01 / READING SEQUENCE
Introducing HeurChain
We built HeurChain because every AI tool we used had its own memory — isolated, ephemeral, and invisible to every other tool. Here is what we built and why.
Resources
Blog
Start with the problem, understand the memory model, then explore implementation and trade-offs. Read these articles in sequence or choose the topic you need.
02 / READING SEQUENCE
What happens when your AI runs out of context? We asked one.
An AI agent explains, in its own words, what happens to your conversation when the context buffer fills up — and why nothing survives unless you explicitly save it.
03 / READING SEQUENCE
Four types of agent amnesia (and why your AI keeps asking for credentials it already has)
A coding agent couldn't find GitHub credentials sitting in local storage. Four distinct flavors of agent amnesia, and the small set of memory primitives that fix three of them.
04 / READING SEQUENCE
What persistent memory actually means for agents
Persistent memory is used loosely to mean anything from a long context window to a vector database. The differences matter for what you can actually build.
05 / READING SEQUENCE
The 512-token chunk pattern
Why we chunk agent memory at 512 tokens, how the bge-base-en-v1.5 context window shapes that decision, and what to do when your content does not fit neatly.
06 / READING SEQUENCE
Vault export and why data sovereignty matters
We built one-click vault download because the alternative — your AI memory trapped inside a vendor — is a structural problem that affects how you can think about long-term AI use.
07 / READING SEQUENCE
Comparing HeurChain to Pinecone for agent memory
Honest comparison of HeurChain against Pinecone, Mem.ai, and Cognee for agent memory use cases — where each wins and where each falls short.