Developers · Stack
The AI-Native App Tech Stack (2026)
The modern AI-native stack layers models, orchestration, retrieval, a memory layer, vector storage and observability — here’s each component, how to choose it and where memory fits in production agents.
2026 stack
Overview
Stack layers at a glance
Seven layers — each with a distinct role in agentic apps.
| Layer | Role | Common options | Choose when |
|---|---|---|---|
| Models | Reasoning, generation, tool use | GPT-4o, Claude, open weights (Llama, Qwen) | Latency, cost, compliance, tool-calling quality |
| Orchestration | Agent loops, tool routing, state | LangGraph, Letta, custom loops, n8n | Team skills, graph vs paging model, framework lock-in tolerance |
| Retrieval / RAG | Document-grounded answers | LlamaIndex, custom chunk+embed pipelines | Static knowledge bases, compliance docs, product catalogs |
| Memory layer | Cross-session facts, user prefs, agent learning | Engram, Mem0, Zep, Letta, LangMem | Stateful agents, personalization, multi-turn continuity |
| Vector store | Embedding storage and similarity search | Weaviate, Pinecone, pgvector, Qdrant | Scale, hybrid search needs, existing Postgres investment |
| Observability | Traces, evals, memory audit logs | LangSmith, Arize, custom OpenTelemetry | Production SLAs, LOCOMO/LongMemEval in CI |
| App / API | User-facing surface, auth, tenancy | Next.js, FastAPI, existing SaaS backend | user_id scoping, delete APIs, rate limits |
Prime layer
The memory layer in the stack
Sits between agent orchestration and raw data infrastructure — extraction, retrieval orchestration, update policies and multi-tenant scoping.
Without a memory layer, agents reset every session. With one, they remember user preferences, prior decisions and conversation facts. Mem0 reports LOCOMO J 66.9 with median search 0.148 s and p95 total 1.44 s vs 17.1 s full-context (Chhikara et al., 2025). Zep scores LongMemEval 71.2% gpt-4o (+18.5% vs baseline) on temporal reasoning (Rasmussen et al., 2025).
Engram is the Weaviate-native memory layer — dynamic extraction, hybrid search and scoped collections (GA June 2026). If your stack already uses Weaviate, Engram adds agent semantics without a separate memory vendor. Public LOCOMO/LongMemEval numbers for Engram are not yet published.
- Engram — Weaviate-native; best when vector infra is already Weaviate
- Mem0 — framework-agnostic managed API; fastest POC path; LOCOMO J 66.9
- Zep — temporal knowledge graph; LongMemEval +18.5%; DMR 94.8%
- Letta — virtual-context paging; MemGPT DMR 93.4% (Packer et al., 2023)
- LangMem — LangGraph-integrated; LOCOMO J 58.1
→ Memory layer deep dive · Engram explained · Best AI memory tools
Storage
Vector store layer
Stores embeddings — distinct from the memory layer that decides what to remember and how to retrieve it.
Weaviate, Pinecone, pgvector and Qdrant handle similarity search at scale. The memory layer sits above: extracting facts from conversations, deduplicating, applying forget policies and formatting retrieved context for the LLM. Engram illustrates the split — Weaviate is storage; Engram is the agent-oriented layer on top.
Decision
Choosing your stack
Match team skills, compliance requirements and use case — not hype.
- Startup POC — Mem0 API + LangGraph; validate on LOCOMO before scaling
- Weaviate shop — Engram memory layer on existing Weaviate cluster
- Temporal reasoning — Zep knowledge graph for fact invalidation over time
- Long-context agents — Letta paging when context window limits bite
- Enterprise compliance — OSS stack (pgvector + LangMem) or self-hosted Zep
- Managed vs OSS — trade speed-to-ship against data residency and cost at scale
FAQ
Frequently asked questions
What layers does an AI-native stack need?
Models, orchestration, retrieval/RAG, memory layer, vector store, observability and app/API. Memory is the layer most teams skip — and the one that separates demos from production agents.
Memory layer vs vector database?
Vector DB stores embeddings. Memory layer adds extraction, deduplication, update/forget policies and agent-oriented retrieval. See memory layer guide.
Where does Engram fit in the stack?
Between agent orchestration and Weaviate — a Weaviate-native memory layer with async extraction and hybrid search (GA June 2026). See Engram explained.
Mem0 vs building your own memory layer?
Engram is the Weaviate-native managed memory layer (GA June 2026). Mem0 is a framework-agnostic API alternative. DIY gives control but costs engineering time.
Typical LangGraph stack in 2026?
GPT-4o/Claude + LangGraph loops + Engram or Mem0 for memory + Weaviate or pgvector + LangSmith for evals.
Open-source vs managed memory in the stack?
Managed (Engram, Mem0, Zep cloud) ships faster. OSS (LangMem, self-hosted Zep) suits data residency. See OSS vs managed.
Is RAG a separate layer from memory?
Yes — RAG grounds answers in static documents. Memory stores per-user facts across sessions. Many production agents use both. See memory vs RAG.
What changed in AI-native stacks in 2026?
Memory layers went GA (Engram June 2026), LOCOMO/LongMemEval became standard CI gates, and context-engineering + memory replaced raw long-context stuffing.
AI-native stack examples?
Weaviate shop: LangGraph + Engram. Support bot: LangGraph + Mem0 + Pinecone. Temporal agent: Zep + Neo4j. See use cases.
Deep dive on the memory layer?
See the memory layer in the AI-native stack — placement, evaluation criteria and implementation checklist.