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

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