Streaming Is the Memory AI Is Missing
Streaming Is the Memory AI Is Missing
The AI industry has a memory problem, and they’re solving it the hard way.
Every week there’s a new paper on “context management” or “memory architectures” for LLMs. Researchers are building elaborate systems to help AI remember: vector databases for retrieval, summarization layers that compress old conversations, graph structures that track relationships between facts.
They’re reinventing event sourcing. They just don’t know it yet.
The Problem Everyone Is Solving
LLMs are stateless. Every conversation starts fresh. The context window is the only “memory” they have, and it’s expensive, limited, and volatile.
Researchers have coined a term for what happens when you try to work around this: context rot. As Chroma’s research demonstrates, LLMs don’t maintain consistent performance across input lengths. Even on simple tasks, performance degrades unpredictably as context grows. The 10,000th token is not as reliable as the 10th.
Anthropic’s engineering team frames it this way: context must be treated as a finite resource with diminishing marginal returns. Like humans with limited working memory, LLMs have an “attention budget” that gets depleted with every new token.
The solutions being proposed all follow a pattern:
- Store interactions somewhere durable
- Retrieve relevant context when needed
- Compress or summarize to fit constraints
Sound familiar? That’s a stream processor with a materialized view.
What Streaming Already Solved
Kafka and its ecosystem solved these problems years ago for a different reason: making sense of high-volume, time-ordered data across distributed systems.
The primitives map directly:
| AI Memory Problem | Streaming Solution |
|---|---|
| Durable history | Immutable event log |
| Retrieve relevant context | Stream processing + materialized views |
| Compress old information | Compaction, windowing, aggregation |
| React to new information | Consumer groups, real-time processing |
| Replay and debug | Log replay from any offset |
The event log isn’t just storage. It’s a timeline you can query, replay, and reinterpret as understanding evolves.
The AI Memory Landscape
The AI community is converging on architectures that look remarkably like what streaming engineers have built for years.
Mem0, which raised $24M in late 2025, offers a “memory layer for AI applications” that dynamically extracts, consolidates, and retrieves information from conversations. Their research shows 26% higher accuracy and 90% token savings compared to stuffing everything into context. What are they building? A system that stores interactions as events, retrieves relevant ones on demand, and compresses history intelligently.
MemGPT/Letta takes inspiration directly from operating systems, proposing “virtual context management” that pages data in and out of the LLM’s context window, much like an OS manages RAM and disk. The MemGPT paper explicitly draws the analogy: main context is RAM, external storage is disk, and the LLM learns to manage its own memory through function calls.
VentureBeat recently covered a new architecture called GAM (General Agentic Memory) that keeps a full, lossless record and layers smart retrieval on top, essentially performing “just-in-time compilation” of context. Instead of pre-compressing memory, it stores everything and compiles a tailored context on the fly.
These are all variations of the same insight: treat memory as a stream of events, not a static store.
Kafka Is Already the Backbone
The streaming community has noticed this convergence. Confluent’s blog argues that “the future of AI agents is event-driven,” positioning Kafka as the infrastructure layer for agentic AI. Red Hat’s developer blog makes the case that Kafka is “the invisible infrastructure backbone” for AI systems that need to coordinate across multiple steps, decisions, and actions.
Kai Waehner’s analysis connects the dots explicitly: agentic AI requires real-time data to act autonomously, and Kafka + Flink provide the event-driven foundation that makes this possible. Traditional batch processing introduces delays and data staleness, exactly the problems that cause AI systems to “forget.”
Sean Falconer’s PodPrep AI is a concrete example: an AI research assistant where MongoDB changes trigger Kafka events, which kick off agentic workflows, with results flowing back as new events. The application layer doesn’t know anything about AI; it just consumes events when they’re ready.
The pattern is clear: events in, agents process, events out, repeat.
Your Second Brain Already Has an Architecture
The “second brain” concept has been popular for years: capture everything, process later, trust the system to surface what matters. But most implementations are static. Notes sit in folders. Links rot in bookmarks.
Streaming makes the second brain alive.
Every signal you capture becomes an event: a saved link, a voice note, a half-formed thought. Agents watch the stream, enrich events with context, and emit new events based on patterns they observe. The system doesn’t just store; it interprets, continuously.
This is what I’m building with Vibe Decoding. But the idea is bigger than one project. It’s an architectural pattern that applies to any system that needs to remember, learn, and act over time.
Why Now?
Two things changed:
1. AI agents can consume streams intelligently. Before LLMs, you needed explicit rules for every enrichment. Now an agent can watch events flow past and decide what’s worth acting on. Akka’s analysis puts it directly: “Event sourcing is the central supporting column for all of the key features of agentic systems like memory, RAG, multi-agent operation, tool integration, and vector embeddings.”
2. The cost of running Kafka dropped. Redpanda ships as a single binary with no JVM or ZooKeeper dependencies, and you can run it on a laptop. Confluent Cloud has a free tier. You don’t need a platform team to experiment.
The builders creating AI applications today are hitting the memory wall. They’re duct-taping context into prompts, losing information between sessions, rebuilding state from scratch every time.
Streaming is the answer they haven’t discovered yet.
The Missing Piece
Here’s the gap: developers building AI apps don’t think in streams. They think in requests and responses. They reach for REST, for databases, for queues.
Streaming feels like enterprise infrastructure because that’s how it’s been sold. But the core pattern (capture signals, process over time, materialize views, surface insights) is exactly what personal AI needs.
The tooling exists. The mental model is what’s missing.
Most developers have an AWS account. They know what S3 is, what EC2 does. They’ve probably spun up a Kubernetes cluster for a side project. But Kafka? That’s still something you encounter at work, not something you’d reach for on a weekend.
Stephen O’Grady’s The New Kingmakers thesis, that developers drive technology adoption bottom-up, applied to REST APIs, to containers, even to Kubernetes eventually. But it hasn’t happened with streaming yet. Developers weren’t the ones choosing Kafka; platform teams were.
With the reset happening around AI tooling, that could change. The people building AI applications are feeling the pain of statelessness. They’re searching for solutions. They just don’t know the solution already exists in a different aisle.
An Invitation
If you’re building AI applications and fighting with memory, context, or state, consider this: you might not need a smarter retrieval system. You might need an event log.
Streaming isn’t just for data pipelines at scale. It’s an architecture for systems that need to remember.
Your AI’s memory problem might already have a solution. It’s just waiting to be discovered.