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AgentTrust

Registre officiel Sécurité

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

Code source Site web Documentation

Que pouvez-vous faire avec AgentTrust ?

  • 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
  • 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

Installation

Ajoutez ceci à la configuration MCP de votre client (Claude Desktop : claude_desktop_config.json ; Cursor : .cursor/mcp.json ; VS Code et les autres utilisent la même forme « mcpServers »).

PYPI package mcp-agenttrust
Claude Code 
claude mcp add agenttrust -e 'GROQ_API_KEY=<your groq_api_key>' -e 'CEREBRAS_API_KEY=<your cerebras_api_key>' -e 'MONGODB_URL=<your mongodb_url>' -- uvx mcp-agenttrust==0.1.2
Configuration JSON (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "agenttrust": {
            "command": "uvx",
            "args": [
                "mcp-agenttrust==0.1.2"
            ],
            "env": {
                "GROQ_API_KEY": "<your groq_api_key>",
                "CEREBRAS_API_KEY": "<your cerebras_api_key>",
                "MONGODB_URL": "<your mongodb_url>"
            }
        }
    }
}

Variables d'environnement

  • GROQ_API_KEY secret Groq API key for LLM judge (free tier available at console.groq.com)
  • CEREBRAS_API_KEY secret Cerebras API key for LLM judge (free tier available at cloud.cerebras.ai)
  • MONGODB_URL secret MongoDB connection URL for persistent storage

Documentation

Tirée du README du projet sur GitHub, actualisée chaque semaine. Les liens ouvrent GitHub.

Sommaire
  1. AgentTrust
  2. Why
  3. Features
  4. Quick Start
  5. API Endpoints
  6. Architecture
  7. Tests
  8. Configuration
  9. License
  10. Links

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

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

Services:

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:

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

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

Built by Assisterr

Questions fréquentes

Qu'est-ce que AgentTrust ?

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

Comment installer AgentTrust ?

Il s'exécute sur votre machine. Dans Claude Code : claude mcp add agenttrust -e 'GROQ_API_KEY=<your groq_api_key>' -e 'CEREBRAS_API_KEY=<your cerebras_api_key>' -e 'MONGODB_URL=<your mongodb_url>' -- uvx mcp-agenttrust==0.1.2. Pour les autres clients, collez la configuration JSON de cette page dans leurs réglages MCP.

Faut-il une clé API pour AgentTrust ?

Oui. Il attend GROQ_API_KEY, CEREBRAS_API_KEY, MONGODB_URL, à renseigner dans sa configuration.

AgentTrust est-il open source ?

Oui. Son code est sur GitHub sous licence MIT.

Qui maintient AgentTrust ?

Il est publié par assister-xyz. AI.tn le référence sans le maintenir ni le recommander.

Issu du registre MCP officiel. Être listé n'est pas une recommandation : vérifiez le code et les permissions d'un serveur avant de lui donner accès à vos données.