Multi-Agent Edge Swarm & Chat
3-Agent SwarmCoordinate a 3-agent autonomous edge team (Coder, Tester, Reviewer) or launch an interactive multi-turn terminal chat.
📖 Full Explanation
Why rely on a single model when you can deploy a coordinated edge team? \1 orchestrates a 3-agent pipeline: Agent 1 (Qwen 2.5 Coder 32B) writes the implementation; Agent 2 (Llama 3.3 70B) generates comprehensive tests; and Agent 3 (DeepSeek R1 32B) conducts forensic code review and edge safety hardening. \1 provides an interactive multi-turn terminal chat session with persistent context.
⚙️ Command Options & Flags
| Option / Flag | Description | Default |
|---|---|---|
| -o, --output <dir> | Directory to write solution, tests, and review report | ./ai-team-output |
| --show-thoughts | Print intermediate reasoning and deliberation from all agents | false |
| -m, --model <id> | Default chat model | @cf/meta/llama-3.3-70b-instruct-fp8-fast |
🔬 Technical Corollary & Edge Mechanics
The team coordinator passes structured artifacts between models sequentially: code is fed into the tester's context, and both code and test specifications are evaluated by the reviewer. Any security warnings or memory limits flagged by DeepSeek R1 are annotated with high-priority warnings in the final review report.
🎯 Real-World DevOps Use Cases
End-to-End Edge Feature Delivery
Implement, test, and audit a complete JWT verification worker in one command.
Interactive Terminal Debugging Chat
Start a conversational session to diagnose edge routing behavior.
💻 Live Output Simulation
$ cflare ai team "Distributed IP rate limiter" [1/3] 🤖 Coder (Qwen 2.5 32B) generating worker implementation... (1,240ms) [2/3] 🧪 Tester (Llama 3.3 70B) synthesizing unit tests... (1,820ms) [3/3] 🔍 Reviewer (DeepSeek R1) conducting forensic edge audit... (2,100ms) ✔ Solution verified with 0 vulnerabilities 📁 Output written to ./ai-team-output/ (worker.js, worker.test.js, audit-report.md)