agent-tasks

agent-tasks

Pipeline-driven task management for AI coding agents. An MCP server with stage-gated pipelines, multi-agent collaboration, and a real-time kanban dashboard. Tasks flow through configurable stages — backlog, spec, plan, implement, test, review, done — with dependency tracking, approval workflows, artifact versioning, and threaded comments.

Built for AI coding agents (Claude Code, Codex CLI, Gemini CLI, Aider) but works equally well with any MCP client, REST consumer, or WebSocket listener.

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Why agent-tasks?

When you run multiple AI agents on the same codebase, they need a shared task pipeline — not just a flat todo list. They need stages, dependencies, approvals, and visibility.

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Features

• Pipeline stages — configurable per project: backlog > spec > plan > implement > test > review > done
• Task dependencies — DAG with automatic cycle detection; blocks advancement until resolved
• Approval workflows — stage-gated approve/reject with auto-regress on rejection
• Multi-agent collaboration — roles (collaborator, reviewer, watcher), claiming, assignment
• Subtask hierarchies — parent/child task trees with progress tracking
• Threaded comments — async discussions between agents on any task
• Artifact versioning — per-stage document attachments with automatic versioning and diff viewer
• Full-text search — FTS5 search across task titles and descriptions
• Real-time kanban dashboard — drag-and-drop, side panel, inline creation, dark/light theme
• 3 transport layers — MCP (stdio), REST API (HTTP), WebSocket (real-time events)
• TodoWrite bridge — intercepts Claude Code's built-in TodoWrite and syncs to the pipeline
• Stage gates — configurable per-project gates with per-stage rules: require named artifacts, minimum artifact counts, comments, or approvals before advancing
• Decisions log — structured decision artifacts (chose X over Y because Z) via task_artifact(type: "decision")
• Learnings propagation — task_artifact(type: "learning") captures insights (technique, pitfall, decision, pattern); auto-propagated to parent and sibling tasks on completion
• Agent affinity — task_list(next: true) prefers routing tasks to agents with related history (parent, dependency, project) as a tie-breaker
• Heartbeat-based cleanup — auto-fails tasks from dead agents using agent-comm heartbeat data
• Task cleanup hooks — auto-fails orphaned tasks on session stop and cleans up stale tasks on session start
• Agent bridge — notifies connected agents on task events (claim, advance, comment, approval)
• Knowledge bridge — auto-pushes learning and decision artifacts to agent-knowledge on task completion, with embedding indexing and auto-linking

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Quick Start

Install from npm

npm install -g agent-tasks

Or clone from source

git clone https://github.com/keshrath/agent-tasks.git
cd agent-tasks
npm install
npm run build

Option 1: MCP server (for AI agents)

Add to your MCP client config (Claude Code, Cline, etc.):

{
  "mcpServers": {
    "agent-tasks": {
      "command": "npx",
      "args": ["agent-tasks"]
    }
  }
}

The dashboard auto-starts at http://localhost:3422 on the first MCP connection.

Option 2: Standalone server (for REST/WebSocket clients)

node dist/server.js --port 3422

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Claude Code Integration

Once configured (see Quick Start above), Claude Code can use all 8 MCP tools directly — creating tasks, advancing stages, adding artifacts, commenting, and more. See the Setup Guide for detailed integration steps.

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MCP Tools (8)