Memento gives your LLM the one thing it's been missing: a memory. Persistent across sessions, shared with your team, written in plain English, limitless in size, and available everywhere.
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. That's why LLMs decimate humans on from-scratch LeetCode tests, but make seemingly childish blunders in a large, pre-existing codebase. They cannot learn the ins and outs of that codebase and retain them over time like a human can.
Or at least that used to be the case.
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 specific committed to memory? Say /remember don't use react-bootstrap, we're on Chakra. Want it to recall something specific from memory? /recall how do we do auth?. Permanent, searchable, plain English.
Every memory 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, and across the tools you use. Cursor today, Claude Code tomorrow, Windsurf next week. Your AI picks up exactly where it left off. Zero local setup, just paste a URL.
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. When your AI reads a detail, it understands the bigger picture. Vector DBs, knowledge graphs, and plain RAG pipelines cannot do this.
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, with semantic, keyword, and graph recall surfacing 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 - because it remembers everything in between.
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 isn't true continuous learning, where model parameters are reweighted in real time. But it is the closest thing we have to that right now. An external memory system that gives an LLM the ability to accumulate knowledge, build on past experience, and get better over time.
What you are actively thinking about right now. Current projects, open questions, recent decisions. Loaded at the start of every session so the AI picks up exactly where it 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.
Your coding style. Your preferred libraries. How you name things, how you structure tests, how you like PRs formatted. The implicit knowledge that turns a generic AI into your AI.
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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