Flumes AI Blog - Insights on Memory Infrastructure for AI

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Build smarter AI memory

Explore updates, guides, and best practices for unified memory in AI applications.

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Context Engineering Is the New Prompt Engineering

Prompt engineering was about crafting inputs. Context engineering is about designing the full information state the model sees. It’s a technical challenge spanning retrieval, summarization, session state, and memory storage.

Update

5 min read

Memory vs Retrieval: Why LLMs Need Both, and How to Build It Right

Vector search ≠ memory. Learn why retrieval isn’t enough for LLM agents, and how the Flumes SDK adds long-term memory, summarization, and continuity in just 3 lines of code.

Update

5 min read

Why Vector DBs Get Expensive Fast (And What to Do Instead)

Understanding the hidden costs of vector search and how a purpose-built AI memory layer changes the game.

Update

5 min read

Building a Memory Stack: What We Learned Designing Flumes

We tried vector DBs, Redis, SQL, and object stores to build agent memory and hit a wall. Here’s what we learned, and why Flumes is a new kind of memory engine for AI.

Update

5 min read

What Do LLMs Actually Forget?

LLMs don’t remember: they just read context windows. Learn why true memory requires external systems and how Flumes bridges the gap between prompts and persistence.

Update

5 min read

The Cost of Remembering: Token Budgets, Compression & Cold Storage for AI

Learn how token usage impacts AI system costs and how to structure memory using tiered storage, summarization, and compression to keep agents efficient.

Update

5 min read

AI Memory Is the Missing Layer

Discover why AI needs more than vector search. Learn what the “AI memory layer” is, how it differs from traditional storage, and why Flumes is redefining how agents remember, reason, and act with continuity.

Unified memory for AI apps

Store, recall, and manage memory with a single API. No more complex stacks or scattered tools—just fast, flexible infrastructure for your AI agents. Learn more about how it works.

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