Give Queue-Driven AI Pipelines Shared Memory Across Every Pipeline Stage
Multi-stage AI pipelines built on SQS, Kafka, RabbitMQ, or Pub/Sub lose context between stages. Each stage gets only what fits in the message. MemoryLake gives queue-driven pipelines shared memory across every stage — so context flows even when messages don't carry it.
Give Queue-Driven AI Pipelines Shared Memory Across Every Pipeline Stage
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The problem: queue messages don't carry enough context
Stage 1 enriched the data. Stage 2 needs that enrichment plus user history. The message grows; queues choke. Or stage 2 re-fetches from databases — slow, expensive, and out of sync. Queue pipelines need shared memory beyond message payloads.
How MemoryLake supports queue-driven pipelines
Shared memory across stages
Each stage reads and writes to the same namespace.
Pipeline-scoped memory namespaces
Memory organized per pipeline, per entity.
Light queue messages
Messages carry IDs; stages retrieve context from MemoryLake.
Audit trail per stage transition
Track context flow across stages.
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How it works for queue-driven pipeline memory
- Connect — Each stage authenticates with MemoryLake.
- Structure — Stage 1 writes context; later stages retrieve.
- Reuse — Messages stay light; context lives in shared memory.
Before vs. after: queue-driven AI pipeline memory
| DIY pipeline state | MemoryLake | |
|---|---|---|
| Cross-stage context | Stuffed in messages | Shared memory |
| Message size | Bloats over stages | Stays light |
| Stage-to-stage re-fetch | Common | Eliminated |
| Audit pipeline flow | Custom | Memory provenance |
Who this is for
Engineering teams running multi-stage AI pipelines on SQS, Kafka, RabbitMQ, Pub/Sub — where queue payload limits and re-fetch overhead are degrading pipeline quality and cost.
Try this use case on your own account
Stage 1 writes what a customer asked for into memory and puts a light message on the queue; stage 2 picks it up a second later and asks memory what to do. But MemoryLake reads a conversation message in the background, in 10 to 77 seconds, so stage 2 reads the memory from before the write and acts on the old region, contact or seat count, as sure of itself as when it is right. Waiting for extraction fixes it slowly; writing the result as a fact and putting its id in the message fixes it at queue speed.
What you will see
- Eight customers, six changing one value and two controls, through three pipelines with their own memory: A appends the note (
conv msg append) and enqueues at once, B waits forconv consumed-messages+cook_finished, C also writes a dated result withfact addand puts the fact id in the queue message - Over 7 runs, stage 2 got the new value for the changed customers A 0 / 126 times (126 old values, read at +1.8 s), B 126 / 126 at a median +37.3 s, C 126 / 126 at +2.4 s
- Waiting has a second cost: extraction wrote a note as a fact with no customer in it ("New environments must be created in eu-central") and MemoryRouter attached it to another customer's question, so B's controls were right 36 / 42 times, A's and C's 42 / 42
- Stage 2 reads through MemoryRouter (
router chat --boundary, three reads at once, an empty-retrieval baseline); a census of what made it into memory,memoryandasksubcommands and a local web UI
You need: a MemoryLake API key · the memorylake CLI · Python 3.9+
Run it
git clone https://github.com/memorylake-ai/memorylake-usecases.git cd memorylake-usecases/stage-handoff-memory export MEMORYLAKE_API_KEY=sk-… python3 demo.py # or: python3 web/server.py
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Frequently asked questions
Queue platform support?
Queue platform support?
SQS, Kafka, RabbitMQ, Pub/Sub, Redis Streams — all supported.
Throughput at scale?
Throughput at scale?
Tested at high throughput; per-namespace concurrency.
Self-host?
Self-host?
Yes — enterprise tier deploys in your VPC.