Plandex
A terminal AI development tool for planning, executing, and reviewing large multi-file coding tasks.
Per-dimension scores and reasoning
Evidence shows: README describes a cumulative diff review sandbox, controlled command execution, rollback capability, and configurable autonomy (from full auto to step-by-step review), indicating user confirmation mechanisms. However, no specific implementation details are provided, such as permission model, data flow transparency, or sensitive data handling policies. Dependency security: no dependency audit or vulnerability scanning evidence. External effects: command execution is controlled, but default permissions are not explicit. Rollback: described but no implementation details. Source attribution: no explicit mechanism for attributing model outputs or code sources. Therefore, most criteria score low due to insufficient or partial evidence.
Evidence shows: README claims support for large projects and files, but no self-consistency tests or detailed failure messages are provided. Dependency availability: no dependency list or availability guarantees. Failure messages: no examples of error handling or user prompts. Therefore, scores are low due to lack of specific evidence.
Evidence shows: README describes multiple use cases (large tasks, real-world projects), capability boundaries (2M token context, 30+ languages), trigger precision (configurable autonomy), and environment fit (terminal, WSL, Docker). However, trigger precision lacks details on specific commands or trigger mechanisms. Therefore, scores are moderate because descriptions are adequate but lack detail.
Evidence shows: README provides installation instructions, examples, documentation links, license (MIT), version control (GitHub releases), and contribution guidelines. However, no changelog or explicit known limitations are provided. Naming stability: no guarantees for API or command stability. Therefore, scores are moderate because information architecture is good but some details are missing.
Evidence shows: README describes output usability (cumulative diff review, version control), marginal value (combining multiple models, automated debugging), and cost-benefit (context caching, model packs). However, no actual performance or cost data are provided. Therefore, scores are moderate because descriptions are adequate but lack empirical evidence.
Evidence shows: Claims in README lack traceable testing or validation evidence. Cross-source corroboration: no independent verification. Fact-inference separation: not explicitly distinguished. Therefore, scores are low due to lack of verifiability.
- Static review cannot verify actual behavior; all scores are inferred from documentation and configuration.
- No dependency security audit or vulnerability scanning evidence provided; users should check dependencies themselves.
- No specific sensitive data handling policies; caution when dealing with sensitive data.
- No explicit permission model; default permissions may be excessive; review configuration.
- No changelog provided; version upgrades may introduce breaking changes.
What does this agent do, and when should you use it?
Plandex is a terminal-based AI development tool for coding tasks that span multiple steps and dozens of files. It exposes both a REPL and CLI workflow, including a project-aware chat mode for discussing a codebase before implementation. The tool can load relevant files, execute commands, and debug builds, linters, tests, deployments, and scripts; with Chrome installed, it can also debug browser applications. Generated edits remain in a cumulative diff review sandbox until they are ready to apply to project files. Plandex offers cloud and Docker-based self-hosted/local modes, although its cloud service stopped accepting new users on October 3, 2025.
Run plandex or pdx inside a project directory to enter a REPL with project-aware chat and a planning-and-implementation workflow. Plandex loads files relevant to each step, generates project maps with tree-sitter, and performs syntax validation; the README states that project maps support 30+ languages. It can edit multiple files, run terminal commands, and automatically debug builds, linters, tests, deployments, and scripts; if Chrome is installed, it can debug browser applications as well. Generated edits are held in a cumulative diff review sandbox, where they can be reviewed, revised, and applied; it also provides version control for plan updates, branches, Git integration, generated commit messages, and optional automatic commits. It can combine models from Anthropic, OpenAI, Google, and open-source providers, with OpenRouter API keys and Claude Pro/Max subscriptions documented as access paths.
- A developer maintaining a large Git repository who needs to break down, implement, and review a feature that changes dozens of files.
- An engineer joining an unfamiliar codebase who wants to use project-aware chat to clarify an approach before making implementation changes.
- A developer repeatedly running builds, linters, or tests from a terminal who wants help debugging failed commands.
- A team using several model providers that needs to select or combine model packs around capability, cost, and speed.
- A browser-application engineer with Chrome installed who wants browser debugging included in a coding-task workflow.
What are this agent's strengths and limitations?
- Its cumulative diff review sandbox keeps generated changes separate from project files until they are reviewed and applied.
- Its large-project context approach is concrete: the README claims a 2M-token effective context window with the default model pack and tree-sitter project-map indexing for directories of 20M tokens or more.
- One terminal workflow covers file loading, code changes, command execution, and debugging failed commands, with browser debugging available for Chrome users.
- It can combine Anthropic, OpenAI, Google, and open-source models, with model packs intended to balance capability, cost, and speed.
- Plan updates have version control and branching, alongside Git integration, commit-message generation, and optional automatic commits.
- The primary interfaces are a terminal REPL and CLI, rather than a graphical IDE workflow.
- Windows use is limited to WSL; the README explicitly says Windows CMD and PowerShell do not work correctly.
- Local self-hosting requires Docker, and model use requires the relevant provider account or API key; the documented example uses an OpenRouter key.
- Because it can execute commands and offer full-autonomy mode, adoption requires evaluating command execution and automatic-commit settings in the target project.
- Plandex Cloud stopped accepting new users on October 3, 2025, so new users need a self-hosted or local deployment path.
How do you install or deploy this agent?
Install the CLI:
curl -sL https://plandex.ai/install.sh | bashOn Windows, use Plandex only from a WSL shell; Windows CMD and PowerShell are not supported. For local self-hosted mode, the README documents running with Docker locally or hosting on your own server. When using OpenRouter, set a provider credential:
export OPENROUTER_API_KEY=...How do you use this agent?
Change into the target project directory; for a new project, git init is optional. Start the REPL with:
cd your-project-dir
plandexpdx is the short command. The REPL starts in chat mode, where you can discuss the project and task before moving into planning and code-writing. Claude Pro or Max subscribers can connect their subscription when prompted on first run.