Meta Launches Muse Code AI Coding Agent to Challenge OpenAI Codex and Anthropic's Claude Code

Meta launches Muse Code, an AI agent for large code bases

Meta, considered a bit of a straggler in the AI harnesses realm, is making strides to catch up. This week, the company released a new terminal coding agent aimed at programmers looking for assistance with complex tasks across large software code bases. Muse Code, which is currently available in beta, can accomplish "complete software engineering tasks across large repos," Meta CEO Mark Zuckerberg said in a social media post on Wednesday. Those tasks include "planning changes, writing code, validating the results," he added. Code, which can be installed with a single command, is powered by Meta's previously released coding model, Muse Spark. It handles large projects by launching its own agents, which then work simultaneously. "When a job is big enough, it fans out to separate sub-agents working in parallel in isolated worktrees," Zuckerberg explained. "Your working copy is never touched. In testing we had it build six features for a game simultaneously with no collisions." The move attempts to position Meta more competitively, and more affordably, with AI Lab peers like OpenAI and its coding agent Codex, and Anthropic with Claude Code. "We think that for a lot of workflows and a lot of use cases, this can be an incredibly good option, especially from a cost perspective," Alexandr Wang, Meta's AI chief, who leads Meta Superintelligence Labs, told the Wall Street Journal. Meta has been attempting to grow its AI presence by pouring money into development. In June, it expanded beyond its core focus of using AI to support its advertising business and entered the enterprise AI market with an agent aimed customer service and support.

Meta Is Challenging Claude Code and Codex With New Muse Code - CNET

Katelyn is a reporter with CNET covering artificial intelligence, including chatbots, image and video generators.... Read full bio Meta is making strides to catch up to AI heavyweights like OpenAI and Anthropic. The company announced on Wednesday a new agentic coding tool and the new AI model that powers it. The terminal coding tool is called Muse Code, and it's powered by Meta Muse Spark 1.2, the latest update to Meta's revamped AI model family. Meta says it can run agents asynchronously in the background and execute complex coding tasks. Spark 1.2 has coding-specific improvements from last month's Spark 1.1, particularly for code generation, debugging and codebase understanding. Agentic coding uses AI tools called agents (or bots or, sometimes, claws, depending on your vernacular preference) to complete tasks autonomously. It's accelerated the trend of vibe coding, where you use agentic AI to create new websites and apps, with little coding experience required. Claude Code's release in 2025 was the first major tool to do so, followed by OpenAI's version called Codex. Right now, Muse Code and the model behind it are only available to developers on Meta's developer website and OpenRouter, a site that hosts AI models. You'll need an API key to access them. Anyone can technically buy an API key, but the technical nature of the launch means it's likely best suited for software developers and engineers. Meta has a pay-as-you-go system; Muse Spark 1.2 has similar pricing as with Spark 1.1, with $1.25 per million token input and $4.25 per million token output. But be careful: Companies that were "token-maxxing" this year quickly learned that heavy AI use comes with an equally hefty price tag.

Meta launches new AI coding tool powered by Muse Spark 1.2

Aug 5 (Reuters) - Meta Platforms (META.O), opens new tab on Wednesday launched Muse Code, a coding tool powered by its latest AI model, Muse Spark 1.2, designed to help developers write and debug software. The launch highlights the social media giant's push into AI-powered coding tools, where it competes with Anthropic's Claude and OpenAI's Codex amid a broader race to monetize AI assistants. Muse Code can write ⁠code and verify results, and has been trained to handle long, complex coding projects while also running multiple sub-agents simultaneously to speed up difficult tasks, the company said. Meta also said it trained Muse Spark 1.2 and Muse Code together to operate smoothly as a pair. Launched in beta, Muse Code keeps a log of its actions, ⁠so it can pick up where it left off after a crash rather than start over, the company said. Developers can access Muse Code through a pay-as-you-go plan, ⁠with the standard tier priced at $1.25 per million input tokens and $4.25 per million output tokens. The launch comes nearly a ⁠month after Meta introduced Muse Spark 1.1 for developer testing, with the earlier model used to generate and ⁠evaluate difficult coding challenges that helped improve Muse Spark 1.2's ability to follow complex instructions. Reporting by Anhata Rooprai in Bengaluru; Editing by Diti Pujara Our Standards: The Thomson Reuters Trust Principles., opens new tab

Meta introduces Muse Code, its take on a coding agent - Engadget

The terminal-based coding tool is powered by a new AI model, Muse Spark 1.2. Meta has announced an early beta of Muse Code, a new coding agent meant to compete with Anthropic's Claude Code and OpenAI's Codex. The new terminal-based coding tool is powered by Muse Spark 1.2, a new version of Meta's AI model which offers "improvements in code generation, complex debugging, codebase understanding, and end-to-end developer workflows." Like its competition, Muse Code can handle software engineering tasks like writing code, planning changes and validating results, making it possible to build working software with text prompts. It can also manage multiple sub-agents and delegate tasks to complete work more efficiently. Meta's announcement includes sample projects like an interactive model of a photon sphere and a Plants vs. Zombies knockoff, and a demo video of the tool building a webpage based on an MP4 file. Affordability is a key way Meta appears to want to differentiate Muse Code from its competitors. The company says the tool uses the same pay-as-you-go pricing as Muse Spark by default -- $1.25 per million input tokens and $4.25 per million output tokens -- and in an interview with CNBC, Alexander Wang, Meta's Chief AI Officer, said the company would also offer "a contributor tier that gets you in at a significantly lower cost." That tier would require users to agree to provide feedback to improve the coding agent, according to The Wall Street Journal, and cost $0.10 per million input tokens and $0.20 per million output tokens. Those prices are cheaper than Anthropic's Sonnet 5 model, which typically costs $3 per million input tokens and $15 per million output tokens. Provided Muse Code works well, the difference could lead companies to choose Meta over its competitors. The same logic has already proven effective for Chinese AI companies: multiple companies switched to Chinese models like DeepSeek to avoid the growing cost of using US AI tools.

Meta debuts first AI coding agent to take on Anthropic and OpenAI

Meta is rolling out its first coding agent called Muse Code as the company tries to challenge leading AI labs Anthropic and OpenAI. Muse Code is the latest major release from AI chief Alexandr Wang, who leads Meta Superintelligence Labs and oversees foundation model development. Wang joined in June of last year as the centerpiece of CEO Mark Zuckerberg's effort to revamp his company's flailing artificial intelligence strategy. The new tool, like Anthropic's Claude and OpenAI's Codex assistants, makes it easier for people to build apps within a single user interface while managing fleets of AI-powered digital agents that can help underpin the software development process. "Muse Code is a terminal coding agent, like many of the other coding agents on the market," Wang said. "So you can install it with one command and then use it to take on complete software engineering tasks across a wide variety of use cases, planning changes, writing code, validating the results." Meta's coding agent, available in a so-called beta, or preview version, works alongside Meta's latest AI model, Muse Spark 1.2. Similar to the MuseSpark 1.1 model that Meta released in July, the newest version specializes in coding. The latest Muse Spark model differs from the prior version, however, because it was developed and trained alongside Muse Code, which Wang said improves the overall coding performance. Developers can access Muse Code via pay-as-you-go option that Wang said contains similar API pricing as the Muse Spark 1.1 release, in which the company charges $1.25 per million tokens in input and $4.25 per million tokens of output. Wang added that Muse Code also has "a contributor tier that gets you in at a significantly lower cost," which he characterizes as being "more than 10 times cheaper than than even the pay-as-you-go tier." "And as part of that, you opt-in to help improve the model in an industry standard way in-line with all the other coding agents," Wang said.

Meta finally enters the AI coding space with a surprisingly competitive agent

Meta is finally entering the AI coding agent space dominated by the likes of Anthropic's Claude Code and OpenAI's Codex. The social media giant has released a Muse Code beta that it claims can be affordable while matching rivals in performance. The terminal-based agent is built on the Muse Spark 1.2 model and handles "complete" tasks even across large code repositories, according to Meta CEO Mark Zuckerberg. It can plan changes, write code, and validate results. Background agents build context, and larger projects are assigned to isolated "sub-agents" that won't interfere with each other. Every model call and tool use is logged, and the agent will resume its place in the event of a crash. The hook is the low price of entry, AI chief Alexandr Wang tells AI in an interview with CNBC. There's still a pay-as-you-go choice that costs $1.25 per million input tokens and $4.25 for a million output tokens, but you can also use a contributor tier that Wang claims is "more than 10 times cheaper." It requires that you help improve the model like you do with other agents, but might be better if you're just starting out or are part of a larger team. Meta is now accepting requests to avoid data retention, helping companies that don't want their potentially sensitive information used to train AI models. How does Muse Code compare to Claude Code and Codex? It works best within Meta's universe Muse Code is a coding harness that lets you manage multiple models, and can be used for third-party platforms. However, Wang explains that Muse Spark 1.2 will work best as it was developed in tandem with the agent. In those conditions, Meta claims Muse Spark 1.2 performs on par with or better than the competition. It edges out GPT 5.6 Terra (there's no mention of Sol) in the Terminal-Bench 2.1 software engineering benchmark, and comes close to Claude Opus 5. In the long-horizon DeepSWE 1.1 test, it falls behind both but is still ahead of X.ai's Grok and Google's Gemini 3.6 Flash. With Meta's in-house coding benchmark, its model sits between Claude Opus 5 and GPT 5.6 Terra. You're not choosing Muse Code for raw speed at this stage. Rather, it's that Meta is already competitive with peers in coding four months after releasing the first Muse Spark model. Its pricing strategy could also be appealing in the right circumstances, although you could quickly find yourself looking at subscriptions with frequent AI coding.

Meta launches Muse Code AI coding agent for macOS and Linux

Meta is entering the AI coding-agent race with Muse Code, a new terminal-based tool now available in beta for macOS and Linux. Muse Code arrives alongside Meta's new Muse Spark 1.2 model Powered by the new Muse Spark 1.2 model, it is designed to handle complex software engineering work across large repositories. For Mac users, Muse Code installation is a one-line command. Unlike ChatGPT Codex and Claude Code, there's currently no dedicated app for Muse Code. Meta says Muse Code can plan changes, write code, validate its work, and coordinate persistent background agents that remain active throughout a session. Its local event log records model calls, tool use, approvals, and edits, allowing work to resume after a crash. Built-in commands can also generate a plan, stress-test it, or continue working toward a specified goal. Muse Spark 1.2 is described as a coding-focused update with gains in code generation, debugging, codebase understanding, and long-running developer workflows. The model is available through both Muse Code and the Meta Model API with expanded global access. The release follows Meta's July launch of Muse Spark 1.1, which upgraded the model's agentic and multimodal abilities and introduced Meta's paid API service for developers. Before that, Meta replaced Llama with the original Muse Spark in April, then used it to expand the Meta AI app and power new image-generation features across Meta's apps.

Meta enters the AI coding wars with Muse Spark 1.2 and Muse Code with persistent async background agents

Meta today released Muse Code, a terminal-based AI coding agent now in beta, alongside Muse Spark 1.2, a coding-focused update to its Muse Spark family of frontier models -- a one-two punch that puts the company in direct competition with Anthropic's Claude Code, OpenAI's Codex, and the growing field of agentic coding harnesses that have rapidly become the primary way many professional developers ship software. "Releasing Muse Code in beta today," Meta CEO Mark Zuckerberg wrote in a post on rival social network X (under his longtime handle @finkd). "It's a terminal coding agent that takes on complete software engineering tasks across large repos: planning changes, writing code, validating the results." The launch marks Meta's most serious entry yet into a category it has largely watched from the sidelines. While Anthropic and OpenAI turned their coding agents into flagship products -- and startups like Cursor built billion-dollar businesses on the workflow -- Meta's developer story long centered on Llama, the open-weight model family it gave away to the tune of more than a billion downloads. Muse Code changes that in more ways than one: it's a full harness, installable on macOS or Linux with a single curl command, co-trained with the model that powers it -- and, like the Muse Spark models behind it, entirely proprietary. Developers and prospective users can install it now on their Terminal using the following one-line command -- but be warned, if that's you, you'll need to log in with a Meta account and provide billing details first in order to begin: Persistent background agents and parallel worktrees Muse Code's headline architectural bet is what Meta calls async background agents. Rather than spawning helper agents fresh for each task -- the pattern most rival harnesses use -- Muse Code keeps a set of specialized background agents alive for the entire session. According to Meta's blog post, these agents "remain active throughout each session, rather than being spawned for individual tasks, helping avoid redundant information gathering," carrying out next steps on their own and choosing when to report back to the main agent. The practical pitch is less latency and less babysitting: an agent that already knows the repository doesn't have to re-explore it every time the developer asks for something new. When a job is large enough, Muse Code fans out to separate sub-agents working in parallel, each in its own isolated git worktree, so the developer's working copy is never touched. "In testing we had it build six features for a game simultaneously with no collisions," Zuckerberg wrote on X. Worktree isolation and parallel sub-agents exist in competing tools, but Meta is leaning on the combination of persistence plus parallelism as its differentiator. The second notable design choice is auditability. Every model call, tool run, approval, and edit is appended to a local event log before it executes -- a single source of truth that Meta says makes the runtime "replay-exact and restart-safe." If Muse Code crashes 20 hours into a long-running task, it resumes precisely where it stopped, with no lost work and no re-prompting. For engineering leaders who have been burned by opaque agent runs, a complete local audit trail may prove to be the feature that matters most in enterprise evaluations. Muse Code also ships with bundled "skills" that will look familiar to users of rival tools: /plan turns a task into an approval-gated plan, /grill stress-tests that plan until it holds up, and /goal drives the agent toward completion of a stated objective. Muse Spark 1.2: co-trained with its own harness Under the hood is Muse Spark 1.2, which Meta describes as a coding-focused update to Muse Spark 1.1 with "significantly scaled up training compute on coding tasks" and broader training environment diversity, improving code generation, complex debugging, and codebase understanding while maintaining general agentic capability. The update lands squarely on the Muse family's weakest flank. When the original Muse Spark debuted in April, it vaulted Meta back into the top five on frontier reasoning and vision benchmarks -- but trailed on the agentic coding evaluations that matter most to this market, scoring 77.4 on SWE-Bench Verified against Claude Opus 4.6's 80.8 and Gemini 3.1 Pro's 80.6, and lagging well behind GPT-5.4 on GDPval's measure of long-horizon work tasks. Four months later, a coding-specialized checkpoint paired with a purpose-built harness reads as Meta's direct answer to that gap. Two training details stand out. First, Meta co-trained the model with Muse Code itself, using rejection-sampled harness trajectories and recipe optimizations for goals, context compaction, and sub-agents -- meaning the model was explicitly tuned to perform best inside this particular tool. That mirrors an industry-wide shift away from treating models and harnesses as separable products. Second, Meta used a self-improvement loop: Muse Spark 1.1 generated challenging coding environments and instruction-following templates, then graded candidate solutions against those requirements, producing a scalable training dataset for its successor. Meta credits the loop with making 1.2 measurably better at following complex instructions. Meta published benchmark charts comparing Muse Spark 1.2 against other coding models on Terminal-Bench 2.1, DeepSWE 1.1, and an internal Meta coding benchmark, pointing readers to a separate methodology report for details -- though the company did not headline specific scores in the announcement itself, a notable omission in a field where rivals trumpet leaderboard placement. The company's most striking demonstration is a long-horizon case study: Meta pointed Muse Spark 1.2 at GPU kernel optimization and let it run for more than 1,000 tool calls over up to 24 hours on NVIDIA Hopper hardware. Working in Triton and barred from simply wrapping existing third-party kernel libraries, the agent wrote, compiled, and profiled its way to what Meta calls "substantial improvements" over baseline implementations of KDA and MLA kernels -- including genuinely non-obvious optimizations like re-centering gated cumulative decay at a chunk midpoint. "It kept finding substantial improvements well beyond the initial exploration phase," Zuckerberg wrote. Sustained improvement over a 24-hour autonomous run, if it holds up outside Meta's demos, addresses one of the most persistent criticisms of coding agents: that they plateau or drift once past their initial burst of progress. Your data for a discount? The pricing structure may be the most consequential -- and most scrutinized -- part of the launch. Meta is offering Muse Spark 1.2 through its Meta Model API in two tiers. The standard tier is priced at $1.25 per million input tokens and $4.25 per million output tokens (with cached input at $0.15), and Meta commits that prompts and completions on this tier are not used to train its models. There is no long-context premium, and rate limits run to 3,000 requests and 4 million tokens per minute, per team. It's about mid-range price, compared to other leading AI models available over API. The contributor tier is where Meta's strategy diverges sharply from its rivals: $0.10 per million input tokens and $0.20 per million output tokens -- roughly 12x and 21x cheaper than standard, respectively, with cached input at a near-free $0.002 -- in exchange for explicit permission to use your prompts and completions to train future Meta models. It's the cheapest available on the market, but you pay with your data -- as described below. This is the tier Zuckerberg is steering new users toward: "It's easy and low-cost to get started," he wrote. "Install Muse Code with one line and you can start on our contributor tier." In VentureBeat's own testing on a Mac mini, the one-line installer worked as advertised -- a 97 MB download and a sign-in -- but the agent stopped short of running anything, reporting that no models were visible and that payment was "required to finish setting up your account." In other words, even the heavily discounted contributor tier requires a payment method on file before Muse Code will do any work: low-cost is accurate, but free is not. Meta frames the contributor tier as lowering the barrier for prototyping and experimentation "where training on your data is acceptable." But it also means the default on-ramp for Muse Code sends developers' code and prompts into Meta's training pipeline -- a tradeoff enterprises with proprietary codebases will need to consciously opt out of by moving to standard pricing. The contributor tier also carries much tighter rate limits (60 requests per minute versus 3,000), a clear signal it's aimed at individuals and small experiments rather than production workloads. The approach is classically Meta: subsidize access, harvest data at scale, and use it to close the gap with the frontier. Zuckerberg made no secret of the ambition, calling Muse Spark 1.2 "our next step as we push toward frontier, with larger, more capable models on the way." However, for developers and enterprises who want or are required legally to keep their code secure, the tradeoff may not be one they're willing or able to make. No Llama in sight What today's announcement conspicuously lacks is any mention of open source -- a striking omission from the company that spent three years positioning itself as the standard-bearer of open AI. From the original LLaMA's debut in February 2023 -- whose weights famously leaked onto 4chan within weeks, inadvertently kickstarting the movement to run capable models on consumer hardware -- through Llama 2's commercially usable license, the coding-specialized Code Llama, and the 405-billion-parameter Llama 3.1, which Zuckerberg launched in July 2024 with a manifesto titled "Open Source AI Is the Path Forward," Meta's entire pitch to developers was that frontier-class weights should be free to download, self-host, and fine-tune. The strategy worked: by early 2026, the Llama family had been downloaded roughly 1.2 billion times, averaging about a million downloads a day, with self-hosting offering enterprises cost reductions VentureBeat has previously reported at as much as 88% versus proprietary API providers. Then came the unraveling. Llama 4 debuted in April 2025 to mixed reviews and, eventually, admissions that its benchmark results had been fudged -- while Chinese open-weight rivals from DeepSeek, Alibaba, and Zhipu AI surged to account for some 41% of downloads on Hugging Face by late 2025, eroding Llama's claim to leadership of the very movement it started. The rocky rollout spurred Zuckerberg's summer 2025 overhaul of Meta's AI operations into Meta Superintelligence Labs (MSL), with Scale AI co-founder Alexandr Wang recruited as chief AI officer. The Llama era effectively ended this past April 8, when MSL shipped the original Muse Spark -- "the most powerful model that meta has released," in Wang's words -- as Meta's first proprietary model: cloud-only, with no downloadable weights and no self-hosting, initially confined to Meta's apps and a private API preview. Asked directly at the time whether Llama development would continue, a Meta spokesperson told VentureBeat only that "our current Llama models will continue to be available as open source" -- pointedly silent on future ones. Wang, for his part, said bigger models were already in development "with plans to open-source future versions" -- but four months on, today's release does nothing to advance that promise: no weights, no license, and neither the blog post nor Zuckerberg's thread so much as uses the word "open." The reversal is all the sharper because Meta's rivals have been moving in the opposite direction. OpenAI released its Codex CLI as open source under the permissive, enterprise-friendly Apache 2.0 license and followed with its gpt-oss open-weight models; Google's Gemini CLI harness is likewise Apache-licensed. With Muse Code, Meta lands closest to the posture of Anthropic -- whose Claude Code remains proprietary -- while the company that once argued open source was the path forward now asks developers to pay per token for a model they cannot inspect, or to subsidize that access with their own data. Seen in that light, the contributor tier reads as the successor to the Llama strategy itself: the ecosystem flywheel is no longer free weights in exchange for mindshare, but cheap tokens in exchange for training data. Why it matters Terminal coding agents have become the fastest-growing surface in enterprise AI, and until today the category has effectively been a two-horse race between Anthropic and OpenAI, with Google and a crowd of startups in pursuit. Meta's entry brings a genuinely different architecture (persistent background agents, an append-only local event log), a credible long-horizon demo, and an aggressive pricing wedge. The open questions are the ones benchmarks charts can't answer: whether Muse Spark 1.2 actually matches Claude and GPT-class models on real-world repositories, whether developers trust Meta with their code, and whether the contributor tier's discount is enough to make them stop asking. Muse Code is available in beta today; Muse Spark 1.2 is live in the Meta Model API with expanded global access.

Meta Debuts AI Coding Agent Muse: Here's How It Compares to Claude Code and Codex

On Meta's own charts, Muse Spark 1.2 trails Anthropic's Opus 5 on every coding benchmark shown, while beating OpenAI's Codex and Google's Antigravity on most. Meta is the latest tech giant to ship a coding agent, racing to compete with leading AI behemoths Anthropic and OpenAI. "We're excited to release Muse Code (beta), a terminal coding agent powered by Muse Spark 1.2, our newest model," the company wrote in an official announcement. "This marks our next step toward the frontier, with larger and much more capable models on the way." As an agentic coding tool, Muse Code is built for software engineering across large repositories. Per Meta, it "takes on complex software engineering tasks across large repositories: planning changes, writing code, and validating the results. It can coordinate multiple persistent subagents for each task, solving difficult problems faster, more accurately, and with less intervention." The detail that stands out is the runtime. Muse Code logs every model call, tool run, approval, and edit to a local event log that acts as a single source of truth. "This single source of truth makes the runtime replay-exact and restart-safe: after a crash, the agent can resume precisely where it stopped," Meta said. For long-running jobs, that's the feature that matters more than raw speed -- and it's the part competitors haven't made a selling point. It also ships with default skills. The "/plan" command turns a task into an approval-gated plan, while "/grill" stress-tests that plan until it holds up and "/goal" works toward successful completion of the objective similar to what Hermes does. Meta said it co-trained Muse Spark 1.2 with Muse Code so the core LLM and the agent work together in synergy. The benchmarks, and the catch Muse Spark 1.2 is a coding-focused update to Muse Spark 1.1. Meta said it "significantly scaled up training compute on coding tasks while expanding training environment diversity, delivering improvements in code generation, complex debugging, and end-to-end developer workflows." The charts tell a clear story. On Terminal-Bench 2.1, Muse Spark 1.2 with Muse Code scored 82.9%, behind Claude Code on Opus 5 at 86.7% but ahead of GPT-5.6 Terra on Codex (81.8%) and Grok Build (81.6%). DeepSWE 1.1, which measures agentic coding capabilities, was closer: 59.3% for Muse versus 65.0% for Opus 5 and 64.8% for Codex. On Meta's internal coding bench, Muse hit 70.6% to Opus 5's 79.4%. The speedup charts flip the order. Over 1,000-plus tool calls, Opus 5 posted the biggest gain versus baseline (about 74-75%), with Muse Spark 1.2 mid-pack at roughly 61-69% depending on the run. Meta's point is that the agent keeps improving as tool calls accumulate, the behavior you want from a long-horizon coder. The most interesting demos are long-horizon and multimodal. In stress testing, Meta said Muse Code "iteratively optimized GPU kernels over 1,000+ tool calls (up to 24 hours) on Nvidia Hopper GPUs." That means it was able to improve over time. There's also a visual-coding angle. In one demo, a user drops a fly-through video of a house into the terminal as an mp4, and Muse Code "interprets the video and produces a visually rich website with booking capabilities." Reading raw video into a working web app is the multimodal pitch Meta has been making across the Muse line. See the launch thread: The field is already crowded That said, Meta is late to the fight. OpenAI's Codex already runs parallel cloud agents; DeepSeek has built its own rival to Claude Code and agentic tools like Hermes or OpenClaw are already good substitutes with more capabilities. Muse Code's edge is the crash-safe runtime and the subagent design, not benchmark supremacy. The risk is the usual one for agentic coding: an agent that resumes after a crash and keeps calling tools for 24 hours is powerful and unpredictable. Meta is betting developers want that autonomy, and it's shipping now. Muse Code is available for testing upon installation entering this command: curl -fsSL https://dev.meta.ai/install.sh | bash

Meta Releases Coding Agent in Beta Amid Pressure to Monetize AI | PYMNTS.com

"Releasing Muse Code in beta today," Meta Founder, Chairman and CEO Mark Zuckerberg said in a Wednesday (Aug. 5) post on X. "It's a terminal coding agent that takes on complete software engineering tasks across large repos: planning changes, writing code, validating the results. Powered by Muse Spark 1.2, a coding-focused model update." In replies to his initial post, Zuckerberg said Muse Code runs specialized background agents that build up context over time instead of starting from scratch on each task; keeps a local event log so that if it crashes mid-task, it picks up where it left off; can be installed with one line; and allows users to start on a contributor tier at low cost. "Muse Spark 1.2 is our next step as we push toward frontier, with larger, more capable models on the way," Zuckerberg said in another reply. "Install it, use it, tell us what you think." Meta introduced Muse Spark in April, saying that it would be the first in a series of new AI models and that it "excels at visual coding," allowing users to create custom websites and mini-games straight from a prompt. It was reported in June that Meta was facing substantial pressure to prove it can monetize its AI tools such as its Muse Spark model and to provide meaningful growth to justify its massive spending on AI. Other AI companies have also been working to enhance their offerings in AI coding. OpenAI announced in June that it plans to expand the capabilities of its AI coding agent, Codex, by acquiring Ona, a provider of secure cloud execution and orchestration technology. OpenAI said that the addition of Ona's technology will allow Codex users to delegate work that may take hours or days to the coding agent without being tied to a single device or active session. It was reported Wednesday that Google is in talks with AI coding startup Mechanize to hire some of Mechanize's talent and sign a non-exclusive licensing agreement for the company's technology. About a year earlier, in July 2025, it was reported that Google successfully recruited several top executives and researchers from AI code generation startup Windsurf. For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.

Meta releases Muse Code coding agent in beta By Investing.com

Investing.com -- Meta Platforms (NASDAQ:META) released Muse Code in beta on Wednesday, a terminal-based coding agent designed to handle complete software engineering tasks across large repositories. The tool plans changes, writes code, and validates results. Meta powers the agent with Muse Spark 1.2, an updated model focused on coding tasks. Muse Code operates specialized background agents that remain active throughout a user's session, building context over time rather than restarting with each new task. When a task reaches sufficient size, the system deploys separate sub-agents that work in parallel within isolated worktrees, leaving the working copy untouched. During testing, Meta ran the system to build six features for a game at the same time without conflicts. The company said users can install Muse Code with a single line of code and begin using it on a contributor tier. Meta described the pricing as low-cost for initial use. This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.