I initially built Kontexta as a simple MCP tool for storing and retrieving development context across AI workflows. As I continued experimenting with AI-assisted engineering and local LLM workflows, the project evolved into a broader local-first system for persistent memory, targeted context retrieval, and secure agent execution. One of the most interesting parts of the project is that Kontexta was used to help build itself. By exposing the tool through MCP to locally hosted LLMs, I was able to iteratively improve the system through carefully designed specifications, structured planning, and autonomous implementation loops supervised by me. The goal is to make AI-assisted software development more reliable, efficient, and privacy-focused without depending on heavyweight cloud infrastructure. After refining and modularizing, I decided to open-source it for the community.

kontexta

Save your tokens. Remember Forever

Save your tokens. Remember Forever