Muse Code
Muse Code is Meta’s terminal coding agent, powered by Muse Spark 1.2, for handling complex software engineering tasks across large repositories. The agent can plan changes, write code, validate results, and coordinate multiple persistent subagents during development sessions. Muse Code uses async background agents that stay active throughout a session to reduce repeated information gathering and help complete multi-step tasks with less steering. Its runtime uses a local event log that records model calls, tool runs, approvals, and edits so sessions can be replayed and resumed after failures. Muse Code includes bundled skills such as /plan for approval-gated planning, /grill for stress-testing plans, and /goal for working toward completion. Built for AI developers and software teams, Muse Code helps automate coding workflows, long-running engineering tasks, debugging, and repository-level development.
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Muse Spark 1.1
Muse Spark 1.1 is a multimodal reasoning model from Meta Superintelligence Labs built for agentic tasks, coding, computer use, tool use, and multimodal understanding. The model improves on the original Muse Spark with stronger performance in planning, orchestration, long-context work, coding workflows, and external app interactions. Muse Spark 1.1 can manage a 1 million token context window, remember earlier actions, retrieve important information, compact context, and delegate tasks across parallel subagents. It is designed to operate across tools, MCP servers, custom skills, browsers, native apps, scripts, images, video, PDFs, and audio-based workflows. Developers can access Muse Spark 1.1 through the new Meta Model API public preview, while users can try it in Thinking mode in the Meta AI app and on meta.ai.
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Meta Model API
Meta Model API is a new developer API for building with Muse Spark 1.1, Meta’s multimodal reasoning model built for agentic tasks, coding, tool use, computer use, and multimodal understanding. Now in public preview, it gives developers a way to access Muse Spark 1.1 through an OpenAI-compatible package, making it easier to point existing clients at the API, keep the same code structure, and set the model to muse-spark-1.1. Muse Spark 1.1 is designed for personal agentic tasks that require planning and orchestration across external apps and services, with the ability to generalize to new native tools, MCP servers, and custom skills. As a main agent, it can gather context, make a plan, and delegate execution across parallel subagents; as a subagent, it follows its role, understands available tools, and knows when to escalate back. The model can actively manage a 1 million-token context window, remember actions, retrieve information from much earlier work, and compact context.
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Amazon Bedrock AgentCore
Amazon Bedrock AgentCore enables you to deploy and operate highly capable AI agents securely at scale, offering infrastructure purpose‑built for dynamic agent workloads, powerful tools to enhance agents, and essential controls for real‑world deployment. It works with any framework and any foundation model in or outside of Amazon Bedrock, eliminating the undifferentiated heavy lifting of specialized infrastructure. AgentCore provides complete session isolation and industry‑leading support for long‑running workloads up to eight hours, with native integration to existing identity providers for seamless authentication and permission delegation. A gateway transforms APIs into agent‑ready tools with minimal code, and built‑in memory maintains context across interactions. Agents gain a secure browser runtime for complex web‑based workflows and a sandboxed code interpreter for tasks like generating visualizations.
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