pi-orchestration – OpenClaw Skill

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name: pi-orchestration description: Orchestrate multiple AI models (GLM, MiniMax, etc.) as workers using Pi Coding Agent with Claude as coordinator. homepage: https://github.com/mariozechner/pi-coding-agent metadata: {"clawdis":{"emoji":"🎭","requires":{"bins":["pi"]}}}

Pi Orchestration

Use Claude as an orchestrator to spawn and coordinate multiple AI model workers (GLM, MiniMax, etc.) via Pi Coding Agent.

Supported Providers

Provider	Model	Status
GLM	glm-4.7	✅ Working
MiniMax	MiniMax-M2.1	✅ Working
OpenAI	gpt-4o, etc.	✅ Working
Anthropic	claude-*	✅ Working

Setup

1. GLM (Zhipu AI)

Get API key from open.bigmodel.cn

export GLM_API_KEY="your-glm-api-key"

2. MiniMax

Get API key from api.minimax.chat

export MINIMAX_API_KEY="your-minimax-api-key"
export MINIMAX_GROUP_ID="your-group-id"  # Required for MiniMax

Usage

Direct Commands

# GLM-4.7
pi --provider glm --model glm-4.7 -p "Your task"

# MiniMax M2.1
pi --provider minimax --model MiniMax-M2.1 -p "Your task"

# Test connectivity
pi --provider glm --model glm-4.7 -p "Say hello"

Orchestration Patterns

Claude (Opus) can spawn these as background workers:

Background Worker

bash workdir:/tmp/task background:true command:"pi --provider glm --model glm-4.7 -p 'Build feature X'"

Parallel Army (tmux)

# Create worker sessions
tmux new-session -d -s worker-1
tmux new-session -d -s worker-2

# Dispatch tasks
tmux send-keys -t worker-1 "pi --provider glm --model glm-4.7 -p 'Task 1'" Enter
tmux send-keys -t worker-2 "pi --provider minimax --model MiniMax-M2.1 -p 'Task 2'" Enter

# Check progress
tmux capture-pane -t worker-1 -p
tmux capture-pane -t worker-2 -p

Map-Reduce Pattern

# Map: Distribute subtasks to workers
for i in 1 2 3; do
  tmux send-keys -t worker-$i "pi --provider glm --model glm-4.7 -p 'Process chunk $i'" Enter
done

# Reduce: Collect and combine results
for i in 1 2 3; do
  tmux capture-pane -t worker-$i -p >> /tmp/results.txt
done

Orchestration Script

# Quick orchestration helper
uv run {baseDir}/scripts/orchestrate.py spawn --provider glm --model glm-4.7 --task "Build a REST API"
uv run {baseDir}/scripts/orchestrate.py status
uv run {baseDir}/scripts/orchestrate.py collect

Best Practices

1. Task Decomposition: Break large tasks into independent subtasks
2. Model Selection: Use GLM for Chinese content, MiniMax for creative tasks
3. Error Handling: Check worker status before collecting results
4. Resource Management: Clean up tmux sessions after completion

Example: Parallel Code Review

# Claude orchestrates 3 workers to review different files
tmux send-keys -t worker-1 "pi --provider glm -p 'Review auth.py for security issues'" Enter
tmux send-keys -t worker-2 "pi --provider minimax -p 'Review api.py for performance'" Enter  
tmux send-keys -t worker-3 "pi --provider glm -p 'Review db.py for SQL injection'" Enter

# Wait and collect
sleep 30
for i in 1 2 3; do
  echo "=== Worker $i ===" >> review.md
  tmux capture-pane -t worker-$i -p >> review.md
done

Notes

• Pi Coding Agent must be installed: npm install -g @anthropic/pi-coding-agent
• GLM and MiniMax have generous free tiers
• Claude acts as coordinator, workers do the heavy lifting
• Combine with process tool for background task management