Developers hub · agentic apps
Developer’s Guide to Agentic Applications
A developer’s guide to building agentic and AI-native applications — architecture, the modern tech stack, and the memory layer that makes agents reliable across sessions.
Agentic app stack
Core component
Memory as a core component
Every agentic application needs memory alongside the model, tools and orchestration layer — without it, agents are stateless and forget every session.
Collection
What this guide covers
Agentic apps
What they are and why memory is core.
Read →AI-native apps
Built around models, agents and memory.
Read →AI-native tech stack
The modern stack for agentic apps.
Read →Memory layer
Where memory fits in the stack.
Read →Build an AI agent
From zero to working agent.
Read →Agentic architecture
ReAct, planner-executor, multi-agent patterns.
Read →AI-enabled vs AI-native
Bolted-on vs built-around AI.
Read →AI-native examples
Reference applications and patterns.
Read →Paths
Developer paths
| Goal | Start here |
|---|---|
| Building your first agent | Build an AI agent |
| Choosing the tech stack | AI-native tech stack |
| Evaluating memory products | Memory layer + best tools |
| Adding memory to existing agent | Add memory guide |
FAQ
Frequently asked questions
Agentic app vs AI-native app — what's the difference?
Agentic apps let AI act autonomously with tools and planning. AI-native apps are built around models, agents and memory from day one. See AI-enabled vs AI-native.
Is a memory layer required for agentic apps?
For any app with multi-session users, yes. Without memory, agents forget every session. The memory layer handles write, retrieve, consolidate and forget. See memory layer.
Where does Engram fit in the stack?
Engram is a vector-native memory layer on Weaviate — sits between your agent orchestration and the vector database. See memory layer and Engram.
What's the fastest Mem0 quickstart?
Mem0 Cloud API for per-user memory in minutes. For stack decisions, see best tools and add memory guide.
How do I add memory to LangGraph?
Use LangMem for native integration or Engram/Mem0/Zep as external APIs. See LangMem and add memory guide.
When do I need a vector database?
When you need semantic search over long-term memories at scale. Smaller apps may start with Redis or pgvector. See vector databases.
What's the production checklist for agentic apps?
Model + tools + memory layer + orchestration + evaluation + monitoring. Memory needs eviction, conflict handling and benchmark POC. See memory management.
Where can I see AI-native app examples?
See AI-native app examples for reference patterns and architectures.