Viewing profile — jackmpcollins

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 me…

I've already merged my first Engine PR! Being able to review PRs like normal and it updates its work is very cool.

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…

I would love some feedback on my library magentic which aims to have the best features of both approaches! https://github.com/jackmpcollins/magentic

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 avoi…

I'm building magentic for use cases like this! https://github.com/jackmpcollins/magentic It's based on pydantic and aims to make writing LLM queries as easy/compact as possible by …

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 […

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 integ…

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 use…

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 a…

Pydantic also have support for parsing partial JSON. https://docs.pydantic.dev/latest/concepts/json/#partial-json... from pydantic_core import from_json partial_json_data = '["aa",…

That gif is really cool! I built a Python package magentic [0] which similarly parses the LLM streamed output and allows it to be used before it is finished being generated. There …

It's a great start but there's a little more work to do for full OpenAI API compatibility, namely streaming support and the tool_choice parameter. Making it fully compatible would …

Thanks for the explanation! Do you see auto-optimization as something that is useful for every use case or just some? And what determines when this is useful vs not?

Looks quite like magentic [0] that I've been building, though broader in scope? I'm (clearly) a huge advocate of pydantic, structured outputs, and keeping control flow in python co…

Is AdalFlow also focused on automated prompt optimization or is it broader in scope? It looks like there are also some features around evaluation. I'd be really interested to see a…

I've built a lightweight package that provides a standard interface to the LLM providers, as well as taking care of boilerplate around structured outputs, function calling, and ope…

I haven't used LangGraph myself, but the latest magentic release is compatible with it if you'd like to check out the examples here https://github.com/jackmpcollins/magentic/issues…

Please try out https://magentic.dev/ ! It is a light wrapper that is standard across LLM providers and handles the boilerplate code related to structured outputs and function calli…

I completely agree, and built magentic [0] to cover the common needs (structured output, common abstraction across LLM providers, LLM-assisted retries) while leaving all the prompt…

I'm building magentic https://github.com/jackmpcollins/magentic which has basically this syntax, though it queries the LLM to generate the answer rather than writing + running code…

Does the dashboard/UI support traces? I would love a tool in which to view opentelemetry traces, that can neatly display full prompt and response for the spans that represent LLM q…

Update: I've added the ability to add chat messages using a new decorator `@chatprompt` in v0.7.0. See https://github.com/jackmpcollins/magentic/releases/tag/v0.7....

Ellipses is actually used in quite a few places. See the answers and comments on this stackoverflow post[0]. The usage most similar to what I have in the magentic examples is with …

Right now we just pass a single user prompt to the chat model. Setting the system prompt could also be done in the `@prompt` decorator. I've added a github issue to track https://g…