Forge Code
Forge Code is a terminal-based AI pair-programmer and software-engineering agent that lets you interact with your entire codebase using natural language commands. It runs directly in your shell, integrating seamlessly with any IDE or existing workflow; you don’t need to switch editors or abandon your regular tools. Forge Code understands project files, Git history, dependencies, and environment context, so once it loads your project, it comprehends the codebase structure and can respond to prompts without repeated explanations. It offers a multi-agent architecture: a “Forge Agent” for implementing changes and real code execution, and a “Muse Agent” for planning, analyzing, and reviewing code without altering files. You can use Forge with your preferred AI provider or self-hosted LLMs, giving full control over where your code goes and how the model is run.
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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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Favur
Favur is an autonomous software-building system that takes a written statement of work and turns it into a complete, tested repository without a human steering the run. A team of agents plans, builds, reviews, tests, and ships the project on its own, while scoring its own work, catching mistakes, and steering itself back on track. Every run follows the same lifecycle, architecture, sprints, review, and tests, whether the task is small or a serious project. It first reads the ask, commits to an architecture, and records its decisions before code is written. Then it breaks the work into sprints and writes pseudocode before building. One agent writes the code, a separate reviewer checks the diff against the plan, and a tester proves the result. Agents can run on different models within the same job, allowing teams to mix models for boilerplate, judgment calls, supervision, and other roles.
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Buzz
Buzz is a self-hostable workspace where humans and AI agents build together in the same rooms, on a relay the team owns. One community brings conversations, agents, workflows, repositories, artifacts, documents, search, and automation into a single workspace with one identity model and one event log. Every message, reaction, workflow step, review approval, canvas update, huddle event, and Git action is recorded as a cryptographically signed Nostr event, giving people and processes the same audit trail. Agents are members, not bots: teams add them to channels like colleagues, give them their own keys and scoped memberships, and let them open repositories, send patches, review code, run workflows, edit canvases, orchestrate other agents, create channels, join voice huddles, and bring the right people into the work. Buzz can search project history and return answers with supporting threads, patches, runs, and approvals.
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