SaaS Metrics
Product Positioning & Context
AI memory API with 3 types: semantic (facts), episodic (events), and procedural (learned workflows). One API call extracts all three automatically. Killer feature: your agent completes a task → Mengram saves the steps → next time it already knows the optimal path with success/failure tracking. Works with Claude (MCP), LangChain, CrewAI, OpenClaw. Free, open-source, Apache 2.0.
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
Deep-Dive FAQs
What is Mengram?
Mengram is a digital product or tool described as: AI memory API with 3 types: facts, events, and workflows
Where did Mengram originate?
Data for Mengram was aggregated directly from the Product Hunt community ecosystem, representing raw developer and early-adopter sentiment.
When was Mengram publicly launched?
The initial public indexing or launch date for Mengram within our tracked developer communities was recorded on February 19, 2026.
How popular is Mengram?
Mengram has achieved measurable traction, logging over 114 traction score and facilitating 9 recorded discussions or engagements.
Which technical categories define Mengram?
Based on metadata extraction, Mengram is categorized under topics such as: Open Source, Developer Tools, Artificial Intelligence.
What are some commercial alternatives to Mengram?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as TrustedRouter, which offers overlapping value propositions.
How does the creator describe Mengram?
The original author or development team describes the product as follows: "AI memory API with 3 types: semantic (facts), episodic (events), and procedural (learned workflows). One API call extracts all three automatically. Killer feature: your agent completes a task → Men..."
Community Voice & Feedback
How do you handle the hard edge cases of long-term memory in production—contradictions, staleness, and “what happened last week” temporal queries—without making developers build their own maintenance rules around your API?
Memory is one of those things that sounds simple until you're actually building with it. Three types makes sense as a decomposition but I'm curious about edge cases where something blurs between categories - like a user preference that's both a fact AND affects how workflows run. Do you reconcile that at write time or does the consumer figure it out?
Hey! This sounds really cool, do you have any example code for using this with LangChain?
Building with agents means hitting memory walls fast - semantic search on a flat vector store doesn't cut it once you have multiple intent types to track. The 3-way split makes sense to me because facts, events, and workflows really do need different retrieval patterns.One thing I haven't seen clean answers to: preferences that should trigger recurring actions. A user likes X -> that becomes a workflow trigger eventually. Is that modeled in Mengram or application concern?
Hey Product Hunt! I'm Ali, a 32-year-old developer from Almaty, Kazakhstan.
I built Mengram because every AI memory tool I tried only stored facts. But human memory has 3 types — we remember facts (semantic), events (episodic), and how to do things (procedural). So I built an API that does all three.
The killer feature: your AI agent completes a task → Mengram saves the steps as a procedure → next time, it already knows the optimal path. No other memory API does this.
It's free, open-source, and takes 60 seconds to set up. Would love your feedback!
I built Mengram because every AI memory tool I tried only stored facts. But human memory has 3 types — we remember facts (semantic), events (episodic), and how to do things (procedural). So I built an API that does all three.
The killer feature: your AI agent completes a task → Mengram saves the steps as a procedure → next time, it already knows the optimal path. No other memory API does this.
It's free, open-source, and takes 60 seconds to set up. Would love your feedback!
Discovery Source
Product Hunt Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
No mainstream media stories specifically mentioning this product name have been intercepted yet.
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.