# samwit/langchain-tutorials

A set of LangChain Tutorials from my youtube channel

Repository: https://github.com/samwit/langchain-tutorials
Canonical: https://ross.abutalabs.com/products/samwit-langchain-tutorials
Language: Jupyter Notebook
License Family: other
Last push: 2024-07-01T09:44:46+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 84
- inputs: {"age_days": 1177, "days_push": 793, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1553, forks 392 (observed 2026-08-28T04:05:02.866703+00:00)

## What it is
A collection of Jupyter Notebook tutorials for LangChain accompanying a YouTube playlist. It covers hands-on examples of building LLM applications with the LangChain framework.

## Use cases
- learn langchain from scratch
- tutorials for building llm apps with langchain
- example notebooks for langchain agents
- how to do rag with langchain
- langchain youtube course companion code
- get started with chains and prompts in langchain

## When to choose
- you prefer learning via runnable notebooks paired with video walkthroughs
- you want practical langchain examples rather than reference docs
- you are new to building llm applications

## When to avoid
- you need production-ready code or maintained library features
- you want a framework or tool rather than educational material
- you need a licensed or actively updated codebase

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: llm-inference, agent-framework, rag, prompt-engineering
- domain: large-language-models, tutorials, artificial-intelligence
- platform: python
- tags: langchain, jupyter-notebooks, youtube-tutorials, llm, educational, ai-agents, retrieval-augmented-generation

## Member repositories
- samwit/langchain-tutorials (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:02.866703+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:10.685559+00:00, confidence not recorded.
  - readme: https://github.com/samwit/langchain-tutorials (fetched 2026-08-28T04:05:02.866703+00:00, sha ac76a484da62)
- Data as of 2026-08-30T08:39:29.467469+00:00.