Plannotator is an open-source platform designed to review and validate plans and code generated by AI coding agents.
The platform provides a centralized interface where developers can:
Review AI-generated plans
Annotate proposed actions
Approve or reject tasks
Inspect code changes
Provide structured feedback
Improve agent outputs
Maintain human oversight
Rather than replacing developers, Plannotator aims to keep humans involved in critical decision-making during AI-assisted development.
As AI coding agents become increasingly capable, developers face a growing challenge: ensuring that AI-generated plans and code changes align with project requirements before they are executed. Plannotator addresses this problem by acting as a review layer between developers and AI coding agents.
Instead of generating code itself, Plannotator focuses on making AI-driven development more transparent and controllable. It allows teams to review, annotate, approve, reject, and refine agent-generated plans and code changes before they affect a project.
Download Plannotator v0.24.2 - Software Mirrors |
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Plannotator v0.24.2 for Windowsplannotator-paste-win32-arm64.exe | 106.86 MB plannotator-paste-win32-x64.exe | 110.08 MB |
Plannotator v0.24.2 for macOSplannotator-darwin-arm64 | 112.19 MB plannotator-darwin-x64 | 116.6 MB |
Plannotator v0.24.2 for Linuxplannotator-linux-arm64 | 147.7 MB plannotator-linux-x64 | 148.24 MB |
Plannotator v0.24.2 Release Notes:Follow @plannotator on X for updatesMissed recent releases?
What's New in v0.24.2This release opens annotate to config files, adds XDG data-directory support, and gets ahead of OpenAI's July 23 model shutdowns. Twelve pull requests landed since v0.24.1. Four came from community members, and all three external authors are first-time contributors.Annotate YAML, JSON, TOML, and other config filesplannotator annotate config.yaml used to fail with "File type not supported", even though the pipeline handles any plain text. Annotate now accepts .yaml, .yml, .json, .jsonc, .json5, .toml, .ini, .cfg, .conf, .properties, .csv, .tsv, .log, .xml, and .env.example, rendered as plain text the same way .txt is. The file browser lists them in folder mode across all runtimes.
Some details worth knowing. Multi-document YAML keeps its first document (the markdown parser no longer strips a leading --- pair as frontmatter for non-markdown files). Files over 2MB get a clear error instead of freezing the server; the same cap the code viewer has always had now protects every annotate read. .env is deliberately excluded, since annotate's version history copies file contents into the data directory and .env files commonly hold secrets. Code-file links inside plans keep their syntax-highlighted popout; only file-browser selections render as annotatable documents.
XDG data directory supportPlannotator stores everything under~/.plannotator. Linux users who keep their home directory clean have asked for XDG Base Directory support several times, in two full pull requests and an issue. The answer used to be the PLANNOTATOR_DATA_DIR override; now placement is also automatic: if ~/.plannotator does not exist and $XDG_DATA_HOME is set to an absolute path, Plannotator uses $XDG_DATA_HOME/plannotator.
Nothing changes for existing users. An existing ~/.plannotator always wins, PLANNOTATOR_DATA_DIR remains the top-priority override, and macOS and Windows defaults are untouched. The resolution is honored across the Claude Code, OpenCode, Amp, Pi, and VS Code paths, and the install scripts resolve config.json the same way. PLANNOTATOR_DATA_DIR is now documented in the README.
Codex models: correct IDs, current catalog, Max and Ultra effortsSelecting GPT-5.6 in the Agents tab launched Codex with the baregpt-5.6 id, which Codex rejects on ChatGPT accounts. @rNoz fixed the catalog to the canonical gpt-5.6-sol and migrated saved selections, including per-model reasoning and fast-mode preferences, so existing users stop sending the broken id automatically.
A follow-up aligned the whole catalog with the current Codex CLI. OpenAI retires gpt-5.3-codex, gpt-5.2-codex, gpt-5.1-codex-max, and gpt-5.1-codex-mini at the API level on July 23, so those left the picker and saved picks migrate to OpenAI's recommended replacements. gpt-5.2 stays, since only the ChatGPT product retired it and API-key users still have it. Reasoning effort options are now per-model: minimal is gone (no current model supports it), and the GPT-5.6 family gains Max, with Ultra on Sol and Terra.
Cursor review engine works on NixOS and hardened LinuxReview jobs launch Cursor'sagent CLI with --sandbox enabled as part of their read-only posture. On systems where Cursor's sandbox cannot start (NixOS, AppArmor-restricted Linux), every Guided Review with the Cursor engine failed outright, and the flag overrode the user's own agent sandbox disable configuration.
The default is unchanged. A new escape hatch, PLANNOTATOR_CURSOR_SANDBOX=0 (or "cursorSandbox": false in config.json), omits the flag entirely so the user's own Cursor configuration governs. Opting out means the review job's write protection relies on that configuration, which is why it stays opt-in.
Guided Review: viewed files collapseMarking a file as viewed in a guided review now collapses its diff to the header, matching how the all-files view behaves, so long guides show your progress at a glance. Annotation jumps from the sidebar reopen a collapsed file before scrolling, so clicking a comment never lands on a folded diff.
Pi: review setup progress out of your typing areaStarting a PR review in Pi printed clone and fetch progress straight to the terminal, where it smeared across the input box until the next repaint. Progress now renders on Pi's footer status line and clears when the review opens; warnings like a failed fetch go to the chat history where they leave a trace.
Additional Changes
Install / UpdatemacOS / Linux:/plugin in Claude Code, find plannotator, and click "Update now".
OpenCode: Clear cache and restart:
opencode.json:
What's Changed
New Contributors
ContributorsThree first-time contributors landed code this release. @rNoz spent a weekend stress-testing Plannotator and turned it into two merged fixes, the canonical GPT-5.6 Sol id with a careful settings migration and the installer--global scoping, plus a set of detailed reports still being worked through. @josdirksen brought the viewed-files collapse to Guided Review. @alexanderkreidich cleaned up Pi's review startup so progress lands in the footer instead of the composer.
The XDG work stands on earlier community efforts: @cwrau and @Joao-O-Santos each wrote full XDG implementations in #568 and #612, and @IstPlayer built the PLANNOTATOR_DATA_DIR override in #795 that this release's fallback completes.
Issue reporters drove most of the fix list:
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Key Features of Plannotator
Plan Review System
One of Plannotator's core capabilities is reviewing plans generated by AI agents before execution.
Developers can examine:
Proposed tasks
Implementation strategies
Agent reasoning
Planned file modifications
Workflow sequences
This visibility helps reduce unintended changes and costly mistakes.
Annotation Tools
The platform allows users to add comments, notes, and guidance directly to AI-generated plans.
These annotations can be used to:
Clarify requirements
Correct misunderstandings
Provide context
Guide future agent actions
AI Code Review
Plannotator extends the review process beyond planning by supporting inspection of generated code.
Developers can:
Review modifications
Analyze diffs
Leave comments
Request revisions
Validate implementation details
This workflow resembles modern pull-request review systems.
Human-in-the-Loop Workflows
A major design goal is ensuring that AI actions remain subject to human approval.
Organizations can establish review processes where important actions require validation before execution.
Open Source Foundation
Plannotator is open source, allowing teams to inspect, modify, and self-host the platform according to their needs.
This transparency is particularly valuable for organizations adopting AI-assisted software development.
User Experience
The interface is designed around review workflows rather than direct code generation.
Instead of interacting with a chatbot, users primarily:
Receive agent-generated plans
Review proposed actions
Add feedback
Approve or reject changes
Monitor execution results
The workflow feels familiar to developers accustomed to pull requests, code reviews, and project planning tools.
Productivity Benefits
As AI coding tools become more autonomous, review processes become increasingly important.
Plannotator helps organizations:
Reduce risky AI actions
Improve code quality
Increase accountability
Preserve architectural consistency
Encourage collaboration between developers and AI agents
For teams adopting AI-driven development, these safeguards can be as valuable as the coding agents themselves.
Collaboration Features
The platform supports collaborative review workflows where multiple team members can participate in evaluating AI-generated outputs.
This allows:
Peer review
Team approval processes
Shared annotations
Collective decision-making
Such features are especially useful for larger engineering teams.
Performance
Because Plannotator focuses on workflow management and review rather than model inference, performance largely depends on the connected AI agents and integrations.
The platform itself is lightweight and primarily serves as an orchestration and review layer.
Open Source Advantages
Being open source provides several benefits:
Transparent development
Self-hosting capabilities
Custom integrations
Community contributions
Vendor independence
Organizations concerned about compliance, security, or proprietary workflows may find these advantages particularly appealing.
Limitations
Plannotator is designed as a companion tool rather than a complete AI development platform.
Common limitations include:
Requires external AI coding agents
Best suited for teams already using AI-assisted development
Smaller ecosystem than mature developer platforms
Additional review steps may slow rapid prototyping
Some users may prefer fully autonomous workflows
The software delivers the most value in environments where oversight and quality control are priorities.
Pros
Improves transparency of AI-generated plans
Supports structured review workflows
Human-in-the-loop design
Useful annotation system
Open source
Self-hosting support
Familiar review experience for developers
Helps reduce AI-generated mistakes
Cons
Not a standalone coding agent
Requires integration with AI development tools
Smaller community than established developer platforms
Adds review overhead to workflows
Best suited for teams rather than casual users
Who Should Use Plannotator?
Plannotator is ideal for:
Software development teams
Engineering managers
AI-assisted development workflows
Organizations adopting coding agents
Open-source projects
Teams prioritizing code quality and governance
It is particularly valuable for environments where AI-generated code requires oversight before reaching production systems.
Plannotator fills an increasingly important role in the AI development ecosystem by providing visibility and control over AI-generated plans and code changes. Its focus on human oversight, structured reviews, and collaborative workflows makes it a useful companion for modern coding agents. While it is not a replacement for AI coding tools themselves, it offers a practical solution for teams seeking greater confidence and accountability in AI-assisted software development.
Developer:
backnotprop
Operating System:
Windows / macOS / Linux
Date Added:
2026-07-21T23:01:50.517Z
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