playwright-chrome-recorder
waggle-dance
| playwright-chrome-recorder | waggle-dance | |
|---|---|---|
| 1 | 5 | |
| 16 | 150 | |
| - | - | |
| 3.9 | 9.9 | |
| about 1 year ago | 5 months ago | |
| TypeScript | TypeScript | |
| - | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
playwright-chrome-recorder
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Autotab – Boring AI Agents for real world tasks
Similarly, https://github.com/AndrewUsher/playwright-chrome-recorder
waggle-dance
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Show HN: Demystifying Advanced Rag Pipelines
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.
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Autotab – Boring AI Agents for real world tasks
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)
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Ask HN: Show me your half baked project
- source code: https://github.com/agi-merge/waggle-dance
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Language Agent Tree Search Unifies Reasoning Acting and Planning in LMs
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?
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Why AutoGPT engineers ditched vector databases
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.
What are some alternatives?
testing-library-docs - docs site for @testing-library/*
webdriver-bidi - Bidirectional WebDriver protocol for browser automation
autotab-starter - Build browser agents for real world tasks
RVS_GTDriver - A "Pure Swift" Low-Level SDK for Bluetooth Low-Energy Devices (Work In Progress)
selenium-python-helium - Lighter web automation for Python [Moved to: https://github.com/mherrmann/helium]
rag-demystified - An LLM-powered advanced RAG pipeline built from scratch
Playwright - Playwright is a framework for Web Testing and Automation. It allows testing Chromium, Firefox and WebKit with a single API.
paperless-ngx - A community-supported supercharged version of paperless: scan, index and archive all your physical documents
completions - Node.js SDK for interacting with OpenAI Chat API.