* fix(agent-core): report EXIF-rotated image dimensions and raise edge cap to 3000px
Image compression now reports original dimensions in the decoded
(EXIF-rotated) space, matching the coordinate system of the sent image
and of ReadMediaFile region readback; previously portrait JPEGs
(orientation 5-8) got swapped width/height in captions. The longest-edge
downscale cap rises from 2000px to 3000px, and the default jimp resize
path is documented as the anti-aliased area-average one so it is not
accidentally switched to a point-sampled interpolation mode.
* test: shrink oversized image fixtures to fit CI timeouts
The 3600x3600 fixtures introduced for the 3000px edge cap nearly doubled
the pixel area jimp has to decode and deflate, pushing the slowest
compression tests past the 5s vitest timeout on CI runners. 3600x1800
keeps every fixture over the cap while restoring roughly the workload of
the old 2600x2600 fixtures that CI handled comfortably.
* test: pin anti-aliased downscale quality with executable guards
A 1px checkerboard probe pins the compressor to full-coverage averaging
at integer and fractional ratios, with jimp's point-sampled BILINEAR
mode kept as the executable aliasing counter-example (it collapses the
50%-gray pattern to solid black at 4:1). Also guards the other classic
downscale bugs: transparent-pixel color bleed, mean-brightness drift,
iterative recompression degradation, and zero-size collapse on extreme
aspect ratios.
* fix(agent-core): report decoded EXIF-rotated dimensions in ReadMediaFile notes
The media note derived its original-dimensions line from the header
sniff, which reports pre-rotation values for EXIF orientation 5-8
JPEGs. The sent image and region readback both live in the decoded
(rotated) space, so portrait photos got axis-swapped coordinate
guidance. Once a decode has happened — compression or crop — its
dimensions now overwrite the sniffed ones.
* fix(agent-core): improve handling of EXIF orientation in image dimensions and metadata
* fix(agent-core): sniff EXIF orientation and step budget fallback through 2000px
Two follow-ups to the EXIF and 3000px-cap changes:
sniffImageDimensions now reads the JPEG EXIF Orientation tag (pure
header parse, both byte orders) and reports display-space dimensions
for orientations 5-8. Passthrough images — never decoded — previously
kept the pre-rotation header size in compression results and media
read notes, disagreeing with the decoded space that region readback
uses.
encodeWithinBudget steps the over-budget fallback through 2000px
before the 1000px last resort. Raising the cap to 3000px had left a
regression window: an image whose 2000px encode fits the byte budget
was sent at 1000px where the old 2000px cap used to send it at
2000px.
* fix(kimi-code): record pasted image dimensions in display space
The TUI paste path recorded attachment and original dimensions from its
raw header parser, which ignores EXIF orientation. For a portrait JPEG
the submit-time caption then contradicted the sent image's aspect and
region readback coordinates were axis-swapped. Dimensions now come from
the compression result, which reports display space on both the
compressed and passthrough paths; parseImageMeta remains only the
format/mime gate.
* feat(agent-core): add image compression and crop telemetry
Every image ingestion path now reports an image_compress event —
outcome (compressed / passthrough fast, guard, unsupported, unhelpful,
error), input/output formats, byte and pixel sizes, EXIF transposition,
and duration — and region readback reports an image_crop event with a
failure classification and the region's share of the original area.
Wiring is per call site via a new CompressImageOptions.telemetry
option, so the outcome split and timing are measured inside the
compressor while each caller only names its source: ReadMediaFile
(tool construction, like GrepTool), MCP tool results (McpOutputOptions),
server prompt ingestion (ICoreProcessService now exposes the host
telemetry client), ACP prompts (session track adapter), and TUI paste
(host.track adapter). Properties are numeric/enum only — never paths
or content — and a throwing client can never affect the compression
result.
* fix(agent-core): run the full JPEG quality ladder at fallback sizes
The fallback rescales encoded only at quality 20, so a JPEG whose
ladder failed at the fitted size collapsed straight to the lowest
quality even when the smaller size left budget headroom for a higher
rung (the realistic window is the 1000px step, where the 4x pixel
drop pays for q80/q60). Each fallback edge now walks the same
q80-to-q20 ladder as the fitted size.
* test: shrink heavy JPEG fixtures and add explicit timeouts
The fallback-ladder test runs ~11 pure-JS JPEG encodes and the EXIF
paste test decodes, rotates, and re-encodes a 6.5MP frame; both sat at
the edge of the 5s vitest timeout on CI runners. Narrower fixtures cut
the pixel area (the ladder test keeps its width above 2000px so the
full fallback chain still runs) and explicit 15s timeouts absorb runner
variance.
* fix(server): scope prompt image compression telemetry to the session
The prompt-ingestion image_compress events were emitted with the bare
host telemetry client, while every agent-side source inherits a
session-scoped client — so prompt_inline/prompt_file events could not
be correlated with their session. The route now wraps the client with
withTelemetryContext({ sessionId }) like rpc/core-impl does for
session telemetry.
* chore(changeset): consolidate image compression changesets
One entry covering the cap raise and the EXIF dimension fix, listed
for both the CLI and the SDK so the SDK changelog's compression
description (previously pinned at 2000px) stays accurate.
|
||
|---|---|---|
| .agents/skills | ||
| .changeset | ||
| .github | ||
| apps | ||
| build | ||
| docs | ||
| packages | ||
| plugins | ||
| scripts | ||
| .editorconfig | ||
| .gitattributes | ||
| .gitignore | ||
| .npmrc | ||
| .nvmrc | ||
| .oxfmtrc.json | ||
| .oxlintrc.json | ||
| AGENTS.md | ||
| CLAUDE.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.
