Your agents forget. We fix that. Plug in persistent memory and millisecond recall in three lines of code.
Every feature in CoreCast AI is built for one purpose: give AI agents the memory layer they've always been missing.
Keep users, tasks, and preferences across every session. Agents never start from zero. Write in <10ms. Read in <50ms.
Hybrid vector + time-series retrieval. Query by meaning and by when. Hybrid scoring out of the box.
Automatically surface relevant history every time an agent calls a tool. Automatic pre-fetch before every tool call. No manual retrieval code.
Per-user, per-tenant memory with encryption and access control. Per-tenant encryption keys. Zero cross-tenant leak guarantee.
Long conversations summarize themselves. Long chats shrink to the 10 facts that actually matter. Never blow past the context window.
Three lines of code. LangChain, OpenAI, Claude, CrewAI, AutoGen, LlamaIndex.
From YC-backed agent startups to Fortune 500 AI teams.
We tried rolling our own memory layer twice. Both times we spent three weeks just on retrieval logic before hitting walls with context window management. CoreCast took us from nothing to working agent memory in a single afternoon. Recall latency under 40ms across our entire user base.
CoreCast cut our agent's 'what were we talking about' moments by ~90% in the first week. Our coding agent now carries full project context session to session without a single line of custom retrieval code. It's the primitive we should have had from day one.
Our support CSAT went from 4.1 to 4.6 after giving our agents real memory. Customers stopped repeating themselves. Agents stopped sounding like they'd never met the user before. The improvement was immediate and measurable within 72 hours of going live.
Start free. Add persistent memory to your agent in minutes. No credit card required.
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