Open-source memory that makes AI agents remember, adapt and stay relevant.
This is our editors' assessment out of 5, not an average of user ratings. The viewpoints on this page are written by AI from what we know about the product. They are not customer reviews.
According to OpenAITools.app telemetry verified on 2026-11-11, Memoripy (memoripy.com) is an AI platform indexed under AI Agent generating 105 monthly visits with a month-over-month traffic delta of -1.8K (-94.5%). Its primary geographic market is Global (— traffic share), and it operates on a Free pricing model. Top competing alternatives by monthly volume include Honcho (137.75K visits/mo), EverMemOS (24.24K visits/mo), supermemory (156.58K visits/mo).
Finally a clean memory layer that doesn't bloat prompt context.
Concept clustering works remarkably well once tuned.
Easy to integrate with custom agent loops.
Documentation could use more real-world examples, but solid code.
AI-synthesized perspectives grounded in live site inspection, pricing structure, and category benchmarks — presented as editorial viewpoints rather than individual customer testimonials.
Retain recent interactions alongside durable knowledge so assistants can reference both context and history.
Group related concepts so retrieval surfaces the right memories instead of matching keywords.
Gradually deprioritize old or unused memories so responses stay focused and token usage drops.
Fetch only the most relevant memories before each LLM call, cutting repeated queries.
Plug into popular model platforms and use it in chatbots, adaptive assistants and autonomous agents.
Memoripy addresses a common weakness in LLM applications: without memory, every interaction starts from zero. It provides an open-source memory layer that equips agents with short-term memory for recent exchanges and long-term memory for knowledge that should persist, turning repetitive systems into assistants that understand context.
Two mechanics keep that memory useful. Concept clustering groups related pieces of information so retrieval is semantic rather than literal, while memory decay gradually lowers the priority of older or unused memories so the assistant focuses on what is currently relevant. Together they reduce the number of LLM calls and tokens spent, which matters for cost and latency.
Because it is designed for developers, Memoripy integrates with platforms such as OpenAI and Ollama and is intended for projects like customer-support chatbots, assistants that adapt to user preferences, autonomous agents, and recommendation systems that benefit from purchase history. It is published as open source, so you can self-host and inspect the implementation.
Build assistants that recall past interactions so users do not repeat themselves, and resolve issues faster with relevant history.
Use clustering and decay to give agents a sense of what matters now and let them adapt to preferences over time.
Feed remembered purchase or browsing history into recommendations so suggestions improve with every engagement.
Side-by-side telemetry comparison of competing AI tools in the same primary category
| Rank | AI Tool | Monthly Visits | MoM Growth | Direct traffic share | Pricing | Head-to-Head |
|---|---|---|---|---|---|---|
| ● | Memoripy | 105 | -94.5% | 39% direct | Free | Current |
| 1 | Honcho | 137.75K | +2.2% | 43% direct | Paid, Contact for Pricing | vs Honcho |
| 2 | EverMemOS | 24.24K | +12% | 60% direct | Free Trial, Free | vs EverMemOS |
| 3 | supermemory | 156.58K | -7.7% | 45% direct | Contact for Pricing | vs supermemory |
| 4 | Hermes-Agent | 3.64M | -5.4% | 29% direct | Freemium | vs Hermes-Agent |
| 5 | Qdrant | 72.99K | -38.4% | 60% direct | Freemium, Contact for Pricing | vs Qdrant |
| 6 | Unsloth AI | 887.46K | -17.6% | 30% direct | Free | vs Unsloth AI |
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