WhipCode
A coding agent built for work that doesn’t fit in one context window.
Quickstart · What is this? · How it works · Benchmarks · Development
Quickstart
Download Desktop Beta — Apple Silicon · macOS 14 or newer.
Use a release built after the clean-project reset; old Desktop releases are not an upgrade path.
- Download
whipcode-desktop-darwin-arm64.dmg, open it, and drag Whip Beta into Applications. - Launch it and choose Set up this Mac if prompted. The app includes its matching backend; no separate CLI installation is needed.
- Connect a model provider, open your project, and describe what you want to do.
Bring an API key or use a supported subscription login. Provider access is separate from installing WhipCode. See providers and authentication.
Prefer the terminal? Install the standalone CLI on macOS or Linux:
curl -fsSL https://raw.githubusercontent.com/context-labs/whip/main/install.sh | sh
whipcode
For an exact version, use the release's install.sh command from its notes—no
version environment variable is needed for releases with the two-script split.
The companion latest.sh always selects stable v1+, never an alpha.
The new CLI track starts at v1.0.0-alpha.N. Prerelease installation is explicit;
default stable installation becomes available with v1.0.0. Existing internal
installs must follow the manual reset checklist, not upgrade in place.
Desktop-managed installations update through Desktop; standalone CLI installations use whipcode update. Setup and upgrades →
For the web client, start the daemon with whipcode daemon start, then run
whipcode web. It opens your browser and stays running as a separate gateway;
Ctrl+C stops web access, not daemon work. Ordinary daemon and Desktop startup
open no web listener. See web access
for --no-open, managed startup, and trusted-network configuration.
What is this?
WhipCode is an open-source coding agent with a Desktop app, terminal UI, and web client. A shared daemon owns the work, so closing a window doesn’t end the session.
- Delegate recursively. Agents split work into focused tasks, launch their own agents, and coordinate through messages.
- Keep context within reach. Search and read history, files, and large tool outputs in bounded slices instead of cramming everything into a prompt.
- See what’s happening. Follow conversations, inspect the execution tree and timeline, and track model usage and cost.
- Stay in control. Choose models, set tool permissions, and connect local or remote execution hosts. Extend agents with skills and MCP servers.
How it works
- Describe the task. Give an agent a goal and the context it needs.
- Let it work. A recursive language model (RLM) loop uses short Starlark or JavaScript programs to inspect context, call tools, and delegate—not just a growing chat transcript.
- Inspect and steer. Follow the trace
