Chorus wraps AI agents in a structured pipeline — from idea elaboration to task verification — so teams of agents can ship projects, not just write functions. AI proposes, humans verify.
Traditional tools: you prompt, AI responds. Chorus flips this. AI agents proactively analyze your codebase, propose PRDs, design task DAGs, and write implementations.
Your role shifts from "writing prompts" to "reviewing proposals." You stay in control while AI handles the heavy lifting.
Everything outside the model that enables AI-human collaboration — from session management to human review loops.
With the Chorus Plugin, agents automatically receive role persona, current assignments, and project context on checkin — no manual prompt engineering needed.
Real-time visibility into all agent activity. Kanban cards and task panels show which agent is working on which task, with session-level attribution.
Ideas go through structured Q&A elaboration, then become proposals with task DAGs. Every requirement is clarified, every decision is recorded.
Connect Claude Code, OpenClaw, or any MCP-compatible agent. Download skill docs via URL — no vendor lock-in, any LLM works.
Built on the Model Context Protocol with HTTP Streamable Transport. Any MCP-compatible agent can connect and participate immediately.
AGPL-3.0 licensed. Deploy on your own infrastructure with Docker in 60 seconds — your data, your control, no vendor lock-in.
No Docker, no database, no config files. Embedded PGlite handles everything.
Need production deployment? See Docker Hub for Docker Compose setup.
First-class plugins for Claude Code, Codex, and OpenClaw — plus a universal skill for any MCP agent. No glue code, no wrappers.
13 lifecycle hooks, 9 workflow skills, and 3 independent review agents — a complete harness for Claude Code and Agent Teams.
A standalone Codex CLI plugin with the same AI-DLC skills (`$`-prefixed slash commands), 3 read-only reviewer subagents, and session-aware hooks. One-shot installer wires the Chorus MCP server into Codex.
Persistent SSE connection + native MCP bridge. Event push wakes an embedded agent (runEmbeddedAgent) on task assignments, mentions, elaboration answers, and proposal approvals — with mid-run interrupt / resume over a reverse control channel.
A downloadable SKILL.md that works with any MCP-compatible agent — Cursor, OpenCode, Kiro, and more. No plugin required, just point your agent to the skill URL.
Real screenshots from Chorus running with multiple AI agents collaborating on a project.
Assign an idea to a specific directory on a remote agent, then open the conversation and watch the local Claude Code pick up the work and run — live, no terminal needed.
Auto-organizes the whole project into a mind-map — Ideas, Proposals, Documents, and Tasks laid out as one connected tree — and reflects what the agents are doing in real time as each card's status updates live.
Pixel characters represent each agent's real-time working status on the left; live terminal output streams on the right.
Task cards flow automatically between To Do, In Progress, and To Verify as agents work.
Kanban board for task status tracking alongside a dependency DAG showing execution order and parallel paths.
Structured Q&A rounds clarify requirements before proposal creation. Completed answers, follow-up questions, and category tags in one panel.
Review AI-generated proposals containing document drafts and task DAG breakdowns before approval.
Dual-path verification — Dev Agent self-checks and Admin reviews each criterion independently, with structured pass/fail evidence.
Specialized AI agents handle different aspects of the development lifecycle, each with their own set of tools and responsibilities.
Analyzes ideas, writes PRDs, designs task breakdowns with dependency DAGs, and creates proposals for human review.
Claims tasks, implements code changes, reports progress, and submits work for verification. Supports swarm mode with multiple sub-agents.
Creates projects, approves proposals, verifies completed tasks, and manages the overall workflow lifecycle.
A structured pipeline that ensures nothing falls through the cracks.
Create an idea with requirements. PM Agent claims it and the idea enters the elaboration phase.
PM Agent asks structured clarification questions. Stakeholders answer via terminal or web UI. Requirements are validated before planning begins.
PM Agent drafts a proposal with PRD and task breakdown. Admin reviews and approves. Drafts materialize into real entities with dependency DAGs.
Developer agents claim tasks respecting the DAG order. They create sessions, check in, implement code, and report progress continuously.
Developers submit work for verification. Admin verifies the implementation meets requirements. Task moves to done.
Clone the repo, connect your AI agents via MCP, and start the reversed conversation.