0

Deploy local LLMs behind your own managed API server - vLLM or llama.cpp in isolated environments, with API keys, workspaces, and usage tracking.

outo-llms

Deploy local LLMs behind your own managed, OpenAI-compatible API server.

vLLM or llama.cpp runs in an isolated environment managed by outo-llms, kept apart from the Python environments you already have. Users sign up, receive API keys, organize work into workspaces, and every request is metered per workspace. Open the dashboard, point your OpenAI SDK at the URL, and the rest is just an HTTP call.

Full documentation lives in docs/.

Principles

1. Fragmentation - small, single-purpose modules.
2. Fluidity - features can be added or removed as needs change.
3. Simplicity - a handful of commands and a small API cover the common workflow.
4. Automation - one guided command gets you a working deployment.
5. Explicitness - every automated action is announced and logged; the system is never touched without your knowledge.

Features

• Isolated engine virtualenvs. vLLM and llama.cpp install into their own virtual environments, never into the system interpreter.
• Choice of engines. Bring Hugging Face models through vLLM, or GGUF models through llama.cpp, and switch between them with one command.
• Account, keys, and workspaces. Open signup issues an outo_sk_ API key, creates a default workspace, and lets each user create more.
• Per-workspace usage metering. Token usage is recorded by workspace and surfaced at GET /v1/usage.
• OpenAI-compatible proxy. POST /v1/chat/completions, POST /v1/completions, and GET /v1/models speak the same shapes clients already know.
• Optional HTTPS. A self-signed certificate is generated under data/certs/ when you ask for it.
• Explicit automation with an action log. Every setup step is announced, confirmed when destructive, and appended to actions.log.
• Dashboard and Swagger UI. Visit the root URL for a tiny status page, or /docs for the full OpenAPI explorer.

Install

pip install outo-llms

Prefer an isolated install? pipx install outo-llms works too.

Quickstart

outo-llms setup                              # interactive, fully explicit setup
curl -s -X POST http://127.0.0.1:8611/v1/account/signup \
  -H 'Content-Type: application/json' \
  -d '{"username": "me"}'                    # returns an api_key + default workspace
outo-llms models add tinyllama               # register a Hugging Face model
curl -s http://127.0.0.1:8611/v1/chat/completions \
  -H "Authorization: Bearer outo_sk_..." \
  -H 'Content-Type: application/json' \
  -d '{"model": "tinyllama", "messages": [{"role": "user", "content": "hi"}]}'
curl -s http://127.0.0.1:8611/v1/usage \
  -H "Authorization: Bearer outo_sk_..."    # per-workspace token accounting

The setup wizard prints the exact base URL it configured (HTTP or HTTPS), along with the path to the action log, and writes a short summary you can copy into your shell history.

Commands

Command	Purpose
outo-llms setup	Automated, explicit server setup (engine, HTTPS, firewall, launch)
outo-llms models add <name>	Register a model in the registry
outo-llms models list	Show every registered model
outo-llms models remove <name>	Unregister a model (asks first)
outo-llms engine list	List known engines and their installed state
outo-llms engine use <name>	Switch the active engine (llamacpp or vllm)
outo-llms engine install [name]	Install an engine into its isolated virtualenv
outo-llms engine status	Show the active engine's runtime status
outo-llms start	Start the API server in the background
outo-llms stop	Stop the background API server
outo-llms restart	Restart the API server
outo-llms status	Show server, engine, and path status
outo-llms reset	Wipe everything back to factory state (asks twice)
outo-llms version	Print the version

How it works

The outo-llms server (python -m outo_llms.server) binds 127.0.0.1:8611 by default and exposes an OpenAI-compatible HTTP API. Engines are internal services that bind their own loopback ports (llama.cpp on 8612, vLLM on 8613); clients never reach them directly.

When a request hits POST /v1/chat/completions or POST /v1/completions, the server looks up the requested model in the registry, asks the engine manager to ensure the right engine is running with that model, then forwards the request. The active engine streams or returns its response unchanged, and the server records prompt and completion tokens against the calling workspace.

Every state change, install step, and external command goes through outo_llms.core.consent, which announces what is about to happen, asks for confirmation on destructive actions, and writes a timestamped line to logs/actions.log. The dashboard at the root URL and Swagger UI at /docs round out the picture.

Documentation

• docs/index.md — entry point for installation, API, configuration, operations, and testing guides.

License

Apache License 2.0. See LICENSE.