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* feat(agent-core): guide the model away from repeating denied or failed tool calls - system.md: add a diagnose-before-retrying paragraph next to the existing permission-denial guidance, covering failed tool calls - permission: when the user rejects an approval on the main agent, tell the model not to re-attempt the exact same call (sub agents already had an equivalent hint) * fix(agent-core): close abandoned tool exchanges and dedupe duplicate tool_use ids A turn that dies between a recorded tool.call and its paired tool.result (e.g. a transcript write failure mid-batch) used to leave pendingToolResultIds open forever: every later message was stranded in deferredMessages and user input was silently swallowed. - runOneTurn now defensively closes any dangling tool calls when a turn ends (completed, cancelled, or failed), synthesizing an error result that names the cause, with a warn log and a tool_exchange_abandoned telemetry event - the projector drops assistant tool calls whose id already appeared earlier (first occurrence wins): a duplicate id is wire-invalid on strict providers and not repairable by the strict resend; reported via the existing projection-repair log and telemetry - resume-side closePendingToolResults now logs what it closes (warn for a mid-history gap, info for the routine trailing interruption) * chore: add changesets for tool exchange fixes * fix(agent-core): scope duplicate tool_use id dedup to the strict resend Unconditional dedup regressed providers that emit per-response counter ids (e.g. call_0 in every step) and accept their own duplicates: later tool exchanges silently vanished from the projected history, and a duplicate call's own recorded result was left dangling. - the dedupe pass is now opt-in via dedupeDuplicateToolCalls and enabled only in strictMessages, so the normal projection keeps the history the provider produced - the pass also drops every tool result after the first for an id, so no dangling tool message survives; when the kept call has no result of its own, the surviving one is reattached by the adjacency repair - kosong now classifies the Anthropic "tool_use ids must be unique" 400 as a recoverable request-structure error so it triggers the strict resend |
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Kimi Code CLI
Documentation · Issues · 中文
What is Kimi Code CLI
Kimi Code CLI is an AI coding agent that runs in your terminal — it can read and edit code, run shell commands, search files, fetch web pages, and choose the next step based on the feedback it receives. It works out of the box with Moonshot AI’s Kimi models and can also be configured to use other compatible providers.
Install
Install with the official script. No Node.js required.
- macOS or Linux:
curl -fsSL https://code.kimi.com/kimi-code/install.sh | bash
- Homebrew (macOS/Linux):
brew install kimi-code
- Windows (PowerShell):
irm https://code.kimi.com/kimi-code/install.ps1 | iex
On Windows, install Git for Windows before first launch because Kimi Code CLI uses the bundled Git Bash as its shell environment. If Git Bash is installed in a custom location, set
KIMI_SHELL_PATHto the absolute path ofbash.exe.
Then, run it with a new shell session:
kimi --version
For npm install, upgrade, uninstall, see Getting Started.
Quick Start
Open a project and start the interactive UI:
cd your-project
kimi
On first launch, run /login inside Kimi Code CLI and choose either Kimi Code OAuth or a Moonshot AI Open Platform API key. After login, try your first task:
Take a look at this project and explain its main directories.
Key Features
- Single-binary distribution. Install with one command: no Node.js setup, PATH gymnastics, or global module conflicts.
- Blazing-fast startup. The TUI is ready in milliseconds, so starting a session never feels heavy.
- Purpose-built TUI. A carefully tuned interface, optimized end to end for long, focused agent sessions.
- Video input. Drop a screen recording or demo clip into the chat and let the agent watch what is hard to describe in words — turn a reference clip into a LUT, a long video into a short, a screen recording into working code, and more.
- AI-native MCP configuration. Add, edit, and authenticate Model Context Protocol servers conversationally with
/mcp-config, without hand-editing JSON. - Rich plugin ecosystem. Install skills, MCP servers, and data sources from the marketplace or any GitHub repo, with each install's trust level surfaced up front.
- Subagents for focused, parallel work. Dispatch built-in
coder,explore, andplansubagents in isolated contexts while keeping the main conversation clean. - Lifecycle hooks. Run local commands at key points to gate risky tool calls, audit decisions, trigger desktop notifications, or connect to your own automation.
- Editor & IDE integration (ACP). Drive a Kimi Code CLI session straight from Zed, JetBrains, or any Agent Client Protocol client with
kimi acp.
Use it in your editor (ACP)
Kimi Code CLI speaks the Agent Client Protocol, so ACP-compatible editors and IDEs (Zed, JetBrains, …) can drive a session over stdio. Log in once, then point your editor at the kimi acp subcommand — no extra login needed.
For Zed, add this to ~/.config/zed/settings.json:
{
"agent_servers": {
"Kimi Code CLI": {
"type": "custom",
"command": "kimi",
"args": ["acp"],
"env": {}
}
}
}
Then open a new conversation in Zed's Agent panel. See Using in IDEs for JetBrains setup and troubleshooting, and the kimi acp reference for the full capability matrix.
Docs
- Getting Started
- Interaction and approvals
- Sessions
- Using in IDEs (ACP)
- Configuration
- Command reference
Develop
Requirements: Node.js ≥ 24.15.0, pnpm 10.33.0.
git clone https://github.com/MoonshotAI/kimi-code.git
cd kimi-code
pnpm install
pnpm dev:cli # run the CLI in dev mode
pnpm test # run tests
pnpm typecheck # TypeScript check
pnpm lint # oxlint
pnpm build # build all packages
See CONTRIBUTING.md for the full contribution guide.
Community
- Issues
- For security vulnerabilities, see SECURITY.md.
Acknowledgements
Our TUI is built on top of pi-tui. We thank the authors of pi-tui for their valuable work.
License
Released under the MIT License.
