Weaviate Guide to Agent Memory With Engram
Every tool you've ever set up has an onboarding screen you just straight click past. Usually the defaults are chosen by someone who thought about them harder than you have time to.
Engram's setup has one of those screens too, and it takes a couple of minutes to get through:
name a project → pick a template → click past topics that are already filled in for you → generate an API key.
It keeps working, too. That's the interesting part, because nothing ever nudges you back to that screen. But somewhere between "it works" and "it works the way I meant it to", four decisions turn out to be yours rather than Engram's:
- What gets remembered (the topic descriptions)
- How many memories, and whose (bounded topics and scope)
- What comes back (the retrieval mode)
- Where it goes (placement in your prompt)
In this post we walk through all four, with real outputs from a project mostly running the stock personalization template.
How Engram turns raw data into memories
Engram runs an asynchronous pipeline over whatever raw data you send it. By default, it:
- extracts the memories that matter,
- transforms them against what's already stored,
- commits the result.
Victoria's walkthrough video covers that end-to-end, including the console setup and the SDK:
For how the pipeline itself works, the Engram: Memory by Weaviate blog goes through it step by step. Which memories it pulls out of the data you send, though, is decided by your topic descriptions, so that is where we start.
How topic descriptions decide what gets remembered
When you create a new Engram project in the console, the
"I'm Prajjwal, a developer advocate at Weaviate. I write all my demos in Python, and I have a hard rule that they stay under 100 lines."
"I'm Prajjwal, a developer advocate at Weaviate. I write all my demos in Python, and I have a hard rule that they stay under 100 lines."
Five memories came back across the three topics:
Other ready-to-use project templates exist as well, like
We didn’t write any routing logic or a formatter. The descriptions did both, because a topic description is the memory extraction prompt, and it controls three things:
Take a throwaway message like:
"Two hours lost to a Docker rebuild, my headphones died mid-call, and it started raining right as I stepped out. Anyway, I finally swapped the demo over to qwen3-embedding-8b."
"Two hours lost to a Docker rebuild, my headphones died mid-call, and it started raining right as I stepped out. Anyway, I finally swapped the demo over to qwen3-embedding-8b."