mycontext-ai v0.5.0 is live on PyPI. This release focuses on three capabilities that matter in production: non-blocking async execution, token-accurate context assembly, and validated structured output — plus a set of reliability improvements that make the library safer and more observable.
Welcome to the official documentation site for mycontext-ai — the context engineering SDK for LLMs.
We're building the most comprehensive documentation for context engineering. Stay tuned for tutorials, deep dives into cognitive patterns, and best practices for building production-grade LLM applications.
The AI framework landscape in early 2026 is messy in a specific way. There are good tools — LangChain, LlamaIndex, CrewAI, AutoGen, DSPy, Semantic Kernel — and they serve genuinely different use cases. But they all want their own format. Their own message structure, their own agent config, their own prompt template shape.
The result is that if you want to experiment with your prompt across frameworks — or migrate from one to another — you end up rewriting the same context multiple times in different dialects.
We built mycontext-ai to fix this. The answer we landed on: build your context once as a structured object, then export it to whatever shape the framework needs.
Software development has a long history of finding the right abstraction and watching it unlock an entire class of capabilities. Relational databases gave us SQL and normalized data. Version control gave us reproducible history. Containers gave us environment portability.
Each time, the abstraction wasn't just a convenience — it changed what was possible to build and reason about.
I think context engineering is at a similar inflection point. And the abstraction we're missing is treating context as a structured, version-controllable, measurable artifact — Context as Code.
When I first started using LLMs seriously for work, I noticed something that took me a while to articulate. The same question, asked slightly differently, would get radically different quality answers. And when I looked at what "slightly differently" actually meant in the cases that worked, I kept seeing the same thing: structure.
The best prompts I wrote weren't better because of their vocabulary. They were better because they embedded a reasoning methodology. They told the LLM not just what to do but how to think about the problem.
The issue was that I had to reinvent that structure from scratch every time.
I've been building with LLMs since early 2023. In that time I've written thousands of prompts. Some were good. Most were mediocre. And for a long time, I couldn't tell the difference until I saw the output — sometimes days later in production logs.
That frustration is what eventually led me to build mycontext-ai. And it started with a simple realization: the problem was never how I was asking the question. It was the absence of any structure around the question itself.