architectural-skill

Generate services, agents, workflows, and orchestration logic for a LangGraph multi-agent platform.

Updated Mar 12, 2026
One-click install
npx skills add https://github.com/tendercoconut174/ai-agent-platform --skill architectural-skill-tendercoconut174
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: architectural-skill
Source: https://github.com/tendercoconut174/ai-agent-platform/tree/main/.cursor/skills/architectural-skill
Command: npx skills add https://github.com/tendercoconut174/ai-agent-platform --skill architectural-skill-tendercoconut174

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Building a goal-oriented multi-agent platform requires consistent architectural decisions across gateways, orchestrators, agent pools, and delivery services. This Skill encodes the target architecture, layering rules, and technology constraints so generated code stays loosely coupled and consistent. ## Core Features & Use Cases - Service Generation: Scaffold FastAPI gateway and orchestrator services with defined endpoints, session management, and HTTP-based inter-service communication. - Agent & Workflow Design: Generate LangGraph supervisor graphs (classify, plan, execute, evaluate, deliver), CrewAI sub-agents, and MCP tool integrations following strict communication rules. - Use Case: When asked to add a new agent or endpoint, the Skill ensures agents remain stateless, access external systems only through MCP tools, and never touch the database directly. ## Quick Start Use the architectural skill to generate a new orchestrator node that dispatches a research agent through the supervisor graph.

Frequently Asked Questions about architectural-skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a multi-agent orchestration system with LangGraph?▼

Define a LangGraph StateGraph supervisor with nodes for classify, plan, execute, evaluate, and deliver. The supervisor dispatches stateless agents from a registry, evaluates whether the goal is achieved, and replans up to five iterations before delivering results.

How to combine CrewAI agents with LangChain MCP tools?▼

Wrap tool implementations as LangChain @tool functions, then convert them with Tool.from_langchain() before passing them to CrewAI crews. Each CrewAI crew has one agent with a role, goal, and backstory, and a TOOL_REGISTRY maps agent types to allowed tool subsets.

Should agents access the database directly in a multi-agent system?▼

No. Agents must remain stateless, receive a message, and return a string without database access. Session and message history persistence is handled by the gateway's session manager using PostgreSQL with an in-memory fallback.

How do microservices communicate in this agent architecture?▼

The gateway communicates with the orchestrator over HTTP using an httpx client, keeping services loosely coupled. The gateway exposes /message and /message/upload endpoints, while the orchestrator exposes /orchestrate on a separate port.

What are the limitations of a supervisor-based replanning loop?▼

Replanning is capped at five iterations to prevent infinite loops when the evaluation node determines the goal is not achieved. Workflows requiring unbounded retries or long-running asynchronous tasks need additional scheduling logic outside the supervisor graph.