Poe API Orchestrator

Poe API Orchestrator

Autonomous subagent orchestration using Poe API with specialized models.

🎯 Decision Matrix

Main Agent (GLM-5) autonomously decides:

🔧 How It Works

Autonomous Flow:

User Request
    ↓
Main Agent (GLM-5) analyzes task
    ↓
Main Agent decides:
  - Is this coding? → spawn coding-agent
  - Is this UI/UX? → spawn design-agent
  - Is this analysis? → spawn analysis-agent
  - Is this complex? → spawn reasoning-agent
    ↓
Subagent uses Poe API
    ↓
Calls specialized model
    ↓
Returns result to Main Agent
    ↓
Main Agent delivers to user

No User Intervention Needed!

• ✅ Main agent detects task type
• ✅ Spawns appropriate subagent
• ✅ Subagent calls Poe API
• ✅ Uses specialized model
• ✅ Returns result

🚀 Quick Start

1. Set API Key

export POE_API_KEY="w0womy7-r0RmMP-C1nFH2f_RXnBbPr1dfy34VHqzWck"

2. Main Agent Usage

I (GLM-5) will automatically:

For Coding Tasks:

User: "Write a Python script to scrape data"
Me: [Detects coding task] → spawns coding-agent → uses GPT-5.3-Codex

For UI/UX Tasks:

User: "Design a dashboard"
Me: [Detects design task] → spawns design-agent → uses Gemini-3.1-Pro

For Analysis Tasks:

User: "Analyze these requirements"
Me: [Detects analysis task] → spawns analysis-agent → uses Claude-Sonnet-4.6

For Complex Problems:

User: "Design system architecture"
Me: [Detects complex task] → spawns reasoning-agent → uses Claude-Opus-4.6

📋 Subagent Model Assignments

1. Coding Agent → GPT-5.3-Codex

Best for:

• Writing code (Python, JS, etc.)
• Debugging
• Refactoring
• API integration
• Algorithm implementation

2. Design Agent → Gemini-3.1-Pro

Best for:

• UI/UX design
• Visual mockups
• Frontend design
• User experience
• Creative solutions

3. Analysis Agent → Claude-Sonnet-4.6

Best for:

• Data analysis
• Requirements gathering
• Task breakdown
• Structured planning
• Documentation

4. Reasoning Agent → Claude-Opus-4.6

Best for:

• Complex problems
• Deep reasoning
• Architecture design
• Multi-step logic
• Hard decisions

🎯 Decision Logic

Main agent checks for keywords:

# Coding triggers
if any(word in task for word in ["code", "implement", "debug", "script", "function"]):
    spawn("coding-agent", model="GPT-5.3-Codex")

# UI/UX triggers
elif any(word in task for word in ["design", "UI", "mockup", "visual", "frontend"]):
    spawn("design-agent", model="Gemini-3.1-Pro")

# Analysis triggers
elif any(word in task for word in ["analyze", "requirements", "breakdown", "plan"]):
    spawn("analysis-agent", model="Claude-Sonnet-4.6")

# Complex triggers
elif any(word in task for word in ["complex", "difficult", "architecture", "reasoning"]):
    spawn("reasoning-agent", model="Claude-Opus-4.6")

📝 Example Scenarios

Scenario 1: Coding Task

User: "Write a WebSocket client for real-time data"

Main Agent:
  → Detects: "WebSocket", "client" (coding keywords)
  → Decision: Spawn coding-agent
  → Model: GPT-5.3-Codex
  → Result: Working code

User receives: Complete WebSocket client implementation

Scenario 2: UI/UX Task

User: "Design a trading bot dashboard"

Main Agent:
  → Detects: "design", "dashboard" (UI/UX keywords)
  → Decision: Spawn design-agent
  → Model: Gemini-3.1-Pro
  → Result: Dashboard mockup + design specs

User receives: Complete UI/UX design

Scenario 3: Analysis Task

User: "Analyze requirements for building a chatbot"

Main Agent:
  → Detects: "analyze", "requirements" (analysis keywords)
  → Decision: Spawn analysis-agent
  → Model: Claude-Sonnet-4.6
  → Result: Detailed requirements document

User receives: Complete analysis

Scenario 4: Complex Problem

User: "Design microservices architecture for e-commerce"

Main Agent:
  → Detects: "architecture", complex task
  → Decision: Spawn reasoning-agent
  → Model: Claude-Opus-4.6
  → Result: Architecture diagram + explanation

User receives: Complete architecture design

⚙️ Configuration

Environment Variables

POE_API_KEY=w0womy7-r0RmMP-C1nFH2f_RXnBbPr1dfy34VHqzWck
POE_API_URL=https://api.poe.com/v1

Token Control ⚠️ IMPORTANT

Correct Understanding (Updated 2026-03-03):

# 1. max_tokens = Response length limit (NOT total usage!)
task_max_tokens = {
    "coding": 8000,      # Coding needs complete code
    "design": 4000,      # UI/UX design
    "analysis": 3000,    # Data analysis
    "reasoning": 5000,   # Complex reasoning
}

# 2. Real cost control ⭐
max_calls_per_task = 10        # Limit API calls (MOST IMPORTANT!)
max_total_tokens = 100000      # Total token budget
track_usage = True             # Track usage

# 3. Monitor output
# 📊 Tokens: 77 | Total: 77 | Calls: 1/10

Key Points:

• ✅ max_tokens = Maximum response length (not total usage)
• ✅ max_calls_per_task = Real cost control (limit API calls)
• ✅ Coding tasks need 8000+ tokens (not 2000)
• ✅ Main agent monitors all subagent usage

Violation Handling:

• Call limit exceeded → Reject request
• Auto-notify main agent

Model Endpoints

MODELS = {
    "coding": "GPT-5.3-Codex",
    "design": "Gemini-3.1-Pro",
    "analysis": "Claude-Sonnet-4.6",
    "reasoning": "Claude-Opus-4.6"
}

🔄 Workflow

1. User makes request → Main agent (GLM-5)
2. Main agent analyzes → Detects task type
3. Main agent decides → Which subagent to spawn
4. Subagent spawned → Uses Poe API
5. Subagent calls model → Specialized AI
6. Model processes → Returns result
7. Subagent returns → To main agent
8. Main agent delivers → To user

All automatic! No user intervention needed!

📊 Benefits

✅ Autonomous Decision-Making - Main agent decides automatically
✅ Specialized Models - Right model for right task
✅ No Manual Spawning - Automatic subagent creation
✅ Parallel Processing - Multiple subagents can work simultaneously
✅ Best Results - Combining strengths of different models

🎯 Summary

Main Agent (GLM-5) Responsibilities:

1. ✅ Analyze user request
2. ✅ Detect task type
3. ✅ Decide which subagent to spawn
4. ✅ Spawn subagent with Poe API
5. ✅ Collect results
6. ✅ Deliver to user

User Just Asks → Everything Else is Automatic!

I will autonomously decide and spawn the right subagent for each task! 🚀