Multi Agent AI Projects

Multi-Agent AI Projects

Guidelines for working with multi-agent AI learning projects and experimental codebases.

Project Structure Recognition

Common Patterns

• .spec/ directory - Learning specifications and experimental code
• lessons/ or similar learning directories
• STATUS.md - Progress tracking for learning journey
• Per-lesson or per-module structure
• Self-contained lesson directories

Workflow Patterns

Before Starting Work

1. Check for STATUS.md - Understand current progress and next steps
2. Identify lesson structure - Each lesson may be self-contained
3. Check for lesson-specific dependencies - Each module might have its own requirements
4. Look for .env files per lesson - API keys typically organized by lesson

Execution Patterns

• Use uv run python for execution (most AI projects use modern Python tooling)
• Each lesson may have its own virtual environment or shared venv
• Check lesson README for specific setup instructions

API Keys and Secrets

• API keys typically in per-lesson .env files
• Each lesson might require different API credentials
• Always check .env.example or .env.template in lesson directories
• Never commit .env files

Progress Tracking

STATUS.md Pattern

• Update after completing lessons
• Note blockers and next steps
• Document learnings and insights
• Track which lessons are complete

Session Management

• Always check STATUS.md before starting
• Update STATUS.md before ending sessions
• Note any experimental findings

Common Project Types

Learning Spike Projects

• Focus on exploration and experimentation
• Code may not be production-quality
• Documentation of learnings is important
• Test different approaches

Multi-Agent Frameworks

• Agent coordination patterns
• Tool usage and integration
• Message passing between agents
• State management across agents

Quick Reference

Always check:

• ✅ STATUS.md for current progress
• ✅ Lesson-specific README files
• ✅ Per-lesson .env files
• ✅ .spec/ or lessons/ directory structure

Execution:

• Use uv run python for modern projects
• Check for per-lesson dependencies
• Respect lesson isolation if present

Documentation:

• Update STATUS.md with progress
• Document experimental findings
• Note what worked and what didn't

Note: These projects are often learning-focused - prioritize understanding and documentation over production perfection.