SNHP

Math-optimal negotiation moves for AI agents, in plain dollars. Your agent brings the LLM; SNHP brings the game theory — single-price and multi-issue, LLM-free, runs locally.

 ·  snhp.dev  ·  Manifesto

🏆 The Negotiation Leaderboard

arena.snhp.dev/leaderboard.html — which AI walks away with the most money? Claude models, a naive splitter, a genome evolved in a live sim, and community bots all negotiate the same held-out multi-issue deals against the SNHP engine, scored against the exact Pareto frontier. Every match is a real recorded negotiation, replayable in the browser. Headline result: frontier models, solo, lose to the naive split-the-difference bot — wired to the engine mid-deal, they're near-optimal.

Put your bot on the board: expose one HTTP endpoint speaking snhp-gauntlet/1 and DM @ryuxik the URL. The runner lives in arena/gauntlet/ — protocol, seats, scoring, and the 25-line starter bot. Machine-readable spec: arena.snhp.dev/llms.txt.

Install

uvx snhp            # zero-install: runs the stdio MCP server on demand
# or
pip install snhp

Wire it into any MCP client (Claude Desktop, Cursor, Cline, …):

{ "mcpServers": { "snhp": { "command": "uvx", "args": ["snhp"] } } }

Or call the math directly — plain dollars in, the move out:

from gametheory.server.mcp_server import gt_negotiate_turn

gt_negotiate_turn(
    side="sell", walk_away=4000, target=6000,
    counterparty_offers=[4200, 4500], rounds_left=6,
)
# -> {'action': 'counter', 'recommended_price': 5714.0,
#     'message': 'Thanks for the offer. The best I can do on this is $5,714.00.', ...}

Multi-issue deals logroll automatically — SNHP infers the other side's priorities and proposes the package that maximises joint surplus (concede what you value least to hold what you value most):

from gametheory.server.mcp_server import gt_negotiate_bundle

gt_negotiate_bundle(
    issues=[
        {"name": "price",   "options": [100, 120, 140], "my_utility": [1.0, 0.5, 0.0], "their_utility": [0.0, 0.5, 1.0]},
        {"name": "support", "options": ["basic", "priority"], "my_utility": [1.0, 0.0], "their_utility": [0.0, 1.0]},
    ],
    my_priorities={"price": 0.8, "support": 0.2},
)
# -> recommended_offer {'price': 100, 'support': 'priority'} + the trade logic behind it

Hosted agent card, streamable MCP, and a live demo: snhp.dev.

What's here

snhp/                   Core algorithm + NegMAS agent + B2B tournament harness
gametheory/             Productization layer (FastAPI, MCP, Tier 1/2/3 endpoints)
gametheory/negotiation/ Plain-terms single- + multi-issue (logrolling) engines
gametheory/server/      HTTP + MCP entry points
gametheory/tests/       pytest suite
SNHP_Whitepaper/        Protocol description + 3 component PRDs

Develop from source

git clone https://github.com/ryuxik/snhp && cd snhp
python -m venv venv && source venv/bin/activate
pip install -e ".[test]"

python -m pytest gametheory/tests/                  # test suite
uvicorn gametheory.server.http:app --reload         # local API (catalog at /v1/catalog)
snhp                                                # stdio MCP server

Empirical anchor

Two different numbers — keep them straight

There are two distinct measurements; conflating them is the easy mistake.

1. Head-to-head competitive margin (the product-relevant number). In a SNHP-scaffolded LLM vs a non-SNHP LLM, how much more of the surplus does the SNHP side capture? On the committed cross-vendor run (gametheory/server/static/e6_cross_vendor.json, Sonnet+SNHP vs Haiku, n=20 paired seeds) the pooled margin is ~+12.5% (mean h3_margin ≈ 0.125, 29/40 positive signs). This is the number the shipped tools cite as "~12% better head-to-head." Caveats: n=20, LLM-vs-LLM, single-issue price, and the opponent is a general vanilla prompt — see the strong-baseline note below.

2. Joint-welfare lift in self-play (a cooperation metric, NOT the same thing). Two-Sonnet B2B contract negotiation, n=20 paired seeds:

Lift from both sides adopting the SNHP tool: +0.186 joint welfare, sign test 18/20, p=0.0004. (The 1.59/1.61 slightly exceed the 1.57 frontier estimate — the frontier was estimated on a coarse grid, so treat these as "at the frontier," not "beyond it.") Cost: $0.025 per matchup at 2026-04 pricing.

3. The build-vs-buy test: SNHP vs a STRONG production prompt

Both numbers above are vs a general vanilla prompt. The sharper question — "why not just prompt the LLM well?" — is answered by running SNHP against a strong production prompt (snhp/llm_strong_baseline.py, whose system prompt even includes logrolling advice). On the 4-issue contract, Haiku+SNHP-tool vs Haiku+strong-prompt, n=12 paired seeds (python -m snhp.strong_baseline_headtohead, result committed at gametheory/server/static/strong_baseline_headtohead.json):

SNHP beats even a strong production prompt — but by roughly half the edge it shows against a weak one. Caveats: n=12, Haiku (not Sonnet), one contract domain; re-run at larger n / a stronger model to tighten the CI.

Network effect: the cooperation premium requires both sides to be SNHP-staked. Asymmetric matchups (Sonnet+SNHP vs vanilla Sonnet) lose 0.11 utility vs symmetric scaffolded play. Peer-mode advisor only fires when counterparty has posted a verifiable SNHP attestation.

Live demo (replay of the actual API trace at seed=42): https://snhp.dev/demo.html

Tournament rank (honest, per-market)

In the committed round-robin (leaderboard/results/leaderboard.json, n_rounds=20), SNHP's rank by average utility depends on the market:

So SNHP is #1 in the asymmetric markets and mid-pack in the symmetric one — do not read this as "#1 overall." Its variance is the smallest in the field. At n_rounds=100 the symmetric field restabilizes further and Aspiration leads.

This NegMAS agent (snhp/negmas_agent.py) is a research artifact and is NOT the shipped product recommender — the product claims below are measured on the shipped code, not on this tournament.

See gametheory/evals/README.md for the eval/tuning runbook.

Tiers

• Tier 1 — Negotiation: sell-side + buy-side recommenders, anchor-attack detection, cryptographic first-strike commit-reveal, LLM-drafted reply emails (paid).
• Tier 2 — Auctions: Vickrey / first-price BNE / English ascending, Myerson optimal reserve, format recommendation, MC simulation.
• Tier 3 — Mechanism design: Gale-Shapley, asymmetric Myerson optimal auction, Gallego-van Ryzin posted-price.

Tier 4 (coalition games) deferred until a paying buyer asks for it.