Articles tagged "agent memory".
7 articles. Sorted newest first.
Why Agent Memory Is Broken (And the Path to Fix It)
The standard agent loop has no idea who you are between sessions. Conversation buffers forget, embeddings retrieve the similar but not the true. Here is what typed memory looks like instead.
Typed Memory vs Vector Embeddings for AI Agents
Why storing 'Alice is in UTC' as an embedding and retrieving it later by cosine similarity is the wrong primitive — and what to use instead.
ONTO vs LangChain: When Typed Memory Beats Chains
LangChain is the largest agent orchestration ecosystem in the world. So why pick ONTO instead? A direct comparison on memory, planning, consent, and operational concerns.
ONTO vs Mem0: Choosing a Memory Layer for AI Agents
Mem0 is a popular dedicated memory layer for LLMs. ONTO is an agent SDK with memory built in. What's the difference, and which one is right for your project?
ONTO vs Letta (MemGPT): Two Approaches to Long-Term Agent Memory
Letta (formerly MemGPT) pioneered the idea of agents managing their own memory through tool calls. ONTO takes a typed-graph approach. Same problem, different solutions.
Building a Personal AI Assistant That Actually Remembers You
Long-running personal AI fails on memory more than anything else. Here's an architecture for an assistant that learns who you are across sessions, with the user's facts staying on the user's side.
Multi-Tenant Memory: Scoping AI Agents for SaaS
If you're running an agent SDK in a multi-tenant SaaS, memory leakage between tenants is the bug that ends your company. Here's the architecture that prevents it structurally.