Cheaplane

Cheaplane 🛣️

Keep your premium subscription on the main thread. Offload the grunt work to cheap models — Cheaplane even picks the right one for you. Stop burning premium tokens on boilerplate.

Cheaplane is a tiny single-file MCP server (~250 lines, stdlib + mcp only) that gives your main agent — e.g. Claude Code on a Max subscription — one extra tool: delegate. Your agent keeps doing the thinking (planning, architecture, final review) and hands replaceable grunt work — boilerplate code, formatting, translation, summarizing long docs — to cheap models behind a local LiteLLM proxy (DeepSeek, Kimi, Qwen, …). Think of it as a cheap intern for your premium agent — it churns out the boring parts while you keep thinking.

The trick that makes it safe: the delegated calls and your subscription live in physically separate processes and never share credentials. (why that matters ⬇️)

The trick that makes it effortless: auto-routing. delegate(task) picks the right cheap model from the task itself — code → DeepSeek, long docs → Kimi, Chinese → Qwen. (how ⬇️)

The trick that makes it stick: a per-turn reminder hook so your agent doesn't forget the tool exists — the part most "delegate" tools skip. And a savings ledger shows you what it kept off your quota.

See it in action — your agent hands a chore over; auto-routing sends it to the cheap code model:

delegate("convert to a TypeScript interface: {id, name, email, isAdmin, roles[]}")

interface User {
  id: number;
  name: string;
  email: string;
  isAdmin: boolean;
  roles: string[];
}

↑ a real call's output — not a mockup, and no model picked by hand. That token cost ~90× less than your premium model, and your subscription quota never moved.

The problem

Premium models earn their price on hard problems — but every token counts against your plan, and you burn through quota on churn: reformatting JSON, translating UI strings, summarizing a doc you'll read once. The usual "just use a cheap model" setups force an ugly choice:

• Downgrade the whole agent → you lose main-thread quality on the work that actually matters.
• Route everything through an API key → you stop using the subscription you're already paying for.

Cheaplane keeps the sweet spot: premium main thread for judgment + cheap models for the churn + billing that physically can't cross.

How Cheaplane compares

The popular 2026 move is to swap your whole agent onto a cheap model (DeepClaude-style). Great for raw cost — but it downgrades the thread you actually think with, breaks your other MCP tools, and doesn't even apply if you're on a Pro/Max subscription. Cheaplane takes the opposite bet:

	Swap whole agent → cheap model (DeepClaude-style)	Everything via one API key	Cheaplane
Main thread	⬇️ downgraded	⬇️ no more subscription	✅ stays premium
Your other MCP tools	❌ break	✅	✅ (it is an MCP server)
Works on a Pro/Max subscription	❌ API-key only	❌ replaces it	✅ built for it
Picks the cheap model for you	❌ one model for everything	❌	✅ auto routing
Shows what you saved	❌	❌	✅ savings ledger
Billing	merged into one	one per-token bill	🔒 subscription + cheap, isolated

Comparison reflects how backend-swap setups (DeepClaude-style) behaved per public reports in mid-2026; specifics vary by tool and can change.

How cheap is "cheap"?

The grunt work is the easy part — paying premium rates for it is pure waste. Per million tokens (public list prices, mid-2026):

Model	Input	Output	Best for
Claude Opus (API, for reference)	$5.00	$25.00	the judgment work you keep
DeepSeek V4 Flash	$0.14	$0.28	code / formatting
Kimi K2	$0.60–0.95	$2.50–4.00	long docs (very large context)
Qwen	$0.05–0.40	$0.20–1.20	Chinese copy

That's an output token costing ~$25 on Opus vs ~$0.28 on DeepSeek — about 90× more for work that doesn't need the smarts. You're on a subscription, so you don't pay that $25 directly — your main thread spends quota, not dollars. That's the whole point: every routine task you offload is premium quota you keep for the hard problems. (Summarizing a 40-page doc on DeepSeek Flash runs ~$0.005 — your quota never even notices.)

Prices are public list rates, mid-2026, and vary by tier/caching — check each provider. The stable takeaway is the order-of-magnitude gap, not an exact dollar saving.

Billing isolation (the whole point)

Most "save money" hacks blur your bills together. Cheaplane keeps them physically apart:

flowchart LR
    A["Main agent<br/>premium subscription"] -->|"delegate(task)"| B["Cheaplane MCP<br/>own process, own key"]
    B -->|HTTP| C["LiteLLM proxy<br/>localhost:4000"]
    C --> D["DeepSeek / Kimi / Qwen<br/>cheap, pay-per-use"]

The Cheaplane process never imports your subscription provider's SDK, never reads its auth, never touches its OAuth token. It knows exactly one thing: an HTTP endpoint (your proxy) and its key. Your main thread bills to your subscription; delegated calls bill to your cheap proxy. The two can't cross — not by policy, by architecture.

Quick start

Fastest path — Claude Code, one script:

git clone https://github.com/millennialdreamer/cheaplane && cd cheaplane
cp litellm.yaml.example litellm.yaml          # then: export DEEPSEEK_API_KEY=sk-...
litellm --config litellm.yaml &               # start the cheap-model proxy on :4000
bash setup.sh                                 # deps + register MCP + reminder hook + verify

setup.sh is idempotent (safe to re-run): it installs deps, registers the delegate MCP server with Claude Code, installs the per-turn reminder hook, and verifies the chain end-to-end. Then start a fresh Claude Code session — done.

Prefer a package? Cheaplane is on PyPI — no clone, no path to hard-code:

pip install cheaplane          # or: uvx cheaplane  /  pipx install cheaplane
claude mcp add delegate cheaplane

You still want the proxy from step 1 below, and the reminder hook is worth it — that part needs the repo.

1. Get an OpenAI-compatible endpoint for the cheap models. Most people run LiteLLM locally as a proxy in front of DeepSeek / Kimi / Qwen. A minimal config is ~5 lines:

# litellm.yaml — exposes DeepSeek under the model_name "deepseek"
model_list:
  - model_name: deepseek
    litellm_params:
      model: deepseek/deepseek-chat        # swap for any provider/model LiteLLM supports
      api_key: os.environ/DEEPSEEK_API_KEY

pip install 'litellm[proxy]'
litellm --config litellm.yaml        # serves http://localhost:4000

That model_name: deepseek lines up with Cheaplane's default alias, so it works out of the box. (deepseek is a built-in LiteLLM provider — no api_base needed; you'd add one only for a custom or self-hosted endpoint.) Already have an OpenAI-compatible endpoint (LiteLLM, OpenRouter, Ollama, vLLM…)? Skip this and just point DELEGATE_BASE_URL at it.

2. Install Cheaplane — from PyPI, or from a clone if you also want the reminder hook and probe.py:

pip install cheaplane        # installs a `cheaplane` command; that's the whole install

git clone https://github.com/millennialdreamer/cheaplane && cd cheaplane
uv sync     # or:  python -m venv .venv && .venv/bin/pip install mcp

3. Register it with your MCP client. Installed from PyPI — the command is already on your PATH:

{
  "mcpServers": {
    "delegate": { "command": "cheaplane" }
  }
}

From a clone — copy .mcp.json.example to .mcp.json in the repo root and fix the path (or use claude mcp add):

{
  "mcpServers": {
    "delegate": {
      "command": "uv",
      "args": ["run", "--directory", "/ABSOLUTE/PATH/TO/cheaplane", "python", "server.py"]
    }
  }
}

4. Verify it end-to-end — with your proxy from step 1 running (handshake → list tools → a real delegated call):

uv run python probe.py
# ✅ chain works (main → MCP → cheap model → back)

Using delegate

Your agent now has delegate(task) — routing is automatic; override only when you want to:

delegate("convert this JSON to a TypeScript interface: …")      #  auto → deepseek (code)
delegate("summarize this 40-page contract: …")                  #  auto → kimi (very long input)
delegate("…Chinese text in the task auto-routes here…")        #  auto → qwen (Chinese copy)
delegate("translate these UI strings to Japanese", "flash")     #  explicit alias still wins

alias	good for
auto	default — picks one of the below from the task itself
deepseek	code / balanced
mimo	reasoning / multi-step
flash	fast / formatting / translation
kimi	long documents (very large context)
qwen	Chinese copywriting

Aliases map to your LiteLLM model_names. Point them at your proxy without editing code — set the DELEGATE_MODEL_MAP env var (a JSON object), or drop a ~/.claude/delegate-model-map.json (hot-reloaded — no restart needed); editing MODEL_ALIASES in server.py also works.

Delegate (let the cheap model do it):

• boilerplate / scaffolding from a clear spec
• mechanical refactors, formatting, lint fixes
• translation; summarizing or extracting facts from long docs
• routine prose: changelogs, docstrings, commit messages

Keep (you do it yourself):

• planning, architecture, technical trade-offs
• final review of delegated output — always you
• talking to the user; judgment calls
• anything where being subtly wrong is expensive

The delegated model sees only your task string — it has no access to your conversation. Make each task self-contained: spec + the actual input + the exact output format you want.

See what you saved

Every delegated call appends one line of metadata only — never the task content — to ~/.cheaplane/usage.jsonl. Ask your agent for savings any time (sample output):

Cheaplane savings — all time
  delegated calls : 184
  tokens offloaded: ~412,300 in / ~365,800 out
  premium cost avoided (Opus list): ~$11.21
  actually spent (DeepSeek-class) : ~$0.16  (≈70× cheaper, in+out blended)
  last 7 days     : 31 calls, ~$2.04 avoided

Numbers are estimates at public list prices — the real win is the premium quota that never left your subscription. The ledger records token counts and model names only; delete the file any time, or set DELEGATE_NO_LOG=1 to turn logging off entirely.

Make your agent actually use it

Here's the dirty secret of every "delegate to a cheap model" tool: installing it isn't the hard part — getting your agent to actually use it is. Drop a tool into an agent and, a few turns into a real task, it forgets the tool exists and grinds through the grunt work itself on premium tokens. The instruction sinks down the context; attention moves on.

Cheaplane ships the fix in the box — three layers you can stack:

1. Skill (SKILL.md) — teaches the agent when to delegate. Works on any client; passive, so treat it as the baseline.
2. A one-line default in your CLAUDE.md / system prompt: "Before doing replaceable grunt work yourself, delegate it." Stronger — but a static instruction still drifts down a long conversation.
3. A per-turn reminder hook — the reliable one (Claude Code). It re-injects the nudge on every prompt, so the habit never sinks out of view. This is what turns an installed tool into a used one.

On other MCP clients (no UserPromptSubmit hook system), use layers 1–2 — wire the one-liner into whatever system prompt your client supports.

Install the hook — safe and idempotent (backs up your settings, merges instead of overwriting, de-dupes on re-run):

bash install-hook.sh            # registers hooks/delegate-reminder.sh as a UserPromptSubmit hook
# verify it's wired up:
python3 -c "import json,os;s=json.load(open(os.path.expanduser('~/.claude/settings.json')));print([h['command'] for e in s.get('hooks',{}).get('UserPromptSubmit',[]) for h in e.get('hooks',[])])"

Start a fresh session, and your agent self-checks every turn: "is this replaceable grunt work? → delegate it."

The reminder costs ~60 tokens per turn — trivially less than the hundreds of premium tokens a single forgotten delegation burns. The hook uses Claude Code's UserPromptSubmit mechanism.

Config

Env var	Default	Meaning
DELEGATE_BASE_URL	http://localhost:4000	OpenAI-compatible endpoint (your proxy)
DELEGATE_API_KEY	sk-litellm	key for that endpoint
DELEGATE_TIMEOUT	120	per-call timeout (seconds)
DELEGATE_MODEL_MAP	(none)	JSON remapping aliases, e.g. {"deepseek":"deepseek-v4-flash"} — overrides defaults, no code edit
DELEGATE_LOG	~/.cheaplane/usage.jsonl	where the savings ledger lives
DELEGATE_NO_LOG	(unset)	set to 1 to disable the ledger entirely

FAQ

Will this leak my subscription credentials? No. The delegate tool runs in its own process and only ever makes a plain HTTP call to the endpoint you configure. It never imports your subscription SDK and never sees its auth — see Billing isolation.

What exactly does the savings ledger record? One JSON line per call: timestamp, alias, model name, and token/character counts. Never the task text, never the model's output. Delete ~/.cheaplane/usage.jsonl any time, or set DELEGATE_NO_LOG=1.

How does auto decide which model to use? A small deterministic heuristic in server.py (_pick_model, ~20 lines you can read and tweak): code signals → deepseek, very long input → kimi, Chinese-heavy → qwen, multi-step language → mimo, short mechanical chores → flash. An explicit alias always overrides it.

How is this different from just using one API key for everything? With a single API key you stop using your subscription entirely and pay per token for all work — including the hard parts. Cheaplane keeps your subscription as the premium main thread and sends only the cheap, replaceable churn elsewhere.

Does it work with anything besides Claude Code? Yes — any MCP-compatible client (Cursor, Cline, Windsurf, …). The main agent just needs to support MCP tools; see Manual setup for the generic JSON config.

Do I have to use DeepSeek / Kimi / Qwen? No. Anything reachable through an OpenAI-compatible endpoint works; the aliases are just convenience labels you can remap with DELEGATE_MODEL_MAP.

Why a proxy instead of calling providers directly? One endpoint, one key, usage logging, and easy model swaps — and it keeps provider keys out of the MCP server entirely.

Roadmap & ideas (help wanted)

Cheaplane's core stays deliberately tiny — but the surface it opens up is big. Shipped so far: ✅ auto-routing (v0.2), ✅ savings ledger (v0.2). Still worth building — proposals and PRs welcome, and most are small enough to be good first issues:

• Smarter routing — the current router is a readable heuristic; better signals (or a learned router) are an open playground.
• Richer savings dashboard — the ledger is plain JSONL; a cheaplane stats HTML view would be lovely.
• Result cache — skip re-delegating identical tasks.
• Auto-review — lint/test code that comes back before you trust it.
• Batch / parallel delegate — hand off several chores in one call.
• More client adoption recipes — the reminder hook targets Claude Code's UserPromptSubmit; Cursor / Cline / others want their own nudge.

Design rule: keep the core single-file and dependency-light — that's the whole point. Build extensions as opt-in, so the 5-minute read stays a 5-minute read.

Contributing

Issues and PRs welcome — it's ~250 lines of single-file Python with no heavy deps, easy to hack on. Add a useful model alias, a routing signal, or a client recipe and send it over.

License

MIT — see LICENSE.