Tutorials, tool guides, and deep dives on persistent AI memory for developers.
Install MemNexus, save your first memory, and connect your AI tools — all with a single setup command. Your AI assistant remembers everything from here on out.
How to structure your memories so your AI coding assistant walks into every session knowing your architecture, conventions, and where you left off — without re-explaining anything.
How a 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.
Every AI coding assistant resets at session end — not a bug, but an architectural constraint baked into LLMs. Why it happens and what you can do about it.
Learn how to add persistent memory to AI apps built on the Anthropic or OpenAI API — architecture, what to store, and a TypeScript SDK walkthrough.
One developer, five vertical teams of agents — MCP server team, CommitContext team, SDK team, customer portal team, etc. Before shared memory: they contradicted each other, built incompatible features, rediscovered the same bugs. After: they stay coherent because they can read from the same knowledge base.
Most AI agents have no memory between sessions. Here are three patterns — session memory, preference memory, and knowledge memory — that make agents genuinely useful over time.
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 constantly — between projects, months apart. Persistent AI memory means you never re-explain a project's conventions or architecture to your AI again.
New engineers spend weeks learning undocumented conventions, past decisions, and tribal knowledge. Shared AI memory makes that context instantly accessible.
What AI pair programming can be when your coding assistant carries context between sessions — instead of starting fresh every time you open a new chat.
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 the context it needs to write accurate, consistent docs — without you re-explaining the system every time.
ChatGPT and Claude have built-in memory. It works well — until you hit the API, switch tools, or need to build something. Here's the architectural difference.
Prompt engineering gets all the attention, but context engineering — systematically managing what your AI knows at the start of each session — is what separates productive AI users from frustrated ones.
MCP tools lose context when you restart. Learn the MCP memory pattern — how to wire a memory server so every session starts with relevant context.
When you're the only one who knows the codebase, persistent AI memory turns your AI into a second engineer who actually understands the full context — accumulated over months, not sessions.
Every developer on your team has an AI assistant. None of them share context. Here's how shared team memories fix that — and where to start.
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.
CommitContext captures the reasoning behind every commit — decisions, debugging paths, and gotchas — giving your agent the context to investigate issues and connect changes across your codebase.
Build-context delivers a structured briefing — active work, key facts, gotchas, recent activity — before your agent starts. One command, under 60 seconds. No more cold starts.
MemNexus search now follows connections between memories — entities, facts, topics — not just similar words. 90% recall on broad queries, stale results filtered by default.
mx setup auto-detects your AI agents and configures MemNexus across Claude Code, Copilot, Cursor, and more. Built-in MCP bridge, no external binary, no secrets in config files.
We tested MCP vs CLI across three AI agents. GPT-based agents were 2x faster with MCP. Claude-based Kiro performed equally well with CLI. The right interface depends on the agent.
We built and tested an agent-help feature for our CLI. AI agents ignored it. Here's what actually helps agents use CLI tools effectively.
Recursive digest synthesis partitions large memory sets into focused groups, synthesizes each at full fidelity, and merges into one comprehensive briefing. Full content fidelity at any scale.
We're opening MemNexus to a small group of developers. Here's what you get, how it works, and what we're looking for.
Memory Digest assembles complete project briefings with a single command. Gathers up to 100 memories, expands via topic and entity graphs, and synthesizes with LLM. No more missed context.
Named memories let you assign meaningful names to your most important memories. Updates auto-create versioned chains with full history preserved.
MemNexus now auto-extracts topics, facts, and entities from your memories using LLM analysis. Richer metadata, more retrieval paths, same search speed.
Complete transparency on what happens when you delete your MemNexus account — the 7-day grace period, what gets deleted, and how to permanently erase your AI memory data.
MemNexus CLI v1.7.29 introduces batch memory retrieval: fetch multiple memories in a single API call with piping support from search.
New conversation-based memory retrieval helps developers find work sessions, not just individual memories. Filter by time, group search results by conversation.
Introducing --exclude-topics: the complement to topic filtering that gives you complete control over your memory search results.
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.
Timeline Search in MemNexus CLI optimizes memory search for temporal understanding — reconstruct debugging sessions, review decision evolution, and brief teammates in one query.
Most AI memory systems treat each piece of information as an isolated fact. Narrative Reconstruction understands how your knowledge evolves over time.
Today we're launching MemNexus, a persistent memory layer that helps AI assistants remember context across conversations.
Learn how to effectively use persistent memory to improve your AI assistant workflows and productivity.