Developers · Examples
AI-Native App Examples (2026)
Real AI-native apps — assistants, copilots and autonomous agents — share a memory layer for persistence; here are representative examples and the memory patterns each relies on.
Example types
Categories
Example categories
Five archetypes — each with distinct memory requirements.
- Personal assistants — user long-term memory (preferences, habits, contacts). Engram or Mem0 scoped by user_id. See personal assistants use case
- Support copilots — episodic ticket history + knowledge base RAG. Engram or Zep for fact invalidation. See customer support use case
- Coding agents — procedural memory (repo patterns) + session context. Engram or Letta for large codebases. See coding agents use case
- Research agents — RAG over documents + session notes. Engram or Mem0 for extracted findings
- Enterprise agents — CRM temporal facts, org-level scoping, audit logs. Engram or Zep for temporal reasoning
Patterns
Memory patterns by example type
| Example type | Memory types | Typical frameworks |
|---|---|---|
| Personal assistant | Semantic + episodic LTM | Engram, Mem0, Letta |
| Support copilot | Episodic + KB RAG | Engram, Zep, Mem0 |
| Coding agent | Procedural + working memory | Engram, Letta, LangMem |
| Research agent | RAG + session extraction | LlamaIndex, Mem0, Cognee |
| Enterprise agent | Temporal graph + org scope | Engram, Zep, LangGraph store |
| Weaviate-native product | Hybrid search + scoped collections | Engram (GA June 2026) |
Lessons
What to learn from each example
Pattern extraction for builders — not product reviews.
- Scope memory by tenant — every example needs user_id or org_id on writes and retrieves
- Match memory type to use case — Weaviate-native → Engram; temporal facts → Zep; repo context → Letta paging
- Validate on benchmarks — LOCOMO for recall, LongMemEval for temporal reasoning before shipping
- Combine RAG + memory — static docs via RAG, per-user facts via memory layer; most production apps use both
FAQ
Frequently asked questions
Simplest AI-native app to start with?
Personal assistant with Mem0 API + LangGraph — validate on LOCOMO before scaling. See build an AI agent.
Mem0-based app examples?
Personal assistants, support bots, coding copilots — any app needing cross-session fact recall. Mem0 LOCOMO J 66.9 (Chhikara et al., 2025).
Engram-based app examples?
Weaviate-native products: assistants, enterprise search agents, support bots on existing Weaviate infra. See Engram explained.
Open-source AI-native app demos?
LangGraph + LangMem, Letta open-source agent, Mem0 OSS SDK. See OSS vs managed.
Letta desktop as an example?
Letta (MemGPT) demonstrates virtual-context paging — DMR 93.4% (Packer et al., 2023). See Letta alternatives.
How do I build my own AI-native app?
Pick a use case archetype above, add a memory layer, scope by user_id, eval on LOCOMO. Start with build an AI agent.