What can you do with this server?

Pipepie is a self-hosted, end-to-end encrypted tunneling platform for webhooks, AI pipelines, and local development, with extensive management, monitoring, and AI tool integration capabilities. * Secure Tunneling: Create encrypted HTTP/TCP tunnels from local ports to public URLs, supporting custom subdomains, password protection, and multi-tunnel configs via pipepie.yaml. Dynamically register, create, start, stop, and delete tunnels. * Webhook Inspection & Replay: Stream live requests, inspect full details (headers, request/response bodies, status codes, duration, source IP), list recent requests with pagination, and replay captured webhooks. * AI Pipeline Tracing: Auto-detect and correlate webhooks from major AI providers (Replicate, fal.ai, RunPod, Modal, OpenAI) into step-by-step execution timelines, viewable in terminal or a web UI. * Dashboard & Monitoring: Get aggregate overviews of all tunnels including request counts, success/error rates, uptime, and online status. * Server Administration: Run an interactive setup wizard (DNS, TLS, firewall), diagnose configurations, monitor tunnel activity, and manage multiple server accounts. * MCP Server Integration: A built-in Model Context Protocol server exposes 13 tools, allowing AI assistants (Claude, Cursor) to programmatically manage tunnels, inspect requests, replay webhooks, and analyze pipeline traces.

Which integrations are available for this server?

Supports TLS certificate management through Cloudflare during server setup, allowing automatic SSL configuration for secure tunnel connections. Provides Jaeger-style timeline visualization for AI pipeline traces in the web dashboard, enabling distributed tracing of AI workflow executions. Supports automatic TLS certificate provisioning through Let's Encrypt during server setup, enabling secure HTTPS connections for tunnels. Auto-detects webhooks from Modal AI provider, extracts call ID and status information, and correlates them into pipeline traces for monitoring serverless AI executions. Provides a preset tunnel configuration for n8n workflow automation on port 5678, enabling secure external access to n8n webhook nodes and workflows. Auto-detects nginx configuration during server setup and integrates with existing nginx installations for tunnel routing and management. Provides a preset tunnel configuration for Ollama with authentication enabled on port 11434, enabling secure external access to local Ollama instances. Auto-detects webhooks from OpenAI batch operations, extracts batch ID and model information, and correlates them into pipeline traces for monitoring AI batch processing. Auto-detects webhooks from Replicate AI provider, extracts job ID, status, and prediction time information, and correlates them into pipeline traces for monitoring AI model inferences. Provides a preset tunnel configuration for Telegram Mini Apps on port 5173, enabling secure external hosting of Telegram web applications. Supports pass-through streaming for Vercel AI SDK compatibility, enabling seamless integration with Vercel's AI streaming patterns without buffering.

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pipepie

Official

by pipepie

Overview Schema Related Servers Score Discussions

Hybrid

Pipepie

Self-hosted, encrypted tunnel for webhooks, AI pipelines, and local development. End-to-end Noise encryption, pipeline tracing, web dashboard.

pipepie connect

pie connect 3000
# ✓ https://my-app.tunnel.dev → localhost:3000

Why Pipepie?

End-to-end encrypted — Noise NK protocol. The relay server never sees your data.
AI-first — auto-detects Replicate, fal.ai, RunPod, Modal, OpenAI webhooks. Pipeline tracing with timeline visualization.
Self-hosted — your server, your domain, your data. No vendor lock-in.
Fast — 577 req/s parallel, 2ms overhead. Protobuf + zstd + yamux.
MCP server built-in — pie mcp gives Claude, Cursor, and AI tools direct access to tunnels, requests, and pipeline traces.
Beautiful CLI — Dracula theme, interactive forms, 17 commands.
Zero-config start — pie setup on server, pie login on client, done.

Related MCP server: LivePort MCP

Install

# Homebrew
brew install pipepie/tap/pipepie

# Script
curl -sSL https://raw.githubusercontent.com/pipepie/pipepie/main/install.sh | sh

# Or build from source
git clone https://github.com/pipepie/pipepie && cd pipepie && make build

Quick Start

Server (your VPS, one time)

pie setup

Interactive wizard handles DNS, firewall, TLS (Let's Encrypt auto or Cloudflare), nginx detection, systemd service — everything.

Client (your dev machine)

# Save server connection (key from pie setup)
pie login --server tunnel.mysite.com:9443 --key a7f3bc21...

# Tunnel any local port
pie connect 3000

AI Tool Presets

pie connect --ollama       # Ollama (port 11434, auth enabled)
pie connect --comfyui      # ComfyUI (port 8188, WebSocket)
pie connect --n8n          # n8n workflows (port 5678)
pie connect --tma          # Telegram Mini App (port 5173)

Features

Tunneling

pie connect 3000                    # HTTP tunnel
pie connect 3000 --name my-app     # Stable subdomain
pie connect --tcp 5432              # TCP (databases, gRPC)
pie connect 3000 --auth secret      # Password-protected URL
pie up                              # Multi-tunnel from pipepie.yaml

Webhook Inspection

pipepie logs

pie logs my-app --follow --body     # Stream with request/response bodies
pie inspect <request-id>            # Full headers, body, metadata
pie replay <request-id>             # Re-send a captured webhook
pie dashboard                       # Open web UI in browser

AI Pipeline Tracing

Pipepie auto-detects webhooks from AI providers — no configuration needed:

Provider	Detection	What's extracted
Replicate	`webhook-id` header + payload	Job ID, status, predict_time
fal.ai	`x-fal-signature` header	Request ID, status
RunPod	UPPERCASE status in payload	Run ID, execution time
Modal	`call_id` in payload	Call ID, status
OpenAI	`batch_` prefix in ID	Batch ID, model
MCP	JSON-RPC 2.0 `method` field	Tool name, call ID

Webhooks from the same pipeline are auto-correlated into traces:

pie traces my-app                   # Terminal timeline view
pie dashboard                       # Web UI with Jaeger-style bars

Or use headers for manual correlation:

curl -X POST https://my-app.tunnel.dev/webhook \
  -H "X-Pipepie-Pipeline: image-gen" \
  -H "X-Pipepie-Step: generate" \
  -H "X-Pipepie-Trace-ID: trace-001"

Multi-Service Config

# pipepie.yaml
server: tunnel.mysite.com:9443
key: a7f3bc21...

tunnels:
  api:
    subdomain: my-api
    forward: http://localhost:3000
  frontend:
    subdomain: my-app
    port: 5173

pipeline:
  name: image-gen
  steps:
    - name: replicate-sdxl
      webhook: /replicate
      forward: localhost:3000/on-image
    - name: fal-upscale
      webhook: /fal
      forward: localhost:3000/on-upscale

pie up

Multi-Account

pie login --server work.example.com:9443 --key abc...
pie login --server personal.example.com:9443 --key def...

pie account                         # List all, see active
pie account use work.example.com    # Switch
pie logout personal.example.com     # Remove

Server Management

pipepie status

pie setup                           # Interactive setup wizard
pie server --config pipepie.yaml    # Start server
pie doctor --config pipepie.yaml    # Diagnose configuration
pie status                          # Tunnel overview
pie status --json                   # JSON output for scripts

Architecture

Client (pie connect)                    Server (pie server)
     │                                       │
     │◄──── Noise NK handshake ────►│
     │       (ChaChaPoly + BLAKE2b)          │
     │                                       │
     │◄──── yamux multiplexing ────►│
     │       (parallel streams)              │
     │                                       │
     │◄──── Protobuf + zstd ──────►│
     │       (binary, compressed)            │
     │                                       │
  localhost:3000              https://sub.domain.com

Noise NK — server authenticated by public key. Know the key = have access. yamux — multiplexed streams, no head-of-line blocking. Protobuf — binary wire format, ~10x smaller than JSON. zstd — bodies >1KB auto-compressed. SSE/streaming — pass-through without buffering (Vercel AI SDK, Ollama compatible).

MCP Server (Claude, Cursor, AI tools)

Pipepie includes a built-in Model Context Protocol server. Your AI tools can inspect webhooks, replay requests, manage tunnels, and debug pipelines — directly from the chat.

# Claude Code (one command)
claude mcp add --transport stdio pipepie -- pie mcp

Add to claude_desktop_config.json or Cursor MCP settings:

{
  "mcpServers": {
    "pipepie": {
      "command": "pie",
      "args": ["mcp"]
    }
  }
}

13 tools available:

Tool	What it does
`overview`	Dashboard with all tunnels, stats, success rates
`list_tunnels`	All registered tunnels with online/offline status
`tunnel_status`	Check if a specific tunnel is online
`connect`	Start a tunnel (e.g. port 3000 → public URL)
`disconnect`	Stop a running tunnel
`active_tunnels`	List tunnels running in this session
`list_requests`	Recent webhook requests for a tunnel
`inspect_request`	Full request details: headers, body, response
`replay_request`	Re-send a captured webhook
`pipeline_traces`	AI pipeline execution traces
`trace_timeline`	Step-by-step timeline for a trace
`create_tunnel`	Register a new subdomain
`delete_tunnel`	Remove a tunnel and its data

CLI Reference

Command	Description
`pie connect [port]`	Create a tunnel
`pie connect --tcp [port]`	TCP tunnel
`pie connect --ollama`	Ollama preset
`pie connect --comfyui`	ComfyUI preset
`pie connect --n8n`	n8n preset
`pie connect --tma`	Telegram Mini App preset
`pie login`	Add server connection
`pie logout`	Remove account
`pie account`	List & switch accounts
`pie dashboard`	Open web UI in browser
`pie status`	Show tunnels and activity
`pie logs [name]`	Stream requests
`pie inspect [id]`	Full request details
`pie replay [id]`	Replay a webhook
`pie traces [name]`	Pipeline trace timelines
`pie up`	Multi-tunnel from pipepie.yaml
`pie setup`	Server setup wizard
`pie server`	Start relay server
`pie doctor`	Diagnose server config
`pie mcp`	Start MCP server for AI tools
`pie update`	Self-update to latest
`pie version`	Version + update check

Performance

Metric	Result
Latency	2ms overhead
Sequential	119 req/s
Parallel (20 workers)	577 req/s
1MB body	16ms

What makes Pipepie different

Self-hosted & open source — your infrastructure, your data, no third-party traffic inspection
End-to-end encrypted — Noise NK protocol, the relay server never sees plaintext
AI-native — auto-detects 6 providers, pipeline tracing, MCP support
Complete toolkit — tunneling + inspection + replay + dashboard in one binary
Free forever — no bandwidth limits, no session timeouts, no paid tiers for core features

License

AGPL-3.0 — free to use, modify, and self-host. If you modify and offer as a service, you must open-source your changes.

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

0dRelease cycle

11Releases (12mo)

Commit activity

Resources

GitHub Repository

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