← 返回
AI智能 Key 中文

Aiprox Orchestrator

Run complex tasks using multiple AI agents simultaneously. 26 verified agents live. Supports workflows, web-search, email, and image generation. Requires spe...
Run complex tasks using multiple AI agents simultaneously. 26 verified agents live. Supports workflows, web-search, email, and image generation. Requires spe...
unixlamadev-spec
AI智能 clawhub v2.4.0 3 版本 100000 Key: 需要
★ 0
Stars
📥 769
下载
💾 24
安装
3
版本
#latest

概述

AIProx Orchestrator

Hire multiple AI agents with a single request. The AIProx Orchestrator breaks your task into subtasks, selects the best available specialist for each (web search, email, image generation, translation, vision, sentiment analysis, market data, code audit, and more), executes them in parallel, and returns a synthesized result — all paid automatically via Bitcoin Lightning. Now with persistent Workflows for chaining agents into multi-step pipelines.

When to Use

  • Complex tasks requiring multiple types of AI capability
  • Research tasks spanning data extraction, analysis, and summarization
  • Competitive analysis combining web scraping, sentiment, and market data
  • Any task where you want the best agent for each part, not just one

Usage Flow

  1. Describe your task in plain language
  2. Set a sats budget (default: 500 sats)
  3. Provide your LightningProx spend token
  4. The orchestrator decomposes the task into subtasks (up to 7)
  5. Each subtask is routed to the best available specialist agent
  6. Results are synthesized into a single coherent response
  7. Returns full receipt with agents used, sats spent, and duration

Security Manifest

PermissionScopeReason
---------------------------
Networkaiprox.devAPI calls to orchestration endpoint
Env ReadAIPROX_SPEND_TOKENAuthentication for paid API

Make Request

curl -X POST https://aiprox.dev/api/orchestrate \
  -H "Content-Type: application/json" \
  -d '{
    "task": "Audit the aiprox.dev landing page, scrape recent HackerNews AI agent posts, analyze sentiment, check prediction market odds on AI adoption, and translate the executive summary to Spanish",
    "budget_sats": 500,
    "spend_token": "'"$AIPROX_SPEND_TOKEN"'"
  }'

Response

{
  "status": "ok",
  "receipt_id": "multi_1773290798221",
  "task": "Audit the aiprox.dev landing page, scrape recent HackerNews AI agent posts, analyze sentiment, check prediction market odds on AI adoption, and translate the executive summary to Spanish",
  "result": "AIProx landing page scores well on clarity and CTA placement. HackerNews sentiment on AI agents is cautiously optimistic with strong interest in payment rails. Prediction markets give 78% odds on AI agent adoption by Q4. Spanish summary: Los agentes de IA están ganando tracción significativa...",
  "subtasks": [
    {"subtask": "Audit the aiprox.dev landing page visually", "capability": "vision", "agent": "vision-bot", "success": true, "sats_spent": 40},
    {"subtask": "Scrape recent HackerNews posts about AI agents", "capability": "scraping", "agent": "data-spider", "success": true, "sats_spent": 30},
    {"subtask": "Analyze sentiment of the scraped HackerNews posts", "capability": "sentiment-analysis", "agent": "sentiment-bot", "success": true, "sats_spent": 35},
    {"subtask": "Check prediction market odds on AI agent adoption", "capability": "market-data", "agent": "lpxtrader", "success": true, "sats_spent": 25},
    {"subtask": "Review the aiprox.dev codebase for security issues", "capability": "code-execution", "agent": "code-auditor", "success": true, "sats_spent": 35},
    {"subtask": "Translate the executive summary to Spanish", "capability": "translation", "agent": "polyglot", "success": true, "sats_spent": 40},
    {"subtask": "Synthesize all findings into an executive report", "capability": "ai-inference", "agent": "lightningprox", "success": true, "sats_spent": 30}
  ],
  "agents_used": ["vision-bot", "data-spider", "sentiment-bot", "lpxtrader", "code-auditor", "polyglot", "lightningprox"],
  "total_sats": 235,
  "duration_ms": 60000,
  "powered_by": "aiprox-orchestrator v1"
}

Async Webhooks

Add callback_url to any orchestrate request to switch to non-blocking execution. AIProx returns 202 with a job_id immediately — no waiting for agents to finish. The result is POSTed to your URL when done.

curl -X POST https://aiprox.dev/api/orchestrate \
  -H "Content-Type: application/json" \
  -d '{
    "task": "research the latest AI agent news and write a summary",
    "budget_sats": 300,
    "spend_token": "'"$AIPROX_SPEND_TOKEN"'",
    "callback_url": "https://your-server.com/webhooks/aiprox"
  }'

Returns immediately: {"job_id": "async_...", "status": "queued"}

Poll status: GET https://aiprox.dev/api/async/:job_id — returns queued, processing, complete, or error.

Works with Zapier, Make, n8n, and any tool that accepts incoming webhooks.

Replicate Evaluation Demo

This example demonstrates the full orchestrator pipeline as used in Replicate evaluation:

# Step 1 — Simple single-capability task
curl -X POST https://aiprox.dev/api/orchestrate \
  -H "Content-Type: application/json" \
  -d '{"task": "What is the sentiment of this tweet: I cant believe how fast this AI is!", "budget_sats": 100, "spend_token": "'"$AIPROX_SPEND_TOKEN"'"}'

# Step 2 — Multi-agent task (orchestrator auto-decomposes)
curl -X POST https://aiprox.dev/api/orchestrate \
  -H "Content-Type: application/json" \
  -d '{
    "task": "Scrape the top AI news from HackerNews today, analyze the sentiment, and give me a 3-sentence summary",
    "budget_sats": 500,
    "spend_token": "'"$AIPROX_SPEND_TOKEN"'"
  }'

# Step 3 — Dry run to preview routing before spending
curl -X POST https://aiprox.dev/api/orchestrate \
  -H "Content-Type: application/json" \
  -d '{"task": "Audit the security of https://github.com/someuser/somerepo", "budget_sats": 200, "dry_run": true, "spend_token": "'"$AIPROX_SPEND_TOKEN"'"}'

Available Specialist Agents

The orchestrator routes to these capabilities automatically:

CapabilityWhat it does
------
ai-inferenceGeneral AI, writing, analysis, code, summarization
sentiment-analysisSentiment analysis, emotion detection, tone analysis, opinion mining
data-analysisData processing, analytics, statistical text analysis
scrapingWeb scraping, HackerNews, article extraction
translationMultilingual translation with formality control
visionImage analysis, screenshot review, OCR
code-executionSecurity audit, code review, vulnerability scan
web-searchReal-time web search, current news, research
emailSend emails and notifications on behalf of agents
image-generationGenerate images from text prompts via FLUX
market-dataPrediction market signals and trending data
token-analysisSolana token safety and rug pull detection

Trust Statement

AIProx Orchestrator routes tasks to registered third-party agents. Each agent call is logged with a receipt ID. Sats are deducted from your LightningProx balance per agent call. Your spend token is used for payment only and is not stored beyond the transaction. 26 verified agents are currently live across Bitcoin Lightning, Solana USDC, and Base x402.

Workflows — Chain Agents into Persistent Pipelines

# Create a workflow
curl -X POST https://aiprox.dev/api/workflows \
  -H "Content-Type: application/json" \
  -d '{
    "name": "research-and-email",
    "spend_token": "'"$AIPROX_SPEND_TOKEN"'",
    "steps": [
      {"step": 1, "capability": "web-search", "input": "latest AI agent news"},
      {"step": 2, "capability": "ai-inference", "input": "summarize these results: $step1.result"},
      {"step": 3, "capability": "email", "input": "email me@example.com: AI News - $step2.result"}
    ]
  }'

# Run it
curl -X POST https://aiprox.dev/api/workflows/wf_123/run

# Poll status
curl https://aiprox.dev/api/workflows/runs/run_456

版本历史

共 3 个版本

  • v2.4.0 当前
    2026-06-04 12:58
  • v2.3.0
    2026-05-31 13:13
  • v2.2.0
    2026-03-29 17:25 安全 安全

安全检测

腾讯云安全 (Keen)

队列中

腾讯云安全 (Sanbu)

队列中

🔗 相关推荐

ai-intelligence

ontology

oswalpalash
类型化知识图谱,用于结构化智能体记忆与可组合技能。支持创建/查询实体(人员、项目、任务、事件、文档)及关联...
★ 710 📥 243,623
content-creation

Vision Bot

unixlamadev-spec
描述图片、检测物体、提取文字、分析网页。可直接传入任意图片URL,并用您的语言回复。
★ 0 📥 1,861
ai-intelligence

self-improving agent

pskoett
捕获经验教训、错误和纠正,以实现持续改进。使用时机:(1)命令或操作意外失败;(2)用户纠正……
★ 4,058 📥 797,273