Modo Review 2026: Open-Source AI IDE That Plans Before It Codes

#cursor #kiro #opensource #review

This article was originally published on aicoderscope.com

TL;DR: Modo is a free, MIT-licensed AI IDE that forces a planning step before writing any code — requirements, design, and tasks documents first, implementation second. Built on the Void editor (a VS Code fork), it supports Anthropic, OpenAI, Ollama, Groq, and five other LLM providers with zero subscription cost. The blockers: manual compile-from-source install, no official binary release, and its Void foundation paused active development in mid-2025. Try it for structured side projects or spec-driven experiments at $0. Don't swap it into a production team stack yet.

	Modo	Kiro	Cursor
Best for	Free spec-driven dev, local LLM, no subscription	Spec-driven workflows with AWS integration	Daily professional coding, best-in-class completions
Price / Cost	Free (MIT, open-source)	Free (50 credits) / Pro $20/mo / Pro+ $40/mo / Power $200/mo	Hobby free / Pro $20/mo ($20 model credits) / Ultra $200/mo
The catch	Manual source install; no binary; upstream base paused	$0.04/credit overage; credits don't roll over	$20 plan empties fast with heavy frontier model use

Honest take: If you want spec-driven AI coding without the credit anxiety, Modo is the best free option right now — just accept you're adopting a beta project built on a dormant upstream.

Why Modo Showed Up on Hacker News

The submission hit HN in late May 2026: "Show HN: Modo – I built an open-source alternative to Kiro, Cursor, and Windsurf." GitHub user mohshomis built Modo starting from the Void editor and layered a structured planning system on top of Void's existing AI capabilities.

The thesis is stated plainly in the first line of the README: "The open-source AI IDE that plans before it codes." Most AI editors go prompt → code. Modo goes prompt → requirements → design → tasks → code. That's either discipline you didn't know you needed or an overhead step you'll resent by Thursday, depending on how you work.

The timing matters. AWS Kiro launched with a similar spec-driven workflow in May 2026 and immediately attracted developers who found Cursor's free-form agent loop too chaotic for complex features. We covered Kiro's credit system in depth in our full Kiro review. Modo is betting the same planning approach has demand — but wants to serve it without a credit meter ticking in the corner.

At 183 GitHub stars and 18 forks as of late May 2026, Modo is not a mainstream project. But the idea is coherent enough to warrant a close look.

The Spec-Driven Workflow in Practice

Running Modo: Initialize Workspace from the command palette creates a .modo/ directory with three subdirectories: specs/, steering/, and hooks/. That structure is the working surface of the whole IDE.

Starting a feature in Spec mode: Type what you want to build in the Modo sidebar. The IDE generates three markdown files inside .modo/specs/<feature-name>/:

requirements.md — what the feature must do
design.md — how the code will be structured
tasks.md — a numbered checklist of implementation steps

You review and edit each document before a single line of code gets written. Tasks arrive with inline CodeLens buttons — "Run Task" appears directly in the markdown file next to each checklist item. Click one, and Modo's agent handles only that task and waits. Click "Run All," and it works autonomously down the list.

The contrast with Cursor's Agent mode is concrete: with Cursor, you describe a feature and the agent decides the architecture, writes the files, and you review the diff. With Modo, the architecture lives in design.md — a document you approved before implementation started. That produces more auditable output at the cost of 5–10 minutes of spec writing per feature.

Vibe mode is the flip side: free-form chat, no spec structure, same as Claude Code's chat or Cursor's inline composer. You toggle between modes per session. The dual-mode design means you don't have to commit to spec-driven for every task — quick one-off fixes stay fast.

Steering Files and Agent Hooks

Steering files live in .modo/steering/ and inject project rules into every AI interaction. Three inclusion modes: always (injected automatically into every prompt), fileMatch (fires when the active file matches a glob pattern), and manual (you invoke them explicitly via a slash command). These are functionally similar to Cursor's .cursorrules — covered in our Cursor rules deep dive — but with more granular control over when they activate.

Agent Hooks are JSON configuration files in .modo/hooks/ that automate actions around the agent lifecycle. Supported triggers include file saves, prompt submissions, and specific tool invocations. The system handles 10 event types and 2 action types and includes circular dependency detection. If you've written GitHub Actions workflows, the mental model maps cleanly.

Both features are where Modo distinguishes itself from a plain chat-on-top-of-VS-Code setup. A well-tuned .modo/steering/ directory effectively encodes your team's architecture decisions and coding standards into every agent interaction.

LLM Providers and Local Model Support

Modo inherits Void's multi-provider architecture. Supported out of the box: Anthropic (Claude Sonnet / Haiku / Opus), OpenAI, Gemini, Mistral, Groq, OpenRouter, and Ollama.

The Ollama integration is the reason privacy-conscious developers will look twice. Run Modo entirely locally with a model like Qwen2.5-Coder or DeepSeek-Coder-V2 via Ollama, and no data leaves your machine. The IDE costs $0 regardless of token volume when running locally. For the hardware side — which models perform at which VRAM tier — runaihome.com's best local AI models by VRAM guide has the benchmarks.

Cloud inference costs whatever your API provider charges; there's no Modo markup on top. Compare that to Kiro's $0.04/credit overage or Cursor's $20 monthly credit pool that evaporates mid-month on heavy Claude Sonnet use. If you want cloud GPU access to run larger models without buying hardware, RunPod lets you spin up an Ollama-compatible inference endpoint on-demand at roughly $0.50–$1.00/hour on an RTX 4090.

The Full Feature Set

Subagents and parallel sessions: Modo can spawn independent sub-agents for parallel subtasks. Each gets its own context thread, tool access, and history. Tasks from tasks.md can fan out across multiple agents and reassemble results — which is where the "alternative to Kiro" claim holds up technically.

Autopilot / Supervised toggle: A status bar button switches between fully autonomous execution (Autopilot) and a mode where every agent action requires your approval before proceeding. Supervised mode is valuable when you're running Modo on an unfamiliar codebase and don't yet trust your steering documents to constrain it appropriately.

Powers: Modo's term for installable knowledge packages — bundled documentation, steering files, and MCP configurations packaged together. Think of them as project templates with AI context baked in. The ecosystem is tiny right now (the repo lists a handful of examples), but the concept is sound.

MCP integration: Inherited from Void, so any MCP server that works with a VS Code-based editor works here. You get the same MCP ecosystem as Cursor or Claude Code. For the servers worth installing, see our MCP servers roundup.

Installation: The Honest Version

There is no binary download. No .dmg, no .exe, no .AppImage. You compile from source:

git clone https://github.com/mohshomis/modo.git
cd modo
npm install          # requires Node 20+
npm run buildreact
npm run watch
./scripts/code.sh    # Linux/macOS