Run Claude Code's UltraCode mode on any model you pay for

Use Claude Code's UltraCode mode (xhigh effort + the Workflow/deep-reasoning
harness) with any model you already pay for - pick it live from the /model
menu.

One icon. Open Claude Code, type /model, and choose any backend you've set up -
all running with the full UltraCode harness. Your normal Claude Code install is
left untouched.

The example config ships ready-to-use entries for GPT‑5.5 (Codex login),
MiniMax‑M3, MiMo v2.5 Pro, DeepSeek V4 Pro/Flash, Step Flash,
Ollama Cloud, OpenCode Go, OpenRouter, Requesty, and local models - keep
the ones you have a plan for, delete the rest. (Cursor's Composer needs the
cursor-agent CLI and isn't HTTP-based - see
docs/ADD_A_MODEL.md.)

How it works

How is this possible? At the API level, "UltraCode" is just
effort=xhigh + adaptive thinking + a big max_tokens + one system reminder -
there is no secret model. The proxy adds that envelope to every request, so any
backend gets the UltraCode treatment. Full breakdown (with the reverse‑engineering
evidence) in docs/HOW_IT_WORKS.md.

Orchestrator + Worker: two models, one workflow 🪄

Claude Code's /model menu is single-slot - and its dynamic-workflow engine
quietly issues most of its background/sub-agent traffic as the stock model
(claude-opus-4-8) no matter what you pick. So the dozens of parallel workers
that do the bulk of a workflow don't follow your selection (and can bill a model
you didn't choose).

This proxy turns that single slot into two. The launcher opens a two-column
selector before Claude Code starts: choose an orchestrator (the main
interactive loop) on the left and a worker (every Workflow/Task sub-agent) on
the right. The same choices are also available later in /model: for every model
you configure, the proxy auto-adds a Worker → <model> entry.

• Pick one model (or choose Same as orchestrator in the selector) → it runs
  everything, orchestrator and every parallel worker. One pick, your model
  end-to-end.
• Pick an orchestrator plus a worker model → the smart model plans while a
  cheaper/faster model fans out the parallel work.

How it routes: the proxy classifies each request by a structural signal (the main
loop carries interactive-only tools like AskUserQuestion; sub-agents never do),
then sends the orchestrator tier to your orchestrator model and every worker to
your worker model. The workflow's stock-model background calls are remapped to
your picks too - so "use MiniMax" really means MiniMax everywhere, not Opus
behind the scenes. Toggle off with UC_ORCH_WORKER=0. Workers run fully in
parallel (threaded proxy, no artificial concurrency cap).

Auto Router: the right model for every task, automatically 🧭

Don't want to pick at all? Choose Auto (smart routing) and the proxy
decides per task which of your backends to use - trivial turns go to a cheap
model, hard turns escalate to your strongest one. Same idea as Factory Droid's
model router ("frontier quality, lower cost"), rebuilt on the models you
already pay for.

A tiny, cheap classifier model you nominate scores each candidate 0–1 on
how likely it is to nail the current task (reading a short capability card you
write for each). The proxy then routes to the cheapest candidate that clears a
quality bar (default 0.7). The classifier never sees price, so it can't be
biased toward expensive models; decisions are cached per task; and it degrades
safely (any failure falls back to a sensible default and never breaks a request).
It's off until you configure it - the shipped config.example.json has a
ready-to-use block. Full guide: docs/AUTO_ROUTER.md.

"router": {
  "enabled": true,
  "classifier": "claude-mimo",        // your cheapest fast model does the scoring
  "threshold": 0.7,                   // cheapest candidate scoring >= this wins
  "candidates": [
    { "id": "claude-minimax-m3",    "cost": 0.3, "card": "cheap; single-file edits, codegen, simple refactors" },
    { "id": "claude-gpt-5.5-codex", "cost": 5.0, "card": "frontier; big refactors, hard debugging, images" }
  ]
}

Works as your orchestrator, your worker, or both. Watch it decide with
UC_ROUTER_LOG=1.

See it route, offline (no keys):

python3 examples/auto_router_demo.py

#  Task                                         Classifier scores                Routed to      Cost
1  add a docstring to the foo() helper          cheap=0.90 mid=0.92 strong=0.95  claude-cheap   $0.3
2  write a CRUD REST endpoint with tests        cheap=0.50 mid=0.85 strong=0.95  claude-mid     $1.0
3  refactor the auth module across 8 files ...  cheap=0.40 mid=0.55 strong=0.95  claude-strong  $5.0
4  what does this screenshot show?  [image]     cheap=0.90 mid=0.92 strong=0.95  claude-strong  $5.0  ← only vision-capable
5  (repeat task #1)                             served from cache                claude-cheap   $0.3  ← classifier not re-called

Built for long, dynamic workflows ✨

UltraCode shines on long, autonomous runs - deep reasoning, multi-step
Workflows, multi-agent fan-out. The catch with any "route to a third-party
backend" shim is that those backends occasionally hiccup, and on a 40-minute
agent run a single unhandled hiccup can wedge the whole session. We hardened
the proxy against the three failure modes we actually hit in production, so it
keeps going instead of stalling:

• 🔁 Empty turns auto-retry. A backend that returns a turn with no text and no
  tool call (a transient blip, or a budget-exhausted reasoning turn at high
  effort) is transparently re-issued. It buffers only until the first real token,
  so a normal turn adds zero latency and output is never duplicated - and it
  never retries after real output or a fatal error.
• ⏱️ A stalled stream can't freeze the run. If a GPT‑5.5/codex stream opens and
  then goes silent mid-turn, a bounded idle timeout turns the stall into a quick
  retry instead of a ~10-minute hang - so one stuck sub-agent no longer freezes an
  entire multi-agent / dynamic-workflow run.
• 🛠️ Rejecting a tool call just works. Declining (or skipping) a tool mid-run no
  longer 400s strict backends like DeepSeek - the proxy repairs the tool-call
  sequence and synthesizes a stub reply for anything you didn't answer, including
  partial parallel calls. (#3)
• 💬 No "dead air" while a model thinks. Reasoning models (MiniMax‑M3, etc.)
  can think for seconds before the first answer token. The proxy keeps the
  connection live during that phase, so a workflow step looks busy instead of
  frozen - without leaking the chain-of-thought into the answer.

All of these are tunable via env vars and locked down by the offline self-test in
CI. Details and knobs: docs/HOW_IT_WORKS.md → Reliability.

Demo

There's a ready-to-run scenario in examples/demo/ - a buggy
little Game of Life. Launch UltraCode there, pick any model, enable auto mode,
and paste the prompt: it fixes the bug, adds an
animated color renderer + starting patterns, and runs its own self-test, ending
on a glider crawling across the screen.

_{The demo's payoff: an animated, colored glider crawling across the terminal. Record your own run over this - see assets/demo/README.md.}

Verified live against real backends: GPT‑5.5 (Codex login) and Cursor
Composer, plus an offline self-test that runs in CI on Linux/Windows ×
Python 3.8/3.12.

What you need

• Claude Code CLI with UltraCode access (npm i -g @anthropic-ai/claude-code).
• Python 3.8+ (standard library only - there is nothing to pip install).
• At least one backend credential, e.g. an API key (MiMo / OpenRouter / OpenAI /
  a local server) and/or a codex login for GPT‑5.5. You only set up the ones you have.

Tested on Windows 11 (no WSL needed). macOS/Linux/WSL work too via bin/ultracode.

Quick start

One command gets the code, runs the offline self-test, creates your
config.json, and installs a ultracode launcher on your PATH. Then you edit
one file and run ultracode.

macOS / Linux / WSL

curl -fsSL https://raw.githubusercontent.com/OnlyTerp/UltraCode-Shim/main/install.sh | bash

Windows (PowerShell)

irm https://raw.githubusercontent.com/OnlyTerp/UltraCode-Shim/main/install.ps1 | iex

Already cloned the repo? Just run ./install.sh (or .\install.ps1) from inside
it - same result, no network clone.

Then:

1. Pick your models - edit config.json (created for you): keep the backends
   you have a key/plan for, delete the rest, drop your keys in. See
   Configure your models.
2. Run it - ultracode. The launcher starts the proxy, opens the two-column
   orchestrator/worker selector, then launches Claude Code. Type /model anytime
   to change either tier. (UC_SELECTOR=0 skips the selector and uses /model
   only.)

Prefer Desktop icons on Windows? Run .\install.ps1 -DesktopIcons (or, in a
clone, .\windows\Install-DesktopIcons.ps1) to get "UltraCode (All Models)"
and "Claude Code (Normal)" shortcuts. Uninstall the launcher anytime with
./install.sh --uninstall (or .\install.ps1 -Uninstall).

git clone https://github.com/OnlyTerp/UltraCode-Shim.git
cd UltraCode-Shim
python3 scripts/doctor.py                 # sanity-check + offline self-test
cp config.example.json config.json        # then edit it (gitignored)
./bin/ultracode                           # mac/linux/WSL
#   windows: .\windows\Start-UltraCode.ps1   (or .\windows\Install-DesktopIcons.ps1)

Configure your models

Everything is in one file: config.json (copied from config.example.json).
It has two sections you edit:

• models - what shows up in the /model menu. Every id must start with
  claude or anthropic (Claude Code filters the rest out).
• routes - where each of those ids actually goes. The route key must match
  the model id.

Real Claude (Opus / Sonnet / Haiku) is always in the picker. You don't list
it in config.json - the proxy adds the stock Claude models to /model
automatically and keeps them there even when there's no Anthropic key to fetch
the list, so Opus never silently disappears. Picking one routes straight to
real Claude with the UltraCode envelope. The list is self-updating: the
proxy learns the real Claude ids from any successful upstream /v1/models fetch
and caches them, so when Anthropic ships the next Opus it shows up here
automatically - no update to this tool needed. Don't want any of this? Set
proxy.include_stock_models: false (or UC_INCLUDE_STOCK_MODELS=0); disable
just the learning with proxy.learn_stock_models: false (or UC_STOCK_LEARN=0).

Example - MiMo and an OpenRouter model:

{
  "models": [
    { "id": "claude-mimo",       "display_name": "MiMo v2.5 Pro" },
    { "id": "claude-openrouter", "display_name": "Llama 3.3 70B (OpenRouter)" }
  ],
  "routes": {
    "claude-mimo": {
      "type": "openai_compat",
      "upstream": "https://token-plan-sgp.xiaomimimo.com/v1",
      "model": "mimo-v2.5-pro",
      "auth": "Bearer ${MIMO_API_KEY}"
    },
    "claude-openrouter": {
      "type": "openai_compat",
      "upstream": "https://openrouter.ai/api/v1",
      "model": "meta-llama/llama-3.3-70b-instruct",
      "auth": "Bearer ${OPENROUTER_API_KEY}"
    }
  }
}

Put your key right in config.json (it's gitignored) or use ${ENV_VAR} and
export it - or drop keys into a gitignored ultracode.env the launchers load.

Requesty works the same way - another openai_compat
gateway with upstream https://router.requesty.ai/v1, provider/model naming
(e.g. openai/gpt-4o-mini), and auth Bearer ${REQUESTY_API_KEY}.

Route types:

Reasoning models (MiniMax‑M3, etc.): an openai_compat route can carry a
"body": { ... } dict of extra params merged into every request. MiniMax‑M3
needs "body": { "reasoning_split": true } so its <think> chain‑of‑thought is
returned separately instead of leaking into the visible answer - the shipped
example already sets this. See docs/ADD_A_MODEL.md.

Full walkthrough: docs/ADD_A_MODEL.md.

Is my normal Claude Code safe?

Yes. The UltraCode launcher only sets environment variables for the launched
process and uses a session-scoped --settings file. It never edits your global
Claude config or credentials. On Windows the -DesktopIcons install also gives
you a "Claude Code (Normal)" icon, so you can always start the plain version.
Remove the launcher with ./install.sh --uninstall (or .\install.ps1 -Uninstall); remove Windows icons + session state with windows\Uninstall.ps1.

Telling your AI assistant to set this up

This repo is built so you can hand it to an assistant. Point it at
AGENTS.md - that's a step-by-step runbook (install → configure →
test → troubleshoot) written for an AI to follow.

Docs