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Storia in 5 fonti

Memora scales agent memory to boost long-horizon productivity

AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this with a scalable memory system separating what’s stored from how it's retrieved.

Raccontata daventurebeat.comdev.tomicrosoft.comcomputerworld.cominfoworld.com

Confronto fonti

5 prospettive sulla stessa storia
AI · summaries
microsoft.comStai leggendo3 g fa

Memora scales agent memory to boost long-horizon productivity

AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this with a scalable memory system separating…

originale

Timeline cronologica

  1. sabato 27 giugno 2026·venturebeat.com

    AI agent memory: MRAgent cuts token use up to 27x | VentureBeat

    NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — using step-by-step reasoning.

  2. domenica 28 giugno 2026·dev.to

    Mem0 Memory Layer: 5 Hidden Uses of the 60K-Star Agent Memory Engine

    What if your AI agent could remember every user preference, every past conversation detail, and every...

computerworld.com
3 g fa

Microsoft unveils Memora to tackle AI agents’ memory problem

The research project promises more efficient long-term recall by organizing knowledge around abstractions and cue-based retrieval instead of raw conversation history.

Leggi questa versione → originale
infoworld.com2 g fa

How to improve the memory of AI agents

Retrieval-augmented generation enhances the performance of AI agents by expanding their recall. It can do this in three important ways.

Leggi questa versione → originale
venturebeat.com6 g fa

AI agent memory: MRAgent cuts token use up to 27x | VentureBeat

NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — using step-by-step reasoning.

Leggi questa versione → originale
dev.to4 g fa

How I Build AI Agents That Actually Remember

Checkpoint + vector store enables AI agents to resume multi-step workflows without state loss on crash or timeout. For production automation teams, pattern eliminates restarts and ensures reliable execution on complex tasks (job scoring, resume generation, browser automation).

Leggi questa versione → originale
  • domenica 28 giugno 2026·dev.to

    How I Build AI Agents That Actually Remember

    Three production-tested patterns for AI agent memory: structured logs, vector stores, and hybrid checkpointing.

  • domenica 28 giugno 2026·dev.to

    How I Built a Memory-Backed Sales Agent That Actually Remembers Deals

    Most AI sales assistants are great at summarizing a meeting. The problem is that they forget...

  • lunedì 29 giugno 2026·microsoft.com

    Memora scales agent memory to boost long-horizon productivity

    AI agents can't remember past conversations. They must constantly reload or retrieve context, which grows less efficient as tasks get longer and more complex. Memora solves this…

  • martedì 30 giugno 2026·computerworld.com

    Microsoft unveils Memora to tackle AI agents’ memory problem

    The research project promises more efficient long-term recall by organizing knowledge around abstractions and cue-based retrieval instead of raw conversation history.

  • mercoledì 1 luglio 2026·infoworld.com

    How to improve the memory of AI agents

    Retrieval-augmented generation enhances the performance of AI agents by expanding their recall. It can do this in three important ways.