Memento gives LLMs what they have been missing the most: a real memory. Limitless in size. Persistent across sessions, IDEs, and machines. Shared with your team. Browseable and editable on the web. Eerily human.
Works with Cursor · Claude Code · Windsurf · Copilot
While the rest of the world chases IQ, knowledge, and ever-larger models in pursuit of a truly autonomous coding agent, Memento knows the gap was never in those things.
It was in the LLM's inability to learn anything new and retain it. That's why LLMs crush humans on from-scratch LeetCode tests, but make seemingly childish blunders in a large, pre-existing codebase. Beyond the confines of a single chat session, they can't learn it over time the way a human does: the styles, the conventions, the architecture, the tribal knowledge, the buried gotchas, which functions even exist and how they chain together. That lived-in memory is what every human developer has and what every LLM lacks.
At least until now.
As you work with your LLM, Memento quietly saves the things worth remembering: architectural decisions, gotchas, patterns it figures out about your code. You don't have to ask. Next session, the right memories surface automatically before the AI answers you.
Want something committed to memory? Say /remember everything we just learned about the OAuth flow. Want to pull it back later? /recall database structure for the billing service. Permanent. Searchable. Yours.
Every memory node is a plain-English page in your dashboard. Open it, read what your AI thinks it knows, fix anything that's wrong, add or upload new knowledge nodes, delete what's stale. No black box.
Memories live in your personal scope by default. Drop them in a team org and every teammate's AI can recall them too. A new developer's AI shows up on day one already knowing your architecture.
Memento is a fundamentally different approach to AI memory. Here's what sets it apart.
Your AI's memory is a graph of linked, plain-English nodes. Browse them in a web dashboard, read exactly what your AI knows, and correct it when it's wrong.
Your memory lives in the cloud and travels with you: across machines, across projects, across every tool you use. Your AI picks up exactly where it left off.
Create an org, invite your team, and share codebase knowledge instantly. When one person's AI learns something, everyone's AI can recall it.
Memories are organized in a hierarchy that imparts context automatically. Your AI can drill down to a specific detail or zoom out to the surrounding architecture. Vector DBs, knowledge graphs, and RAG pipelines on their own can't do that. Memento uses all three, under a hierarchy that gives every memory its context.
Memento blends semantic similarity, keyword matching, knowledge-graph traversal, and tiered summaries layered over the hierarchical knowledge base. The right memory surfaces whether your query matches the exact words, the underlying meaning, or just the part of the system you're working in.
Not just storage - a complete workflow. Wake up your AI, recall knowledge, remember new things, hand off between sessions, investigate codebases, manage tasks. Each command is a battle-tested procedure.
Most AI memory today is a single file: .cursorrules, AGENTS.md, a chat's system prompt. Those work great for a handful of rules. Memento is designed for a different scale: a hierarchical graph that grows from 200 memories to 200,000 without losing its granular, context-rich structure. Semantic, keyword, and graph recall surface the right one at the right moment. It's why Memento holds up on mature, 500K-line codebases.
LLMs today cannot learn anything new after training. They have no mechanism to retain information between conversations. Memento changes this. Every session builds on the last. Your AI at month six is dramatically more capable than your AI on day one.
The main thing standing between us and artificial general intelligence is continuous learning. LLMs today cannot retrain - cannot reweight their parameters in real time - without catastrophic forgetting.
This is an enormous handicap. Humans, though not as capable as LLMs in many ways, can run circles around them when it comes to learning and retaining new things.
Memento closes that gap. An external memory system that lets an LLM accumulate knowledge, build on past experience, and get better with every session.
What your AI is actively thinking about right now: current projects, open questions, recent decisions. Loaded at the start of every session so it picks up exactly where you left off.
Formative experiences that shape behavior. The time a deployment broke. The architectural decision that saved months. Lessons that permanently change how the AI approaches problems.
A record of what happened, when, and in what order. Session logs, handoff documents, daily activity stacks. The AI can trace back through its own history to understand how it arrived at a decision.
Three search strategies working together: semantic (by meaning), keyword (exact matches), and knowledge graph (connected concepts). The AI finds relevant memories even when your phrasing is different.
Not all memories are equal. Some fade; others stay sharp. Memories carry weight that affects how likely they are to surface. Unimportant details decay naturally. Critical lessons persist.
At the end of a session, /sleep reviews what happened, consolidates what matters into long-term memory, hands off to tomorrow, and clears the daily stack. Your AI wakes up with yesterday already processed. A quiet parallel to how biological brains turn a day of experience into lasting knowledge overnight.
For individual developers
$0
For power users
$20/mo
per developer
Autonomous AI development
$49/mo
Early adopter pricing · per developer
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