StackMap
Subscribe
Explore / whip
context-labs

whip

WhipCode: open-source coding agent in Go built on a recursive language-model loop; agents delegate to sub-agents through short programs, with daemon-owned sessions across desktop, TUI and web.

1,065 153 Go Apache-2.0updated today
View on GitHubDispute this mapping →
Curator's take

Interesting when your tasks outgrow one context window: instead of a growing transcript, an RLM loop writes small Starlark or JavaScript programs to slice history, files and large tool outputs, and delegates recursively to its own sub-agents, while a daemon keeps sessions alive after you close the window. Built with open-source models in mind, with provider routing, MCP and skills. Its benchmark (20 of 30 on a mixed Terminal-Bench and repo set) is honestly caveated as not a matched comparison. The churn is real: a v1 alpha CLI track, an Apple-Silicon-only desktop beta, and a clean-project reset that is not an upgrade path. Stay on Claude Code or Codex for a stable daily driver; try this when long-horizon delegation is the actual problem.

Mapped by ShipWithAI editors · links verified

Continue your stack

What teams reach for next — and why each earns a place beside whip. Ranked by curator confidence.

alternativealternativealternativeprime-agentgooseopeninterpreterwhip
pairs wellalternativebuilt withpick a node for the why · open it from the panel
Weekly digest
README.md2 min read


WhipCode logo
WhipCode

A coding agent built for work that doesn’t fit in one context window.

Apache 2.0 license Desktop beta

Quickstart · What is this? · How it works · Benchmarks · Development

WhipCode Desktop with a conversation, three working subagents, and an execution trace timeline side by side

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.

  1. Download whipcode-desktop-darwin-arm64.dmg, open it, and drag Whip Beta into Applications.
  2. Launch it and choose Set up this Mac if prompted. The app includes its matching backend; no separate CLI installation is needed.
  3. 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

  1. Describe the task. Give an agent a goal and the context it needs.
  2. 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.
  3. Inspect and steer. Follow the trace