Neurocache: A library for augmenting language models with external caching mechanisms

Deterministic LLM caching layer with context optimization, LRU eviction, model-aware token counting, and concurrency-safe request deduplication.

Web+Electorn model

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A lightweight, plug-and-play local memory module for LLM agents. Works offline, remembers past conversations and tasks, integrates into any Python project.

Adaptive Memory-Aware Caching

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Designed a high-performance, configurable cache simulator in C++ for the ANN/SNN Loads. Supports tuning of cache parameters, replacement policies, and trace-based analysis. Enabled architectural insights for embedded systems.

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A self-hosted CDN and predictive cache built from scratch using Raft Consensus for cache metadata

A Transformer-based code autocompletion engine with a FastAPI backend and VSCode extension, trained on open-source Python code for intelligent, low-latency code suggestions.

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A cache for your mind - AI chat grounded in what you actually know

Smart caching manager

Reduce LLM API costs and speed up responses by caching completions with NeuroCache’s intelligent, provider-agnostic caching layer.

Memory for AI systems. Speed for everything else.

A brain-inspired adaptive caching framework that learns what to keep or forget. Public demo version — commercial release coming December 2025.

Second Brain Application

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NeuroCache is an intelligent cache that uses learning-based algorithms (like heuristics or RL) to predict and evict less useful items. Unlike static methods like LRU/LFU, it adapts to usage patterns, reducing latency, cache misses, and bandwidth in dynamic systems like browsers or CDNs.