A practical guide to choosing AI agent memory — comparing Mem0, Hindsight, Letta, Zep, Cognee, Supermemory, LangMem, LlamaIndex Memory, and MemNexus across architecture, MCP support, pricing, and best-fit use cases.
A practical guide to which AI coding assistants support persistent memory today — via MCP, APIs, or built-in features — and how to set up each one.
Everything developers need to know about persistent AI memory — what it is, why it matters, and how to set it up across your tools.
Five common misconceptions developers have about persistent AI memory — and what actually works for keeping structured context across tools and sessions.
Every AI coding assistant resets at session end. Here's why, what options exist today for persistent memory, and how they compare.
How the MemNexus team diagnosed a recurring CI failure pattern across 5 incidents in 10 days — and why the sixth incident took 2 minutes instead of 2 hours.
Most developers debug the same classes of bugs repeatedly. Here's a workflow that uses persistent memory to make each debugging session faster than the last.
How to load architectural context before reviewing a PR — so your AI reviewer knows why things were built the way they were, not just what the code does.
Open source contributors context-switch between projects months apart. Persistent AI memory means you never re-explain a project's conventions or patterns.
How to use persistent memory to generate accurate standup updates in seconds — without reconstructing what you did from git history or memory.
Technical writing is hard when your AI doesn't know your product. Persistent memory gives AI context to write accurate, consistent docs without re-explaining.
Claude Code's memory resets between sessions. Here's how to extend it with a persistent layer that spans projects and gives your whole team shared context.
Prompt engineering gets all the attention, but context engineering — managing what your AI knows at session start — separates productive from frustrated.
When you're the only one who knows the codebase, persistent AI memory turns your assistant into a second engineer who understands the full context over months.
LLMs are stateless by design. Built-in memory helps for simple use cases, but if you're building on the API or working across tools, you need a different approach.
Most AI memory is a jumbled pile of everything you've ever said. MemNexus introduces memory versioning and instant recaps to fix the fundamental problems with AI memory.