oumigo 0.2.0

Vertical-integration toolkit for running and managing GPU fleets with vLLM and Transformer backends.

oumigo

Oumigo is a vertical-integration toolkit for running and managing GPU fleets. A GPU fleet is a group of computer instances, running locally or on a cloud environment, made up of a manager node and one or more GPU worker nodes coordinated by the manager.

GPU worker nodes work independently as a whole to provide LLM based generation capability, and the manager node exposes a unified data plane interface to calling applications.

Key functions provided by an Oumigo fleet (as of v0.2.0):

1. Multiple GPU instance dynamic lifecycle management, supporting vLLM and Transformer backends
2. Data and control interfaces unification, runtime request routing
3. Performance monitoring
4. Vertical integration of administration and programming interfaces:
   • Command line interface (CLI) administration
   • Python API function with built-in Agent loop, supporting Agent chat, tool call usage
   • Python API interface for guardrail extension development

Architecture

Two roles:

• Manager (oumigo.service.manager): coordinates the fleet, split into sub-layers:

  • control plane (manager.control): tracks worker registrations and state, drives worker lifecycle, reconciles desired vs. actual. Low-frequency, correctness-critical.
  • data plane / router (manager.router): forwards client inference calls to healthy workers — on the hot path of every request. Exposes an OpenAI-compatible HTTP surface (/v1/chat/completions, /v1/completions, /v1/models, including SSE streaming), so any OpenAI-API client can talk to the fleet through a single endpoint.
  • provisioning (oumigo.providers): how workers come into existence — a minimal, lifecycle-shaped Provider protocol used by the control plane. Ships with StaticProvider (LAN: workers are hand-started and self-register, no provisioning); cloud backends (e.g. ConoHa, OpenStack-based) are future implementations of the same protocol.
  • dashboard (manager.dashboard): performance & diagnostics — later.

• Worker (oumigo.service.worker): a long-lived coordinator supervises a LLM server as a child process, monitors health, executes start/stop/restart from the manager, and owns the node state machine + restart-with-give-up policy. Workers self-register with the manager and heartbeat.

Programming interfaces layered on top of the two roles:

• Python API (oumigo.api): the client-side inference layer. Agent(tools) → Chat → request drives a built-in agent loop with callback tools, response parity across streaming/non-streaming, and reasoning-content surfacing. Talks to the manager's data plane so applications never address workers directly.
• Guardrails (oumigo.guard): an agent-layer interceptor chain with a narrow-waist Guard protocol and tiered rule→model add-ins, for developing guardrail extensions around the API without touching fleet internals.

Shared foundations: oumigo.config (typed settings + precedence resolution) and oumigo.protocol (the wire contract both roles import so it can't drift).

Using the fleet as an OpenAI-compatible endpoint

Because the data plane speaks the OpenAI API, the manager can drop in wherever an OpenAI-compatible base URL is accepted — no oumigo client required. Point the tool at the manager's router and it addresses the whole fleet through one endpoint:

Base URL:  http://<manager-host>:<router-port>/v1
Model:     <a model id from GET /v1/models>
API key:   any non-empty string (the data plane does not check it; clients still require one)

This has been verified with other editing or coding assistant agents, and works the same for any OpenAI-API SDK or tool (e.g. the openai Python client).

Installation

Oumigo is published on PyPI. Install the extra that matches the node's role:

pip install "oumigo[worker]"     # on a GPU worker box (pulls vLLM + Transformers)
pip install "oumigo[manager]"    # on the manager box
oumigo version

On a GPU worker box, do not install torch separately — vLLM hard-pins it and pulls the matching CUDA wheel transitively.

To use oumigo from another project, add it as a dependency (pip install oumigo, or the [worker] / [manager] extra as needed).

Development

Working on oumigo itself uses an editable install from a source checkout:

# from oumigo/
python -m venv .venv
source .venv/bin/activate
pip install -e ".[worker,dev]"     # on a GPU worker box
pip install -e ".[manager,dev]"    # on the manager box
oumigo version

Documentation

• docs/api.md — Python API: Agent loop, chat, tool calls, streaming.
• docs/metrics.md — performance monitoring and metrics.
• docs/worker-node-states.md — worker node state machine.