Ophir
БесплатноНе проверенOphir protocol tools for AI agent discovery, negotiation, and settlement
Описание
Ophir protocol tools for AI agent discovery, negotiation, and settlement
README
An open protocol for AI agents to autonomously discover, negotiate, and transact with each other.
AI agents collectively spend billions on API calls but operate without any commercial infrastructure. There is no standard way for one agent to find another, negotiate a price, agree on service quality, or enforce the terms of a deal. Every integration is bespoke. Every price is fixed by the provider. Every failure goes unresolved.
Ophir changes this. It defines a structured negotiation lifecycle where agents discover service providers through a shared registry, exchange signed requests for quotes, counter-offer up to five rounds, and arrive at a binding agreement. Agreements are dual-signed with Ed25519 and committed via SHA-256 hash. The protocol tracks eight SLA metrics against committed targets during execution, and if a provider fails to deliver, the buyer files an on-chain dispute backed by cryptographic evidence. Settlement happens through Solana escrow vaults denominated in USDC.
The protocol is transport-agnostic at its core but ships with a JSON-RPC 2.0 binding over HTTP. Identity is built on the did:key standard using Ed25519 public keys. The state machine has twelve states, from IDLE through RESOLVED, covering the full lifecycle of discovery, negotiation, margin assessment, escrow, execution, and dispute resolution.
Everything ships as TypeScript libraries you can drop into any agent framework.
Quick Start
Add to any MCP client:
{
"mcpServers": {
"ophir": {
"command": "npx",
"args": ["@ophirai/mcp-server"]
}
}
}
Your agent now has tools to discover sellers, request quotes, negotiate terms, accept agreements, monitor SLAs, and file disputes.
Or use the SDK directly:
npm install @ophirai/sdk @ophirai/protocol
import { BuyerAgent } from '@ophirai/sdk';
const buyer = new BuyerAgent({ endpoint: 'http://localhost:3001' });
await buyer.listen();
const session = await buyer.requestQuotes({
sellers: ['http://seller-a:3000'],
service: { category: 'inference', requirements: { model: 'llama-70b' } },
budget: { max_price_per_unit: '0.01', currency: 'USDC', unit: 'request' },
sla: { metrics: [
{ name: 'p99_latency_ms', target: 500, comparison: 'lte' },
{ name: 'uptime_pct', target: 99.9, comparison: 'gte' },
]},
});
const quotes = await buyer.waitForQuotes(session, { minQuotes: 1, timeout: 30_000 });
const best = buyer.rankQuotes(quotes, 'cheapest')[0];
const agreement = await buyer.acceptQuote(best);
OpenAI-compatible inference gateway:
npx @ophirai/router --port 4000
How It Works
A buyer agent queries the Registry to find sellers offering the services it needs. Sellers register with their capabilities, pricing, and SLA commitments.
The buyer sends a signed RFQ (Request for Quote) specifying service requirements, budget constraints, and SLA targets. Sellers respond with signed quotes. Either party can counter-offer. After at most five rounds, both sides either accept or walk away.
On acceptance, both parties sign the final terms with Ed25519. The agreement hash, computed as the SHA-256 of the canonicalized terms (RFC 8785), binds the deal cryptographically.
The Clearinghouse then assesses each party's Probability of Delivery, a score derived from historical SLA performance across completed agreements. Agents with strong track records post as little as 5% margin instead of the full contract value. The clearinghouse also runs a multilateral netting engine that detects cycles in the obligation graph and cancels offsetting debts.
The buyer locks USDC in a Solana PDA escrow vault, with the deposit amount determined by the margin assessment. During execution, the SDK monitors eight SLA metrics (latency, uptime, accuracy, throughput, error rate, time to first byte, and two custom metrics) against committed targets.
If violations exceed the agreed threshold, the buyer files an on-chain dispute with arbiter co-signature. The escrow program splits funds according to the penalty rate defined in the agreement, automatically compensating the buyer.
Protocol
| Transport | JSON-RPC 2.0 over HTTP |
| Identity | did:key with Ed25519 public keys (0xed01 multicodec prefix) |
| Signing | Ed25519 via tweetnacl, JCS canonicalization (RFC 8785) |
| State Machine | 12 states: IDLE, RFQ_SENT, QUOTES_RECEIVED, COUNTERING, ACCEPTED, MARGIN_ASSESSED, ESCROWED, ACTIVE, COMPLETED, REJECTED, DISPUTED, RESOLVED |
| Settlement | Solana Anchor program with PDA-derived USDC escrow vaults |
| Messages | RFQ, Quote, Counter, Accept, Reject, Dispute |
All messages are signed and verified. Replay protection uses time-windowed message ID deduplication.
Packages
| Package | Description |
|---|---|
| @ophirai/protocol | Core types, Zod schemas, 12-state FSM, SLA metrics, error codes |
| @ophirai/sdk | BuyerAgent, SellerAgent, Ed25519 signing, did:key identity, escrow |
| @ophirai/clearinghouse | Multilateral netting, fractional margin, Probability of Delivery scoring |
| @ophirai/registry | Agent discovery with rate limiting, authentication, reputation |
| @ophirai/mcp-server | MCP tools for LLM-powered agents |
| @ophirai/router | OpenAI-compatible inference gateway with automatic negotiation |
| @ophirai/providers | AI inference provider wrappers (OpenAI, Anthropic, Together, Groq, OpenRouter, Replicate) |
| @ophirai/openai-adapter | OpenAI function calling adapter |
| escrow | Solana Anchor program for USDC escrow with arbiter disputes |
Specifications
| Document | |
|---|---|
| Protocol Specification | Full protocol reference |
| State Machine | Negotiation state transitions and guards |
| Security Model | Cryptographic layers, threat model, replay protection |
| SLA Schema | Metric definitions, comparison operators, penalty calculation |
| Escrow Lifecycle | Deposit, release, dispute, and arbiter flows |
| Identity | did:key derivation, key management, agent authentication |
Development
npm install
npx turbo build
npx turbo test
Build order is managed by Turborepo. The protocol package builds first, then sdk, then everything else.
License
MIT
Установить Ophir в Claude Desktop, Claude Code, Cursor
unyly install ophirСтавит в Claude Desktop, Claude Code, Cursor и VS Code — сам разбирается с npx, uvx и сборкой из исходников.
Впервые? Поставь CLI: curl -fsSL https://unyly.org/install | sh
Или настроить вручную
Выполни в терминале:
claude mcp add ophir -- npx -y @ophirai/mcp-serverFAQ
Ophir MCP бесплатный?
Да, Ophir MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Ophir?
Нет, Ophir работает без API-ключей и переменных окружения.
Ophir — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Ophir в Claude Desktop, Claude Code или Cursor?
Открой Ophir на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
Похожие MCP
Fetch
Web content fetching and conversion for efficient LLM usage.
AWS KB Retrieval
Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.
автор: modelcontextprotocolSpring AI MCP Server
Provides auto-configuration for setting up an MCP server in Spring Boot applications.
llm-analysis-assistant
A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and can also view request responses through the /logs page. It also
автор: xuzexin-hzCompare Ophir with
Не уверен что выбрать?
Найди свой стек за 60 секунд
Автор?
Embed-бейдж для README
Похожее
Все в категории ai
