Rope Notes is an offline-first, local-network agentic workspace built for high-performance engineering. It runs a dedicated LSP client on-device and routes AI context pipelines over secure LAN/WAN to your private infrastructure.

Is Rope Notes a privacy focused IDE?

Yes, Rope Notes is built for air-gapped privacy. It never sends your source files through public cloud aggregators. All processing stays on-device or on your private network infrastructure.

Does Rope Notes support local LLM agents?

Yes, Rope Notes routes agentic prompt matrices to localized private server nodes including Ollama, vLLM, and private endpoints for complete data sovereignty.

Is Rope Notes available as a Mobile Dart IDE?

Yes, Rope Notes is built with Dart and targets mobile platforms, functioning as a Mobile Dart IDE that operates seamlessly across devices with a locked 60/120 FPS UI.

How does Rope Notes pricing work?

Rope Notes has $35 pricing for a lifetime license for the first 100 customers. After 100 licenses are sold, the price will increase to $50. After reaching 10,000 customers, the software will be completely open-sourced.

Kimi K3 in Rope Notes: Frontier Coding Without Leaving Your Editor

Yesterday

How to integrate Kimi K3 — Moonshot AI's 2.8-trillion-parameter open-weight model — into Rope Notes for long-running software engineering, Flutter refactors, and vision-assisted UI iteration.

Moonshot AI released Kimi K3 today — a 2.8-trillion-parameter open-weight model with a 1-million-token context window, native vision, and a focus on long-running software engineering. If you use Rope Notes’ AI agent for Dart, Flutter, or large multi-file projects, K3 is one of the best cloud models you can plug in right now.

This guide covers why the fit is strong, how to connect Kimi K3 in Preferences, and a few workflows that play to both products’ strengths.

How Kimi K3 fits Rope Notes

Rope Notes is built as a local-first, IDE-style editor: fast rope-backed editing, Dart analysis, Git and terminal on desktop, and an agent that can chat, plan, call tools, and propose edits you accept or reject. Kimi K3 was built for that same shape of work.

Kimi K3 strength	How Rope Notes uses it
Long-horizon coding with tools	Agent tool loop (`read_file`, search, definitions, …) over many turns
Huge context window	Open tabs, `@` mentions, plans, and memory stay coherent longer
Vision-in-the-loop	Iterate on Flutter UI with screenshots and layout feedback
Competitive price vs Western flags	Frontier-class sessions without Fable-level output rates
Open weights coming July 27	Same agent UX later on your own or LAN-hosted weights

Moonshot positions K3 for autonomous engineering sessions: navigate large repos, orchestrate tools, and keep going with minimal babysitting. That is exactly what Rope Notes’ Plan and Execute modes are for — write a plan into .rope_notes/, then walk the checklist turn by turn while you review ghost previews and diffs.

Independent and provider benchmarks place K3 near the top proprietary models (Claude Fable 5, GPT-5.6 Sol), with especially strong showings on front-end / coding arenas. Treat marketing scores cautiously, but for day-to-day agent coding the early signal is clear: this is a serious coding model, not a budget toy.

Pricing in plain terms

Moonshot’s API (flat across the full 1M context):

$0.30 per million input tokens on cache hits
$3.00 per million input tokens on cache misses
$15.00 per million output tokens (including reasoning)

Coding agents re-send a lot of the same prompt and file context. High cache-hit rates matter; Moonshot reports 90%+ cache hits on coding workloads. Compared with Fable-class pricing, that is a large savings for the same style of multi-file sessions.

Open weights are expected July 27, 2026. Until then, use the hosted API or OpenRouter.

Set up Kimi K3 in Rope Notes

You need a desktop or mobile build with cloud agent backends (web builds do not run the agent).

Path 1 — OpenRouter (fastest if you already have a key)

Open Preferences → Agent (or the model picker in the Agent panel).
Add a provider with backend OpenRouter.
Set the model to moonshotai/kimi-k3.
Paste your OpenRouter API key (prefer secure credential storage).
Test connection, then select the provider in the agent panel.

Path 2 — Moonshot API directly

Create a key on the Kimi API platform.
In Preferences → Agent, add a provider:
- Backend: OpenAI-compatible (or equivalent preset)
- Base URL: https://api.moonshot.ai/v1
- Model: kimi-k3
Save credentials securely, test connection, select the provider.

Provider configs live with the project under .rope_notes/preferences.toml, so teammates can share the setup — not the secret keys.

Workflows that shine with K3

1. Large Flutter refactors (Plan → Execute)

Open the project and pin the relevant files with @lib/... or by dragging from Explorer.
Switch the agent to Plan and describe the end state (e.g. “split this feature into a clean MVVM boundary”).
Review plan-*.md / checklist under .rope_notes/.
Switch to Execute and let K3 work checklist items while you accept or reject proposals.

K3’s long context helps it remember decisions from earlier turns instead of re-deriving architecture every prompt.

2. UI polish with vision in the loop

K3 can look at screenshots, change code, and check the visual result again. In Rope Notes:

Keep the widget files in context.
Describe the visual bug and attach or paste screenshot context when your workflow supports it.
Review ghost edits and diffs before accepting.
Re-run the app and feed the next screenshot into the following turn.

Especially useful for layout, theming, and mobile portrait/landscape browser layouts.

3. Repo-wide questions without losing the thread

With a 1M window you can ask deeper “how does this subsystem fit together?” questions while keeping:

Open editor tabs
/remember project notes
Prior plan artifacts
Session history under .rope_notes/sessions/

Still prefer targeted @ mentions — precision stays faster and cheaper even when the model can swallow more.

4. Mobile + desktop continuity

On Android, Rope Notes supports cloud backends (including K3) even though local Ollama is a desktop feature. Use session transfer (ZIP or network) to move open files, unsaved buffers, and agent artifacts between machines, then keep chatting with the same Kimi provider.

Local-first stays the default

Rope Notes remains Ollama-first on desktop. Use local models when you want source to stay on the machine. Reach for Kimi K3 when you need:

Multi-hour agentic coding
Stronger reasoning over a large Dart/Flutter tree
Vision-assisted UI iteration
Quality close to top proprietary models at a lower output price

After open weights ship, you can point a self-hosted or LAN OpenAI-compatible endpoint at K3 and keep the same Preferences entry — only the base URL changes.

Permissions and safety

Autonomous models are most useful when they cannot touch everything. Before long Execute sessions:

Review .rope_notes/permissions.toml
Deny .git and secret paths as needed
Prefer accept/reject on destructive edits

The agent proposes; you still own the rope.

Quick start checklist

Add OpenRouter or Moonshot provider in Preferences → Agent
Model = moonshotai/kimi-k3 or kimi-k3
Test connection and watch the context budget bar
Try a Plan on a real feature, then Execute with accept/reject
Tighten permissions before unattended tool use