Aashi Dutt

Five papers show LLM agents often misuse the memories they retrieve. What the research says, how much to trust it, and what builders can do.

Understand memory staleness in AI agents, why outdated information leads to errors, and the best techniques to maintain accurate long-term memory.

Persistent memory gives field service AI agents what the business already knows, past repairs, equipment history, and customer preferences, so the next technician arrives prepared.

Open-source AI agents: built-in memory, persistent context, and how to enable personalized behavior in production.

Learn the differences between structured and unstructured memory in AI agents, how each stores information, and when to use them for better retrieval and reasoning.

Compare semantic, episodic, and procedural memory in AI agents. Learn how each memory type stores knowledge, experiences, and skills—and why all three are essential for autonomous AI.

Compare stateful vs stateless AI agents, understand how memory impacts context, personalization, and decision-making, and learn when to use each approach.

Learn what a confidence score in AI memory is, how models calculate prediction confidence, common calibration techniques, and why confidence scores matter for reliable AI systems.

Learn what AI agent memory governance is and how it controls who can read, write, update, and delete memories using access controls, audit logs, retention policies, and security best practices.

Learn how event-based memory systems help long-running AI agents retain important interactions, adapt over time, and make better context-aware decisions.

Explore procedural memory in AI agents, including how agents store learned behaviors, improve over time, and execute complex workflows with greater efficiency.

Cross-session identity resolution unifies persistent user identity for LLM agents, isolates users, and merges duplicate memory graphs. With runnable Mem0 code.

Five breakthrough ICML 2026 papers show how structured retrieval, reconstruction, and new benchmarks are reshaping AI agent memory for production use.

Compare Mem0 with building a custom vector store for AI agent memory, covering identity, extraction, lifecycle, retrieval, and production tradeoffs.

Voice AI remembers nothing between calls. Mem0 gives outbound sales agents memory of every prior call .

Learn how Mem0 gives healthcare AI agents reliable, compliant long-term memory for triage, care coordination, RAG, and clinical workflows at scale.

Programmatic memory management in Mem0 for AI agents, including search, update, soft delete, and hard delete patterns with Python examples.

Learn how to build a continual learning AI agent that stores outcomes, reuses past lessons, and improves over time using Mem0 as a persistent memory layer.

Learn how Perplexity-style memory works, how it models preferences and history, and how to implement the same pattern in ~50 lines using Mem0.

Most AI companion apps forget users the moment a session ends. Here's how to build one with voice input, cross-session memory, and working Python code using Mem0.