Meta's Superintelligence Labs just dropped Muse Code (beta), a terminal coding agent built for the kind of software engineering work that breaks most AI tools: large repos, multi-file changes, and tasks that take hours rather than seconds. It ships alongside Muse Spark 1.2, a coding-focused model update that was co-trained specifically to run inside Muse Code's agent harness.

A new entrant in a crowded arena

The terminal coding agent space is already packed. Unlike ChatGPT Codex and Claude Code, there is currently no dedicated app interface for Muse Code -- it lives entirely in your terminal. That is a deliberate design choice. Muse Code is a terminal agent that installs with a single command and takes on complete software engineering tasks, including planning changes, writing code, and validating results. The install command is literally one line:

curl -fsSL https://dev.meta.ai/install.sh | bash

The release follows Meta's launch of Muse Spark 1.1, which upgraded the model's agentic and multimodal abilities and introduced Meta's paid API service for developers. With Muse Code, Meta is now competing directly with Claude Code and OpenAI Codex on the autonomous, long-horizon coding agent front.

The architecture: agents that don't quit

The core insight behind Muse Code is that most coding agents fail on long tasks not because they're dumb, but because they're stateless. Every hard problem requires re-gathering context, re-planning, and re-steering. Muse Code attacks this with two architectural ideas.

First, persistent background agents. Muse Code uses persistent background agents, repository-scale execution, and built-in verification. Muse Code operates specialized background agents that remain active throughout a user's session , rather than being spun up and torn down for each subtask. They carry out next steps autonomously and choose when to report back to the main agent, cutting down on redundant context-gathering and the need for human steering.

Second, a crash-proof event log. Muse Code uses a local event log in which every model call, tool run, approval, and edit is appended. This single source of truth makes the runtime replay-exact and restart-safe: after a crash, the agent can resume precisely where it stopped. That ability lets Muse Code take on long-running tasks without being derailed by failures. For tasks that run for hours, this is not a nice-to-have -- it is the difference between finishing and starting over.