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AgentPrizm
AI agent memory + skills; API over REST + MCP
5.0•10 reviews•14 followers
AI agent memory + skills; API over REST + MCP
5.0•10 reviews•14 followers
AgentPrizm gives AI agents persistent, governed memory, reusable skills, and an audit receipt for every recall over REST and MCP. It resolves contradictions, tracks fact-validity windows, preserves history, and supports right-to-forget. Built for coding, support, sales, and regulated workflows. Try a live recall in the no-signup playground, then connect Claude Code, Cursor, OpenClaw, or any MCP-capable agent. Plus, it is already available as an OpenClaw skill on ClawHub.
I already have a RAG system using Postgres, pgvector, vector search, and metadata filters. I would be interested in understanding where AgentPrizm fits.
Would I keep my current vector database and use AgentPrizm for validity, confidence, containers, and recall receipts? Or does AgentPrizm also handle the storage and retrieval layer?
The distinction matters because many teams already have a working retrieval system. Replacing everything would be difficult, but adding governance and visibility on top of it could be very attractive
@wajeeha_ishfaq Thanks, Wajeeha — Gene here, founder and CEO of AgentPrizm. The short answer is: you can keep your current pgvector setup. AgentPrizm does handle its own storage, embeddings, and retrieval, but it is not meant to force you to replace a RAG pipeline that already works. It is meant to be your agent's operational memory, not the operational storage that is the core of your app's main function.
A clean setup would be to keep pgvector for your document corpus and general similarity search, while using AgentPrizm for the agent’s durable, changing memory — facts, preferences, decisions, lessons, and directives that need containers, confidence, validity windows, contradiction handling, right-to-forget, and a receipt explaining each recall.
Your agent can query both systems and combine the results. Using AgentPrizm only as a governance layer over vectors that remain in your own tables would be a different integration model, and it is not the primary model today.
Would you prefer one merged recall receipt covering both sources, or separate document and memory results so your application can control how they are combined?
honestly the fact-validity windows thing is super useful, but i'd love to see a visual diff tool in the playground so you can see how the memory evolves over time as new info comes in. would make debugging weird agent behavior way easier.
@dorttepeil49832 Thanks, İlyas — Gene here, founder and CEO of AgentPrizm. I’m imagining something similar to GitHub’s contribution map, but for an agent’s memory.
The map could show memory subjects across time, with different colors for different subjects and darker blocks showing greater activity or frequency. Clicking a block could reveal what changed and why a particular memory was used.
For something like competitor research, this could make it much easier to spot which subjects the agent has been following and when its understanding changed. Would you find the frequency of memory updates or the frequency of recalls more useful?
the fact-validity windows and audit receipts show real thought about regulated use, not just a generic memory layer slapped together.
@abdulkadirgyt6 Thanks, Abdulkadir — I appreciate that. A marketing analytics agent is a good example: the same ROAS number can mean different things depending on the date range, ad account, currency, and attribution model.
Saving the number alone is not enough. The agent also needs to know where it came from, when it was valid, and what later replaced it. That’s where validity windows and recall receipts become useful.
For blended ROAS, would you want to drill down by account and source first, or compare how the metric changed over time?