Once you get comfortable with one AI coding agent, the next question is obvious: why only run one? A growing set of tools now let you point several AI agents at a codebase at the same time — each in its own isolated copy of the code — so you can parallelize work across tickets, or have multiple agents attempt the same problem and pick the best result. Here are 7 real tools built specifically for that, from cloud services to free terminal tools.
Cloud-based parallel agents
Ascii sends out multiple AI coding agents to work on your repositories at once, each in an isolated sandbox — like hiring a small team of junior developers who each take a ticket, write the code, run the tests, and hand back a pull request. Pricing is by number of agents rather than unpredictable usage: Base is $19/month (50 agents), Max $75/month (1,000 agents), Ultra $199/month (10,000 agents), with a 7-day free trial. It cites enterprise customers like Airbnb, Google, Datadog and BlackRock, but it's competing in a fast-moving space against Devin, Charlie Labs and Codegen, and the cost scales with agents launched, not value delivered — serious human review is still required.
Terminal-based orchestration
Herdr is a terminal tool — think tmux, but built specifically for AI agents — that runs several agents at once and keeps them alive even if you close your laptop or lose your SSH connection, thanks to persistent sessions and a socket API for agent-to-agent communication. It's 100% free and open source, no account, no telemetry. It's terminal-only (no GUI) and younger than an established multiplexer like tmux, so treat it as a lightweight, still-maturing tool rather than a battle-tested one.
Visual, browsable orchestration
Juggler is the point-and-click alternative to a terminal: it shows the whole agent conversation as a browsable tree, lets you inspect exactly what tool calls happened, and lets you branch off to try a different approach without losing your place. It's free and open source (Apache 2.0 core, AGPLv3 app), built by the developer behind JUCE (a well-respected audio dev framework), and works across Claude Code, OpenAI, Gemini and Ollama. The trade-off is a real workflow change if you're already comfortable in a terminal, plus a young ecosystem and an extra desktop app to maintain.
Desktop dashboards for isolated workspaces
Superset is a desktop app for running 10+ AI coding agents (Claude Code, Codex and others) at once, each in its own isolated git worktree, with a unified dashboard to review diffs, terminal into a workspace, and merge each agent's work — zero conflict between agents by design. Free is 1 user with local workspaces only; Pro is $15/month/user (billed annually) with unlimited users, remote workspaces and Linear integration; Enterprise is quote-based with SSO and audit logs. It's already used internally at YC companies and by engineers from OpenAI, Google and Vercel, though it's a very young product (YC P26) and its name collides in search results with the unrelated open-source Apache Superset BI tool.
Orca takes a similar isolation approach (clean git worktrees) but for a different goal: running several AI coding assistants on the *same* problem at once so you can compare their different solutions and pick — or merge — the best one, rather than betting on a single agent blind. It's completely free and open source (MIT), built by Stably AI, with mobile apps and a daily development cadence. You still pay each agent's own API costs multiplied by however many you run in parallel, and at 954 open GitHub issues at last check, it's worth watching for stability before relying on it for anything critical.
Directing a fleet from chat
Linzumi is a team chat app built specifically for directing a whole fleet of AI coding agents at once — kick off work, watch it happen, and approve it, all from the same chat thread your team already uses, with directory-level access controls and explicit approval gates for sensitive operations. Personal is free for solo builders; Company is a flat $100/month for the entire team (no per-seat pricing); Enterprise is custom with SSO and SOC 2. It's very new (YC S26), still in beta, and macOS-only for now — no long track record yet to judge reliability at scale.
Remote approval when you're away from the terminal
Pushary solves a narrower but real problem once you're running agents unsupervised: it sends a notification to your phone whenever Claude Code, Cursor or a similar agent wants to do something risky, so you can approve or deny it from your lock screen instead of babysitting a terminal. Setup is 4 lines of JSON, source code never leaves your machine, and you can configure auto-approval rules for low-risk actions. It's a 7-day free trial then $9.99/month — a recurring cost for a fairly narrow function, and less useful if you're already watching the agent directly.
How they compare
| Tool | Format | Best for | Starting price |
|---|---|---|---|
| Ascii | Cloud SaaS | Parallelizing well-scoped tickets across many agents | $19/mo (50 agents) |
| Herdr | Terminal (free) | Persistent SSH sessions running several agents | Free / open source |
| Juggler | Desktop GUI (free) | Browsable, branchable conversation trees | Free / open source |
| Superset | Desktop app | Dashboard to review/merge 10+ isolated agents | Free (1 user), $15/mo Pro |
| Orca | Desktop (free) | Comparing multiple agents on the same task | Free / open source |
| Linzumi | Team chat app | Directing a fleet from a shared chat thread | Free solo, $100/mo team |
| Pushary | Mobile notifications | Remote approval for risky agent actions | $9.99/mo |
Which one to pick depends on how you actually work: if you want a managed cloud service that just runs agents on tickets, Ascii is the closest to hands-off. If you're terminal-native, Herdr is free and purpose-built for that. If you'd rather see everything visually, Juggler and Superset both give you a browsable or dashboard view — Superset leans toward a polished team product, Juggler toward a free, hacker-friendly tool. Orca is the one to reach for specifically when you want several agents racing on the same problem, and Linzumi and Pushary both address the "now what" after the agents are running: directing them as a team, or getting pinged when one needs a decision.