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76
A2A v1.6.0

asiai

asiai.dev · asiai (Jean-Marc Nahlovsky / druide67)

Apple Silicon LLM inference benchmark and monitoring agent. Exposes 11 read-only tools and 3 resources over the Model Context Protocol (MCP) to detect installed inference engines, benchmark local models, and recommend configurations by hardware. Runs locally (stdio) or over SSE/streamable-HTTP.

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🔔 Watch this agent for changes. Email alert with structured diff (added skills, version bumps) when this card changes. Enterprise feature. Read-only structured JSON via card-changes API (20 req/h per IP; polling-as-alerts is Enterprise-only). Sign in to subscribe
Trust score
49/100
grade D · 9 criteria
Uptime
98.7%
156 probes
~21 ms response
Revenue · 30d
no payment wallet declared
Usage · 7d
0
no recent activity
Card drift · 7d
stable
1 snapshots tracked
Owner
unverified
claim this listing →
D
Conformance score: 49/100
D-grade: significant issues, auth-gated, partially broken, or stale.
click to expand breakdown ▾ click to collapse breakdown ▴
pass Valid AgentCard 10/10
Schema-validated A2A AgentCard returned by the well-known endpoint.
fail Live JSON-RPC 5/25
Endpoint replies but body isn't a valid JSON-RPC 2.0 A2A response.
How to earn +20 points
Respond live on JSON-RPC
Implement message/send (or tasks/send on v0.x). Return a 200 with a valid JSON-RPC response. Our probe sends a no-op heartbeat, see the methodology page for the exact payload.
Docs →
fail Protocol version 0/10
No protocolVersion in card.
How to earn +10 points
Declare protocolVersion
Add `"protocolVersion": "1.0"` to the AgentCard root. Without it, callers can't negotiate v0.x vs v1.0 compatibility.
Docs →
info JWS signature 0/10
Card is unsigned (most published agents are).
pass Uptime track record 15/15
154/156 probes succeeded (99% uptime).
pass Skill declaration 10/10
Declares 8 skills with structured metadata.
partial Verified Identity 5/10
Provider declared: asiai (Jean-Marc Nahlovsky / druide67) (https://asiai.dev). Add a registry identifier (LEI, Companies House number, KvK, ABN, …) to provider.legalEntity for full verified-business credit.
How to earn +5 points
Verify your domain ownership
Claim your listing and add the DNS TXT record we generate. Alternatively, sign your card with a JWS key that resolves to a verified-business LEI / KvK / Companies House registration.
Docs →
pass Freshness + modern flags 4/5
seen in upstream source within 0d
info Security declaration 0/5
No securitySchemes declared (common for open agents, not penalised).

Activity (audit trail)

last 24h · 0 calls Public aggregate · no PII recorded

No calls observed in the last 7 days. Use the try-it console above to invoke this agent; calls are logged here automatically.

Card history

1 snapshot Every change to agent-card.json
Captured Hash
2026-05-23 01:44:52 current 5c4137ec905f… view →
Uptime
98.7%
156 probes
Response
167ms
last probe
Skills
8
declared
Streaming
SSE-capable

Endpoints

Agent cardhttps://asiai.dev/.well-known/agent-card.json
Providerhttps://asiai.dev
Docshttps://asiai.dev/commands/mcp/
Discovered via
manifests recrawl_hot recrawl_warm

Skills · 8 declared · mapped to canonical taxonomy

Check Inference Health

Quick health check of all local LLM inference engines. Returns ok/degraded/error, memory pressure, thermal state, GPU. Responds in <500ms.

canonical Model Inference Serving match 84%
healthmonitoringapple-silicon
List Loaded Models

List all models currently loaded across inference engines (VRAM, quantization, context length).

canonical Model Inference Serving match 85%
modelsinventoryinference
Detect Inference Engines

Auto-detect running LLM inference engines (Ollama, LM Studio, mlx-lm, llama.cpp, vLLM-MLX, Exo, TurboQuant).

canonical Model Evaluation and Benchmarking match 85%
discoveryenginesapple-silicon
Run Inference Benchmark

Benchmark a local model's performance (tok/s, TTFT, VRAM, power) with statistical rigour (CI 95%, P50/P90/P99). Supports multi-engine and cross-model comparison…

canonical Model Evaluation and Benchmarking match 87%
benchmarkperformanceinference
Recommend Engine and Model

Hardware-aware engine+model recommendations optimized for throughput, latency, or power efficiency.

canonical Model Inference Serving match 86%
recommendationhardwareinference
Compare Engines

Side-by-side comparison of inference engines or models from benchmark history.

canonical Benchmark Execution match 87%
comparisonbenchmarkanalysis
Full Inference Snapshot

Complete system + inference state: CPU load, memory, thermal, GPU, engines status, loaded models, recent activity.

canonical Model Inference Serving match 85%
snapshotmonitoringsystem
Run Diagnostics

Comprehensive diagnostic checks: Apple Silicon compat, engines health, DB integrity, daemon status, alerting config.

canonical Error Diagnosis and Debugging match 87%
diagnosticstroubleshooting

Health · last 30 probes

When HTTP Live JSON-RPC Latency
2026-07-17 20:04:34 200 167ms
2026-07-17 08:33:46 200 138ms
2026-07-17 02:18:45 200 147ms
2026-07-16 19:16:05 200 132ms
2026-07-16 14:38:50 200 160ms
2026-07-16 08:00:37 200 139ms
2026-07-16 01:35:19 200 153ms
2026-07-15 18:15:37 200 129ms
2026-07-15 11:43:14 200 139ms
2026-07-15 06:25:40 200 142ms

Cheaper or better alternatives per-skill

↑ 4 higher quality

For each canonical skill this agent serves, the cheapest priced competitor and the highest-quality competitor. Only shown when at least one beats the current agent. Skills where this agent is already best on both axes are hidden.

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Embed your Agenstry badge

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Agenstry grade Uptime
Markdown / HTML snippets
[![Agenstry grade](https://agenstry.com/badge/asiai.dev.svg)](https://agenstry.com/agents/asiai.dev)
[![Verified Business](https://agenstry.com/badge/asiai.dev/identity.svg)](https://agenstry.com/agents/asiai.dev)
[![Uptime](https://agenstry.com/badge/asiai.dev/uptime.svg)](https://agenstry.com/agents/asiai.dev)
[![A2A version](https://agenstry.com/badge/asiai.dev/protocol.svg)](https://agenstry.com/agents/asiai.dev)

Audit-grade evidence bundle

JSON snapshot for vendor-review files. Add ?sign=true for a JWS-signed envelope verifiable against our JWKS. See the methodology.

audit.json audit.json (JWS-signed) verification history
Raw agent card JSON
{
  "name": "asiai",
  "description": "Apple Silicon LLM inference benchmark and monitoring agent. Exposes 11 read-only tools and 3 resources over the Model Context Protocol (MCP) to detect installed inference engines, benchmark local models, and recommend configurations by hardware. Runs locally (stdio) or over SSE/streamable-HTTP.",
  "url": "https://asiai.dev",
  "version": "1.6.0",
  "documentationUrl": "https://asiai.dev/commands/mcp/",
  "provider": {
    "organization": "asiai (Jean-Marc Nahlovsky / druide67)",
    "url": "https://asiai.dev"
  },
  "protocols": [
    "mcp"
  ],
  "mcpServerCard": "https://asiai.dev/mcp-server.json",
  "preferredTransport": "stdio",
  "supportedInterfaces": [
    {
      "url": "local://asiai-mcp",
      "transport": "stdio",
      "description": "MCP server over stdio \u2014 invoke via `asiai mcp`"
    },
    {
      "url": "http://127.0.0.1:8765/sse",
      "transport": "sse",
      "description": "MCP server over Server-Sent Events \u2014 invoke via `asiai mcp --transport sse`"
    },
    {
      "url": "http://127.0.0.1:8765/mcp",
      "transport": "streamable-http",
      "description": "MCP server over streamable HTTP \u2014 invoke via `asiai mcp --transport streamable-http`"
    }
  ],
  "additionalInterfaces": [
    {
      "url": "https://asiai.dev/mcp-server.json",
      "transport": "http+mcp-card",
      "description": "MCP Server Card (static discovery manifest)"
    }
  ],
  "capabilities": {
    "streaming": true,
    "pushNotifications": false,
    "stateTransitionHistory": false
  },
  "defaultInputModes": [
    "text"
  ],
  "defaultOutputModes": [
    "text",
    "application/json"
  ],
  "skills": [
    {
      "id": "check-inference-health",
      "name": "Check Inference Health",
      "description": "Quick health check of all local LLM inference engines. Returns ok/degraded/error, memory pressure, thermal state, GPU. Responds in <500ms.",
      "tags": [
        "health",
        "monitoring",
        "apple-silicon"
      ],
      "examples": [
        "Is local LLM inference available right now?"
      ]
    },
    {
      "id": "list-models",
      "name": "List Loaded Models",
      "description": "List all models currently loaded across inference engines (VRAM, quantization, context length).",
      "tags": [
        "models",
        "inventory",
        "inference"
      ],
      "examples": [
        "What models are loaded right now?"
      ]
    },
    {
      "id": "detect-engines",
      "name": "Detect Inference Engines",
      "description": "Auto-detect running LLM inference engines (Ollama, LM Studio, mlx-lm, llama.cpp, vLLM-MLX, Exo, TurboQuant).",
      "tags": [
        "discovery",
        "engines",
        "apple-silicon"
      ],
      "examples": [
        "Which inference engines are installed on this Mac?"
      ]
    },
    {
      "id": "run-benchmark",
      "name": "Run Inference Benchmark",
      "description": "Benchmark a local model's performance (tok/s, TTFT, VRAM, power) with statistical rigour (CI 95%, P50/P90/P99). Supports multi-engine and cross-model comparison.",
      "tags": [
        "benchmark",
        "performance",
        "inference"
      ],
      "examples": [
        "Benchmark Qwen 3.6 on Ollama NVFP4",
        "Compare Qwen 3.5 vs 3.6 on this Mac"
      ]
    },
    {
      "id": "recommend-engine",
      "name": "Recommend Engine and Model",
      "description": "Hardware-aware engine+model recommendations optimized for throughput, latency, or power efficiency.",
      "tags": [
        "recommendation",
        "hardware",
        "inference"
      ],
      "examples": [
        "What's the fastest engine for my Mac?",
        "Which model fits my RAM?"
      ]
    },
    {
      "id": "compare-engines",
      "name": "Compare Engines",
      "description": "Side-by-side comparison of inference engines or models from benchmark history.",
      "tags": [
        "comparison",
        "benchmark",
        "analysis"
      ],
      "examples": [
        "Compare Ollama MLX vs LM Studio for Qwen 3.6"
      ]
    },
    {
      "id": "get-inference-snapshot",
      "name": "Full Inference Snapshot",
      "description": "Complete system + inference state: CPU load, memory, thermal, GPU, engines status, loaded models, recent activity.",
      "tags": [
        "snapshot",
        "monitoring",
        "system"
      ],
      "examples": [
        "Give me a full status report of local inference"
      ]
    },
    {
      "id": "diagnose",
      "name": "Run Diagnostics",
      "description": "Comprehensive diagnostic checks: Apple Silicon compat, engines health, DB integrity, daemon status, alerting config.",
      "tags": [
        "diagnostics",
        "troubleshooting"
      ],
      "examples": [
        "Diagnose why inference is failing"
      ]
    }
  ],
  "related": {
    "mcpServerCard": "https://asiai.dev/mcp-server.json",
    "agentSkills": "https://asiai.dev/.well-known/agent-skills.json",
    "apiCatalog": "https://asiai.dev/.well-known/api-catalog",
    "openapi": "https://asiai.dev/openapi.json",
    "llmsTxt": "https://asiai.dev/llms.txt"
  }
}