Pipeline-driven task management for AI coding agents — stages, dependencies, artifacts, and multi-agent collaboration
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
git clone https://github.com/keshrath/agent-tasks{
"mcpServers": {
"agent-tasks": {
"command": "node",
"args": ["/path/to/agent-tasks/dist/index.js"]
}
}
}Resumen de MCP Servers
# agent-tasks
[](LICENSE)
[](https://nodejs.org/)
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**Pipeline-driven task management for AI coding agents.** An [MCP](https://modelcontextprotocol.io/) 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.
---
| Light Theme | Dark Theme |
| -------------------------------------------------------- | ------------------------------------------------------ |
|  |  |
---
## 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.
---
## 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
---
## Quick Start
### Install from npm
```bash
npm install -g agent-tasks
```
### Or clone from source
```bash
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.):
```json
{
"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)
```bash
node dist/server.js --port 3422
```
---
## Claude Code Integration
Once configured (see [Quick Start](#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](docs/SETUP.md) for detailed integration steps.
---
## MCP Tools (8)
| Category | Tools |
| ---------------------- | ---------------------------------------------------------------------------------------------------------- |
| **Task CRUD** (4) | `task_create`, `task_get` (include subtasks/artifacts/comments), `task_list` (search, next), `task_delete` |
| **Metadata** (1) | `task_update` (title, description, priority, tags, project, assignment, dependencies) |
| **Lifecycle** (1) | `task_stage` (claim, advance, regress, complete, fail, cancel) |
| **Artifacts** (1) | `task_artifact` (general, decision, learning, comment) |
| **Config & utils** (1) | `task_config` (pipeline, session, cleanup, rules) |
See [full API reference](docs/API.md) for detailed descriptions of every tool and endpoint.
## REST API (18 endpoints)
All endpoints return JSON. Loopback only: foreign `Host`/`Origin` headers get 403, request bodies must be `application/json`, no wildcard CORS. See [full API reference](docs/API.md#rest-api-18-endpoints) for details.
```
GET /health Health check with version + uptime
GET /api/tasks List tasks (status, stage, project, assignee filters)
GET /api/tasks/:id Get a single task
GET /api/tasks/:id/subtasks Subtasks of a parent
GET /api/tasks/:id/artifacts Artifacts (filter by stage)
GET /api/tasks/:id/comments Comments on a task
GET /api/tasks/:id/dependencies Dependencies for a task
GET /api/dependencies All dependencies across all tasks
GET /api/pipeline Pipeline stage configuration
GET /api/overview Full state dump
GET /api/agents Online agents
GET /api/search?q= Full-text search
POST /api/tasks Create a new task
PUT /api/tasks/:id Update task fields
PUT /api/tasks/:id/stage Change stage (advance or regress)
POST /api/tasks/:id/comments Add a comment
POST /api/cleanup Trigger manual cleanup
```
---
## Testing
```bash
npm test # 355 tests across 13 files
npm run test:watch # Watch mode
npm run test:coverage # Coverage report
npm run check # Full CI: typecheck + lint + format + test
```
---
## Environment variables
| Variable | Default | Description |
| -------------------------- | ------------------------------- | ------------------------------------------------------------ |
| `AGENT_TASKS_DB` | `~/.agent-tasks/agent-tasks.db` | SQLite database file path |
| `AGENT_TASKS_PORT` | `3422` | Dashboard HTTP/WebSocket port |
| `AGENT_TASKS_HOST` | `127.0.0.1` | Dashboard bind address (`0.0.0.0` exposes it to the network) |
| `AGENT_TASKS_INSTRUCTIONS` | enabled | Set to `0` to disable response-embedded instructions |
| `AGENT_COMM_URL` | `http://localhost:3421` | Agent-comm REST URL for bridge notifications |
| `AGENT_KNOWLEDGE_URL` | `http://localhost:3423` | Agent-knowledge REST URL for knowledge bridge |
---
## Dependencies
**Required**: Node.js >= 20.11, better-sqlite3 (bundled)
**Optional (soft dependencies — fail-open, HTTP-only, no npm dep):**
- [agent-comm](https://github.com/keshrath/agent-comm) — Heartbeat-based task cleanup and event notifications. agent-comm tracks heartbeats → agent-tasks checks heartbeats → auto-fails tasks from dead agents. Also sends direct messages on claim/advance and posts to channels on comments/approvals. Without agent-comm, stale agent detection and notifications are skipped gracefully.
- [agent-knowledge](https://github.com/keshrath/agent-knowledge) — Knowledge persistence for task learnings and decisions. On task completion, the KnowledgeBridge pushes `learning` and `decision` artifacts to agent-knowledge via `POST /api/knowledge`. Entries are auto-indexed with embeddings, auto-linked to similar entries, and git-synced. Without agent-knowledge, artifacts stay in agent-tasks only.
---
## Documentation
- [API Reference](docs/API.md) — all 8 MCP tools, 18 REST endpoints, WebSocket protocol
- [Architecture](docs/ARCHITECTURE.md) — source structure, design principles, database schema
- [Dashboard](docs/DASHBOARD.md) — kanban board features, keyboard shortcuts, screenshots
- [Setup Guide](docs/SETUP.md) — installation, client setup (Claude Code, OpenCode, Cursor, Windsurf), hooks
- [Changelog](CHANGELOG.md)
---
## License
MIT — see [LICENSE](LICENSE)
Lo que la gente pregunta sobre agent-tasks
¿Qué es keshrath/agent-tasks?
+
keshrath/agent-tasks es mcp servers para el ecosistema de Claude AI. Pipeline-driven task management for AI coding agents — stages, dependencies, artifacts, and multi-agent collaboration Tiene 20 estrellas en GitHub y su última actualización registrada es del 2026-10-01.
¿Cómo se instala agent-tasks?
+
Puedes instalar agent-tasks clonando el repositorio (https://github.com/keshrath/agent-tasks) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
¿Es seguro usar keshrath/agent-tasks?
+
Nuestro agente de seguridad ha analizado keshrath/agent-tasks y le ha asignado un Trust Score de 95/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.
¿Quién mantiene keshrath/agent-tasks?
+
keshrath/agent-tasks es mantenido por keshrath. La última actividad registrada en GitHub es del 2026-10-01, con 0 issues abiertos.
¿Hay alternativas a agent-tasks?
+
Sí. En ClaudeWave puedes explorar mcp servers similares en /categories/mcp, ordenados por popularidad o actividad reciente.
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