# benman1/generative_ai_with_langchain

Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph. This is the companion repository for the book on generative AI with LangChain.

Repository: https://github.com/benman1/generative_ai_with_langchain
Canonical: https://ross.abutalabs.com/products/generative_ai_with_langchain
Homepage: https://amzn.to/4dErkya
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: chatgpt, gpt, huggingface, langchain, llms, openai, claude, claude-3-5-sonnet, deepseek, deepseek-r1, ollama, agent, gpt-4o, langgraph, llamacpp
Last push: 2026-08-14T08:45:56+00:00

## Health v2 (maintenance only)
Score: 72/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 82
- inputs: {"age_days": 1149, "days_push": 19, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1411, forks 583 (observed 2026-08-28T04:04:38.928055+00:00)

## What it is
Companion code repository for the Packt book 'Generative AI with LangChain, Second Edition', containing Jupyter notebook examples for building production-ready LLM applications and agents with Python, LangChain, and LangGraph. It covers multi-agent architectures, RAG pipelines, reasoning techniques, testing, evaluation, and deployment of LLM systems.

## Use cases
- learn to build LLM applications with LangChain
- build multi-agent systems with LangGraph
- implement RAG pipelines with re-ranking and hybrid search
- learn agent design patterns and error handling
- evaluate and test LLM applications
- deploy generative AI apps to production
- work with OpenAI, Claude, and Ollama models in Python

## When to choose
- you are reading the book and want runnable code examples
- you want hands-on notebooks covering LangChain and LangGraph
- you want practical coverage of RAG, agents, and LLM deployment

## When to avoid
- you need a production library rather than educational example code
- you want a framework-agnostic guide without LangChain
- you need a maintained software dependency for a production system

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, rag, llm-inference, prompt-engineering, chatbot
- domain: large-language-models, machine-learning, tutorials
- platform: python, cross-platform
- tags: langchain, langgraph, book-companion, jupyter-notebooks, openai, huggingface, ollama, multi-agent-systems, generative-ai, ai-agents, retrieval-augmented-generation

## Member repositories
- benman1/generative_ai_with_langchain (main) score 72

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.928055+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 2026-08-30T04:38:23.232373+00:00, confidence not recorded.
  - readme: https://github.com/benman1/generative_ai_with_langchain (fetched 2026-08-28T04:04:38.928055+00:00, sha 282cef80ec17)
  - homepage: https://amzn.to/4dErkya (fetched 2026-08-29T11:51:39.078960+00:00, sha cc1b32e27072)
- Data as of 2026-08-30T08:39:29.467469+00:00.