TaskSync
Human-in-the-loop feedback for AI coding agents. Pause your agent mid-task, give it new directions, attach screenshots, and keep it on track — without restarting the conversation.
README
TaskSync
Human-in-the-loop feedback for AI coding agents. Pause your agent mid-task, give it new directions, attach screenshots, and keep it on track — without restarting the conversation.
Reply from anywhere. Enable Remote Mode and get Telegram notifications when your agent needs input. Tap a quick-reply button or type a response from your phone — the agent picks up instantly.
Quick Start
npx tasksync-mcp-http
Server starts on port 3011 (MCP) and 3456 (web UI). Open http://localhost:3456 to see the feedback dashboard.
OpenCode Plugin (Recommended)
The opencode-tasksync plugin is the best way to use TaskSync. Add one line to your OpenCode config:
{
"plugin": ["opencode-tasksync"]
}
What the plugin gives you that raw MCP can't:
- Zero-config agent behavior — A
daemonagent is injected automatically with the complete feedback loop protocol. No prompt pasting, no manual setup. - Augment any agent — Add feedback loops to your existing
coder,ask,buildagents with one config line. They start callingget_feedbackbetween tasks. - Unbreakable connections — SSE transport with 30s keepalives and automatic reconnection (exponential backoff up to 15s). Server restarts, network blips — the agent never notices.
- Native image support — Attached images appear directly in the LLM conversation via OpenCode's
tool.execute.afterhook. No temp files, no workarounds. - Remote Mode (OpenCode exclusive) — The plugin captures the agent's latest response and forwards it to Telegram. You see what the agent is asking and can reply without touching your laptop.
- FYI status updates — When the agent works for 30+ seconds without asking for feedback, you get a status update on Telegram so you know it's still making progress.
How the plugin makes this possible: It hooks into OpenCode's event system — experimental.text.complete for capturing agent context, tool.execute.after for native image injection, and the config hook for automatic agent/prompt injection. The SSE connection lives inside the tool's execute() function, which is a regular async function that can do anything before returning a string. All of this happens transparently — agents just call get_feedback and get back your response.
See the OpenCode Plugin Guide for configuration, agent augmentation, and remote mode setup.
MCP Server (Universal)
For VS Code Copilot, Claude Desktop, or any MCP-compatible client — connect directly to the MCP endpoint.
Add to .vscode/mcp.json:
{
"servers": {
"tasksync": {
"url": "http://localhost:3011/mcp"
}
}
}
Or for OpenCode in MCP mode (opencode.json):
{
"mcp": {
"tasksync": {
"type": "remote",
"url": "http://localhost:3011/mcp"
}
}
}
MCP gives you the tasksync_get_feedback tool with full image support (ImageContent blocks). You'll need to provide the daemon agent prompt manually — see prompts/ for ready-to-use templates.
How It Works
- Agent calls
get_feedbackand blocks - You see the waiting session in the web UI at
http://localhost:3456 - Type your feedback, attach images if needed, and submit
- Agent receives your response and continues working
- Repeat — the agent stays in a feedback loop until you tell it to stop
Feedback UI
The web dashboard at http://localhost:3456 provides:
- Multi-session support — Route feedback to different agent sessions
- Image attachments — Paste, drag-drop, or file-pick images
- Markdown toolbar — Bold, italic, code, lists, headings with keyboard shortcuts
- Live status — See which sessions are waiting, idle, or have queued feedback
- Desktop notifications — Get alerted when an agent is waiting
- Remote mode toggle — Enable/disable Telegram notifications per session
- Session management — Rename, prune stale, delete sessions
See Feedback UI Guide for details.
Remote Mode (Telegram)
Get notified on Telegram when agents are waiting for feedback, and reply directly from your phone.
Setup
- Create a Telegram bot via @BotFather and copy the token
- Set the token:
# .env file (recommended) TASKSYNC_TELEGRAM_BOT_TOKEN=your-bot-token-here # or CLI flag npx tasksync-mcp-http --telegram-token=your-bot-token-here - Start the server, then send
/startto your bot in Telegram - Enable remote mode per session via the web UI toggle
When an agent calls get_feedback, you'll receive a Telegram message with the agent's question and quick-reply buttons (Approve / Reject / Continue / Stop / Pause). Reply with text or tap a button — the feedback goes straight to the agent.
One bot per server. Telegram only allows one process to poll for updates per bot token. If you run multiple TaskSync servers, create a separate bot for each via @BotFather.
CLI Options
| Option | Default | Description |
|---|---|---|
--port=<n> |
3011 |
MCP Streamable HTTP port |
--ui-port=<n> |
3456 |
Feedback UI port |
--telegram-token=<tok> |
— | Telegram bot token for remote notifications |
--heartbeat |
off | Enable legacy [WAITING] timeout mode |
--timeout=<ms> |
3600000 |
Wait timeout (only with --heartbeat) |
--no-ui |
off | Disable embedded feedback UI |
Environment Variables
| Variable | Description |
|---|---|
TASKSYNC_TELEGRAM_BOT_TOKEN |
Telegram bot token (alternative to CLI flag) |
TASKSYNC_TELEGRAM_CHAT_IDS |
Pre-authorized Telegram chat IDs (comma-separated) |
TASKSYNC_LOG_LEVEL |
debug, info, warn, error (default: info) |
TASKSYNC_LOG_FILE |
Path to log file (default: stderr) |
A .env file in the project root is loaded automatically. See .env.example for all options.
Updating
Server
npm install -g tasksync-mcp-http@latest
If you run via npx, just restart — npx fetches the latest version automatically.
OpenCode Plugin
OpenCode caches npm plugins locally in ~/.cache/opencode/. Restarting OpenCode does not fetch the latest version — it reuses the cache. To update:
rm -rf ~/.cache/opencode/packages/opencode-tasksync@latest
Then restart OpenCode. It will re-install the latest version on startup.
Building from Source
git clone https://github.com/vikrant82/tasksync-mcp.git
cd tasksync-mcp
npm install && npm run build
node dist/index.js
Documentation
- Architecture — Current module map and Excalidraw architecture diagram
- OpenCode Plugin Guide — Plugin setup, configuration, agent augmentation, remote mode
- API Specification — MCP tools, HTTP endpoints, REST API
- Session Workflow — Session lifecycle and routing
- Feedback UI Guide — Web UI features and controls
- Examples — Curl commands, client configs, multi-session flows
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.