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plandex

by plandex-ai 15.5k stars MITDeveloper Tools
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About plandex

Plandex is an open source AI coding agent designed for handling large-scale development tasks and real-world projects. It operates as a terminal-based tool that can plan and execute complex coding operations across multiple files and steps.

The tool manages context efficiently with support for up to 2M tokens, using tree-sitter project maps for larger directories. It maintains a sandbox environment for reviewing AI-generated changes before applying them to project files, with version control and debugging capabilities.

Key features

  • Handles up to 2M tokens of context with tree-sitter project maps
  • Cumulative diff review sandbox for AI-generated changes
  • Supports models from Anthropic, OpenAI, Google and open source providers
  • Configurable autonomy from full auto to manual control
  • Automated debugging of terminal commands and browser applications
  • Project-aware chat mode for codebase discussion
  • Git integration with commit message generation
  • One-line CLI installation with Docker support for self-hosting

Download server/v2.2.1

Jul 16, 2025
View all 10 versions

Frequently asked questions

Is plandex free?
Yes. plandex is free and open-source software, distributed under the MIT license.
Which platforms does plandex support?
plandex provides downloads for macOS, Linux.
What is the latest version of plandex?
The latest version is server/v2.2.1, released on Jul 16, 2025.
What platforms does Plandex support?
Plandex runs in terminal environments. Windows is supported via WSL, but not in native CMD or PowerShell.
How do I install Plandex?
The primary installation method is a one-line curl command that installs the CLI tool. Docker is supported for self-hosting.
What model providers does Plandex support?
It works with Anthropic, OpenAI, Google models, and open source providers, including through OpenRouter.ai.
Is there a hosted version available?
The cloud-hosted version is winding down as of October 2025. Self-hosting via Docker is the recommended approach.

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