It would be more interesting to compare trading agents with index tracking ETFs. The better version of an ETF could maybe be a model where you zoom in on the companies and add/remove to your portfolio on the company related news, but keeping a broader portfolio.

Maybe agentic trading still performs worse than ETFs. But alternatively, if it were meaningfully better then it would be okay to opensource, similarly how ETFs are publishing their portfolios.

If you like building but have no capital you might release something like this that works.

Given what a know about the 2008 financial crisis, wouldn't an AI analysis in the years before that crisis of the real state funds helped to understand the risk of them better and avoid the big exposure.

As local contexts/skills get better at depicting what's to be expected and what an agent can work with and work to get better at, having more and more little specialized agents working as a swarm get you, with a field-skilled human as a supervisor, really good results even in highly niche and technical fields

103K stars in a few months, smells of purchased stars IMO.

I feel like a lot of the stars for repos like this are from people who are riding the AI hype train and not actually interested in using the software

>Isn't a single agent with a good harness better than any multi agent env?

Multiple agents can each have their own isolated LLM context that is focused on their respective narrow scoped tasks. That way, the accumulated "memories", "instructions", etc do not stomp on each other. E.g. the agent loop that is gathering "signals" from the market has a context memory full of instructions related to scraping websites and using REST APIs and it doesn't interfere with the other agent loop that does sentiment analysis that has context memory dedicated to text of news transcripts correlated with historical price signals.

You can have a single agent acting as a top-level "orchestrator" to manage other agents. However, you can't have a single agent that does "everything" for long complicated workflows with high quality. The drawback is the more agents you spawn to take advantage of isolating contexts to improve task completion quality, the more you multiply the tokens consumption and costs.

Another other driver for multi-agents is scaling out cpu usage, especially across different machine boundaries.

It helps you spend more token, is more expensive, and thus is obviously more AI. Also novelty and more complexity means less scrutiny of the approach.

These are necessary and perfectly sufficient for an investment firm thesis I believe.

Modularity maybe. Remember, agents are basically workflows that call LLMs in certain nodes.

If it's a single agent then people will just call it a chatgpt wrapper, can't have that can we

I've built agents that call different LLMs and keep separated memories. Remember, agents are just long-running workflows with some nodes calling LLMs and that sometimes can be started as tool from other "agent".

There are times when I wonder if couldn't just draw then in a BPMN designer that allowed me to write custom code for nodes. Is BPMN still a thing?

Experimented with multi-llm analysis for problem solving over summer, combined with multi-agent approaches it can tease out interesting angles to problems that I never considered. Expensive but use only for my high value problems.

https://github.com/monkeydust/rightmind