pipeshub-ai
projectother scannedThe open-source context layer for AI agents. PipesHub turns your company's knowledge (Slack, Drive, Jira, GitHub, Microsoft 365 and 40+ connectors) into a permission-aware workspace that agents can search, grep, navigate and cite. MCP, SDKs and built-in agents. Self-hosted.
run it
on your own machines, one way per deploy method# once, as root:
docker build -t mcp-local/pipeshub-ai:main 'https://github.com/pipeshub-ai/pipeshub-ai.git#main'
#!/usr/bin/env bash
# pipeshub-ai (io.github.pipeshub-ai/pipeshub-ai), recipe docker (generated).
# Generated by Hub for toolset "my-agent"; started by mcp-my-agent-pipeshub-ai.service.
set -euo pipefail
export HOST=127.0.0.1 PORT=8101
exec docker run --rm --name mcp-my-agent-pipeshub-ai -p 127.0.0.1:${PORT}:8000 mcp-local/pipeshub-ai:main
[Unit]
Description=MCP server pipeshub-ai (toolset my-agent), generated by Hub
After=network-online.target docker.service
Wants=network-online.target
[Service]
Type=simple
User=mcp
Group=mcp
SupplementaryGroups=docker
EnvironmentFile=/etc/mcp/my-agent.env
Environment=HOME=/var/lib/mcp/pipeshub-ai
WorkingDirectory=/var/lib/mcp/pipeshub-ai
ExecStart=/opt/mcp/my-agent/bin/pipeshub-ai.sh
Restart=on-failure
RestartSec=5
NoNewPrivileges=true
ProtectSystem=strict
ProtectHome=true
PrivateTmp=true
ReadWritePaths=/var/lib/mcp/pipeshub-ai
[Install]
WantedBy=multi-user.target
docker build -t pipeshub-ai 'https://github.com/pipeshub-ai/pipeshub-ai.git#main' docker run --rm -p 127.0.0.1:8101:8000 pipeshub-ai
# Generated by Hub for toolset "my-agent" (2026-10-19T03:50:42+00:00).
# Each package server runs behind a stdio->HTTP bridge and listens on localhost only.
# cp .env.example .env # fill the ${VAR}s
# docker compose up -d && bash smoke.sh
# Remote servers are not here: clients reach them directly (see clients/).
name: mcp-my-agent
services:
pipeshub-ai:
build:
context: https://github.com/pipeshub-ai/pipeshub-ai.git#main
ports:
- 127.0.0.1:8101:8000
restart: unless-stopped
x-note: 'CHANGE_ME: built from the repository''s Dockerfile, assuming it serves streamable HTTP at
:8000/mcp. Nothing has run this yet; the worker replaces it with a tested recipe.'
volumes:
npm-cache: {}
uv-cache: {}
# Generated by Hub for toolset "my-agent" (2026-10-19T03:50:42+00:00).
# kubectl create namespace mcp
# kubectl -n mcp create secret generic mcp-credentials --from-env-file=.env
# kubectl apply -f kubernetes.yaml
# Services are cluster-internal: <name>.mcp.svc.cluster.local:8000/mcp. Put an ingress
# with auth in front before exposing any of them.
apiVersion: v1
kind: Namespace
metadata:
name: mcp
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: pipeshub-ai
namespace: mcp
labels: &id001
app: pipeshub-ai
spec:
replicas: 1
selector:
matchLabels: *id001
template:
metadata:
labels: *id001
spec:
containers:
- name: pipeshub-ai
image: CHANGE_ME
ports:
- containerPort: 8000
name: http
env: []
---
apiVersion: v1
kind: Service
metadata:
name: pipeshub-ai
namespace: mcp
spec:
selector:
app: pipeshub-ai
ports:
- port: 8000
targetPort: http
name: http
# Generated by Hub for toolset "my-agent" (2026-10-19T03:50:42+00:00).
# Credentials come from the Secret 'mcp-credentials' (kubectl create secret generic ... --from-env-file=.env).
# Each server under `servers` is one Deployment + Service; the keys after it configure
# upstream charts a worker verified.
servers:
pipeshub-ai:
enabled: true
replicas: 1
port: 8000
path: /mcp
container:
name: pipeshub-ai
image: CHANGE_ME
ports:
- containerPort: 8000
name: http
env: []
# helm install my-agent helm/my-agent -n mcp
Scanned github.com/pipeshub-ai/pipeshub-ai at 7608ed150d35 with [email protected][email protected] on 2026-10-17 before the build: 27 dependencies, 66 known vulnerabilities (2 critical, 37 high, 23 medium, 4 low), nothing in the code that looks malicious.
in the cloud
one click: the provider's deploy form opens filled in, in your own account- koyeb
-
Clients connect to the service's https://…koyeb.app address plus /mcp.
- cloud run
-
Cloud Shell clones the repository, builds its Dockerfile and deploys it to Cloud Run.
Cloud Run sends requests to $PORT (8080); the recipe expects :8000, so the server has to read PORT, or set the container port on the service afterwards.
connect to it
copy what your client needs{
"mcpServers": {
"pipeshub-ai": {
"command": "CHANGE_ME",
"args": []
}
}
}
#!/usr/bin/env bash # Registers every server with Claude Code (run once; `claude mcp list` to check). set -euo pipefail set -a; [ -f .env ] && source .env; set +a claude mcp add pipeshub-ai -- CHANGE_ME
{
"mcpServers": {
"pipeshub-ai": {
"command": "CHANGE_ME",
"args": []
}
}
}
{
"servers": {
"pipeshub-ai": {
"type": "stdio",
"command": "CHANGE_ME",
"args": []
}
}
}
name: my-agent target: local servers: - hub: io.github.pipeshub-ai/pipeshub-ai
- not probed yet: tools unpinned, ingest lists them
- no tested recipe for target 'local' yet: build it from https://github.com/pipeshub-ai/pipeshub-ai by hand, or wait for the worker
tools
runs · 1
the latest tests of this entry by the hub and its workers, newest first| date | worker | method | status | sandbox | reason |
|---|---|---|---|---|---|
| 2026-10-17 13:09 | local-mac-vm | docker | failed | gvisor | docker: sandbox: docker build: Dockerfile:1 -------------------- 1 | >>> # syntax=docker/dockerfile:1 2 | # Slow layers (APT, Rust, Python deps, ML models, runt… |