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. Note (April 2026): Microsoft has announced AutoGen is entering maintenance phase, replaced by the new Microsoft Agent Framework. AutoGen will still receive bug fixes and security updates, but no new features. Worth considering if you're starting fresh.
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
- Large community (56K+ GitHub stars)
Cons:
- Entering maintenance mode — Microsoft recommends migrating to Agent Framework
- Heavy framework — significant setup for simple use cases
- Python and .NET only
- Designed for conversational agents, not coding workflows
- No git integration, no worktree isolation
- Overkill for "run 3 coding agents in parallel"
Best for: Existing projects already built on AutoGen. Complex multi-step reasoning and agent conversations in research settings.
Not for: New projects (consider Microsoft Agent Framework instead). 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. Built in Rust with a TypeScript frontend.
Pros:
- Visual interface — see all agents and tasks at a glance
- Drag-and-drop task management with real-time agent log streaming
- Git worktree isolation per agent — same isolation concept as Batty, different interface
- Built-in diff review UI for checking agent output before merging
- MCP integration (both client and server) — agents can manage the board programmatically
- Works with Claude Code, Codex, Gemini CLI, and other coding agents
- Large community (24K+ GitHub stars)
Cons:
- Web UI means leaving your terminal
- No test gating — review is manual through the diff UI
- Requires a running web server
- Different mental model from terminal-native workflows
Best for: Teams that prefer visual interfaces. Developers who want to see diffs and review agent work in a browser. Workflows where drag-and-drop task management and visual oversight are features, 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 (or Microsoft Agent Framework) |
| Visual task management with diff review | vibe-kanban |
| Terminal-native with test gating | Batty |
| Windows-only environment | CrewAI or vibe-kanban |
| Orchestrate existing CLI agents | Batty, vibe-kanban, 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 — you want a supervisor. Batty, vibe-kanban, and tmux scripts operate at this layer, each with different tradeoffs: vibe-kanban gives you a visual board with diff review, Batty gives you terminal-native supervision with test gating, and tmux scripts give you full control with no abstractions.
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 (entering maintenance phase)
- vibe-kanban — Visual AI agent kanban
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