Run Claude Code, Codex, Copilot, Cursor, and Grok in Parallel — From a Single Browser Tab

What happened

A new open‑source project called many‑ai‑cli (by ishizakahiroshi) surfaced on GitHub. Written in Go, it lets developers launch the official command‑line interfaces for Claude Code, OpenAI Codex CLI, GitHub Copilot, Cursor (agent‑mode CLI), and the Grok CLI simultaneously. Every session’s diff or action waits for approval inside one unified web dashboard — built with xterm.js, WebSockets, and a PWA that works from a phone.

The repository is tiny (4 stars at time of writing) and clearly experimental. But the idea is sharply on‑point: rather than running each coding assistant one after another, you fire the same prompt at five agents in parallel and compare their output in real time.

Why running AI coding agents in parallel matters now

The AI‑assisted coding market is crowded. Teams oscillate between Cursor, Copilot, Codex, Claude Code, and emerging players before committing to workflows that touch production code. Parallel execution turns a subjective “I like it” into an observable comparison:

• Side‑by‑side output quality: You see who handles ambiguous requirements better, who hallucinates library APIs, and who writes idiomatic code.
• Risk‑aware approvals: Because each agent’s proposed changes wait behind a single approve/deny gate, you can veto unsafe diffs early — something solo CLI loops make easy to miss.
• Speed vs. cost trade‑offs: Running them in tandem exposes differences in token usage and iteration counts for the same task.
• Reduces evaluation friction: Switching contexts between five terminals is painful. A single browser tab brings the comparison to founders, tech leads, and even non‑terminal users who need to weigh in.

Who should care

Founders & engineering leaders

Before buying into an enterprise plan or an IDE‑bound agent, you can stress‑test candidates on representative internal prompts (refactoring a legacy function, generating boilerplate, or scaffolding a microservice). Parallel‑run dashboards make that test repeatable and quick.

Developers & platform engineers

You can hack on the project itself, extend it with custom model endpoints, or use it to build an internal benchmark suite. The WebSocket + PWA architecture also shows how to make agent tools mobile‑friendly for on‑call approval scenarios.

Marketers & technical content creators

A live, parallel‑output demo is a high‑credibility way to illustrate differences between assistants in a video, tutorial, or pitch deck — far more convincing than cherry‑picked screenshots.

Practical use cases

• Daily model shootout: Every morning, run the same coding challenge across agents to spot regressions or improvements after API updates.
• Security‑first code review: Allow agents to propose changes but force a human to approve each diff from a phone, even when the terminal is inaccessible.
• Team decision sessions: Project a single dashboard during a sprint planning session and collectively evaluate which agent writes the cleanest test fixtures.
• Multi‑provider cost analysis: Track token consumption per agent side by side to inform budget allocation for Anthropic API or OpenAI workloads.

Limitations & risks to watch

This is a pre‑v1 experimental tool with no published benchmarks, and the repository is not yet widely adopted. Keep these points in mind:

• Unverified stability: The Go codebase and its WebSocket dashboard are fresh. Expect edge cases around concurrent sessions and agent‑specific CLI quirks.
• API cost can multiply fast: Running five agents on a single prompt burns tokens from five providers in parallel. There’s no built‑in budget guard yet.
• Security surface: Exposing CLI sessions via a web interface (even locally) demands careful network configuration. The approval UI adds a layer, but default setups might need hardening before production use.
• Agent CLI availability drifts: The tool depends on each provider’s official CLI staying compatible. A breaking change in OpenAI Codex CLI or the Grok CLI can silently break a session until the community patches it.
• No built‑in evaluation metrics: The dashboard shows outputs and diffs but doesn’t score correctness, latency, or cost. You bring your own judgment.

How to evaluate AI coding tools when using a parallel runner

A tool like many‑ai‑cli eliminates context‑switching, but you still need a robust evaluation framework. Without one, parallel output becomes just “more tabs.” Use these criteria:

• Correctness: Does the code compile/run and satisfy the prompt exactly?
• Code quality & style: Check adherence to your project’s conventions and idiomatic language patterns.
• Token efficiency: Measure tokens consumed per correct solution — a whisper‑like agent that burns a huge context window may cost more than a verbose but cheap model.
• Self‑correction ability: If you deny a proposal, how well does the agent adapt on the next iteration?
• Safety defaults: Does it avoid executing dangerous commands unless explicitly allowed?

When you compare tools like Claude Code, OpenAI Codex CLI, GitHub Copilot, and Cursor side by side using those criteria, patterns emerge quickly — often in a single afternoon of testing.

FAQ

Do I need paid API keys for all five agents?

Yes. Each CLI requires authenticated access to its respective service. Plan for a budget cap before launching many parallel sessions, because costs stack in real time.

Can I run this securely from a phone without exposing my machine?

The PWA uses WebSockets and can be served over a local network or through a hardened tunnel. The project provides the approval UI, but secure deployment is your responsibility. Treat it like any remote terminal access.

Is this a replacement for a dedicated copilot IDE?

No. It is an evaluation and comparison layer, not an IDE integration. It complements tools like GitHub Copilot in‑editor or Cursor’s IDE by giving you a neutral environment to benchmark agents before onboarding them into daily editor workflows.

What if I only want to compare two agents, not all five?

The architecture is modular. You can configure only the CLIs you care about, reducing complexity and cost. Start with the pair most relevant to your stack and expand later.