pi-orchestration
Orchestrate multiple AI models (GLM, MiniMax, etc.) as workers using Pi Coding Agent with Claude as coordinator.
Installation
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
View source ↗GuidesSkill instructions
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
- Task Decomposition: Break large tasks into independent subtasks
- Model Selection: Use GLM for Chinese content, MiniMax for creative tasks
- Error Handling: Check worker status before collecting results
- 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
Examples
Read the usage examples in the source repository.
View source ↗Source history
- Indexed on
- 2026-01-20
- Index updated
- 2026-01-30
- Branch
- main
Index and sync dates describe this directory. View the source repository for code history.
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