> Star Us on GitHub and Get Exclusive Day 1 Badge for Your Networks
This made me close the tab.
Stars have been gamed for awhile on GitHub, but given the single demo, my best guess is that this is trying to build hype before having any real utility.
Rather than "being thorough", others are likely to see it as "dubious incentives". Plugging that into your question seems to yield some rather obvious answers.
Milvus is following the playbook that they've been following for years- integrate with and boost any framework or product that they can to maintain the appearance of a use-case.
I can somewhat answer this to best of my knowledge.
Right now, businesses communicate with REST Apis.
That is why we have API gateways like AWS Gateway, Apigee, WSO2 (company i used to work in), Kong, etc so businesses can securly deploy and expose APIS.
As LLMS gets better, the idea is we will evenutally move to a world where ai agents do most of business tasks. And businesses will want to expose ai agents instead of APIS.
This is where protocols like a2a comes in. Google partnering with some other giants introduced a2a protocol a while ago, it is now under linux foundation.
It is a standard for one agent to talk to another agent regardless of the framework (langchain, crewai etc) that is used to build the agent.
I see. If iiuc, it's like an extension to an API endpoint. Instead of exposing only endpoints, you can let a user describe an intent and have the agent do the work. Is this not also the goal of an MCP as well?
I mean, I wrote bots to play MMORPGs when I was a teen/kid, but really, what's the point? Aren't games there to be enjoyed by things that can have experiences?
Can someone please explain what this means? I'm familiar with agentic development workflows but have no clue what this means and what I can do with it?
Is it something like n8n, to connect agents with some work flow and let the work flow do stuff for me?
In the late 90s and early 2000s there was a bunch of academic research into collaborative multi-agent systems. This included things like communication protocols, capability discovery, platforms, and some AI. The classic and over-used example was travel booking -- a hotel booking agent, a flight booking agent, a train booking agent, etc all collaborating to align time, cost, location. The cooperative agents could add themselves and their capabilities to the agent community and the potential of the system as a whole would increase, and there would perhaps be cool emergent behaviours that no one had thought of.
This appears, to me, like an LLM-agent descendent of these earlier multi-agent systems.
I lost track of the research after I left academia -- perhaps someone here can fill in the (considerable) blanks from my overview?
As someone who got into ongoing multi-agent systems (MAS) research relatively recently (~4 years, mostly in distributed optimization), I see two major strands of it. Both of which are certainly still in search of the magical "emergence":
There is the formal view of MAS that is a direct extension of older works with cooperative and competitive agents. This tries to model and then prove emergent properties rigorously. I also count "classic" distributed optimization methods with convergence and correctness properties in this area. Maybe the best known application of this are coordination algorithms for robot/drone swarms.
Then, as a sibling comment points out, there is the influx of machine learning into the field. A large part of this so far was multi-agent reinforcement learning (MARL). I see this mostly applied to any "too hard" or "too slow" optimization problem and in some cases they seem to give impressive results.
Techniques from both areas are frequently mixed and matched for specific applications. Things like agents running a classic optimization but with some ML-based classifications and local knowledge base.
What I see actually being used in the wild at the moment are relatively limited agents, applied to a single optimization task and with frequent human supervision.
More recently, LLMs have certainly taken over the MAS term and the corresponding SEO. What this means for the future of the field, I have no idea. It will certainly influence where research funding is allocated.
Personally, I find it hard to believe LLMs would solve the classic engineering problems (speed, reliability, correctness) that seem to hold back MAS in more "real world" environments. I assume this will instead push research focus into different applications with higher tolerance for weird outputs. But maybe I just lack imagination.
Maybe this article can help you. It mentions the multi-agent research boom back in the 1990s. Later, reinforcement learning was incorporated, and by 2017, industrial-scale applications of multi-agent reinforcement learning were even achieved. Neural networks were eventually integrated too. But when LLMs arrived, they upended the entire paradigm. The article also breaks down the architecture of modern asynchronous multi-agent systems, using Microsoft's Magentic One as a key example.
https://medium.com/@openagents/the-end-of-a-15-year-marl-era...
openagents aims to build agent networks with "open" ecosystems, many agent systems these days are centered around workflows, but workflow is possible when you already know what kinds of agents will be there in your team. But when you allow any agent to join/leave a network, the workflow concept breaks, so this project helps developers to build a ecosystem for open collaboration.
Thanks, but do you realize that you explained it to me using agent systems and ecosystems and open collaboration but i still don't know what it does for the user?
Can it book flights for me?
Is it supposed to be some kind of autonomous intelligent bot that does "stuff" for me? What stuff? From the sibling comments it sounds like "we" are putting LLMs together and hope that something emerges? What?
Ultimately, i ask what openagents.org does for me as a user.
Maybe it's malware, I haven't checked, but that seems like a pretty typical trajectory to me. I posted a project on HN and got a graph of roughly the same shape (though a much more modest magnitude). https://www.star-history.com/#maxbondabe/attempt&type=date&l...
Star counts go vertical when you launch your project and it's warmly received. ~850 stars in 11 days for an AI project doesn't seem at all crazy to me.
The README also contains a mild inducement to star the repo.
> Star Us on GitHub and Get Exclusive Day 1 Badge for Your Networks
Seems sufficient to explain any inauthentic behavior. Growth hacking tactics are certainly not typical of open source projects, but how that should factor into your judgment of this project's trustworthiness, I can't say. Caveat emptor.
Hey, I still remember October 9th so well — that was the day we first went public with our project! I was so excited telling all my friends about it on social media. We'd been working towards this for months, getting everything ready.
Frameworks like AutoGen are used to build individual agents or agent teams, while OpenAgents is designed to connect countless such teams and individuals into a vast, dynamic, and scalable ecosystem.
Readit News
This made me close the tab.
Stars have been gamed for awhile on GitHub, but given the single demo, my best guess is that this is trying to build hype before having any real utility.
If you are a rustacean, We are building something in the a2a space as well. Tho we don't have sudden increase in stars :/
https://github.com/agents-sh/radkit
But I still do not know what a real use case for these would be (and don't say a travel agent). What is the point of these swarms of agents?
Can someone enlighten me?
I can somewhat answer this to best of my knowledge.
Right now, businesses communicate with REST Apis.
That is why we have API gateways like AWS Gateway, Apigee, WSO2 (company i used to work in), Kong, etc so businesses can securly deploy and expose APIS.
As LLMS gets better, the idea is we will evenutally move to a world where ai agents do most of business tasks. And businesses will want to expose ai agents instead of APIS.
This is where protocols like a2a comes in. Google partnering with some other giants introduced a2a protocol a while ago, it is now under linux foundation.
It is a standard for one agent to talk to another agent regardless of the framework (langchain, crewai etc) that is used to build the agent.
Dead Comment
This appears, to me, like an LLM-agent descendent of these earlier multi-agent systems.
I lost track of the research after I left academia -- perhaps someone here can fill in the (considerable) blanks from my overview?
There is the formal view of MAS that is a direct extension of older works with cooperative and competitive agents. This tries to model and then prove emergent properties rigorously. I also count "classic" distributed optimization methods with convergence and correctness properties in this area. Maybe the best known application of this are coordination algorithms for robot/drone swarms.
Then, as a sibling comment points out, there is the influx of machine learning into the field. A large part of this so far was multi-agent reinforcement learning (MARL). I see this mostly applied to any "too hard" or "too slow" optimization problem and in some cases they seem to give impressive results.
Techniques from both areas are frequently mixed and matched for specific applications. Things like agents running a classic optimization but with some ML-based classifications and local knowledge base. What I see actually being used in the wild at the moment are relatively limited agents, applied to a single optimization task and with frequent human supervision.
More recently, LLMs have certainly taken over the MAS term and the corresponding SEO. What this means for the future of the field, I have no idea. It will certainly influence where research funding is allocated. Personally, I find it hard to believe LLMs would solve the classic engineering problems (speed, reliability, correctness) that seem to hold back MAS in more "real world" environments. I assume this will instead push research focus into different applications with higher tolerance for weird outputs. But maybe I just lack imagination.
Deleted Comment
Can it book flights for me? Is it supposed to be some kind of autonomous intelligent bot that does "stuff" for me? What stuff? From the sibling comments it sounds like "we" are putting LLMs together and hope that something emerges? What?
Ultimately, i ask what openagents.org does for me as a user.
Star counts go vertical when you launch your project and it's warmly received. ~850 stars in 11 days for an AI project doesn't seem at all crazy to me.
The README also contains a mild inducement to star the repo.
> Star Us on GitHub and Get Exclusive Day 1 Badge for Your Networks
Seems sufficient to explain any inauthentic behavior. Growth hacking tactics are certainly not typical of open source projects, but how that should factor into your judgment of this project's trustworthiness, I can't say. Caveat emptor.
Just playing devils advocate as I think your accusation isn’t based on much merit and is quite a big claim to make.
Dead Comment