Equilibrium-aware primitives — negotiation, auctions, mechanism design — so LLM agents stop losing to closed-form math they aren't built to solve. Open MCP package, hosted API, opt-in priors corpus.
All-vs-all NegMAS multi-attribute round-robin against B2B archetypes: Anchorer, Nibbler, BATNA Bluffer, Silent Hardliner, Cialdini, Logroller, Deadline Exploiter, Fair Demand, plus cognitive biases (Anchoring, Fairness Norm, Loss Averse, Sunk Cost), plus Aspiration baseline. Raw JSON.
Same primitives, two surfaces — Python library or HTTP API.
pip install gametheory-mcp
from gametheory_mcp.negotiation import sell_next_offer
rec = sell_next_offer(
my_reservation=0.40,
opponent_offer_history=[0.55, 0.62],
my_offer_history=[0.85, 0.78],
deadline_rounds=8,
pareto_knob=0.5,
)
# → {recommended_offer: 0.71,
# acceptance_probability: 0.42,
# rationale: "...",
# posterior: {mu: 0.51, sigma: 0.18}}
curl -X POST https://api.snhp.dev/v1/keys \
-H "Content-Type: application/json" \
-d '{"agent_id": "myagent",
"contact_email": "you@example.com",
"intended_use_summary": "negotiation pilot"}'
curl -X POST https://api.snhp.dev/v1/negotiation/sell/next_offer \
-H "Authorization: Bearer gt_..." \
-H "Content-Type: application/json" \
-d '{"my_reservation": 0.40,
"opponent_offer_history": [0.55, 0.62],
"my_offer_history": [0.85, 0.78],
"deadline_rounds": 8}'
Self-serve, no human approval, free for math endpoints (600 req/min). First-strike commit-reveal and the priors corpus require this key.