I love the ethos of the project! I think your docs link might be broken, however. Looking forward to checking this out!
Just found and fixed a bad link on the bottom of the website - thanks again for pointing that out !
Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
11–20 of 72 posts
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#12I love the ethos of the project! I think your docs link might be broken, however. Looking forward to checking this out!
Hey thanks! Sorry about the broken link - here's a better docs link for now https://docs.llamafarm.dev/docs/intro mind sharing where it's broken?
Yea of course. I was trying to click the docs link from the homepage on llamafarm.dev from two different networks on two different browsers: edge and brave. Neither worked. Phone didn’t either. It takes me to a supabase link that errors out. Hope that helps! Thanks for the link! (Btw I don’t see any errors in the browsers console)
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#13Earlier quoted context omitted.
Hey thanks! Sorry about the broken link - here's a better docs link for now https://docs.llamafarm.dev/docs/intro mind sharing where it's broken?
Yea of course. I was trying to click the docs link from the homepage on llamafarm.dev from two different networks on two different browsers: edge and brave. Neither worked. Phone didn’t either. It takes me to a supabase link that errors out. Hope that helps! Thanks for the link! (Btw I don’t see any errors in the browsers console)
thank you! found and fixed 2 on the website - appreciate the comment and detailed testing
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#14Very cool to see a serious local first effort. Looking back at how far local models have come I definitely believe their usefulness combined with RAG or in domain specific contexts is soon to be (or already is) on par with general purpose gpt5-like massive parameter cloud models. The ability to generate quality responses without having to relinquish private data to the cloud used to be a pipedream. It's exciting to see a team dedicated to making this a reality.
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#15Very cool to see a serious local first effort. Looking back at how far local models have come I definitely believe their usefulness combined with RAG or in domain specific contexts is soon to be (or already is) on par with general purpose gpt5-like massive parameter cloud models. The ability to generate quality responses without having to relinquish private data to the cloud used to be a pipedream. It's exciting to s…
Thanks! It means a lot to hear you say that.
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#16but wait, why should I do this for my first home grown orchestration instead of something else? Like, if I want to set up a local LLM running on my old laptop for some kind of RAG on all my hard drives why is this best? Or if I want agentic monitoring of alarms instead of paying for simplisafe or ring or whatever.
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#17So this sounds like an application layer approach, maybe just shy of a replit or base44, with the twist that you can own the pipeline. While there's something to that, I think there are some further questions around differentiation that need to be answered. I think the biggest challenge is going to be the beachead: what client demographic has the cash to want to own the pipeline and not use SaaS, but doesn't have the staff on hand to do it?
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#18Hope you are right and we decentralize AI properly...
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#19but wait, why should I do this for my first home grown orchestration instead of something else? Like, if I want to set up a local LLM running on my old laptop for some kind of RAG on all my hard drives why is this best? Or if I want agentic monitoring of alarms instead of paying for simplisafe or ring or whatever.
Right...there are lots of ways you could do that. Most of the ways we've seen enabling that sort of thing tend to be programmatic in nature. That's great for some people, but you have to deal with shifting dependencies, sorting out bugs, making sure everything connects properly, etc. Some people will want that for sure, because you do get control over every little piece.
LlamaFarm provides an abstraction over most (eventually all) of those pieces. Something that should work out of the box wherever you deploy it but with various knobs to customize as needed (we're working on an agent to help you with this as well).
In your example (alarm monitoring), I think right now you'd still need to write the agent, but you could use LlamaFarm to deploy an LLM that relied on increasingly accurate examples in RAG and very easily adjust your system prompt.
Re: Launch HN: LlamaFarm (YC W22) – Open-source framework for distributed AI
#20but wait, why should I do this for my first home grown orchestration instead of something else? Like, if I want to set up a local LLM running on my old laptop for some kind of RAG on all my hard drives why is this best? Or if I want agentic monitoring of alarms instead of paying for simplisafe or ring or whatever.
Good question — that’s actually the sweet spot for LlamaFarm.
You can wire things together yourself (LangChain, bash, Ollama, etc.), but LlamaFarm tries to make that repeatable and portable. It’s declarative orchestration for AI systems — you describe what you want (models, RAG, agents, vector DBs) in YAML, and it runs the same way anywhere: laptop, cloud, or fully air-gapped edge.
So instead of gluing frameworks and breaking them every update, you can do something like:
name: home_guarde runtimes: - detect_motion: {model: "phi-3", provider: "lemonade"} - alert: {model: "gpt-5", fallback: "llama3:8b"} rag: embedder: "nomic-embed-text" database: chromaDB
…and it just runs — same config, same behavior, whether you’re doing local RAG or home monitoring. The goal isn’t to replace the DIY route, just to make it composable and reproducible.