selenium-python-helium VS waggle-dance

I don't get why people still use XPaths, CSS selectors or HTML IDs to identify elements, even when they are "recorded". Please please please just use my https://github.com/mherrmann/selenium-python-helium instead. It makes so much more sense.

You can, though it might not be the best tool for this. Automation of web entry is better done with selenium, or my favorite variation helium.

Regarding automatic selectors: check out my open source library https://github.com/mherrmann/selenium-python-helium.

quick example https://github.com/mherrmann/selenium-python-helium

If this all fails you'll have to fall back on a web driver like selenium or one of its wrappers like helium.

Simplest would probably be not using Selenium directly, but a wrapper like helium. Most of its functions you'd need are listed here.

If you're talking about CSS I reckon you want to click/input things on a website inside your browser. In this case you would use a web driver which can automate a web browser like Chrome or Firefox. Something like Helium, Selenium or pyppeteer.

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.