App Mesh

One secure daemon to run, schedule, and remote-control apps across machines.

App Mesh is a lightweight, cross-platform daemon that secures access with OAuth, offers official APIs and SDKs, and automates operations with built-in workflow engine and LLM Agent.

Use App Mesh to:

Operate services — manage long-running processes like systemd does, plus health checks, cgroup limits, Docker apps, multi-tenancy, and a Web GUI.
Execute remotely — run commands, scripts, or in-memory tasks on any node via CLI, REST, or SDK.
Power AI agents — provide sandboxed build-and-run environments for AI coding assistants, MCP servers, and LLM agent runtimes.

⚡ Quick Start

Start the daemon in Docker:

docker run -d -p 6060:6060 --restart=always --name=appmesh --net=host -v /var/run/docker.sock:/var/run/docker.sock laoshanxi/appmesh:latest

Manage applications with the appm CLI — sign in first with appm logon.

# Sign in once (non-interactive)
$ sudo /opt/appmesh/script/appmesh-auth.sh print-initial-password | appm logon -u admin@appmesh.local --password-stdin

# List registered applications
$ appm ls
ID  NAME    OWNER           STATUS    HEALTH  PID  USER  MEMORY    %CPU  RETURN  AGE  DURATION  STARTS  COMMAND
1   py-exec system  disabled  -       -    -     -         -     -       37s  -         0       "python3 ../../bin/py_exec.py"
2   ping    system  enabled   OK      747  root  5.9 MiB   0     -       37s  37s       1       "ping cloudflare.com"
3   py-task system  enabled   OK      748  root  29.7 MiB  0     -       37s  37s       1       "python3 ../../bin/py_task.py"

# Register a new application
$ appm add -a myapp -c "ping www.baidu.com"

# View its live output
$ appm ls -a myapp -o
PING www.baidu.com (183.2.172.17) 56(84) bytes of data.
64 bytes from 183.2.172.17 (183.2.172.17): icmp_seq=1 ttl=52 time=34.9 ms

# appm -h for more usage

Send tasks to a running application and get responses back through the SDK:

import os
from appmesh import AppMeshClient
client = AppMeshClient(bearer_token=os.environ["APPMESH_BEARER_TOKEN"])

result_from_server = "0"
for i in range(10):
    task_data = f"print({result_from_server} + {i}, end='')"
    result_from_server = client.run_task(app_name="py-task", data=task_data)
    print(result_from_server)

For native packages (.deb/.rpm), systemd setup, and cluster initialization, see the Installation Guide and the Dockerfile.

🚀 Core Capabilities

Capability	What you get
Application management	Full remote CRUD and control — cgroup limits, OS user, environment variables, Docker apps, stdin/stdout — with monitoring of start counts, exit codes, errors, and health checks
Scheduling	Long- and short-running apps, periodic jobs, cron expressions, custom timings, and policy-driven start/exit behaviors
Remote execution	Run commands and scripts on any node; send in-memory tasks to running applications for high-performance computing
Workflow engine	GitHub-Actions-style YAML pipelines with DAG scheduling, running natively on App Mesh
Security	OAuth/OIDC bearer authentication (RFC 6750) with Principal-based RBAC and multi-tenant isolation; SSL/TLS on TCP/HTTP/WebSocket; HMAC-PSK internal verification
Observability	Built-in Prometheus exporter, Grafana datasource, Loki integration, host/app resource metrics
Extras	File upload/download API, remote shell execution, hot config reload, bash completion

Runs on Linux, macOS, and Windows (x86 and ARM).

🤖 AI & LLM Integration

App Mesh's secure remote-execution core makes it a natural runtime for AI workloads:

Remote sandbox for AI coding assistants — give agents an isolated build-and-run environment instead of your local shell.
MCP server — manage App Mesh from AI clients over Model Context Protocol (Streamable HTTP with OAuth 2.1, RBAC enforced by the daemon).
LLM agent runtime — host Claude-Agent-SDK-based agents as managed App Mesh applications; see the architecture design (SOP).
Remote execution skill for Codex and Claude Code, and MQTT bridge for IoT scenarios.

🔄 Workflow Pipeline

Define CI/CD pipelines as YAML — similar to GitHub Actions, but running natively on App Mesh with the built-in Workflow Engine:

DAG scheduling — jobs run in dependency order, independent jobs in parallel
4 step types — shell commands, existing Apps, Task API messages, sub-workflows
Error handling — retry with exponential backoff, continue-on-error, finally cleanup blocks
Expressions — ${{ inputs.env }}, ${{ steps.build.stdout }}, success(), failure(), always()
Remote execution — target specific nodes by label or hostname

appm workflow add -f pipeline.yaml        # register
appm workflow run pipeline -e env=prod -f # run and follow output
appm workflow runs pipeline               # view history

🧰 Interfaces & SDKs

Interface	Details
CLI	`appm` command reference
REST	REST APIs · OpenAPI spec
Web GUI	app-mesh-ui
SDKs	Python · Golang · Rust · Java · JavaScript · C++

💡 Success Stories

AI & automation

Remote computing

Operations & observability

Platform & Kubernetes

🆚 Comparison

Feature	App Mesh	Supervisor	crontab
Schedule accuracy	Seconds	Seconds	Minutes
Language	C++17	Python	C
Web GUI	√	√
Command lines	√	√	√
SDK	√
Cron schedule expression	√		√
Manage docker app	√
Session login	√
Manage stdout/stderr	√	√
Health check	√
Authentication	√	√
Multi-tenant	√		√

📚 Documentation

🔗 Library dependencies

Community & License

Questions and discussions are welcome on Gitter. Licensed under the MIT License.

Release files for appmesh 3.0.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for appmesh 3.0.3
File	Interpreter	ABI	Platform	Reset
appmesh-3.0.3-py3-none-any.whl 59.5 kB Sep 16, 2026	Python 3	none	any	Details

Release files / appmesh-3.0.3-py3-none-any.whl

Download URL	appmesh-3.0.3-py3-none-any.whl
Size	59.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`e13523c07b1f0418e5175f36f37a7c6598d427a2d2f743dc1baef9eaf6f3a5e0`
BLAKE2b-256 checksum How to use checksums	`54bebeee3bd97d972dd4988ca2f5f141c995d10ad0d6b73e557f2d2ceb9abaf0`
Upload date	Sep 16, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.14`