Ai_MegaList VS waggle-dance

It can do stuff, sort of, like helping me to create ; https://github.com/tudorw/Ai_MegaList/blob/main/AI_Sector_Br...

After a lot of trial and error, I managed to keep it somewhat on track by using a CSV file, something like;

"An expert manipulate .csv files, read the first URL from the first line, 2nd column of 'raw.csv', pass the URL to browse_website the questions 'summarize the activities, highlight any investment, funding or patents mentioned', regardless of the results or failure, write the data quote delimited and in columns where appropriate to a new line of 'complete.csv', pass the URL to google and summarize the answer to the question 'does this organisation have a good reputation, from reliable sources', regardless of the results or failure, write the data quote delimited and in columns where appropriate to a new line of 'complete.csv', remove the 1 line from 'raw.csv' you have processed, repeat the process until 'raw.csv' has no more URL in it"

On the plus side, it was very quick to iterate, 'programming' in words is an exercise in linguistics, it's ability to scrape from any site was impressive, on the downside, it really struggled to stay on task, and even when things seems to be working well, random behaviour was normal, so it might just decide to delete the csv as a short cut...

On Windows it's ability to engage PowerShell was equally enlightening and terrifying... As an exercise in instructing an AI it was interesting, I'd certainly try again if the requirements fitted.

I think it's a credit to the team that they explored options for vector storage then retreated in the name of complexity, it's a good reason.

This seems very similar to LangSmith’s trace monitoring, which I have been leaning on heavily for observability. You also mention LlamaIndex— how do you see your project fitting into the ecosystem?

This is a great README, but I don’t think I would able to use this because it is serial.

In my experimental agent system, waggledance.ai, I have been working on a pre-agent step of [picking and synthesizing the right context and tools](https://github.com/agi-merge/waggle-dance/blob/main/packages...) for a given subtask of a larger goal, and it seems to be boosting results. It looks like now I have to try sub-question answering in the mix as well.

This is amazing. I will try to have it automate my system of agents web app (turtles all the way down) (shameless plug: https://github.com/agi-merge/waggle-dance)

- source code: https://github.com/agi-merge/waggle-dance

Any advice for trying to implement this in my project over at https://github.com/agi-merge/waggle-dance

Currently I am creating different agent types for planned subtasks using langchain, so perhaps implementing a custom AgentExecutor? Or would I need to lift it up higher in the logic stack? I am not sure that I understand how the graph search and thought-action-reflection selection process is deciding when and how to reflect if a branch fails, and how it backpropogates the failure to other nodes?

I have been working on a system of agents over at https://github.com/agi-merge/waggle-dance - I already split problems up into subtasks for agents to work on independently. I give agents access to vector databases, using a simple global key for now, but soon a context/parent/child key. Access to the vector DBs is proxied via tools (agents have to “call” saveMemory or retrieveMemory). I also check for looping/repetition FREQUENTLY using in-memory vector databases of the langchain agent callback events.

My opinion on this: eh, who cares? AutoGPT and similar are non-standard use cases for Vector DBs right now, and Vector DBs are useful for RAG.