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.

Related MCP server: MCP Workbench MCP Server

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)

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

Services:

• API: http://localhost:8002

• MCP Server: http://localhost:8003

• Health: http://localhost:8002/health

Local Development

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
# Add LLM keys to .env

unset GROQ_API_KEY  # Shell env overrides .env rotation pool
python -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:

{
  "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:

API Endpoints

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

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

• Architecture — Full system design (845 lines)

• Distribution Roadmap — Partner and integration plan

• A2A Agent Card — Machine-readable capabilities

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