* feat(agent-core): compress oversized images before sending to the model
Downsample images to a 2000px longest-edge and per-image byte budget at the
single prompt-ingestion chokepoint (the prompt/steer RPC) and on tool results
(ReadMediaFile, MCP), so every client transport — CLI, web, desktop, ACP, SDK —
is covered uniformly inside the core. PNG screenshots stay lossless and only
degrade to JPEG when the byte budget cannot otherwise be met. Best-effort: the
original image is sent unchanged if compression fails.
* fix(agent-core): serialize prompt/steer RPCs to avoid a turn-claim race
The prompt/steer RPC handlers await image compression before turn.launch()
synchronously claims the active turn, so two overlapping calls could both
compress first — letting the faster-to-compress one win the turn and strand the
other on agent_busy. Run these two RPCs through a per-agent serialization chain
so they claim in submit order; cancel and the other RPCs stay immediate.
* fix: update flake.nix pnpmDeps hash for the jimp dependency
Adding jimp to the workspace changed pnpm-lock.yaml, so the pnpmDeps
fixed-output hash was stale and the nix build failed. Update it to the value
the CI nix build reported.
* fix(agent-core): guard image compression against decompression bombs
A tiny-byte, huge-dimension image (e.g. a solid 30000x30000 PNG) would be fully
decoded into a multi-gigabyte bitmap by Jimp before any resize — an OOM vector
the byte budget never catches. Skip compression when the sniffed pixel count
exceeds MAX_DECODE_PIXELS (~100 MP), before the decode; oversized images pass
through uncompressed as they did before compression existed.
* fix(agent-core): cap decode byte size before compressing images
Compression runs before downstream size caps (e.g. the 10MB MCP per-part
limit), so a huge or invalid base64 image from an MCP tool was Buffer.from-
decoded — and handed to Jimp — just to be dropped afterward. Add a
MAX_DECODE_BYTES ceiling (64MB, overridable) checked before the base64 decode
and before Jimp, the byte-side complement to the pixel-count guard; oversized
payloads pass through uncompressed.
* refactor(agent-core): compress images at ingestion, not on the turn RPC
Move image compression off the prompt/steer RPC path and back to each ingestion
site (CLI paste, server upload resolution, ACP conversion; ReadMediaFile and MCP
already compressed at their producers). Compressing on the RPC control path put
an async step before the synchronous turn-claim, which spawned a series of
races: prompt/steer interleaving, and — with a cancel arriving mid-compression —
an ineffective abort that let a cancelled prompt launch anyway.
Treating compression as a pure input-stage transform (done while the content
part is built, before it ever enters the agent loop) removes those races
structurally: rpc.prompt/steer are plain synchronous handlers again, and the
serialization/cancel-window machinery is gone. Records stay compressed, resume
stays consistent, and coverage degrades gracefully (a new client that skips
compression just sends a larger image, as before this feature).
* fix: compress inline base64 prompts and honor ACP cancels mid-compression
Two contained ingestion-site follow-ups:
- server: resolvePromptMediaFiles now also compresses images submitted as an
inline `{ kind: 'base64' }` source, not just uploaded files, so the REST
inline-base64 path gets the same downsampling.
- acp-adapter: AcpSession tracks a pending-abort flag while prompt() awaits
image compression (before any turn exists). A session/cancel in that window
flips it, so the prompt returns `cancelled` instead of launching a turn the
client already stopped.
* fix(acp-adapter): cover all concurrent pre-turn prompts on cancel
The pending-abort marker was a single session field, so with two
`session/prompt` requests compressing large inline images at once the later
one overwrote it and a `session/cancel` could mark only one — the other
launched after the client had cancelled. Track a token per in-flight prompt in
a set and flip them all on cancel so every pre-turn prompt is covered.
* chore(node-sdk): declare jimp as a devDependency
The SDK re-exports the image compressor, whose lazy `import('jimp')` (inside
the bundled agent-core code) is inlined into the published dist. jimp was
resolved only transitively via agent-core, so declare it as an explicit build
input here — matching the CLI — to make the bundling reliable rather than
phantom. It stays a devDependency: jimp is bundled, not a runtime dependency.
|
||
|---|---|---|
| .agents/skills | ||
| .changeset | ||
| .github | ||
| apps | ||
| build | ||
| docs | ||
| packages | ||
| plan | ||
| plugins | ||
| scripts | ||
| .editorconfig | ||
| .gitattributes | ||
| .gitignore | ||
| .npmrc | ||
| .nvmrc | ||
| .oxfmtrc.json | ||
| .oxlintrc.json | ||
| AGENTS.md | ||
| CONTRIBUTING.md | ||
| flake.lock | ||
| flake.nix | ||
| LICENSE | ||
| Makefile | ||
| package.json | ||
| pnpm-lock.yaml | ||
| pnpm-workspace.yaml | ||
| README.md | ||
| README.zh-CN.md | ||
| SECURITY.md | ||
| tsconfig.json | ||
| vitest.config.ts | ||
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.
