GLM-5 Meets Claude Agent Teams: The Rise of Vibe Coding

Just 10 hours ago, Zhipu AI officially launched its flagship model — GLM-5, a high-density reasoning engine poised to redefine AI-assisted development.

While most developers are still benchmarking its single-thread logical reasoning, forward-thinking engineers have unlocked a far more disruptive paradigm: injecting GLM-5 into Anthropic’s newly released Claude Code Agent Teams architecture.

Beyond Single-Agent Interaction: Enter the AI Engineering Team

Owning one GLM-5 is exciting — but what if GLM-5 could assemble and lead its own engineering team?

That’s the core magic of Agent Teams: you shift from conversing with a single AI to acting as CTO of a fully autonomous, GLM-5–powered engineering squad. These agents are no longer linear “task executors” — they’re collaborative, role-aware teammates capable of:

• Autonomous Team Formation: The Lead Agent dynamically spins up specialized roles — a backend engineer, frontend architect, and even a dedicated QA “devil’s advocate” — based on task requirements.
• Mesh Collaboration: Unlike traditional subagents (star-topology), teammates communicate via Direct Messaging, negotiate API contracts, share a Shared Task List, conduct live code reviews, and debate architectural trade-offs — all in parallel.

Watching tmux auto-split into multiple terminals — each running a GLM-5 agent coding, debugging, and cross-reviewing in real time — embodies Andrej Karpathy’s vision of Vibe Coding: You provide intent; AI delivers organizational intelligence.

Why It’s Now Accessible: Cost-Efficient GLM-5 Inference

Historically, such “full-stack AI software factories” were limited by prohibitive token costs (e.g., Opus 4.5). But with Zhipu’s GLM Coding Plan and high-value inference packages, enterprise-grade team-level coding is now democratized — at the cost of a cup of coffee.

What Are Agent Teams?

Agent Teams is Claude Code’s latest multi-agent orchestration framework. It transforms a single LLM session into an Organization, composed of:

• A Lead Agent (team orchestrator & final reviewer)
• Multiple Teammates (role-specialized executors)

Core Principles

• Task Decomposition & Parallel Execution: Lead Agent breaks down goals; teammates execute frontend, backend, and QA tasks concurrently — slashing end-to-end delivery time.
• Independent Context Windows: Each teammate runs in an isolated process with its own context window — eliminating interference (e.g., CSS history won’t pollute SQL logic), ensuring laser focus.

Technical Pillars

🔹 Mesh Communication

Unlike centralized routing, teammates message directly via a built-in Mailbox system, enabling real-time interface sync without Lead Agent bottlenecks.

🔹 Shared Task System

A local JSON task list powers coordination:
– File Locking: Prevents race conditions (e.g., two agents claiming the same task).
– Dependency Management: Tasks unlock only after prerequisites (e.g., DB schema → ORM code) complete.

Agent Teams vs. Subagents: Key Distinction

Feature	Subagents	Agent Teams
Topology	Star (all traffic through leader)	Mesh (peer-to-peer)
Context	Shared or inherited	Fully independent per agent
Use Case	Fast, atomic subtasks (e.g., error lookup)	Complex systems work (refactoring, full-stack prototyping)

Unlocking the “God View”: Tmux Integration

To witness true parallelism — multiple agents coding side-by-side — you must use Tmux (Terminal Multiplexer). Without it, Claude defaults to single-window scroll mode.

Why Tmux?

Claude Code leverages Unix philosophy: it integrates natively with Tmux (or iTerm2) to dynamically split panes — no custom GUI required. The main process controls terminal layout via Tmux’s API.

Installation Guide

• macOS: brew install tmux
• Linux: sudo apt update && sudo apt install tmux
• Windows: Enable WSL2, install Ubuntu, then run sudo apt install tmux

Workflow: Two-Step Activation

1. Launch Tmux: Run tmux in your terminal → green status bar confirms entry.
2. Start Claude: Inside the Tmux session, run claude. When an Agent Team forms, Claude auto-splits panes and assigns agents.

Pro Tmux Cheatsheet

Action	Shortcut
Navigate Panes	Ctrl+b → o (next) / q (show numbers → jump)
Zoom In	Ctrl+b → z (maximize current pane; press again to restore)
Scroll History	Ctrl+b → [ (enter copy mode → arrow keys)

Configuration & Setup

Agent Teams is experimental — enable it manually:

Step 1: Enable Flag

Edit ~/.claude/settings.json:

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

Step 2: Issue Natural-Language Prompts

• “Create an agent team to review this codebase: one focused on security, one on performance, one on test coverage — output actionable fixes.”
• “Build a to-do app prototype. Launch a team: React frontend engineer, Node.js backend engineer, and QA tester. Begin work in parallel.”

Troubleshooting: Fixing 400 Errors in Sub-Panels

⚠️ Common Pitfall: Sub-terminals fail with Error 400 because environment variables (e.g., ANTHROPIC_AUTH_TOKEN, model mappings) aren’t inherited by new Tmux panes.

✅ Fix: Explicitly declare all required env vars in settings.json:

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1",
    "ANTHROPIC_AUTH_TOKEN": "your_zai_api_key",
    "ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic",
    "API_TIMEOUT_MS": "3000000",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "GLM-5",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "GLM-5",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "GLM-4.5-Air"
  }
}

Save (Ctrl+X → Y → Enter) — problem resolved.

Conclusion: From Pair Programming to Team Engineering

Agent Teams marks the evolution of AI coding from pair programming to team engineering. By combining:

• 🖥️ Tmux-powered visual orchestration,
• 🧠 Isolated-context autonomy, and
• 💡 Cost-efficient GLM-5 inference,

you can now run a full-stack AI dev team locally. This isn’t incremental tooling — it’s the fully realized Vibe Coding paradigm. Try it while the momentum is hot.

Article adapted from WeChat Official Account “AI Xiū Māo Prompt”.