Contextify - ctxfy.com
Scale Your Context. Keep Your State.
What it does
Ctxfy is a Context State Engine that compresses LLM conversation history by up to 95%. It strips chit-chat and redundancy, preserving only "Hard State" — decisions, constraints — into a portable State Object and Artifacts - code, schemas, etc. Drop it into any model as a system prompt, inject it into agent loops, or fast-forward it with new diffs and logs. REST API, model-agnostic, zero-retention mode available.
Does a similar job
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Coding agents don't have long-term memory. But you do have months of full-fidelity agent transcripts stored on your machine. A simple solution that goes a long way: ingest those transcripts and logs into a structured SQLite database, then search them with ranked text match. Everything is fully local and doesn't require anything fancy like a graph database or hosted memory service. This is the idea behind ctx, a Rust CLI that handles the ingestion and searching. We give our agents a skill that tells them to reference past sessions before working in an area. Usually we do this through an…
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Current AI chat assistants face a fundamental challenge: context management in long conversations. While current LLM apps use multiple separate conversations to bypass context limits, a truly human-like AI assistant should maintain a single, coherent conversation thread, making efficient context management critical. Although modern LLMs have longer contexts, they still suffer from the long-context problem (e.g. context rot problem) - reasoning ability decreases as context grows longer. Memory-based systems have been invented to alleviate the context rot problem, however, memory-based…
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