* refactor(agent-core-v2): unify model-facing reminder scheduling Route every model-facing reminder through the contextInjector boundary scheduler. Past-tense events go through a persisted once-reminder queue (reminderQueue) that delivers exactly once at turn, step, compaction, and restore boundaries; present-tense state renders through context-injection providers reconciled against live history. - interruption, goal (cancel/budget/fork-cleared), image-compression captions, btw, and init reminders enqueue into reminderQueue instead of writing the context directly; the interruptionReminder wire model is removed and its recorded type is retired silently on replay - swarm mode announcements render through a provider seeded from the replayed history on restore, replacing live side effects and the ContextModel pop reducer on swarm_mode.exit - loadable-tools announcements become an isNewTurn-gated provider, dropping the compaction boundary flag - plugin session-start guidance re-renders as a supersedes reminder at the next boundary via a dirty flag instead of appending immediately - legacy system_trigger origins of migrated reminders still fold on replay * fix(agent-core-v2): make system reminders undo-aware * test(agent-core-v2): migrate plugin session-start harness * fix(agent-core-v2): preserve reminder boundary ordering * refactor(agent-core-v2): narrow reminder and swarm helper exposure - drop the swarmInjection re-export from the package index; SwarmInjection stays a domain-internal collaborator like permissionMode/plan injections - move INTERRUPTION_REMINDER text back to a private constant in the service; only the variant stays in the Ops module - make reminderQueue.enqueue return void; no caller consumed the entry id * chore(agent-core-v2): keep comments in module headers * refactor(agent-core-v2): track reminder state via injection disclosure - derive swarm active/inactive state from ctx.lastDisclosure instead of byte-matching rendered markdown, with variant-only fallback for legacy swarm_mode/swarm_mode_exit journal entries - record once_reminder disclosure (entry id) on queue-appended messages and dedupe the crash window by the contiguous tail id set, covering multi-entry drains - move reminderQueue draining behind a sync onWillInject event so the injector no longer depends on the queue domain - centralize the system-reminder wrap format behind wrapSystemReminder / systemReminderContent and use injector-provided positions in the plugin session-start provider - spell out the step-boundary fallback and sync-only contract of registerAtTurnStart via shouldRunAtBoundary * fix(agent-core-v2): isolate failing turn-start providers and warn once per missing sessionStart skill * refactor(agent-core-v2): compute injection positions on read Drop the per-provider positions cache from the context injector: the registration scan, the context.spliced index arithmetic, and the post-restore resync all existed only to mirror what the history already records. Each provider call now derives its injected positions by scanning context memory for its surviving injection messages, so silent history edits (such as vacuous-step folds) can no longer desync a cached index. * refactor(agent-core-v2): formalize injector once-channels and raw message results * refactor(agent-core-v2): declare dynamic tool schemas at injection boundaries Move the dynamic-tool schema declaration out of toolSelect.load(): the loaded names are recorded as pending and drained by a dedicated toolSelectSchemas provider through the contextInjector boundary scheduler, so the declaration message lands at a quiescent boundary instead of mid-step inside a streaming tool exchange. The folded history remains the loaded-tool ledger, so undo, compaction, and resume still self-heal by re-folding. * refactor(agent-core-v2): deliver AGENTS.md reminders through the reminder queue The tool hook now only observes and enqueues a once-per-agent reminder through the reminderQueue once-channel instead of prepending text to the tool result: results stay verbatim for the truncation pipeline and the reminder can never be truncated away with an oversized output. The reminderQueue is resolved lazily through the instantiation service at enqueue time, breaking the contextInjector -> loop -> llmRequester -> profile -> agentsMdReminder constructor cycle. * refactor(agent-core-v2): make injection disclosures opaque and domain-owned contextMemory no longer declares the ContextInjectionDisclosure union: InjectionOrigin.disclosure becomes an opaque unknown, and providers bind their own payload type through register<D>, so lastDisclosure arrives at the provider already typed by its own variant. The date, swarm_mode, and once_reminder payload shapes move into the dateChange, swarm, and reminderQueue domains respectively; reminderQueue keeps a runtime guard for its cross-message tail scan, the only place that reads disclosures it did not write. Persisted origin shapes are byte-identical, so existing journals replay unchanged. * fix(agent-core-v2): isolate failing step context providers A step or compaction boundary provider that threw or rejected made the injector's inject() promise reject, which propagated through the onWillBeginStep hook chain and failed the whole turn, and starved every provider registered after it. Log and skip the bad provider instead, matching the turn-start path's existing isolation. * refactor(agent-core-v2): derive injector isNewTurn per injection boundary Replace the shared read-and-clear isNewTurn flag (set by turn.started and injectAfterCompaction, consumed by the first inject()) with values each trigger supplies from an authoritative source: the loop marks a turn's first step via BeforeStepContext.firstStepOfTurn (standalone runs never count), and the compaction follow-up passes true explicitly, so interleaved triggers can no longer consume or steal the marker. A compaction follow-up that lands inside a step hook chain (the auto-compaction path) doubles as that step's new-turn delivery: the enclosing step then injects with isNewTurn false, so the upcoming request receives one new-turn injection, not two. * refactor(agent-core-v2): unify disclosure placement and injector param naming * fix(agent-core-v2): keep pending tool schemas across compaction splices A load announced by select_tools sits in pendingLoaded until the next injection boundary declares it. A compaction fold in that window publishes a replacement splice, and the splice-time reconciliation dropped the pending entries before the post-compaction inject could declare them — the model was told "Loaded: X" yet X never became available. Drop pending entries only on removal splices (undo/clear, which carry no replacement messages); compaction's replacement splice keeps them so the declaration lands at the post-compaction boundary. * fix(agent-core-v2): consume the plugin session-start refresh after a successful render reconcileSessionStartReminder cleared the refresh-pending flag before awaiting the render, so a throwing render (skipped by the injector's provider isolation) lost the forced refresh until the next catalog change. Consume the flag only after the render resolves, and move the warn-once rationale into the module header per the comment convention. * refactor(agent-core-v2): remove the generic reminder queue * chore(agent-core-v2): drop the stale reminder-queue mention in systemReminder * test(node-sdk): align side-question fork parity with event-point reminders * chore(agent-core-v2): address reminder review standards * docs(agent-core-v2): condense the model-facing reminders section * refactor(agent-core-v2): write all system reminders through wrapSystemReminder * fix(agent-core-v2): preserve reminder lifecycle invariants * refactor(agent-core-v2): reconcile context injections at the step head Unify the injector's delivery timings into one point on the onWillBeginStep chain, before the step's request is built: - providers run before every request instead of after every step, so reminders are visible from the first response of a turn - a compaction splice re-arms the new-turn flag via context.spliced; when compaction runs inside the hook chain (full-compaction's beforeStep), a follow-up inject at the chain tail keeps the first post-compaction request covered - registerAtTurnStart and injectAfterCompaction are removed; reconcileWhenIdle stays as the v1-parity surface for SDK-driven triggers (swarm toggle, plugin reload) * refactor(agent-core-v2): clarify the injector's step-hook handler Name the handler reconcileAroundStep, rename the rearm flag to compactionRearmPending with a single takeCompactionRearm() consumer, and extract isCompactionSplice. Consuming the flag into a local before computing isNewTurn also avoids hiding the side effect inside a || short-circuit. |
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| .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 | ||
| GOAL.md | ||
| 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
- 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.
