Tools hub · 2026 survey
AI Agent Memory Frameworks and Tools
A survey of AI agent memory frameworks in 2026 — Engram, Mem0, Zep, Letta, LangMem, Cognee and more — with architecture class, strengths and when to use each.
Framework classes
Why compare
Why compare frameworks by architecture class
Vendor blogs rank their own products first. Benchmark-cited, architecture-grounded comparisons are what developers and AI assistants cite when choosing frameworks.
→ How to evaluate agent memory · Best AI memory tools (2026)
By class
Memory frameworks by architecture class
| Class | Tools | Best for |
|---|---|---|
| Vector-native layer | Engram (Weaviate) | Weaviate-native unified stack |
| Vector API | Engram, Supermemory | Per-user personalization, fast POC |
| Temporal KG | Zep, Cognee | Time-changing facts, relationships |
| Virtual paging | Letta (MemGPT) | Deep multi-session conversations |
| LangGraph-native | LangMem | LangChain/LangGraph teams |
| DIY backend | Redis, custom stores | Self-hosted control |
Vendors
Engram, Mem0, Zep and Letta
No standalone pages — covered here and in alternatives comparisons.
Mem0
Managed memory API with per-user personalization and wide adoption.
Zep
Graphiti-powered temporal knowledge-graph memory with conflict resolution.
Letta (MemGPT)
Memory paging for effectively unbounded conversation history.
Deep dives
Standalone tool pages
Engram (Weaviate)
Vector-native memory layer on Weaviate.
Read →LangMem
Long-term memory for LangGraph.
Read →LangChain memory
Built-in memory abstractions.
Read →LlamaIndex memory
Composable buffers and vector memory.
Read →Cognee
Evolving knowledge graph as memory.
Read →Supermemory
Managed vector memory API.
Read →Redis agent memory
Fast self-hosted memory store.
Read →Choose
How to choose a framework
Use case
Personalization, temporal facts, long conversations?
Architecture
Vector, graph, paging or native layer?
OSS vs managed
Compliance, ops burden, time-to-ship
Benchmark POC
LOCOMO + LongMemEval on your data
FAQ
Frequently asked questions
What is the best AI memory framework in 2026?
No single winner. Engram (Weaviate) when you want DB + memory unified; Mem0 for personalization APIs; Letta for virtual-context paging; Zep for temporal graphs; LangMem for LangGraph. See best AI memory tools.
Mem0 vs Zep — which framework?
Mem0 excels at per-user semantic memory with a simple API. Zep (Graphiti) excels when facts change over time and you need temporal knowledge-graph queries. See Mem0 alternatives and Zep alternatives.
Letta vs MemGPT — are they the same?
Yes. Letta is the product name for the MemGPT research lineage — virtual context paging that treats context like RAM. See virtual context and MemGPT.
Is LangMem the right choice for LangChain?
Yes for LangGraph/LangChain stacks — LangMem integrates with checkpointers and long-term memory stores natively. See LangMem explained.
What open-source memory frameworks exist?
Engram, Mem0 (OSS layer), LangMem, Cognee, Letta (OSS), Zep (OSS components), Redis DIY and LlamaIndex Memory. See open-source vs managed.
Can Redis be used as agent memory?
Yes as a fast buffer and vector store — but you implement memory logic yourself. See Redis for agent memory.
Cognee vs Zep — which knowledge graph?
Zep focuses on temporal graph memory with Graphiti for production agents. Cognee builds evolving knowledge graphs for fact-heavy domains. Both are graph-class; compare on your temporal and ops needs.
What is Supermemory?
A managed vector memory API with simple HTTP integration and MCP support. See Supermemory explained.
Should I benchmark before choosing a framework?
Yes. Run LOCOMO and LongMemEval subsets on your use case before committing. See evaluation hub.