Delegate a code task
Describe the goal; the task runs in a managed cloud environment with build, test, terminal, and preview workflows, and produces a reviewable change.
How execution worksProduct
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Your first task: fix, test, explainProduct update logAI NewsResearchDirect answersTry MonkeyCode ↗GitHub ↗Language
Official product guides and resources for MonkeyCode.
OPEN SOURCE · BUILT FOR ENGINEERING TEAMS
Start coding with DeepSeek V4.1 Flash, already built in. Describe a task, run it in a cloud workspace, and review the change — free to start.
FREE TO STARTFree cloud dev environment + 10M tokens/day (resets daily) — no install, run in the browser. Plan terms are set by the vendor; verify current limits.

Text-edited English mock-up of the original product screenshot, not a real English runtime capture. Interface and account values vary by version.
What teams run on it
Delegate a code task, work with documents, or follow progress across devices. Explore the workflow that fits your team.
Describe the goal; the task runs in a managed cloud environment with build, test, terminal, and preview workflows, and produces a reviewable change.
How execution worksWith the MonkeyWork desktop agent, authorized local files (Word, PDF, Markdown) can be summarized, rewritten, and organized; a paired browser extension assists gathering web material.
Desktop mode boundariesStart a task on the web, continue it on the desktop, and check progress, keep chatting, or receive results on mobile while the work keeps running.
See the feature mapExecution modes
Use a managed cloud environment, work with local files on desktop, or operate the platform on your own infrastructure.
Tasks run in managed cloud dev environments. Browser only, no install; free tier to start.
Cloud env · per-plan quotasA local agent works on authorized local files and can continue cloud tasks; browser pairing assists web work.
Windows · macOS · LinuxRun the platform inside your network with centralized admin control. Verify model egress — it is a deployment condition, not a data guarantee.
Online/offline installer · AGPL-3.0Full boundary table with sources: Execution-mode boundaries →
REPRODUCIBLE EXERCISE · V1
Start with a tiny Python project. You can inspect the input, make one bounded change and reproduce the checks before connecting a real repository.
From the official service
Quotes are published feedback from the official hosted service; some details are generalized for privacy. monkeycode-ai.net/ ↗
What I value most is that it is device-agnostic: a computer at the office, a tablet at home, a phone to check progress. Tasks keep running.
Setting up environments used to take a lot of time and felt tedious. After using MonkeyCode a lot of that wasted effort disappeared—it works out of the box and lets me focus on the actual product.
News and research
Source-checked AI coding news plus durable guides on agents, governance, and self-hosting.
Use one disposable example folder for desktop tasks, inspect file changes independently and distinguish local execution from model-provider data processing.
A source-reviewed GitHub App workflow covering a single repository, project creation, missing repositories and separate installation and user authorization revocation.
A concrete Ubuntu workflow with a tested preflight script, a versioned installer observation, expected checks and failure recovery for a self-hosted evaluation.
Direct answers