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tmdgusya/roach-code

22GoAudience · developerComplexity · 2/5Setup · easy

TLDR

A terminal AI coding agent that reads files, writes code, and runs tests based on plain-text instructions, supporting multiple AI providers through a single TOML config file and shipping as a single static Go binary with no external dependencies.

Mindmap

mindmap
  root((roach-code))
    What It Does
      Terminal AI agent
      File read and write
      Run tests and edits
    AI Providers
      DeepSeek and OpenAI
      Anthropic
      MiniMax and GLM
    Features
      Config-driven TOML
      MCP plugin support
      Permission controls
      Dual model mode
    Installation
      Single static binary
      macOS Linux Windows
      One-line shell install
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Things people build with this

USE CASE 1

Run a terminal AI agent that reads your codebase, writes new code, and runs tests, all driven by a plain-text task description.

USE CASE 2

Wire in multiple AI providers like DeepSeek, Anthropic, and OpenAI through one TOML config file and switch between them easily.

USE CASE 3

Use a separate planner model alongside an executor model so high-level reasoning stays independent from implementation steps.

USE CASE 4

Extend the agent with any MCP-compatible tool server by adding a subprocess config entry pointing to an external plugin.

Tech stack

GoTOMLMCP

Getting it running

Difficulty · easy Time to first run · 30min

Requires an API key for at least one supported AI provider such as DeepSeek, OpenAI, or Anthropic.

License information is not described in the explanation.

In plain English

Roach Code is an AI coding agent that runs in the terminal, letting you describe a task in plain text and have an AI model carry it out: reading files, writing code, running tests, and making edits across your project. It is a multi-provider rewrite of an earlier project called deepseek-reasonix, extended to support many AI services rather than just one. The tool is config-driven. You declare which AI providers to use, which models to assign to which roles, and which built-in tools to allow, all in a single TOML file. Supported providers include DeepSeek, OpenAI and Codex, MiniMax, GLM, and Anthropic, as well as any service that follows the OpenAI API format. You can optionally run two models at once: one that executes tasks step by step and a separate planner model that reasons at a higher level between steps. Roach Code is built in Go and ships as a single static binary with no external dependencies beyond a TOML parser. You can install it on macOS, Linux, or Windows with a one-line shell command that downloads the correct version and adds it to your path. Cross-compilation to six targets is supported with a single build command. Plugins work by running external programs as subprocesses over a JSON-RPC protocol called MCP, which Roach Code supports as a client. This means any MCP-compatible tool server can be wired in through a config entry. A built-in reference plugin is included as a starting point. Permissions control what the agent is allowed to do. Each tool call can be set to always allow, always deny, or prompt you for a decision. File writes are sandboxed to the current project directory by default, and on macOS, shell commands are further restricted from touching paths outside the defined workspace. The readme is thorough and covers configuration, sandboxing, plugins, and the full set of commands.

Copy-paste prompts

Prompt 1
Set up roach-code with my DeepSeek API key and have it refactor a Go function based on a plain-text description I provide.
Prompt 2
Configure roach-code's TOML file to use Anthropic as the executor model and DeepSeek as the higher-level planner model.
Prompt 3
Use roach-code to run my test suite and automatically fix any failures, with file writes sandboxed to my project directory only.
Prompt 4
Walk me through installing roach-code on macOS with the one-line shell command and running my first AI coding task.
Prompt 5
How do I connect a custom MCP plugin server to roach-code so the agent can call external tools during a task?
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