Saturday, June 6, 2026

Moonshot AI Releases Kimi Code CLI: A Terminal AI Coding Agent In-built TypeScript for Subsequent-Gen Brokers

Moonshot AI has launched Kimi Code CLI, an open-source coding agent that runs within the terminal. The device reads and edits code, runs shell instructions, searches information, and fetches net pages. It then chooses its subsequent step based mostly on the suggestions it receives. The challenge is MIT-licensed and lives on GitHub..

Kimi Code CLI is the successor to the older kimi-cli. The brand new agent is written in TypeScript and distributed by way of npm. It really works out of the field with Moonshot AI’s Kimi fashions. It will also be configured to make use of different appropriate suppliers.

What’s Kimi Code CLI

Kimi Code CLI is an AI agent for software program improvement and terminal operations. It will probably implement new options, repair bugs, and full refactors. It will probably additionally discover an unfamiliar codebase and reply structure questions. Batch file processing, builds, and chained take a look at runs are supported too.

The execution mannequin is feedback-driven. The agent plans steps, modifies code, runs exams, and experiences its actions. Learn-only operations run routinely by default. For file edits or shell instructions, the agent asks for affirmation first. This approval circulate retains dangerous actions beneath developer management.

The CLI itself is free and MIT-licensed. Mannequin entry requires Kimi Code OAuth or a Moonshot AI Open Platform API key.

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

Key Options

Moonshot lists a number of options aimed toward lengthy, centered agent periods:

  • Single-binary distribution. One command installs it, with no Node.js setup required.
  • Quick startup. Moonshot says the TUI is prepared in milliseconds.
  • Goal-built TUI. The interface is tuned for prolonged agent periods.
  • Video enter. Drop a display screen recording or demo clip into the chat.
  • AI-native MCP configuration. Add and authenticate Mannequin Context Protocol servers by way of /mcp-config.
  • Subagents for parallel work. Dispatch built-in coder, discover, and plan subagents in remoted contexts.
  • Lifecycle hooks. Run native instructions to gate device calls, audit choices, or set off notifications.

Set up and First Run

Two set up paths exist. The official script wants no pre-installed Node.js.

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

The worldwide npm set up requires Node.js 24.15.0 or later:

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

Confirm the binary, then open a challenge and begin the interactive UI:

kimi --version
cd your-project
kimi

On first launch, sort /login contained in the UI. You may select Kimi Code OAuth or a Moonshot AI Open Platform API key. To run one instruction with out the UI, use kimi -p "your process". To renew the earlier session, add -C.

Use Circumstances

  • Understanding a challenge: Ask for an structure overview and a module dependency diagram.
  • Implementing a function: Describe the signature, choices, and acceptance standards up entrance.
  • Fixing a bug: Give the symptom, replica steps, and anticipated conduct collectively.
  • Writing exams and refactoring: Extract repeated patterns, then run exams to verify conduct.
  • One-off automation: Analyze logs and output name counts with p50 and p99 latencies.
  • Scheduled duties: Ask the agent to set reminders or recurring checks by way of cron.

Plan mode is on the market via Shift-Tab or kimi --plan. It outputs a analysis plan earlier than touching information. For protected batch work, --yolo or /yolo skips approval prompts. The /fork command creates an experimental department you may abandon. The /compact command compresses context to release tokens. For giant investigations, the principle agent can dispatch subagents in parallel.

How Kimi Code CLI Compares

Kimi Code CLI joins a number of established terminal coding brokers. The desk under compares it with three of them. Competitor particulars replicate mid-2026 and might change rapidly.

Attribute Kimi Code CLI Claude Code OpenAI Codex CLI Gemini CLI
Developer Moonshot AI Anthropic OpenAI Google
Backing mannequin Kimi fashions Claude fashions 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
Subagents 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, IDEs VS Code (Code Help)
License MIT Proprietary Open supply Apache 2.0

All 4 brokers assist the Mannequin Context Protocol. They differ on backing mannequin, language, license, and orchestration. Kimi Code CLI and Codex CLI each ship native subagents. Gemini CLI runs duties sequentially with out subagent assist.

Key Takeaways

  • Kimi Code CLI is an MIT-licensed terminal coding agent from Moonshot AI.
  • It’s written in TypeScript and installs by way of script or npm.
  • Constructed-in coder, discover, and plan subagents run in remoted contexts.
  • MCP servers are configured conversationally via /mcp-config, not uncooked JSON.
  • It succeeds kimi-cli and migrates present configuration and periods.

Marktechpost’s Visible Explainer


Kimi Code CLI · Information
01 / 09

Overview

Kimi Code CLI

Moonshot AI’s open-source terminal coding agent that reads code, runs instructions, and plans its subsequent step.

  • Runs in your terminal as an AI coding agent
  • MIT-licensed · written in TypeScript · distributed by way of npm
  • Works with Kimi fashions or different appropriate suppliers

Slide 02

What Is Kimi Code CLI?

  • Reads and edits code, runs shell instructions, searches information
  • Fetches net pages and chooses the subsequent step from suggestions
  • Learn-only actions run routinely by default
  • File edits and shell instructions ask for affirmation first

Slide 03

Key Options

  • Single-binary distribution — no Node.js setup required
  • Constructed-in coder, discover, and plan subagents
  • AI-native MCP configuration by way of /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
  • Kind /login → Kimi Code OAuth or Moonshot API key
  • kimi -p "your process" runs one instruction with out the UI
  • kimi -C resumes the earlier session

Slide 06

Use Circumstances

  • Perceive a challenge: structure overview and dependency map
  • Implement options with clear signatures and acceptance standards
  • Repair bugs from symptom, replica steps, and anticipated conduct
  • Write exams, refactor, and automate log evaluation or batch edits

Slide 07

Modes & Instructions

  • Plan mode: Shift-Tab or kimi --plan
  • --yolo or /yolo skips approvals for protected batch work
  • /fork creates an experimental department you may abandon
  • /compact compresses context to release tokens

Slide 08

How It Compares

Attribute Kimi Code CLI Claude Code Codex CLI Gemini CLI
Mannequin Kimi fashions Claude fashions GPT-5.3-Codex Gemini 2.5 Professional
Language TypeScript Node.js Rust TypeScript
Subagents Sure Sure Sure No
License MIT Proprietary Open supply Apache 2.0

Slide 09

Key Takeaways

  • MIT-licensed terminal coding agent from Moonshot AI
  • Written in TypeScript; installs by way of script or npm
  • coder, discover, plan subagents in remoted contexts
  • MCP configured conversationally, not uncooked JSON
  • Succeeds kimi-cli; migrates config and periods


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Michal Sutter is an information science skilled with a Grasp of Science in Information Science from the College of Padova. With a stable basis in statistical evaluation, machine studying, and knowledge engineering, Michal excels at remodeling complicated datasets into actionable insights.

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