1. Advanced Application Management Platform

App Mesh is a secure platform for executing and managing user-defined process behaviors as managed services, providing control and integration via CLI and RESTful APIs.

App Mesh = systemd + scheduler + remote exec + API

1.1. 1. Application Management

Manages user-defined processes in a way similar to systemd services or Docker-managed processes, while providing more advanced capabilities for control, security, and integration.

# List registered applications
$ appm ls
ID  NAME    OWNER  STATUS    HEALTH  PID  USER  MEMORY    %CPU  RETURN  AGE  DURATION  STARTS  COMMAND
1   pyexec  mesh   disabled  -       -    -     -         -     -       37s  -         0       "python3 ../../bin/py_exec.py"
2   ping    mesh   enabled   OK      747  root  5.9 MiB   0     -       37s  37s       1       "ping cloudflare.com"
3   pytask  mesh   enabled   OK      748  root  29.7 MiB  0     -       37s  37s       1       "python3 ../../bin/py_task.py"
# Add app
$ appm add -a myapp -c "ping www.baidu.com"
# View app
$ 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
64 bytes from 183.2.172.17 (183.2.172.17): icmp_seq=2 ttl=52 time=35.1 ms
64 bytes from 183.2.172.17 (183.2.172.17): icmp_seq=3 ttl=52 time=35.3 ms
# appm -h for more usage

Supports not only long-running services, but also scheduled and policy-driven executions, with remote control and execution status tracking.

1.2. 2. Sending Tasks to a Running Application

Interact with a running application by sending tasks or data to it and receiving responses through the SDK.

from appmesh import AppMeshClient
client = AppMeshClient()
client.login("USER-NAME", "USER-PWD")

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="pytask", data=task_data)
    print(result_from_server)

1.3. 🚀 Features

Feature	Description
App Management	🧩 App CURD with Full Remote Control – including cgroup, OS user, environment variables, Docker, stdin, and stdout – along with comprehensive monitoring (start counts, exit codes, error messages, health checks). 🧩 Fine-Grained Behavior Control & Scheduling – supports long- and short-running tasks, periodic jobs, cron schedules, custom timings, and robust error handling. 🧩 Multi-Tenancy – built-in user ownership model and access controls. 🧩 Unified Access Interface – interact via CLI, REST, SDK or WebGUI.
Computing	🚀 High-performance in-memory computing ▶️ Remote execution 🔄 Workflow Pipeline — GitHub Actions-style CI/CD with DAG workflow
Security	🔐 Authentication: OAuth, 2FA, YAML-based storage (local or Consul for clustering) 🔐 Authorization: JWT, RBAC, multi-tenant isolation 🔐 Protection: SSL/TLS for `TCP`/`HTTP`/`WebSocket`, CSRF tokens, HMAC with PSK for non-token verification
Cloud Native	🌩️ Prometheus Exporter (built-in) 🌩️ Grafana SimpleJson datasource 🌩️ Grafana Loki 🌩️ Dockerfile 🧱 Consul micro-service cluster management
Extra Features	Collect host/app resource usage Remote shell command execution File upload/download API Hot-update support `systemctl reload appmesh` Bash completion Request Forwarding 🌐Web GUI
Ecosystem	LLM: Model Context Protocol (MCP), LLM Agent AI: Claude Code Plugin IoT: MQTT
Platform support	Linux, macOS, Windows (X86, ARM)
SDK	C++, Rust, Python, Golang, JavaScript, Java, Swagger OpenAPI Specification

1.4. 📦 Install

Refer to the Installation doc, this covers:

Docker Compose setup
Native installation
Cluster initialization

1.5. 🔄 Workflow Pipeline

App Mesh includes a built-in Workflow Engine for defining CI/CD pipelines as YAML — similar to GitHub Actions but running natively on App Mesh.

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

📄 LLM RAG Agent workflow — LLM Agent

1.6. 📚 Documentation

1.7. 🆚 Comparison

1.7.1. Standalone mode

Feature	App Mesh	Supervisor	crontab
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	√		√

1.7.2. Mind diagram

mind-diagram

1.8. 💡 Success

Claude Code remote sandbox — edit locally, build/test/run on a remote App Mesh server, driven by AI coding assistants
LLM Agent runtime — reason→act→observe agent running as an App Mesh App with identity, RBAC, and tools-as-Apps
LLM Agent workflow design — design of the agent runtime, sessions, and DAG integration
MCP server (Streamable HTTP + OAuth) — manage App Mesh from Claude and other MCP clients
In-memory remote task execute
Build a powerful monitor system with Grafana/Prometheus/Loki
Customize application start behavior
Open service broker support local PV for Kubernetes
Promote native application to microservice application
Secure REST file server
Standalone JWT server
Kubernetes run non-container applications
Remote execute
Python parallel run
Secure consul cluster
JWT service with REST and UI