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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Build, evaluate and observe LLM apps
| Date | Stars |
|---|---|
| 2026-07-31 | 296 |
| 2026-08-06 | 296 |
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<div align="center">
<h1 align="center">BeyondLLM</h1>
<h2 align="center">Build - Rapid Experiment - Evaluate - Observability</h2>
<a href="https://img.shields.io/badge/Python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-3776AB.svg?style=flat&logo=python&logoColor=white"><img src="https://img.shields.io/badge/Python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-3776AB.svg?style=flat&logo=python&logoColor=white" alt="Python Versions"></a>
<a href="https://discord.gg/4aWV7He2QU"><img src="https://dcbadge.vercel.app/api/server/4aWV7He2QU?style=flat" alt="Discord" /></a>
<a href="https://twitter.com/aiplanethub"><img src="https://img.shields.io/twitter/follow/aiplanethub" alt="Twitter" /></a>
<a href="https://colab.research.google.com/drive/1S1UL2uCahHkfJsurRA3f7dcR6IHjg-IM?usp=sharing" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Try an example"/></a>
[](https://github.com/aiplanethub/beyondllm/graphs/contributors)
[](CODE_OF_CONDUCT.md)
<p>Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems, simplifying the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs, ultimately aiming to reduce LLM hallucination risks and enhance reliability.</p>
<i><a href="https://discord.gg/4aWV7He2QU">👉 Join our Discord community!</a></i>
</div>
Try out a quick demo on Google Colab:
[](https://colab.research.google.com/drive/1S1UL2uCahHkfJsurRA3f7dcR6IHjg-IM?usp=sharing)
## Quick install
```bash
pip install beyondllm
```
## Quickstart Guide- Chat with YouTube Video
In this quick start guide, we'll demonstrate how to create a Chat with YouTube video RAG application using Beyond LLM with less than 8 lines of code. This 8 lines of code includes:
* Getting custom data source
* Retrieving documents
* Generating LLM responses
* Evaluating embeddings
* Evaluating LLM responses
### Approach-1: Using Default LLM and Embeddings
Build customised RAG in less than ``5 lines of code`` using Beyond LLM.
```python
from beyondllm import source,retrieve,generator
import os
os.environ['GOOGLE_API_KEY'] = "Your Google API Key:"
data = source.fit("https://www.youtube.com/watch?v=oJJyTztI_6g",dtype="youtube",chunk_size=512,chunk_overlap=50)
retriever = retrieve.auto_retriever(data,type="normal",top_k=3)
pipeline = generator.Generate(question="what tool is video mentioning about?",retriever=retriever)
print(pipeline.call())
```
### Approach-2: With Custom LLM and Embeddings
Beyond LLM support various Embeddings and LLMs that are two very important components in Retrieval Augmented Generation.
```python
from beyondllm import source,retrieve,embeddings,llms,generator
import os
from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass("Your OpenAI API Key:")
data = source.fit("https://www.youtube.com/watch?v=oJJyTztI_6g",dtype="youtube",chunk_size=1024,chunk_overlap=0)
embed_model = embeddings.OpenAIEmbeddings()
retriever = retrieve.auto_retriever(data,embed_model,type="normal",top_k=4)
llm = llms.ChatOpenAIModel()
pipeline = generator.Generate(question="what tool is video mentioning about?",retriever=retriever,llm=llm)
print(pipeline.call()) #AI response
print(retriever.evaluate(llm=llm)) #evaluate embeddings
print(pipeline.get_rag_triad_evals()) #evaluate LLM response
```
##### Output
```bash
The tool mentioned in the context is called Jupiter, which is an AI Guru designed to simplify the learning of complex data science topicsExcerpt of 6,607 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:40903f6978a6217e, topic:large-language-models, topic:llm
matched fp:40903f6978a6217e, topic:embeddings
matched fp:40903f6978a6217e, topic:rag