Valori Systems
Physics-grade deterministic memory for AI agents.
Valori Systems launched on February 23, 2026 with 3 votes, #3,486 of 16,627 launches that month and more than 50% of that year's launches.
What it does
Standard vector databases use floating-point math, which drifts across hardware and causes AI hallucinations. Valori is a Rust-native, fixed-point memory kernel that guarantees bit-exact vector search on any chip from cloud servers to edge robots.
Store, search, and connect your data with cryptographically verifiable state, built for AI systems that need predictable, auditable infrastructure.
Does a similar job
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Actian VectorAI DBApr 2026 · actian.com · ▲203The portable vector database for AI agents beyond the cloud
Valora.ai - AI orchestration platformMar 2026 · github.com · ▲6Open-source, human-in-the-loop AI orchestration tool
SQLite Memory – Markdown based AI agent memory with offline-first syncApr 2026 · github.com · ▲10Markdown based AI agent memory with semantic search, hybrid retrieval, and offline-first sync between agents. - sqliteai/sqlite-memory
Memori – Open-Source Memory Engine for AI Agents2025 · github.com · ▲17Hey HN! I'm Arindam, part of the team behind Memori (https://memori.gibsonai.com/). Memori adds a stateful memory engine to AI agents, enabling them to stay consistent, recall past work, and improve over time. With Memori, agents don’t lose track of multi-step workflows, repeat tool calls, or forget user preferences. Instead, they build up human-like memory that makes them more reliable and efficient across sessions. We’ve also put together demo apps (a personal diary assistant, a research agent, and a travel planner) so you can see memory in action. Current LLMs are stateless…
Honcho – Open-source memory infrastructure, powered by custom modelsJan 2026 · github.com · ▲8Hey HN, It’s Vineeth from Plastic Labs. We've been building Honcho, an open-source memory library for stateful AI agents. Most memory systems are just vector search—store facts, retrieve facts, stuff into context. We took a different approach: memory as reasoning. (We talk about this a lot on our blog) We built Neuromancer, a model trained specifically for AI-native memory. Instead of naive fact extraction, Neuromancer does formal logical reasoning over conversations to build representations that evolve over time. Its both cheap ( $2/M tokens ingestion, unlimited retrieval), token…
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