Alternatives
Products that do what Memoripy – AI Memory Layer with Short- & Long-Term Memory, Clustering does
Hey HN! I built Memoripy, a memory layer for AI that adds short-term, long-term, and semantic memory capabilities to enhance LLM applications. It helps AI systems retain and prioritize past interactions, adapt over time, and respond with greater context and personalization. Memoripy uses semantic clustering to retrieve relevant memories, along with adaptive memory decay and reinforcement, so interactions stay fresh and context-aware. It’s designed for easy integration with OpenAI, Ollama, and other platforms—giving your AI applications dynamic memory management with minimal setup. Would love…
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Memora gives AI the ability to recall memories during interactions, just like humans do subconsciously. For now, it’s just text-based memories, but our vision extends to the full spectrum of human memory: emotions, audio, video. Key Features: Built-in multi-tenancy for managing multiple organizations, users, and agents. Time-stamped memories to track how information evolves over time. Scalable, modular, and developer-friendly design. GitHub: https://github.com/ELZAI/memora Install: pip install memora-core We’re looking for feedback and contributions, let’s change how we…
2025 · github.com
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The memory layer that decides what's worth remembering
13d ago · skynetlab-cortex.com
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It's an biological inspired decay system for our memories with extended support of temporal reasoning. Created a CLI command to infer knowledge from the context stored in memory system without any token utilization or llm call. It comes with a memory dashboard to monitor and manage your memories it can be extended as audit trail for agents as well !
May 2026
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Hey HN! We're Vasilije, Laszlo and Lazar, the authors of a new paper and part of https://www.cognee.ai. cognee let’s you build memory layers for AI applications and agents, allowing them to personalize results, connect various data sources, and add custom rules. This enables AI apps to deliver increasingly accurate responses, we reached almost 90% on standard industry benchmarks as you can see here https://github.com/topoteretes/cognee/tree/main/evals and our paper can be accessed at: https://arxiv.org/abs/2505.24478 and collab…
2025 · github.com
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I’m not a software engineer or a genius — I just had a weird idea: What if memory wasn’t just stored as text or embeddings, but as symbolic, byte-level thoughts that could be passed between AIs? That idea became MemoryCore Lite: Encodes thoughts into lightweight bytecode Shares them across nodes via peer-to-peer sync Fully decentralized, no GPU needed Designed to evolve into its own AI knowledge mesh I just open-sourced the basic version here: github.com/ProToxicNinja/MemoryCore-Lite-Symbolic-Memory-Engine-for-AI It’s simple — but everything works. You can build better tokenizers,…
2025 · github.com
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Hi HN, I’ve been building AI agents and copilots, and kept running into a frustrating problem: they don’t fail loudly, they forget things quietly. Users re-explain preferences, agents contradict earlier responses, and context resets without any clear visibility into why. I built Memograph CLI as a debugging tool to analyze conversation transcripts and show: - what the agent forgot - where continuity broke - contradictions and repeated context - estimated token waste due to re-prompting It works locally and supports plain text or JSON transcripts. Example: $ memograph Output: Cognitive Drift…
Feb 2026
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