#typescript#12_factor#12_factor_agents#agents#ai#context_window#framework#llms#memory#orchestration#prompt_engineering#rag
The 12-Factor Agents are a set of proven principles to build reliable, scalable, and maintainable AI applications powered by large language models (LLMs). They help you combine the creativity of AI with the stability of traditional software by managing prompts, context, tool calls, error handling, and human collaboration effectively. Instead of relying solely on complex frameworks, you can apply these modular concepts to improve your existing products quickly and reach high-quality AI performance for real users. This approach makes AI software easier to develop, debug, and scale, ensuring it works well in production environments[1][3][5].
https://github.com/humanlayer/12-factor-agents
https://github.com/aio-libs/aiohttp-mako
#mako template renderer for #aiohttp.web based on aiohttp_jinja2. Library has almost same api and support python 3.5 (PEP492) syntax. It is used in aiohttp_debugtoolbar.
#Mako is a #template library written in Python. It provides a familiar, non-XML syntax which compiles into Python modules for maximum performance. Mako's syntax and #API borrows from the best ideas of many others, including #Django and #Jinja2 templates, #Cheetah, #Myghty, and #Genshi. Conceptually, Mako is an embedded Python (i.e. Python Server Page) language, which refines the familiar ideas of componentized layout and inheritance to produce one of the most straightforward and flexible models available, while also maintaining close ties to Python calling and scoping semantics.
http://www.makotemplates.org/