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Agent Stack 2026 v2 — Step-by-step guide

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.

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