Biomni: A General-Purpose Biomedical AI Agent
18 comments
·July 9, 2025Edmond
This is nice, a lot of possibilities regarding AI use for scientific research.
There is also the possibility of building intelligent workspaces that could prove useful in aiding scientific research:
andy99
I'm sure they've thought of this but curious how it fared on evaluations for supporting biological threats, ie elevating threat actor capabilities with respect to making biological weapons.
I'm personally sceptical that LLMs can currently do this (and it's based on Claude that does test this) but still interesting to see.
epistasis
This is great, I've been on the waitlist for their website for a while and am now excited to be able to try it out!
freedomben
Awesome! This is the type of stuff I'm most excited about with AI - improvements to medical research and capabilities. AI can be awesome at identifying patterns in data that humans can't, and there has to be troves of data out there full of patterns that we aren't catching.
Of course there's also the possibility of engineering new drugs/treatments and things, which is also super exciting.
AIorNot
very cool -passed on to my friend who is working a Crispr lab
SalmoShalazar
Not to take away from this or its usefulness (not my intent), but it is wild to me how many pieces of software of this type are being developed. We’re seeing endless waves of specialized wrappers around LLM API calls. There’s very little innovation happening beyond specializing around particular niches and invoking LLMs in slightly different ways with carefully directed context and prompts.
gronky_
I see it a bit differently - LLMs are an incredible innovation but it’s hard to do anything useful with them without the right wrapper.
A good wrapper has deep domain knowledge baked into it, combined with automation and expert use of the LLM.
It maybe isn’t super innovative but it’s a bit of an art form and unlocks the utility of the underlying LLM
mrlongroots
Exactly.
To present a potential usecase: there's a ridiculous and massive backlog in the Indian judicial system. LLMs can be let loose on the entire workflow: triage cases (simple, complicated, intractable, grouped by legal principles or parties), pull up related caselaw, provide recommendations, throw more LLMs and more reasoning at unclear problems. Now you can't do this with just a desktop and chatgpt, you need a systemic pipeline of LLM-driven workflows, but doing that unlocks potentially billions of dollars of value that is otherwise elusive.
lawlessone
>pull up related caselaw
Or just make some up...
epistasis
The application of a new technology to new fields always looks like this. SQL databases become widespread, there's a wave of specialized software development for business practices. The internet becomes widespread, and there's a wave of SaaS solving specialized use cases.
We are going to see the same for anything that Claude or similar can't handle out of the box.
mlboss
By that argument every SaaS is a db wrapper
okdood64
> We’re seeing endless waves of specialized wrappers around LLM API calls.
AFAIK, doing proper RAG is much, much more than this.
What's your technical background if you don't mind me asking?
SalmoShalazar
I’m a software engineer in the biotech space. I haven’t worked with RAG though, maybe I’m underestimating the complexity.
agpagpws
I work at a top three lab. RAG is just Mumbai magic. Throwaway. Hi dang.
jjtheblunt
What is a top three lab?
null
Interesting. It's just an agent loop with access to python exec and web search as standard, BUT with premade, curated, 150 tools like analyze_circular_dichroism_spectra, with very specific params that just execute a hardcoded python function. Also with easy to load databases that conform to the tools' standards.
The argument is that if you just ask claude code to do niche biomed tasks, it will not have the knowledge to do it like that by just searching pubmed and doing RAG on the fly, which is fair, given the current gen of LLM's. It's an interesting approach, they show some generalization on the paper(with well known tidy datasets), but real life data is messier, and the approach here(correct me if im wrong) is to identify the correct tool for a task, and then use the generic python exec tool to shape the data into the acceptable format if needed, try the tool and go again.
It would be useful to use the tools just as a guidance to inform a generic code agent imo, but executing the "verified" hardcoded tools narrows the error scope, as long as you can check your data is shaped correctly, the analysis will be correct. Not sure how much of an advantage this is in the long term for working with proprietary datasets, but it's an interesting direction