Pick the right LLM for your task — without the Twitter vibes.
State what you're using the model for. XFMS aggregates evidence from eight independent benchmark sources, normalizes it onto a common scale, lets your intent decide which dimensions matter, and returns a ranked shortlist with plain-English rationale for every pick.
XFMS is one module of the Xpansion Framework — a unified architecture for governing AI-assisted work.
A thin Python client and command-line tool for calling the
hosted XFMS API at xfms.xpansion.dev. About 250 lines of code. It
turns a one-liner into a ranked LLM shortlist.
What this repository is not: the recommender engine, the score catalog, or the ingestion pipeline. Those run on the hosted service. The methodology behind every pick is published in full at docs/methodology.md — every claim there maps to code that runs at request time, you just don't run it locally.
"Fixing bugs in our Python codebase."
Top picks:
1. 0.842 GPT-5.5 (openai/gpt-5.5) via OpenAI
2. 0.811 Claude Opus 4.7 (anthropic/claude-opus-4.7) via Anthropic
3. 0.798 Gemini 3.1 Pro Preview (google/gemini-3.1-pro-preview) via Google
Inferred quality weights from your purpose:
• structured_output_reliability 42.0% ← BigCodeBench, Aider Polyglot
• instruction_following 28.0% ← LiveBench, Tau-Bench
• factuality 20.0% ← MMLU, GPQA
• coherence 10.0% ← LongBench
─── Explanation ───
Picked GPT-5.5: strong on structured output and instruction following —
the two dimensions that dominate code-edit work. Beats Claude on Aider
Polyglot and matches it on LiveBench reasoning, at roughly 60% of the
per-token cost.
Want to see how the picks actually behave on your kind of query? Add --ab:
─── A/B probe ───
Ran 5 test queries against the top picks.
• GPT-4o-mini avg_latency=5579 ms total_cost=$0.00156 successes=5
• GPT-5.5 avg_latency=8190 ms total_cost=$0.07640 successes=5
• GPT-5.4 avg_latency=8783 ms total_cost=$0.03493 successes=5
Commentary:
Across 5 real test queries, GPT-4o-mini was both cheapest ($0.0016 total)
and fastest (5579 ms avg). Clear winner — 98% cheaper and 36% faster
than the slowest pick.
Beyond ranking, XFMS gives you these levers to honor what you actually meant:
--primary <branch>— sacrosanct user preference. When you say "cheapest model, period", the engine switches to lexicographic ranking: cost wins, other dimensions only break ties. No more weighted-blend surprises.--ab— runs the top 3 picks against 5 real test queries (expanding to 10 or 15 if results trade off) and surfaces commentary on who won what. Grounds the recommendation in actual model behavior, not just benchmarks.--strict-priorities— when you name two co-equal drivers ("cheap but high quality too"), the engine refuses to silently blend; it asks you which way to break the tie.- Latent-requirement suggestions — engine surfaces capabilities you didn't ask for but probably need (streaming for real-time chat, vision for OCR), so you don't get burned by what you didn't know.
- Deterministic by design — every internal model call is content- cached; same input always returns the same answer. The "I got different picks for the same question" failure mode is gone.
XFMS is a hosted Model Context Protocol (MCP) server. There is no package to install on your machine. You point your AI assistant — Claude Code, Cursor, Continue, Cline, or any MCP-speaking host — at the URL below and the tools appear inside your chat:
https://xfms.xpansion.dev/mcp/
That's it. The three discovery tools — rank, pick, discover —
are free and work with no key. Your AI assistant does the small
internal thinking work; we pay for nothing on your behalf, and you
pay for nothing either.
The two live-probe tools — compare and benchmark — actually run
test queries against the real candidate models on OpenRouter. That
inference cost rides with you, so they require your own OpenRouter
key in an X-OpenRouter-Key header. You probably already have one —
you're an OpenRouter model picker's target audience. If not, grab
one at openrouter.ai/keys.
The key travels encrypted to our server, is never logged, never persisted — used once per request and dropped. Same security posture as every other API key in your MCP config.
Concrete install snippets for each AI client are in the next section.
XFMS speaks Model Context Protocol (MCP) — the standard your AI assistant uses to call external tools. Once connected, you can ask the assistant "which model should I use for OCR on shipping manifests?" and it calls XFMS for you. No leaving the chat. No copy-pasting between windows.
The XFMS engine hosts the MCP server itself at
https://xfms.xpansion.dev/mcp/. Two install shapes depending on
which tools you want.
Just point your client at the URL. No key, no signup.
Claude Code:
claude mcp add xfms --transport http https://xfms.xpansion.dev/mcp/Cursor (~/.cursor/mcp.json) — or paste through Settings → MCP:
{
"mcpServers": {
"xfms": {
"url": "https://xfms.xpansion.dev/mcp/"
}
}
}These two run real test queries against the actual candidate models
on OpenRouter, so they require your OpenRouter key in an
X-OpenRouter-Key header. Same install, one extra line:
Claude Code:
claude mcp add xfms --transport http https://xfms.xpansion.dev/mcp/ \
--header "X-OpenRouter-Key: sk-or-v1-your-key-here"Cursor (~/.cursor/mcp.json):
{
"mcpServers": {
"xfms": {
"url": "https://xfms.xpansion.dev/mcp/",
"headers": {
"X-OpenRouter-Key": "sk-or-v1-your-key-here"
}
}
}
}Continue / Cline / any other MCP host — same URL + headers pattern; check your host's docs for the JSON config shape.
Don't have an OpenRouter key yet? Grab one at openrouter.ai/keys. Restart your client, then ask it:
"Use XFMS to pick a model for summarizing long legal contracts."
Five tools are available to the assistant: rank (a ranked
shortlist), pick (the single best pick), discover (which
quality dimensions matter for your purpose, without ranking),
compare (live A/B between models you've already named), and
benchmark (live A/B against the engine's top 3 picks).
compare and benchmark require the X-OpenRouter-Key header
above; the other three don't.
If you know which quality dimension matters most for your task, say so — your preference always wins over the LLM's inference:
xfms rank "code refactor" --leaf-priorities "structured_output_reliability=1.0,factuality=0.5"xfms.rank(
"code refactor",
leaf_priorities={"structured_output_reliability": 1.0, "factuality": 0.5},
)Methodology in full at docs/methodology.md.
The short version:
- No provider self-reports. Every score comes from a third-party evaluator running the same protocol across every model.
- No single-source dependence. Eight independent benchmark sources contribute today; no single leaderboard determines a pick.
- User intent beats LLM inference. The system infers weights
from your purpose, but your stated
leaf_prioritiesalways override the inference. - Honest gaps over invented signal. Missing data is recorded as missing — no interpolation, no synthetic scores. Coverage gaps surface on every pick.
XFMS doesn't stand alone — it's the model-selection layer of the Xpansion Framework.
Humans communicate with intent compressed by contextual experience. AI simply predicts patterns in language. Xpansion is the execution layer that bridges them.
Every sentence a human types carries lifetimes of context that the speaker assumes the other side will decompress — what counts as "good enough," which constraints are non-negotiable, what failures last month taught them, what their house style demands. AI doesn't share that context. It predicts patterns in language, filling in the gaps with whatever's plausible to its training data. The result reads as plausible but isn't intent-honoring: sessions lose context, security holes ship silently, contracts break without warning, and there's no way to verify that what was built actually matches what was asked for. They don't know what they don't know, and neither does AI.
Xpansion closes the gap. It decompresses finite intent upfront, enforces it through code-driven AI behavior, and delivers binary-verified results against the intent across persistent memory that survives every session boundary.
When you say "the best model for this task", you're compressing a lot: what counts as best depends on whether you care about factual reasoning or coherent prose, whether cost matters more than latency, whether you actually need vision or just text, whether the call has to stream, whether a particular benchmark dominates your real workload. AI on its own predicts the pattern — what model do most people pick for queries that look like this? — and gives you a plausible-sounding answer that's often wrong for you.
XFMS does the decompression. It takes your stated purpose, infers which benchmarks actually map to it, honors your stated primary preferences without silently overriding them, surfaces the latent requirements you didn't know to ask about (streaming for real-time chat, vision for OCR), and probes the top picks against your real query to verify the recommendation — not predict it. Then it tells you, in plain English, why it picked what it picked.
The rest of the Xpansion stack enforces the same decompress- enforce-verify contract for different parts of the work:
- Dispatch (
Dispatch) — runtime task router. Watches what kind of work you're doing and routes it to the right tool. - Finite Intent (
XFFI) — turns "build me a feature" into a finite spec with binary terminals before any code gets written. Stops scope drift at the source. - Boundary Auditor (
XFBA) — checks every code edit against contracts. Stops broken function signatures and mismatched types from ever reaching production. - Systemic Impact Analysis (
XSIA) — maps the blast radius of a proposed change before it lands. Tells you what else might break. - Token Conservation (
XFTC) — manages how much of the conversation has to stay in the assistant's working memory. Prevents context loss in long sessions. - Execution Audit (
XFXA) — verifies every promise from the spec was actually met before declaring a task done. The final binary check. - Memory Tree (
XFMT) — session snapshots that stay searchable across conversations. Your assistant remembers what you decided last week. - Security Auditor (
XFSA) — static + AI security scanning on every code edit. Catches secrets, injection paths, and unsafe patterns before they ship.
The full picture, with the rest of the modules, lives at xpansion.dev.
Xpansion is in pre-signup right now. Early access and founding licenses are open at xpansion.dev. XFMS is the first piece to ship public + free — the rest follow.
git clone https://github.com/VisionAIrySE/XFMS.git
cd XFMS
python3 -m venv .venv
.venv/bin/pip install -e .[dev]
.venv/bin/python -m pytest tests/ -vThe tests mock the HTTP layer so they run offline — no API keys needed to develop.
This client library is MIT-licensed. The recommender engine, the
catalog, and the ingestion pipeline are not open source. See
NOTICE for the patent reservation language and the
relationship to the broader Xpansion Framework IP.
- Russ Wright — russ@visionairy.biz
- Xpansion Framework — xpansion.dev
- Security disclosures — see
SECURITY.md