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HeurChain — Agent Memory Infrastructure
Your agents forget everything. HeurChain fixes that.
HeurChain is memory infrastructure for AI agents — not a wrapper, not a prompt trick. Agents connect via MCP and get a persistent, structured memory store that survives across sessions, models, and machines. Memory is organized in three tiers (ops/ notes/ self/) with ACT-R cognitive decay. Agents remember what they keep using. They forget what they don't. Hybrid BM25 + vector retrieval via RRF. Sub-200ms P95. MIT licensed. Docker in under 10 minutes.
Hey Product Hunt — I'm Peter, and I built HeurChain after running into the same wall on every agent project. I jumped on the Openclaw bandwagon very quickly and woke up every morning after a long night and went straight to the terminal to build and play around with agents and AI automation, and I quickly learned about agent amnesia And so I started building a memory system around it unaware of how proliferate memory systems were already. But I figured why not use my education in psychology and neuroscience to put together some computational models around it considering I'm not using that education for much of anything else anyways so . . .
The problem is simple: every AI agent session starts from zero. No memory of what was decided, what context was built, what mistakes were already made. You end up re-explaining the same things over and over, and your agents never compound their knowledge.
The obvious fix — dump everything into context — doesn't scale and doesn't model how memory actually works. Some things should persist. Some things should fade. That distinction matters a lot once you're running agents at any real volume.
HeurChain uses ACT-R, a cognitive architecture from psychology, to model memory as activation that decays over time. ops/ is volatile working memory. notes/ is session context. self/ is long-term identity — it barely decays at all. Memory that keeps getting recalled stays alive. Memory that goes untouched fades out. It behaves the way human memory does.
About HeurChain — Agent Memory Infrastructure on Product Hunt
HeurChain — Agent Memory Infrastructure was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #127 on the daily leaderboard. HeurChain is memory infrastructure for AI agents — not a wrapper, not a prompt trick. Agents connect via MCP and get a persistent, structured memory store that survives across sessions, models, and machines. Memory is organized in three tiers (ops/ notes/ self/) with ACT-R cognitive decay. Agents remember what they keep using. They forget what they don't. Hybrid BM25 + vector retrieval via RRF. Sub-200ms P95. MIT licensed. Docker in under 10 minutes.
On the analytics side, HeurChain — Agent Memory Infrastructure competes within Productivity, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.7M followers on Product Hunt. The dashboard above tracks how HeurChain — Agent Memory Infrastructure performed against the three products that launched closest to it on the same day.
Who hunted HeurChain — Agent Memory Infrastructure?
HeurChain — Agent Memory Infrastructure was hunted by Peter Medina. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of HeurChain — Agent Memory Infrastructure including community comment highlights and product details, visit the product overview.
Peter Medina
Hey Product Hunt — I'm Peter, and I built HeurChain after running into the same wall on every agent project. I jumped on the Openclaw bandwagon very quickly and woke up every morning after a long night and went straight to the terminal to build and play around with agents and AI automation, and I quickly learned about agent amnesia And so I started building a memory system around it unaware of how proliferate memory systems were already. But I figured why not use my education in psychology and neuroscience to put together some computational models around it considering I'm not using that education for much of anything else anyways so . . .
The problem is simple: every AI agent session starts from zero. No memory of what was decided, what context was built, what mistakes were already made. You end up re-explaining the same things over and over, and your agents never compound their knowledge.
The obvious fix — dump everything into context — doesn't scale and doesn't model how memory actually works. Some things should persist. Some things should fade. That distinction matters a lot once you're running agents at any real volume.
HeurChain uses ACT-R, a cognitive architecture from psychology, to model memory as activation that decays over time. ops/ is volatile working memory. notes/ is session context. self/ is long-term identity — it barely decays at all. Memory that keeps getting recalled stays alive. Memory that goes untouched fades out. It behaves the way human memory does.