Running one AI coding agent is easy. Running three in parallel on the same codebase is where things get interesting — and where you need to make a tooling choice.
There's no "best" orchestrator. There's the right one for your workflow. Here's an honest comparison of five approaches, with the tradeoffs I've seen after months of running multi-agent setups.
The Options
1. Raw tmux Scripts
What it is: Shell scripts that launch agents in tmux panes. DIY orchestration.
Pros:
- Zero dependencies beyond tmux
- Full control over every detail
- No abstractions to fight
- You already know how it works
Cons:
- No state management — you track everything manually
- No message routing between agents
- No test gating — agents declare "done" without verification
- Breaks when agents crash or hit context limits
- You become the orchestrator
Best for: One-off tasks where you need 2-3 agents for an afternoon. If your coordination needs fit in a 50-line script, use the script.
Not for: Repeatable workflows, overnight sessions, or anything where "walk away and come back to merged PRs" matters.
2. CrewAI
What it is: Python framework for building multi-agent systems with role-based collaboration.
Pros:
- Rich agent definition (role, goal, backstory, tools)
- Built-in task delegation and sequential/parallel execution
- Large ecosystem of tools and integrations
- Active community, good documentation
- Supports multiple LLM providers
Cons:
- Framework, not a tool — you write Python to configure agents
- Agents are CrewAI agents, not existing CLI tools (Claude Code, Codex)
- No terminal visibility — agents run as Python processes
- Learning curve for the framework concepts
- Token costs can be high with verbose agent interactions
Best for: Building custom multi-agent applications in Python. Research, analysis, content generation workflows where you want programmatic control.
Not for: Orchestrating existing CLI coding agents. If you already use Claude Code or Codex and want to run multiples in parallel, CrewAI means rebuilding your agent setup in Python.
3. AutoGen
What it is: Microsoft's framework for multi-agent conversation and collaboration.
Pros:
- Sophisticated conversation patterns between agents
- Strong research backing (Microsoft Research)
- Group chat, nested conversations, teachable agents
- Good for complex reasoning chains
- Human-in-the-loop support
Cons:
- Heavy framework — significant setup for simple use cases
- Python-only
- Designed for conversational agents, not coding workflows
- No git integration, no worktree isolation
- Overkill for "run 3 coding agents in parallel"
Best for: Research applications, complex multi-step reasoning, scenarios where agents need to debate or negotiate. Academic and enterprise settings.
Not for: Parallel code execution. AutoGen excels at agent conversations, not at managing git branches and test suites.
4. vibe-kanban
What it is: Web-based kanban board for AI agent task management.
Pros:
- Visual interface — see all agents and tasks at a glance
- Drag-and-drop task management
- Browser-based, no terminal required
- Good UX for non-terminal users
- Growing community
Cons:
- Web UI means leaving your terminal
- No git worktree isolation built in
- No test gating
- Different mental model from terminal-native workflows
- Requires a running web server
Best for: Teams that prefer visual interfaces. Project managers who want to see agent status without touching a terminal. Workflows where the UI is a feature, not overhead.
Not for: Developers who live in tmux and want everything in the terminal. If Alt-Tab to a browser feels like context switching, vibe-kanban adds friction your workflow doesn't need.
5. Batty
What it is: Terminal-native Rust CLI that supervises AI coding agents in tmux.
Pros:
- Each agent runs in a real tmux pane — your keybindings, SSH attach, pipe-pane all work
- Git worktree isolation per agent — no file conflicts
- Test gating — nothing merges until tests pass
- Markdown kanban for task dispatch —
catthe board,git diffthe state - File-based everything — YAML config, Maildir inboxes, JSONL logs
- Single binary (
cargo install batty-cli), no runtime dependencies - Works with existing CLI agents (Claude Code, Codex, Aider)
Cons:
- tmux is a hard dependency — doesn't work on Windows without WSL
- No web UI — if you want a visual dashboard, look elsewhere
- Early stage (v0.1.0) — API still settling
- Rust contributor barrier — harder for casual contributions than a Python tool
- Smaller community than framework-based alternatives
Best for: Developers who already live in tmux and want to scale from one agent to many without leaving the terminal. Teams that care about test gating and code quality gates.
Not for: Non-terminal users. Windows-primary developers. People who want to build custom agent systems from scratch (use CrewAI/AutoGen instead).
Decision Matrix
| Need | Best Choice |
|---|---|
| Quick one-off parallel tasks | Raw tmux scripts |
| Custom multi-agent Python app | CrewAI |
| Complex agent reasoning/debate | AutoGen |
| Visual task management | vibe-kanban |
| Terminal-native with test gating | Batty |
| Windows-only environment | CrewAI or AutoGen |
| Orchestrate existing CLI agents | Batty or tmux scripts |
The Question That Matters
Before picking a tool, ask: am I building an agent system or coordinating existing agents?
If you're building from scratch — defining agent behaviors, tool access, conversation patterns — you want a framework. CrewAI and AutoGen give you the building blocks.
If you're already using Claude Code, Codex, or Aider and want to run multiples in parallel with quality gates — you want a supervisor. Batty and tmux scripts operate at this layer.
vibe-kanban sits between: it coordinates agents with a visual interface, which is valuable for teams but adds a web server to your stack.
My Honest Take
I built Batty, so I'm biased. But I built it because the other options didn't fit my workflow:
- CrewAI and AutoGen are frameworks — I didn't want to rewrite my agent setup in Python when Claude Code already works well
- vibe-kanban is web-based — I wanted to stay in tmux
- Raw scripts broke when agents crashed or I needed to walk away
Batty fills a specific niche: terminal-native supervision with test gating for people who already use CLI coding agents. If that's you, try it. If it's not, the other tools are genuinely good at what they do.
Try Batty: cargo install batty-cli — GitHub | Demo
Try the alternatives:
- CrewAI — Python multi-agent framework
- AutoGen — Microsoft's agent conversation framework
- vibe-kanban — Visual AI agent kanban
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