[HN Gopher] Biomni: A General-Purpose Biomedical AI Agent
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Biomni: A General-Purpose Biomedical AI Agent
Author : GavCo
Score : 102 points
Date : 2025-07-09 19:20 UTC (3 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| freedomben wrote:
| 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 wrote:
| very cool -passed on to my friend who is working a Crispr lab
| Edmond wrote:
| 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:
|
| https://news.ycombinator.com/item?id=44509078
| SalmoShalazar wrote:
| 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_ wrote:
| 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 wrote:
| 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 wrote:
| >pull up related caselaw
|
| Or just make some up...
| mrlongroots wrote:
| At the token layer an LLM can make things up, but not as
| part of a structured pipeline that validates an invariant
| that all suggestions are valid entities in the database.
|
| Can google search hallucinate webpages?
| okdood64 wrote:
| > 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 wrote:
| I'm a software engineer in the biotech space. I haven't
| worked with RAG though, maybe I'm underestimating the
| complexity.
| agpagpws wrote:
| I work at a top three lab. RAG is just Mumbai magic.
| Throwaway. Hi dang.
| jjtheblunt wrote:
| What is a top three lab?
| zachthewf wrote:
| We know they don't work at OpenAI or Anthropic, but
| beyond that have no information
| epistasis wrote:
| 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 wrote:
| By that argument every SaaS is a db wrapper
| andy99 wrote:
| 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.
| deepdarkforest wrote:
| 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
| epistasis wrote:
| 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!
| teenvan_1995 wrote:
| I wonder if giving 150+ tools is really a good idea considering
| context limitations. Need to check out if this works IRL.
| Herring wrote:
| There's an inner ToolRetriever which is a LLM call that selects
| the most relevant tools to reduce context size.
| dmezzetti wrote:
| Very interesting work!
|
| If biomedical research and paper analysis is of interest to you,
| I've been working on a set of open source projects that enable
| RAG over medical literature for a while.
|
| PaperAI: https://github.com/neuml/paperai
|
| PaperETL: https://github.com/neuml/paperetl
|
| There is also this tool that annotates papers inline.
|
| AnnotateAI: https://github.com/neuml/annotateai
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