Persistent
Background agents stay active.
Meta says specialized agents remain active during a session instead of being recreated for every subtask.
Terminal coding agent reference
Muse Code Meta’s coding agent
Muse Code is Meta’s beta terminal coding agent, powered by Muse Spark 1.2. This independent guide separates confirmed release facts, Meta’s claims, evaluation limits, and early community questions.
$ muse-code record --checked 2026-08-06
status beta
agent Muse Code
model muse-spark-1.2
platforms macOS · Linux
context 1M tokens [Meta says]
runtime persistent agents + event log
$ evidence --limits
✓ official release sources linked
✓ community anecdotes labeled
! no hands-on benchmark claimed here
$
Muse Code release record
- Status
- Early beta
- Released
- 05 Aug 2026
- Model
muse-spark-1.2- Access
- Muse Code + Model API
- Checked
- 06 Aug 2026
01 · Identity
Agent ≠ model
Two names, two jobs.
Muse Code is the terminal agent and runtime. Muse Spark 1.2 is the coding-focused model that supplies inference. Treating them as the same thing hides where state, tools, approvals, and recovery actually live.
| Dimension | Muse Code | Muse Spark 1.2 |
|---|---|---|
| Role | Plans, acts, validates, and manages sessions. | Generates model outputs for the agent loop. |
| State | Local event log, goals, tool results, and agent state. | Input context and generated output within inference. |
| Actions | Can invoke tools and coordinate isolated worktrees. | Does not edit a repository without a harness. |
| Access | Terminal beta. | Muse Code, Meta Model API, and listed partners. |
02 · What Meta documents
Auditable
Every action enters an event log.
Model calls, tool runs, approvals, and edits are appended locally, according to the research release.
Restart-safe
Resume is built on recorded state.
Meta says the same append-only log lets a session reconstruct its last recorded step after interruption.
03 · Start carefully
One command, with one important pause.
Meta publishes a one-line installer for macOS and Linux. A remote script piped into a shell is powerful; inspect the official source and current requirements before executing it.
$ curl -fsSL https://dev.meta.ai/install.sh | bash
Then, according to Meta:
$ cd /path/to/your/project
$ muse
Before running:
✓ verify the official domain
✓ review current authentication terms
✓ test in a non-critical repository first
04 · Evaluation
Company-reported results
A chart is not a deployment decision.
Meta reports improvements on coding evaluations and a kernel-optimization run exceeding 1,000 tool calls over as long as 24 hours. The case study is evidence of a narrow capability, not proof of reliability in an ordinary production repository.
First workload to run
Choose a real multi-file change with a failing test. Record the base commit, prompts, approvals, token cost, elapsed time, diff quality, test results, and a deliberate crash/resume checkpoint.
05 · Community signal
Community · r/opencodeCLI
Price is attracting attention.
Early discussion focuses on the price-to-quality ratio, but several participants are waiting for broader real-world evidence.
Community · r/opencodeCLI
Contributor access is confusing.
Users asked where the discounted model appears and reported regional limits. Meta’s own article says select countries only.
Community · r/MetaAI
Benchmark breadth is questioned.
Some commenters want more independent tasks and practical repository tests before trusting launch charts.
Community posts are anecdotal snapshots, not controlled evidence or representative surveys.
06 · Sources close to claims
S01 · Official
Meet Muse Spark 1.2 and Muse Code
Primary release, installation, model IDs, pricing, context window, cookbooks, and isolated-worktree fan-out.
S02 · Official
Introducing Muse Code and Muse Spark 1.2
Primary architecture, training, long-horizon, event-log, bundled-skill, and kernel-case-study claims.
S03 · Official
Muse Code product page
Current product access point and documentation path.
S04 · Official
Muse Spark 1.2 model page
Current model identity and access point.
07 · FAQ
Questions the release leaves developers asking.
Answers are dated, qualified, and tied back to the source hierarchy.
What is Muse Code?
Muse Code is Meta’s beta terminal coding agent. Meta says it plans changes, writes code, validates results, coordinates persistent background agents, and records work in a local event log.
Is Muse Code the same as Muse Spark 1.2?
No. Muse Code is the agent and runtime; Muse Spark 1.2 is the coding-focused model that powers it. The agent manages goals, tools, state, worktrees, and the interaction loop.
Is Muse Code available now?
Meta announced an early beta on 5 August 2026 with expanded global access. Access conditions can change, so check the official product page before planning a rollout.
Which operating systems are supported?
Meta’s research announcement lists macOS and Linux. This guide does not infer native Windows support from that statement.
How do you install Muse Code?
Meta publishes a one-line shell installer. Review the official instructions and the script source before executing a remote shell command.
How much does Muse Spark 1.2 cost?
As checked on 6 August 2026, Meta lists standard API pricing of $0.15 per million cached input tokens, $1.25 per million input tokens, and $4.25 per million output tokens. Reasoning tokens are billed as output.
What is the Contributor tier?
The model ID is muse-spark-1.2-contributor. Meta says it is token-rate-limited in a rolling five-hour window, available only in select countries, and its data may be used to improve Meta products.
Does Muse Code use subagents?
Yes, according to Meta. The launch material describes persistent background agents and a fan-out mode where write-capable children can work in isolated git worktrees.
Can Muse Code resume after a crash?
Meta says the append-only local event log makes sessions replay-exact and restart-safe, allowing a run to continue from the last recorded step. This guide has not independently stress-tested that recovery path.
Are Meta’s benchmark results independently verified?
Not by this site. Meta publishes evaluation charts and a methodology report. Independent benchmark projects provide useful context, but scores remain specific to model, harness, effort, environment, date, and cost.