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Moonshot AI has been launched Kimi Code CLI, An open supply coding agent that runs contained in the terminal. This software reads and edits code, executes shell instructions, searches for information, and retrieves internet pages. Then select the next step primarily based on the suggestions you obtain. This undertaking is MIT licensed and on GitHub.

Kim Code CLI is the successor to the outdated kimi-cli. The brand new agent is written in TypeScript and distributed through npm. Moonshot AI’s Kim mannequin is able to use. It will also be configured to make use of different appropriate suppliers.

What’s Kimi Code CLI?

Kim Code CLI is an AI agent for software program growth and terminal operations. You may implement new options, repair bugs, and full refactorings. You too can discover unfamiliar codebases and reply architectural questions. Batch file processing, constructing, and working chained assessments are additionally supported.

The execution mannequin is feedback-driven. Brokers plan steps, modify code, run assessments, and report their actions. By default, read-only operations are carried out mechanically. For file edits or shell instructions, the agent first asks for affirmation. This approval stream places dangerous actions beneath developer management.

The CLI itself is free and requires the MIT license. Entry to the mannequin requires a Kimi Code OAuth or Moonshot AI Open Platform API key.

https://github.com/MoonshotAI/kimi-code

Foremost options

Moonshot lists a number of options aimed toward lengthy, targeted agent periods.

  • Single binary distribution. Installs with one command and requires no Node.js setup.
  • Fast startup. Based on Moonshot, TUI might be prepared inside milliseconds.
  • Devoted TUI. The interface is tailor-made for prolonged agent periods.
  • Video enter. Drop a display screen recording or demo clip into the chat.
  • AI-native MCP configuration. Add and authenticate the Mannequin Context Protocol server. /mcp-config.
  • Subagents for parallel work. Constructed-in dispatch coder, discoverand plan Subagents in remoted contexts.
  • life cycle hook. You may run native instructions to manage software calls, audit choices, and set off notifications.

Set up and first run

Two set up paths exist. Official scripts don’t require Node.js to be put in beforehand.

On macOS or Linux, run the set up script.

curl -fsSL https://code.kimi.com/kimi-code/set up.sh | bash

On Home windows, use PowerShell.

irm https://code.kimi.com/kimi-code/set up.ps1 | iex

International npm set up requires Node.js 24.15.0 or later.

npm set up -g @moonshot-ai/kimi-code

Confirm the binaries and open the undertaking to start out the interactive UI.

kimi --version
cd your-project
kimi

At first startup, sort: /login Contained in the UI. You may select Kim Code OAuth or Moonshot AI Open Platform API key. To execute a single instruction with out utilizing the UI, use: kimi -p "your process". To renew the earlier session, add the next -C.

Utilization instance

  • Perceive the undertaking: Request an structure overview and module dependency diagram.
  • Implementation of performance: Clarify signatures, choices, and acceptance standards upfront.
  • Bug fixes: Enter the signs, steps to breed, and anticipated conduct.
  • Writing and refactoring assessments: Extract repeating patterns and run assessments to substantiate conduct.
  • One-time automation: Analyze the logs and print the variety of calls with p50 and p99 latency.
  • scheduled process: Ask brokers to arrange reminders or recurring checks through cron.

Plan mode is out there from Shift-Tab or kimi --plan. Output the analysis plan earlier than touching the file. For secure batch work, --yolo or /yolo Skip the approval immediate. of /fork The command creates an experimental department that may be deserted. of /compact The command compresses the context and frees the token. For big-scale investigations, the primary agent can dispatch subagents in parallel.

Kim Code CLI comparability

Kim Code CLI joins a number of established terminal coding brokers. The desk beneath compares three of them. Competitor particulars mirror mid-2026 and should change quickly.

attribute Kimi Code CLI claude code OpenAI Codex CLI Gemini CLI
developer Moonshot AI human OpenAI google
backing mannequin Kimi mannequin claude mannequin GPT-5.3-codex gemini 2.5 professional
Language/Runtime TypeScript Node.js rust TypeScript
set up Script or npm (Node.js ≥ 24.15.0) Native installer or npm npm/native npm single binary
MCP assist sure (/mcp-config) sure sure sure
subagent sure (coder, discover, plan) sure sure No (sequential)
plan mode sure (Shift-Tab) sure sure sure
IDE integration ACP (Zed, JetBrains) VS Code, JetBrains VS Code, IDE VS Code (Code Help)
license Massachusetts Institute of Know-how personal open supply Apache 2.0

All 4 brokers assist the mannequin context protocol. They differ in backing fashions, languages, licensing, and orchestration. Kimi Code CLI and Codex CLI each ship with native subagents. Gemini CLI executes duties sequentially with out subagent assist.

Essential factors

  • Kim Code CLI is Moonshot AI’s MIT-licensed terminal coding agent.
  • It is written in TypeScript and put in through scripts or npm.
  • Constructed-in coder, discoverand plan Subagents run in remoted contexts.
  • MCP servers are configured conversationally. /mcp-configIt is not uncooked JSON.
  • Inherit kimi-cli and migrate current configurations and periods.

Visible clarification of Marktechpost


Kimi Code CLI Information
01/09

overview

Kimi Code CLI

Moonshot AI’s open supply terminal coding agent. Learn code, execute instructions, and plan subsequent steps.

  • Runs in-device as an AI coding agent
  • MIT License · Written in TypeScript · Distributed through npm
  • Works with Kim fashions or different appropriate suppliers

Slide 02

What’s Kimi Code CLI?

  • Learn and edit code, run shell instructions, search information
  • Get the online web page and select subsequent steps from suggestions
  • Learn-only actions are carried out mechanically by default
  • File edits and shell instructions ask for affirmation first

Slide 03

Foremost options

  • Single binary distribution – no Node.js setup required
  • Constructed-in coder, discoverand plan subagent
  • AI-native MCP configuration /mcp-config
  • Lifecycle hooks and video enter assist

Slide 04

set up

macOS/Linux

curl -fsSL https://code.kimi.com/kimi-code/set up.sh | bash

Home windows (PowerShell)

irm https://code.kimi.com/kimi-code/set up.ps1 | iex

npm (Node.js 24.15.0+)

npm set up -g @moonshot-ai/kimi-code

Slide 05

first run

kimi --version
cd your-project
kimi
  • sort /login → Kim Code OAuth or Moonshot API key
  • kimi -p "your process" Execute a single instruction with out utilizing the UI
  • kimi -C resume earlier session

Slide 06

Utilization instance

  • Perceive your undertaking: structure overview and dependency map
  • Implement options with clear signatures and approval standards
  • Mounted bugs primarily based on signs, steps to breed, and anticipated conduct
  • Create assessments, refactor, automate log evaluation and batch modifying.

Slide 07

Modes and instructions

  • Planning mode: Shift-Tab or kimi --plan
  • --yolo or /yolo Skip approvals for safe batch work
  • /fork Create an experimental department that you could abandon
  • /compact Compresses the context and releases the token

Slide 08

Comparability technique

attribute Kimi Code CLI claude code Codex CLI Gemini CLI
mannequin Kimi mannequin claude mannequin GPT-5.3-codex gemini 2.5 professional
language TypeScript Node.js rust TypeScript
subagent sure sure sure no
license Massachusetts Institute of Know-how personal open supply Apache 2.0

Slide 09

Essential factors

  • MIT licensed Moonshot AI terminal coding agent
  • Written in TypeScript. Set up through script or npm
  • coder, discover, plan Orphaned Context Subagent
  • MCP is constructed conversationally slightly than uncooked JSON
  • You’ll achieve your goals. Migrate configuration and periods


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Michal Sutter is a knowledge science skilled with a grasp’s diploma in knowledge science from the College of Padova. With a robust basis in statistical evaluation, machine studying, and knowledge engineering, Michal excels at reworking advanced datasets into actionable insights.

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