From zero to a production-grade AI agent: LangGraph, Claude Sonnet 4.5, Mem0, Qdrant, Langfuse, Llama Guard 3, MCP, Railway, and the danic-ai-os hookup.
agent-stack
langgraph
claude-sonnet
mem0
qdrant
langfuse
llama-guard
mcp
railway
danic-ai-os
rag
observability
guardrails
deployment
tutorial
architecture
system-design
From an empty folder to a deployed, observable, guarded AI agent in about 4 to 6 hours. This guide walks through eight architecture layers: LangGraph as the orchestrator, Claude Sonnet 4.5 as the model, Mem0 plus Qdrant for persistent memory, Langfuse for tracing and eval, Llama Guard 3 as the guardrail layer, MCP for tool wiring, Railway as the deployment target, and the danic.ai-os integration. Free-tier-friendly — at the end the agent runs in production and is usable via MCP in Claude Code and Claude Desktop. Comes with copy-paste-ready configurations, decision points between build options, and a production checklist. If you're starting with agents in 2026, this is the stack map that will actually carry.
Read the full article — free
Free signup — we'll send you a one-time magic-link. No password,
no credit card.