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. 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.
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)
🚀 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 |
📦 Install
Refer to the Installation doc, this covers:
- Docker Compose setup
- Native installation
- Cluster initialization
🔄 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,finallycleanup 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
📚 Documentation
🆚 Comparison
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 | √ | √ |
Mind diagram
💡 Success
- Remote build-and-run sandbox for AI coding assistants
- LLM agent runtime hosted as an App Mesh app
- LLM agent architecture and workflow design
- Manage App Mesh from AI clients via MCP (HTTP + OAuth)
- In-memory remote task execution
- Observability stack with Grafana, Prometheus, and Loki
- Customize application startup and exit behavior
- Kubernetes local-PV provisioning via Open Service Broker
- Promote a native application into a managed microservice
- Secure REST-based file server
- Standalone JWT authentication server
- Run non-container applications on Kubernetes
- Remote command and Python script execution
- Parallel task execution with the Python SDK
- Secure multi-node cluster with Consul
- JWT auth service with REST API and web UI
🔗 Library dependency
1 maintainer
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