Seems to me that integrations will be the most important component of tools like this. As an engineer I get my context from video calls with customers and other engineers, slack messages, emails, docs online, using the product myself, etc. So an auto-engineer should do the same.
I believe we'll see two main types of AI/LLM libraries/frameworks emerge like has happened for ORMs (at least in Python).
- The "Sqlalchemy" level: just bridge the gap between code and LLM
- The "Django" level: additionally handle how data is indexed for RAG, standard patterns of connecting the pieces, possibly GUI, support for plugins.
It seems langchain and llamaindex are aiming to be the latter, and I'm building https://magentic.dev to be the former. I'd be interested to get your take on whether these abstractions would allow you stray from the narrow path while still being helpful!
I took a brief look (~5 minutes). My $0.02 is that it's not clear what problem you're trying to solve. I get what some of the features do (e.g., templated prompts) but it would be v helpful to have an example of how you actually use magentic, versus the non-magentic way. It feels like a lot of syntactic sugar, if I'm being honest (not a bad thing, but something you might want to be clear about, if that's the case.)
If you are using Python, check out the package I've been building, magentic https://github.com/jackmpcollins/magentic It supports structured outputs and streaming, and aims to avoid making unnecessary abstractions (but might require some more understanding of LLM patterns as a result).
Also recently released is pydantic-ai, which is also based around pydantic / structured outputs, though works at level of "agents". https://github.com/pydantic/pydantic-ai
It's based on pydantic and aims to make writing LLM queries as easy/compact as possible by using type annotations, including for structured outputs and streaming. If you use it please reach out!
I'd be really interested to hear what abstractions you would find useful for RAG. I'm building magentic which is focused on structured outputs and streaming, but also enables RAG [0], though currently has no specific abstractions for it.
I've been building magentic [0] around similar ideas! Structured outputs and keeping control flow in python is definitely the way to go to keep things lightweight and easy to integrate. Having state management built in is cool! I'd be interested to hear what kinds of projects this has enabled you to build.
I'm working on a very abstraction light package for LLMs called magentic. The goal is to handle the main boilerplate of building with LLMs, but leave the abstractions up to the user. I'd be interested to hear your opinion on it vs others.
Interesting! How does it compare to something like Mirascope? I feel like I'm constantly on the hunt for the slimmest llm interface layer, and mirascope is my most recent attempt.
I'm building magentic, which aims to be a middle ground between the current "no framework" and "too much framework" options. It handles retries, logging/tracing, concurrency with asyncio, and other boilerplate, without mandating any prompts or particular patterns. Please check it out for the next comparison!
