Advaiyt Sane

[Advaiyt Sane @advaiytsane]

1// Each successful run is distilled into procedure nodes: the conditions under which a workflow applies, the actions the agent took, and the outcomes it produced.

2// When a task arrives, Memorable retrieves relevant nodes and constructs an ordered path through them.
Because procedures can be composed, the agent can execute workflows that no previous agent has run end to end. This essentially turns agent reasoning into a graph search problem.

3// The Memorable CLI is live across Claude Code, Codex, Cursor, and Claude Cowork. Memorable is also deployed in @garrytan's GBrain, GStack, and YC's QM Harness.

4// The long-term goal: systems agents whose operational competence compounds across sessions.

Models will change. Context windows will reset. The operational experience your agents earn should persist and remain yours.
Check us out at: https://t.co/DUAVdejTzZ
Demo: https://t.co/QTk10DKQ3i

[Advaiyt Sane @advaiytsane]

Introducing Memorable (YC S27): PROCEDURAL MEMORY FOR AI AGENTS

AI agents today are born, work, and die inside a single context window. They solve a hard problem once, then start from zero when it returns.

Memorable turns successful runs into a graph of reusable procedures.

So every task makes the next one:
- Faster.
- Cheaper.
- More deterministic.

We’re excited to share that Memorable is joining Y Combinator’s S27 batch.

Try it: https://t.co/DUAVdejTzZ

[Advaiyt Sane @advaiytsane]

First post of many here~

next one is gonna be big.