magentic

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 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

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 using type annotations, including for structured outputs and streaming. If you use it please reach out!

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. [0] https://github.com/jackmpcollins/magentic

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. https://github.com/jackmpcollins/magentic

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! https://github.com/jackmpcollins/magentic

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 allow it be swapped in directly to frameworks like langchain and magentic [1] that I am building). [1] https://github.com/jackmpcollins/magentic/issues/207

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 code (rather than inside abstractions / "chains") as much as possible, so it's great to see those values here too! I'd be interested to hear what you consider in vs out of scope for mirascope and the long-term vision. Also would be cool for one of us to do a mirascope vs magentic comparison blog post. [0] https://github.com/jackmpcollins/magentic

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 comparison between AdalFlow, DSPy [0], LangChain [1] and magentic [2] (package I've created, narrower in scope). [0] https://github.com/stanfordnlp/dspy [1] https://github.com/langchain-ai/langchain [2] https://github.com/jackmpcollins/magentic

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 opentelemetry/tracing. It's hopefully a good compromise between ease-of-use and complexity. https://github.com/jackmpcollins/magentic

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/287

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 prompts up to the package user. [0] https://github.com/jackmpcollins/magentic

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. from magentic import prompt from pydantic import BaseModel class Superhero(BaseModel): name: str age: int power: str enemies: list[str] @prompt("Create a Superhero named {name}.") def create_superhero(name: str) -> Superhero: ... I do have plans to also solve the case you're talking about of generating code once and executing that each time.

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 queries. I'm planning to add opentelemetry instrumentation to magentic [1] and looking for a UI that is easy to run locally that makes it easy to see what an agent is doing (via OTEL traces). I have more of my thoughts on the github issue: https://github.com/jackmpcollins/magentic/issues/136 [1] https://github.com/jackmpcollins/magentic

See Magentic for something similar: https://github.com/jackmpcollins/magentic