{"repo":"christopherkarani/ContextCore","free":true,"listed":false,"github":"https://github.com/christopherkarani/ContextCore","clone":"git clone https://github.com/christopherkarani/ContextCore.git","description":"Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers","language":"Swift","stars":30,"topics":["ai-agents","anthropic","context-engineering","metal","on-device-ai","openai","performance-engineering","swift","swift-library","swift-package"],"license":"MIT","category":"ai-agents","readme_excerpt":"ContextCore GPU-accelerated context memory for on-device AI agents on Apple Silicon. Stop forgetting. Build context windows in under 5 ms. --- The Problem LLMs forget. As conversations grow, early turns drop out, irrelevant history burns tokens, and rebuilding context gets slower. ContextCore sits between your agent loop and the model, using Metal compute shaders to score, rank, compress, and pack context on-device. Features Feature What it means for you --- --- Sub-5 ms window builds buildWindow runs at 4.89 ms p99 on M2—users never feel the overhead. Four memory tiers Working, episodic, semantic, and procedural memory each have their own retention and retrieval rules. Metal-accelerated scoring GPU shaders score 63 million chunks/sec and beat the CPU path at scale. Progressive compression When the token budget gets tight, low-signal chunks are compressed automatically. Background consolidation Episodic memory is deduplicated, durable facts are promoted, and obvious noise gets evicted. Attention-aware reranking Chunks are reordered so the model’s attention lands on the most useful content first. Installation Add ContextCore to your Package.swift : Or add it in Xcode: File → Add Package Dependencies… → paste the URL above. Quick Start Persist knowledge across sessions How It Works Every call to buildWindow runs this pipeline: 1. Embed the current task using on-device CoreML (MiniLM, 384-dim). 2. Score episodic and semantic memory in parallel on the GPU. 3. ","default_branch":null,"files":null,"tree":[],"storefront":"/r/christopherkarani","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/christopherkarani/ContextCore/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}