Plannotator: Review AI Agent Plans Before They Run (11K Stars)

Plannotator, an open-source browser-based workspace for reviewing AI agent plans and code diffs, reached 11,523 GitHub stars, including 860 added in a single day on November 15, according to the project's GitHub repository. The tool opens plans, Markdown files, HTML artifacts, local code diffs and pull requests in a browser UI where reviewers can approve, annotate with deletions, replacements, insertions or inline comments, and send feedback back to the agent in one click; it supports Claude Code, OpenAI Codex, GitHub Copilot CLI, Gemini CLI, OpenCode, Pi, Amp, Droid and Kiro CLI, and is dual-licensed under MIT and Apache 2.0. The project targets an oversight gap the article ties to a 13-hour AWS outage caused by an AI coding assistant and to roughly 91% growth in industry-wide code review time, with 38% of developers not reviewing AI-generated code at all.

Your AI coding agent writes a plan. You read two lines, nod, and hit y . The agent runs for eight minutes. Something goes sideways. This is not a hypothetical — 38% of developers don’t review AI-generated code at all https://www.helpnetsecurity.com/2026/06/18/research-ai-coding-agent-oversight/ , and the approval fatigue is getting worse as agent velocity climbs. Plannotator, an open-source browser-based workspace for reviewing agent plans and code diffs, just surged to 11,523 GitHub stars — 860 of them added in a single day on November 15. The timing makes sense. The oversight problem is real and getting worse When AI coding agents were novelties, a terminal yes/no prompt was fine. Now they’re writing production code at scale. Amazon Web Services learned this the hard way: an AI coding assistant deleted and recreated an entire environment during a task, triggering a 13-hour outage. The post-mortem forced company-wide new review requirements on AI-assisted changes. Code review time is up roughly 91% industry-wide as AI-generated PR volume explodes — but the review tooling hasn’t kept pace. Plannotator exists to close that gap. https://agent-finder.co/reviews/plannotator There’s also an anchoring bias problem. When an agent finishes a task, it hands you its code and its own explanation of what it did and why. Reviewers read the agent’s self-assessment and tend to agree with it. The agent grades its own work, and you rubber-stamp the grade. Terminal prompts make this worse by stripping all context from the approval step. What Plannotator actually does Plannotator opens plans, Markdown files, HTML artifacts, local code diffs, and pull requests in a browser UI. From there you can approve a plan outright, annotate specific sections with deletions, replacements, insertions, or inline comments, and send all of it back to the agent as structured feedback in one click. The agent receives your annotations as its next instruction — the feedback loop closes without you having to repaste anything. Three features stand out. First, inline AI chat while reviewing : select any text in a plan or diff, click “Ask AI,” and a response streams into a side panel. You’re using AI to review AI output, which sounds recursive but is exactly the right tool for the job when a plan references an unfamiliar API or design pattern. Second, live app annotation : point Plannotator at a localhost URL and your running application loads inside the review UI. Click any element to attach a comment, hit Escape to use the app normally. This matters for frontend agents generating UI code. Third, the Inbox : a local message thread where agents drop guided reviews, questions, and files between sessions. Replies route back to the originating agent. Install it in under a minute The installer is a single command. It detects which agents you have installed and adds hooks, slash commands, and plugins automatically. macOS / Linux / WSL curl -fsSL https://plannotator.ai/install.sh | bash Windows irm https://plannotator.ai/install.ps1 | iex Supported agents: Claude Code, OpenAI Codex, GitHub Copilot CLI, Gemini CLI, OpenCode, Pi, Amp, Droid, and Kiro CLI. VCS support covers Git, GitButler, Jujutsu, Perforce, GitHub, and GitLab. If you want just the binary and nothing else, pass --minimal . Once installed, three slash commands appear in your agent sessions: /plannotator-review to open a plan or PR, /plannotator-annotate to annotate the last response, and /plannotator-last to pull up the most recent agent message for review. Licensing and the paid tier Plannotator is dual-licensed under MIT and Apache 2.0 https://github.com/backnotprop/plannotator — pick whichever fits your project. The core tool is entirely local: your plans, annotations, and feedback never leave your machine. This matters if you’re working on proprietary code and can’t send agent outputs to a third-party cloud service. The paid tier, called Workspaces, adds hosted team collaboration: share plans via encrypted URLs, let teammates annotate independently, import their feedback, and push it all back to your agent in one operation. Artifact filtering by project, date, and agent is included. Pricing isn’t publicly listed; the hosted layer is clearly aimed at engineering teams rather than individual developers. Worth installing? Yes, if you’re running Claude Code or Codex as part of your daily workflow and the terminal prompt has become a rubber stamp. Plannotator is the right size for this problem — it doesn’t try to be an IDE, an orchestration platform, or a full agent harness. It just puts a proper review surface between you and the agent’s execution. The 11,523 GitHub stars and the 860-in-a-day surge are organic; there was no major announcement or VC-backed launch. That’s the kind of traction that means developers are actually using it. It’s less compelling if your existing IDE and PR review process already give you the oversight and audit trail you need. But for most teams running agentic workflows today, that bar isn’t being cleared by the defaults. Install it, hook it into Claude Code or Codex, and annotate the next plan your agent writes. The documentation https://docs.plannotator.ai/ covers every agent integration in detail.