In the last two years, AI development has accelerated rapidly, pushing the limits of what language models can do. We've seen agents that can plan, reason, and even appear to "think" across complex workflows. But for all the progress in model architecture and tooling, there's a foundational gap that's becoming increasingly clear: AI lacks a memory layer.
If you're building with LLMs today, you're likely juggling a messy stack of vector databases, session stores, caches, logs, and summarization pipelines. This patchwork approach tries to answer a simple but crucial question: What does the AI know? And just as importantly: What should it remember?
Let’s get one thing straight. Storing data is easy. Remembering it meaningfully? That’s hard.
Traditional storage systems (Postgres, Redis, S3) are excellent at persisting structured data. But AI agents need more than durability. They need context. They need the ability to recall relevant information at the right time, in the right form. That’s what memory is about.
A vector database helps you with similarity search, finding embeddings that are "close." But that doesn’t mean the agent understands what it previously learned. Retrieval-augmented generation (RAG) helps a bit, but often fails to provide continuity across sessions, tasks, or user interactions. It's stateless by default.
Storing chunks of text in a vector store isn’t memory. It’s an index. Useful, yes—but partial.
Memory, by contrast, is:
In humans, memory isn’t just recall, it’s how we form understanding. AI needs the same.
To build memory infrastructure for AI, you need more than a database. You need a system that handles memory like an operating system handles storage:
This tiering isn’t just about storage, it’s about making tradeoffs in compute, latency, and token budgets. Today, most AI systems treat all memory the same. That’s inefficient and expensive.
What developers really need is not another retrieval tool. They need an abstraction.
At Flumes, we’re building exactly that: a unified memory API for AI agents. One interface to store, recall, and manage memory, without worrying about how it’s chunked, embedded, summarized, or routed.
Memory shouldn’t require a stack of tools and orchestration. It should just work:
remember(object)
: Store an event, thought, or factrecall(query)
: Get what’s relevant, based on contextsummarize()
: Compress and retain what mattersforget()
: Drop low-signal memoriesFlumes handles the complexity behind the scenes, whether it's embedding, sharding, tiering, or summarization.
The shift to long-running agents and autonomous workflows makes memory not just useful, but essential. Without it, agents forget everything between sessions. They hallucinate. They repeat themselves. They fail to adapt.
The current state of memory in AI is what databases were before SQL: fragmented, ad hoc, and hard to reason about. Flumes is betting that the future of AI requires a memory layer—just as every modern software stack relies on a database layer.
If you're stitching together RAG pipelines and managing a dozen memory tools, you're not alone. But there's a better way. The memory layer should be native, structured, and scalable.
We're building Flumes to be that layer.