AgentTrust

io.github.vitaliiserbynassisterr由 GitHub 账号 vitaliiserbynassisterr 发布v0.1.0更新于 Oct 24, 2026

Quality verification for AI agents and MCP servers. 6-axis scoring, adversarial probes.

已验证STDIO仅桌面安全与监控AI 与机器学习

概览

AI 生成的概览

AgentTrust 用于评估 AI 智能体与 MCP 服务器的质量与安全性，按六个维度打分并签发可验证凭证。

可以做什么
对 MCP 服务器地址执行完整或快速质量检查，涵盖清单、功能工具调用与 LLM 评审打分，覆盖准确性、安全性、可靠性等六个维度，并包含提示注入、PII 泄露等对抗性探测。查询带时效衰减的缓存质量分数，并验证签名凭证令牌。在盲测对战竞技场中对智能体进行两两比较，给出贝叶斯评分与排行榜，并通过 IRT 校准自适应选择测试题目。
适用场景
当你希望在把真实任务或付款交给某个 AI 智能体或 MCP 服务器之前先做质量审查，或需要为正在集成或比较的智能体获取可验证的评分信号时使用。
运行要求
以本地 Python 进程运行（PyPI 包 mcp-agenttrust，stdio）或通过 Docker 运行；需要至少一个 LLM 服务商密钥（CEREBRAS_API_KEY 或 GROQ_API_KEY，其余服务商构成回退链）以及用于持久化存储的 MongoDB 连接地址（MONGODB_URL）；完整部署还涉及 Redis 和 Solana 钱包。
安装前请注意
需要机密凭据：CEREBRAS_API_KEY、GROQ_API_KEY 和 MONGODB_URL。评估会把目标服务器信息和测试提示发送给第三方 LLM 服务商。完整部署包含 x402 Solana 付款验证和 Solana 钱包配置，使用前应审查相关付款设置。评估结果会存入 MongoDB。

安装

在 SourceWeft 中

  1. 打开 控制台中的 AgentTrust，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

AgentTrust

Challenge-response quality verification for AI agents and MCP servers.

AgentTrust evaluates AI agent competency before you trust them with real tasks or payments. It connects to any MCP server, runs challenge-response tests across 6 quality dimensions, and issues W3C Verifiable Credentials as proof.

Why

The AI agent ecosystem has identity (ERC-8004, SATI), post-hoc reputation (TARS, Amiko), and payments (x402) — but no pre-payment quality gate. AgentTrust fills this gap: verify competency first, then trust.

Features

Evaluation Engine

  • 3-level pipeline: Manifest (schema) → Functional (tool calls) → Domain Expert (calibrated questions)
  • 6-axis scoring: accuracy (35%), safety (20%), reliability (15%), process quality (10%), latency (10%), schema quality (10%)
  • Consensus judging: 2-3 LLM judges in parallel with agreement threshold (saves 50-66% LLM calls)
  • 7 LLM provider fallback chain: Cerebras → Groq → OpenRouter → Gemini → Mistral → DeepSeek → OpenAI
  • 5 adversarial probe types: prompt injection, PII leakage, hallucination, overflow, system prompt extraction

Battle Arena

  • Head-to-head blind evaluation with position-swap consistency
  • OpenSkill (Bayesian ELO) rating system with divisions (Bronze → Grandmaster)
  • Fair matchmaking: rating proximity + uncertainty bonus + cross-division challenges
  • Style control penalties to prevent gaming via verbose/formatted responses

IRT Adaptive Testing

  • Rasch 1PL calibration from battle data (pure Python, no numpy)
  • Fisher information maximization for adaptive question selection
  • EAP ability estimation with standard normal prior
  • Reduces evaluation cost by 50-90% while maintaining accuracy

Standards

  • W3C Verifiable Credentials (AQVC format) with Ed25519 DataIntegrityProof
  • Google A2A v0.3 native support (AgentTrust IS an A2A agent)
  • x402 Solana payment verification (USDC + SOL)
  • AIUC-1 protocol mapping

Quick Start

Docker (recommended)

bash
cp .env.example .env# Add at least one LLM key (GROQ_API_KEY, CEREBRAS_API_KEY, etc.)docker compose up -d

Services:

Local Development

bash
python -m venv .venv && source .venv/bin/activatepip install -r requirements.txtcp .env.example .env# Add LLM keys to .env
unset GROQ_API_KEY  # Shell env overrides .env rotation poolpython -m uvicorn src.main:app --host 0.0.0.0 --port 8002 --reload

MCP Server (for Claude, Cursor, Windsurf)

Add to your MCP client config:

json
{  "mcpServers": {    "agenttrust": {      "command": "python",      "args": ["-m", "src.standards.mcp_server"],      "env": {        "GROQ_API_KEY": "your-key"      }    }  }}

Or connect to a running instance via SSE:

http://localhost:8003/sse

Available MCP tools:

ToolDescription
check_quality(server_url)Full evaluation: manifest + functional + judge scoring
check_quality_fast(server_url)Cached score (<10ms) or manifest-only (<100ms)
get_score(server_url)Lookup cached score with freshness decay
verify_attestation(attestation_jwt)Verify AQVC JWT and decode payload

API Endpoints

MethodEndpointDescription
POST/v1/evaluateSubmit target for evaluation
GET/v1/evaluate/{id}Poll evaluation status
GET/v1/score/{target_id}Get quality score
GET/v1/scoresSearch/list scores
GET/v1/badge/{target_id}.svgSVG quality badge
GET/v1/attestation/{id}Get signed attestation (JWT or W3C VC)
POST/v1/attestation/{id}/verifyVerify attestation
POST/v1/feedbackSubmit production feedback (anti-sandbagging)
POST/v1/battlesCreate evaluation battle
GET/v1/arena/leaderboardBattle arena leaderboard
GET/v1/rankingsGlobal rankings by domain/tier
POST/v1/irt/calibrateTrigger IRT batch calibration
GET/v1/irt/recommendAdaptive question selection
GET/v1/pricingx402 pricing table
GET/.well-known/agent.jsonA2A Agent Card

Architecture

src/  api/v1/          # 14 FastAPI routers  core/            # Evaluator, MCP client, scoring, IRT, battle arena  auth/            # API keys (SHA256 + salt), rate limiting by tier  storage/         # MongoDB (Motor) + Redis  payments/        # x402 protocol, Solana verification  standards/       # W3C VC issuer, A2A extension, MCP server, AIUC-1

Stack: FastAPI + MongoDB + Redis | 533 tests | 60 source files | 15 lean dependencies

Tests

bash
python -m pytest tests/ -q# 533 passed in ~2s

Configuration

See .env.example for all 60+ configuration options including:

  • LLM API keys (7 providers, comma-separated for rotation)
  • MongoDB/Redis connection
  • JWT attestation (Ed25519 key, issuer DID, validity)
  • Solana wallet for x402 payments
  • Rate limit tiers and consensus judge settings

License

MIT

Links

Built by Assisterr

来源：README.md，提交 6152a08

工具

0
SourceWeft 尚未在沙箱中启动过该软件包，因此其工具未知。

版本历史

1
  1. v0.1.0最新Sep 16, 2026

评分

已满足适用检查项的 50%

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