agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models.
Good fit when
- When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph.
- For projects needing detailed instruction-following from tools, given preference for larger models that ensure reliable performance.
Avoid when
- If smaller language model sizes are required as they might ignore retrieval instructions or hallucinate details.
- Projects sensitive about Docker and system requirements must carefully review the outlined conditions for deployment.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (20d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- 2 low (2 low)
- As of Jul 15, 2026
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Install
git clone https://github.com/GiovanniPasq/agentic-rag-for-dummiesHow it fits your stack(1)
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Evidence and technical details
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Overview
This repository offers a guide and codebase to build an agentic retrieval-augmented generation system using LangGraph and Ollama models, aimed at simplifying the process of setting up such agents.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 20, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 20, 2026)
```python from langchain_ollama import ChatOllamaSource link
Tags
README
Install Ollama from https://ollama.com ollama pull granite4.1:8b python from langchain ollama import ChatOllama llm = ChatOllama(model="granite4.1:8b", temperature=0, seed=42) `` project/README.md`](./project/README.md Docker Deployment) for full Docker instructions and system re...
For agents
This page has a .md twin and JSON over the API.