airweave
mem0
| airweave | mem0 | |
|---|---|---|
| 2 | 10 | |
| 315 | 25,269 | |
| 14.9% | 4.5% | |
| 9.8 | 9.8 | |
| 5 days ago | 8 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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.
airweave
-
Ask HN: What Are You Working On? (February 2025)
I'm working on Airweave https://github.com/airweave-ai/airweave , an open-source dev tool that makes any app searchable for AI agents. it connects to a source app, db, or api and converts its contents to accessible knowledge for agents. Airweave automates authentication, ingestion, enrichment, mapping, and syncing to vector stores and graph databases of choice. you can use it via our UI, API, or SDKs https://docs.airweave.ai/
we originally built this for our previous agent startup as an internal solution to ensure agents could find the relevant data on apps they're using. We then pivoted to this after some early positive reactions and decided to open-source it.
here's a short demo: https://tinyurl.com/demo-airweave
we're two engineers/friends based in Amsterdam, NL. We just launched the project, so it's rough around the edges ofc, but we're very eager to get some feedback!
feel free to reach out to me personally if you like this!
- Show HN: Airweave – Open-Source Tool That Turns App Data into Agent Knowledge
mem0
-
13 GitHub Projects that Supercharge Your AI and Development Journey 🚀
Stars: 25085 Author: mem0ai Star the mem0 repository⭐
- Show HN: Claude Memory – Long-term memory for Claude
- Show HN: Mem0 – open-source Memory Layer for AI apps
- Mem0: The Memory Layer for Personalized AI
-
Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
You can use embedchain[1] to connect various data sources and then get a RAG application running on your local and production very easily. Embedchain is an open source RAG framework and It follows a conventional but configurable approach.
The conventional approach is suitable for software engineer where they may not be less familiar with AI. The configurable approach is suitable for ML engineer where they have sophisticated uses and would want to configure chunking, indexing and retrieval strategies.
[1]: https://github.com/embedchain/embedchain
- Embedchain
- Framework to easily create LLM powered bots over any dataset
-
[D] Hardest thing about building with LLMs?
Langchain is a big wrapper in itself and people can't be bothered to even use that to write 10 lines of code. Look at the traction this project is getting https://github.com/embedchain/embedchain, at it's heart it's just using few modules from langchain. The whole thing, chunking+embedding+retrieval+promoting can be done in 100 lines without langchain and embedchain.
-
AI — weekly megathread!
Embedchain: a framework to easily create LLM powered bots over any dataset [Link].
- EmbedChain: Framework to easily create LLM powered bots over any dataset.
What are some alternatives?
clientai - A unified client for AI providers with built-in agent support.
gpt-migrate - Easily migrate your codebase from one framework or language to another.
canopy - Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone
openchat - OpenChat: Advancing Open-source Language Models with Imperfect Data
gptme - Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web, vision.
trulens - Evaluation and Tracking for LLM Experiments