Learn how to build better AI agents with memory, context, and knowledge layers. Tutorials, guides, and best practices from the RetainDB team.
High-intent comparison page for buyers evaluating memory layers.
Compare benchmark proof, positioning, and time-to-value.
See how RetainDB differs from framework-first approaches.
Compare against another benchmark-led memory and context platform.
Useful when the team is deciding between memory layers and context infrastructure.
Compare a general memory layer against identity-rich agent memory.
A comprehensive, step-by-step guide to adding persistent memory to AI agents. Learn memory architectures, storage strategies, and implementation patterns for production-ready agents.
Understand the difference between stateful and stateless AI agents. Learn when to use each architecture, common failure modes, and how to build production-ready stateful agents.
Understand when retrieval solves the problem, when persistent memory matters more, and how the two work together in production agent systems.
A practical guide to working context, retrieved knowledge, and persistent memory assembled before every model call.
Learn where LangGraph checkpoints stop, where persistent memory begins, and how to combine both cleanly.
Separate chat history from durable memory so LangChain agents can remember users across sessions.
Add persistent memory to an OpenAI assistant so it can remember preferences, prior decisions, and user context over time.
Preference memory is one of the highest-leverage UX wins in AI. Here is how to implement it without bloating every prompt.
Semantic retrieval and persistent memory solve different product problems. This guide explains where each layer belongs.
A practical guide to assembling retrieval, memory, and working state before each model call.
Learn how to separate working state, retrieved knowledge, and persistent memory in production agent systems.
Most AI product teams are losing users to a problem they haven't fully named yet. It's not the model. It's not the prompt. It's that the agent forgets everything.
Teams spend weeks debating which LLM to use. The model is rarely the bottleneck. Here's what actually determines whether your AI product feels intelligent.
Vector search is the default for AI memory retrieval. For a specific class of queries, it fails quietly and predictably. Here's the hybrid retrieval architecture that fixes it.
A deep technical breakdown of AI agent memory: the four memory types, how extraction and retrieval work under the hood, and what separates a memory system that feels like magic from one that fails silently in production.
It is not a model problem. It is not a prompt problem. The reason your AI agent forgets everything between sessions is architectural, and it is entirely solvable. This is the complete fix.
Vector databases and memory layers are not the same thing. This post explains exactly what each does, where each one belongs in your architecture, and what happens when teams confuse the two.
Adding memory to a demo is easy. Making it work reliably in production, at scale, without data leaks or memory rot, is a different problem. This is the guide for teams past the prototype stage.
RAG and memory are not the same thing. This post explains exactly what each does, why the confusion is so common, and what breaks when you treat them as interchangeable.
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