Kimi Code CLI
Work through coding tasks in an interactive terminal session that can inspect, edit, and run project code.
- Source repo
- MoonshotAI/kimi-code
- Stars
- ★ 7.9k
- Last updated
- today
- License
- MIT
- Primary language
- TypeScript
- FA score
- 50/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- Developers who want a terminal agent to inspect an unfamiliar repository and explain its main directories.
- Not a fit if
- Teams that need to avoid model services or API charges
- Source review
- 50/100 · Major gaps
What does this agent do, and when should you use it?
Kimi Code CLI is an AI coding agent that runs in a terminal and can read and edit code, run shell commands, search files, and fetch web pages. It chooses subsequent steps based on feedback from those actions, with an interactive terminal UI for ongoing work. It works out of the box with Moonshot AI's Kimi models and can be configured for other compatible providers. The official installer provides a single-binary distribution and does not require Node.js; kimi acp also connects it to editors that support the Agent Client Protocol. On Windows, Git for Windows supplies the Git Bash shell environment required for the first launch.
Start kimi in a project directory and describe a task; the CLI can read and edit project files, search files, run shell commands, and fetch web pages, then continue based on the resulting feedback. On first launch, /login accepts either Kimi Code OAuth or a Moonshot AI Open Platform API key. Video input lets users submit screen recordings or demo clips for the agent to inspect, while /mcp-config supports conversational addition, editing, and authentication of MCP servers. The built-in coder, explore, and plan subagents can handle focused work in isolated contexts; kimi acp drives a session over stdio from a compatible editor.
- Developers who want a terminal agent to inspect an unfamiliar repository and explain its main directories.
- Individual developers who want an agent to edit code and run shell commands, iterating from the results.
- Teams that want to describe interface behavior with a screen recording or demo clip for coding work.
- Users who want to configure MCP servers conversationally instead of hand-editing JSON.
- Developers who want to drive a terminal agent session from Zed, JetBrains, or another ACP client.
How do you install or deploy this agent?
On macOS or Linux, use the official installer:
curl -fsSL https://code.kimi.com/kimi-code/install.sh | bashOn Windows PowerShell, run:
irm https://code.kimi.com/kimi-code/install.ps1 | iexOn Windows, install Git for Windows before the first launch. If Git Bash is in a custom location, set KIMI_SHELL_PATH to the absolute path of bash.exe. After installation, check the version in a new shell session:
kimi --versionHow do you use this agent?
Open a project directory and launch the interactive UI:
cd your-project
kimiOn first launch, run /login in the CLI and choose Kimi Code OAuth or a Moonshot AI Open Platform API key. After logging in, try a task such as:
Take a look at this project and explain its main directories.To use ACP with Zed, add this to ~/.config/zed/settings.json:
{
"agent_servers": {
"Kimi Code CLI": {
"type": "custom",
"command": "kimi",
"args": ["acp"],
"env": {}
}
}
}What are this agent's strengths and limitations?
- The official script installs a single-binary distribution, and the README says Node.js is not required.
- The terminal UI is designed for long, focused agent sessions and aims for fast startup.
- It works with Kimi models out of the box and can be configured for other compatible providers.
- It includes video input, conversational MCP configuration, isolated-context subagents, and lifecycle hooks.
- ACP support connects sessions to editors such as Zed and JetBrains over stdio.
- Login is required through Kimi Code OAuth or a Moonshot AI Open Platform API key; model service pricing is not stated.
- Windows requires Git for Windows, and a custom Git Bash location needs an environment variable.
- The agent can run shell commands and read or edit project files, which teams should evaluate against their local execution policies.
- Other compatible model providers are not identified, and the README does not document provider-specific feature differences.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Kimi Code CLI This agent | 50 · Major gaps | CLI | ★ 7.9k | today | TypeScript | — |
| Poolside pool | 67 · Some gaps | CLIFree + model costs | ★ 439 | 2mo ago | — | OpenAI API |
| Maki Coding Agent | 64 · Some gaps | CLIFree + model costs | ★ 1.3k | 2d ago | Rust | OpenAI API · Claude API |
| fx Coding Agent | 63 · Some gaps | CLIFree + model costs | ★ 3.5k | today | Zig | ChatGPT · Codex · OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
The README says the CLI can read and edit code, run shell commands, search files, and fetch web pages, giving it broad capabilities. The supplied material does not show fine-grained permission limits or confirmation for risky operations, so least privilege and user confirmation are deducted. The README mentions plugin trust levels and lifecycle hooks, but does not explain data flows, secret protection, confirmation of external effects, or recovery. Those criteria receive only 1. The repository provides a vulnerability reporting channel, but publisher identity is unverified and the supplied code offers limited evidence about provenance and dependency risk, keeping source attribution and dependency security at 1.
The README lists development build, test, typecheck, and lint commands. CI uses frozen dependency installation and includes sharded tests, a build smoke check, and typechecking, supporting ordinary maintenance; self-consistency scores 2. The evidence does not fully establish dependency availability or known failure handling, so that criterion scores 1. Doctor tests cover missing configuration, parse errors, field paths, and aggregated errors, while CLI tests check error guidance, supporting a score of 2 for failure messages. These static files do not mean this review executed or verified those tests.
The README identifies terminal coding tasks and describes video input, subagents, MCP, and IDE ACP scenarios, supporting a score of 2 for audience and scenarios. It lists many tool capabilities but the supplied material does not clearly define safety boundaries or unsupported tasks, so capability boundaries score 1. The project-directory launch, login steps, IDE setup, and doctor command provide clear entry points, scoring 2 for trigger precision. Installation covers macOS, Linux, and Windows, notes the Git Bash requirement, and gives Node/pnpm development requirements, scoring 2 for environment fit.
The README is organized around installation, quick start, features, IDE use, docs, and development, though it relies on external documentation, scoring 2 for information architecture. Install commands, first-login steps, and the Windows prerequisite are concrete, scoring 2. CLI, ACP, and doctor naming is mostly consistent across README, package scripts, and tests, scoring 2. Quick start and configuration/IDE documentation links are present, but the supplied files include no FAQ or broad example set, scoring 1. The supplied files do not systematically document user-facing limitations, scoring 1. The complete MIT terms are present, scoring 3. Package versioning, Changesets scripts, and CI branches are visible, but no changelog for this revision is supplied, scoring 2. SECURITY.md states that only the latest release is supported and provides reporting channels, but does not give response deadlines, so maintenance responsibility scores 2.
The README describes code editing, command execution, and iterative next-step selection; doctor tests also show diagnostic output and field-level errors, supporting a score of 2 for output usability. IDE ACP, subagents, MCP configuration, and video input expand coding workflows, but these are largely README claims, so marginal value scores 2. Single-command installation and single-binary distribution reduce setup steps, while model login/credentials are required and the agent can perform broad actions; the evidence does not quantify operating costs or risk-benefit, so cost-benefit scores 1.
Most README claims about features, speed, and experience are not directly quantified or individually corroborated by the supplied source files, so claim traceability scores 1. README, tests, package scripts, and CI provide some cross-checks for commands, diagnostics, build, and test workflows, scoring 2 for cross-source corroboration. The evidence mixes checkable installation/configuration facts with unquantified promotional claims without explicitly separating facts from inference, scoring 1.
- The supplied material does not show tool permission controls, confirmation for risky actions, or complete data flows; review the interaction and configuration documentation before use.
- The install examples download scripts with curl or PowerShell and pipe them directly to a shell; verify the script source and contents, and protect model API credentials.