Top AI Repos — open-source AI, indexed and scored

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.

GiovanniPasq/agentic-rag-for-dummies

A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

79MITJupyter Notebook

View on GitHub

Stars: 4,326
Forks: 573
Watchers: 26
Open issues: 0
Contributors: 4

Star history, last 90 days+500 over 78 snapshots

2026-07-31 · 3,8262026-11-04 · 4,326

Star history, last 90 days
Date	Stars
2026-07-31	3826
2026-08-10	3877
2026-08-18	3903
2026-08-19	3909
2026-08-20	3914
2026-08-21	3943
2026-08-22	3948
2026-08-23	3950
2026-08-24	3958
2026-08-25	3966
2026-08-27	3972
2026-08-28	3977
2026-08-29	3979
2026-08-30	3982
2026-08-31	3991
2026-09-01	3994
2026-09-02	3998
2026-09-03	4001
2026-09-04	4005
2026-09-06	4008
2026-09-07	4014
2026-09-08	4022
2026-09-09	4028
2026-09-10	4030
2026-09-11	4034
2026-09-12	4067
2026-09-13	4119
2026-09-14	4146
2026-09-15	4164
2026-09-16	4172
2026-09-17	4178
2026-09-18	4185
2026-09-19	4188
2026-09-20	4189
2026-09-21	4192
2026-09-22	4194
2026-09-23	4201
2026-09-24	4202
2026-09-25	4202
2026-09-26	4205
2026-09-27	4207
2026-09-28	4212
2026-09-29	4216
2026-09-30	4220
2026-10-01	4221
2026-10-02	4220
2026-10-03	4223
2026-10-04	4222
2026-10-05	4226
2026-10-07	4227
2026-10-08	4232
2026-10-09	4236
2026-10-10	4239
2026-10-11	4243
2026-10-12	4246
2026-10-13	4247
2026-10-14	4255
2026-10-15	4256
2026-10-16	4260
2026-10-17	4261
2026-10-18	4262
2026-10-19	4268
2026-10-20	4271
2026-10-21	4272
2026-10-22	4276
2026-10-23	4280
2026-10-24	4282
2026-10-25	4284
2026-10-26	4286
2026-10-27	4293
2026-10-28	4298
2026-10-29	4300
2026-10-30	4303
2026-10-31	4305
2026-11-01	4310
2026-11-02	4311
2026-11-03	4314
2026-11-04	4326

Today

+12 stars today

This week

+28 stars this week

This month

+100 stars this month

Momentum

121.0

growth rate 0.65%/day

From the README

<p align="center">
  <img alt="Agentic RAG for Dummies Logo" src="assets/logo.png" width="350px">
</p>

<h1 align="center">Agentic RAG for Dummies</h1>

<p align="center">
  <strong>Build a modular Agentic RAG system with LangGraph, conversation memory, and human-in-the-loop query clarification</strong>
</p>

<p align="center">
  <a href="#overview">Overview</a> •
  <a href="#how-it-works">How It Works</a> •
  <a href="#llm-provider-configuration">LLM Providers</a> •
  <a href="#implementation">Implementation</a> •
  <a href="#installation--usage">Installation & Usage</a> •
  <a href="#troubleshooting">Troubleshooting</a>
</p>

<p align="center">
  <img src="https://img.shields.io/github/stars/GiovanniPasq/agentic-rag-for-dummies?style=social" alt="GitHub Stars"/>
  <img src="https://img.shields.io/github/forks/GiovanniPasq/agentic-rag-for-dummies?style=social" alt="GitHub Forks"/>
  <img src="https://img.shields.io/badge/license-MIT-green" alt="License"/>
  <a href="https://github.com/von-development/awesome-langgraph">
    <img src="https://awesome.re/badge.svg" alt="Awesome LangGraph"/>
  </a>
</p>

<p align="center">
  <img src="https://img.shields.io/badge/python-3.11%2B-blue?logo=python&logoColor=white" alt="Python"/>
  <img src="https://img.shields.io/badge/LangGraph-1.2%2B-orange?logo=langchain&logoColor=white" alt="LangGraph"/>
  <img src="https://img.shields.io/badge/Qdrant-vector%20db-DC244C" alt="Qdrant"/>
  <img src="https://img.shields.io/badge/LLM%20Providers-Ollama%20%7C%20OpenAI%20%7C%20Anthropic%20%7C%20Google-purple" alt="LLM Providers"/>
</p>

<p align="center">
  <a href="https://colab.research.google.com/github/GiovanniPasq/agentic-rag-for-dummies/blob/main/notebooks/agentic_rag.ipynb">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
  </a>
</p>

<p align="center">
  <img alt="Agentic RAG Demo" src="assets/demo.gif" width="650px">
</p>

<p align="center">
  <strong>If you like this project, a star ⭐️ would mean a lot :)</strong><br>
</p>

## Overview

This repository demonstrates how to build an **Agentic RAG (Retrieval-Augmented Generation)** system using LangGraph with minimal code. Most RAG tutorials show basic concepts but lack guidance on building modular, agent-driven systems — this project bridges that gap by providing **both learning materials and an extensible architecture**.

### What's inside

| Feature | Description |
|---|---|
| 🗂️ **Hierarchical Indexing** | Search small chunks for precision, retrieve large Parent chunks for context |
| 🧠 **Conversation Memory** | Maintains context across questions for natural dialogue |
| ❓ **Query Clarification** | Rewrites ambiguous queries or pauses to ask the user for details |
| 🤖 **Agent Orchestration** | LangGraph coordinates the full retrieval and reasoning workflow |
| 🔀 **Multi-Agent Map-Reduce** | Decomposes complex queries into parallel sub-queries |
| ✅ **Self-Correction** | Re-queries automatically if initial results are insufficient |
| 🗜️ **Context Compression** | Keeps working memory lean across long retrieval loops |
| 🔍 **Observability** | Track LLM calls, tool usage, and graph execution with Langfuse |
| 📊 **Evaluation** | Evaluate retrieval and answer quality with RAGAS metrics |

### 🎯 Two Ways to Use This Repo

**1️⃣ Learning Path: Interactive Notebook**

Step-by-step tutorial perfect for understanding core concepts. Start here if you're new to Agentic RAG or want to experiment quickly.

**2️⃣ Building Path: Modular Project**

Flexible architecture where each component can be independently adapted — LLM provider, embedding model, PDF converter, and agent workflow. The runnable app is Ollama-first, and it can be adapted to any chat model provider supported by LangChain. Examples are included for Anthropic, OpenAI, and Google.

See [Modular Architecture](#modular-architecture) and [Installation & Usage](#installation--usage) to get started.

## How It Works

### Document Preparation:

Excerpt of 60,802 characters

Read on GitHub

Top contributors3 shown

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

79/10079

Maintenance: 90%; How recently the default branch was pushed to
Releases: 89%; Recency and cadence of tagged releases
Community: 15%; Contributor breadth and bus factor
Issue backlog: 100%; Open issues measured against the size of the audience
Documentation: 100%; README depth, plus a homepage and topics
Licensing: 100%; How freely the code can be adopted

What to know before adopting

Bus factor of 1 — one contributor dominates
Usable in commercial products with attribution.

Details

Created: last year
Last push: 18 days ago
Last release: 18 days ago
Release tag: v2.3.1
Releases in the last year: 9
Default branch: main
Size: 25k KB
Owner type: User
Owner location: Italy
Discovered via: topic:retrieval-augmented-generation
First indexed: 3 months ago
Last synced: 4 days ago

GiovanniPasq/agentic-rag-for-dummies

A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.

79MITJupyter Notebook

View on GitHub

Stars: 4,326
Forks: 573
Watchers: 26
Open issues: 0
Contributors: 4

Star history, last 90 days+500 over 78 snapshots

2026-07-31 · 3,8262026-11-04 · 4,326

Star history, last 90 days
Date	Stars
2026-07-31	3826
2026-08-10	3877
2026-08-18	3903
2026-08-19	3909
2026-08-20	3914
2026-08-21	3943
2026-08-22	3948
2026-08-23	3950
2026-08-24	3958
2026-08-25	3966
2026-08-27	3972
2026-08-28	3977
2026-08-29	3979
2026-08-30	3982
2026-08-31	3991
2026-09-01	3994
2026-09-02	3998
2026-09-03	4001
2026-09-04	4005
2026-09-06	4008
2026-09-07	4014
2026-09-08	4022
2026-09-09	4028
2026-09-10	4030
2026-09-11	4034
2026-09-12	4067
2026-09-13	4119
2026-09-14	4146
2026-09-15	4164
2026-09-16	4172
2026-09-17	4178
2026-09-18	4185
2026-09-19	4188
2026-09-20	4189
2026-09-21	4192
2026-09-22	4194
2026-09-23	4201
2026-09-24	4202
2026-09-25	4202
2026-09-26	4205
2026-09-27	4207
2026-09-28	4212
2026-09-29	4216
2026-09-30	4220
2026-10-01	4221
2026-10-02	4220
2026-10-03	4223
2026-10-04	4222
2026-10-05	4226
2026-10-07	4227
2026-10-08	4232
2026-10-09	4236
2026-10-10	4239
2026-10-11	4243
2026-10-12	4246
2026-10-13	4247
2026-10-14	4255
2026-10-15	4256
2026-10-16	4260
2026-10-17	4261
2026-10-18	4262
2026-10-19	4268
2026-10-20	4271
2026-10-21	4272
2026-10-22	4276
2026-10-23	4280
2026-10-24	4282
2026-10-25	4284
2026-10-26	4286
2026-10-27	4293
2026-10-28	4298
2026-10-29	4300
2026-10-30	4303
2026-10-31	4305
2026-11-01	4310
2026-11-02	4311
2026-11-03	4314
2026-11-04	4326

Today

+12 stars today

This week

+28 stars this week

This month

+100 stars this month

Momentum

121.0

growth rate 0.65%/day

From the README

<p align="center">
  <img alt="Agentic RAG for Dummies Logo" src="assets/logo.png" width="350px">
</p>

<h1 align="center">Agentic RAG for Dummies</h1>

<p align="center">
  <strong>Build a modular Agentic RAG system with LangGraph, conversation memory, and human-in-the-loop query clarification</strong>
</p>

<p align="center">
  <a href="#overview">Overview</a> •
  <a href="#how-it-works">How It Works</a> •
  <a href="#llm-provider-configuration">LLM Providers</a> •
  <a href="#implementation">Implementation</a> •
  <a href="#installation--usage">Installation & Usage</a> •
  <a href="#troubleshooting">Troubleshooting</a>
</p>

<p align="center">
  <img src="https://img.shields.io/github/stars/GiovanniPasq/agentic-rag-for-dummies?style=social" alt="GitHub Stars"/>
  <img src="https://img.shields.io/github/forks/GiovanniPasq/agentic-rag-for-dummies?style=social" alt="GitHub Forks"/>
  <img src="https://img.shields.io/badge/license-MIT-green" alt="License"/>
  <a href="https://github.com/von-development/awesome-langgraph">
    <img src="https://awesome.re/badge.svg" alt="Awesome LangGraph"/>
  </a>
</p>

<p align="center">
  <img src="https://img.shields.io/badge/python-3.11%2B-blue?logo=python&logoColor=white" alt="Python"/>
  <img src="https://img.shields.io/badge/LangGraph-1.2%2B-orange?logo=langchain&logoColor=white" alt="LangGraph"/>
  <img src="https://img.shields.io/badge/Qdrant-vector%20db-DC244C" alt="Qdrant"/>
  <img src="https://img.shields.io/badge/LLM%20Providers-Ollama%20%7C%20OpenAI%20%7C%20Anthropic%20%7C%20Google-purple" alt="LLM Providers"/>
</p>

<p align="center">
  <a href="https://colab.research.google.com/github/GiovanniPasq/agentic-rag-for-dummies/blob/main/notebooks/agentic_rag.ipynb">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
  </a>
</p>

<p align="center">
  <img alt="Agentic RAG Demo" src="assets/demo.gif" width="650px">
</p>

<p align="center">
  <strong>If you like this project, a star ⭐️ would mean a lot :)</strong><br>
</p>

## Overview

This repository demonstrates how to build an **Agentic RAG (Retrieval-Augmented Generation)** system using LangGraph with minimal code. Most RAG tutorials show basic concepts but lack guidance on building modular, agent-driven systems — this project bridges that gap by providing **both learning materials and an extensible architecture**.

### What's inside

| Feature | Description |
|---|---|
| 🗂️ **Hierarchical Indexing** | Search small chunks for precision, retrieve large Parent chunks for context |
| 🧠 **Conversation Memory** | Maintains context across questions for natural dialogue |
| ❓ **Query Clarification** | Rewrites ambiguous queries or pauses to ask the user for details |
| 🤖 **Agent Orchestration** | LangGraph coordinates the full retrieval and reasoning workflow |
| 🔀 **Multi-Agent Map-Reduce** | Decomposes complex queries into parallel sub-queries |
| ✅ **Self-Correction** | Re-queries automatically if initial results are insufficient |
| 🗜️ **Context Compression** | Keeps working memory lean across long retrieval loops |
| 🔍 **Observability** | Track LLM calls, tool usage, and graph execution with Langfuse |
| 📊 **Evaluation** | Evaluate retrieval and answer quality with RAGAS metrics |

### 🎯 Two Ways to Use This Repo

**1️⃣ Learning Path: Interactive Notebook**

Step-by-step tutorial perfect for understanding core concepts. Start here if you're new to Agentic RAG or want to experiment quickly.

**2️⃣ Building Path: Modular Project**

Flexible architecture where each component can be independently adapted — LLM provider, embedding model, PDF converter, and agent workflow. The runnable app is Ollama-first, and it can be adapted to any chat model provider supported by LangChain. Examples are included for Anthropic, OpenAI, and Google.

See [Modular Architecture](#modular-architecture) and [Installation & Usage](#installation--usage) to get started.

## How It Works

### Document Preparation:

Excerpt of 60,802 characters

Read on GitHub

Top contributors3 shown

Quality score

Would you bet a product on this? Bounded 0–100 and slow moving.

79/10079

Maintenance: 90%; How recently the default branch was pushed to
Releases: 89%; Recency and cadence of tagged releases
Community: 15%; Contributor breadth and bus factor
Issue backlog: 100%; Open issues measured against the size of the audience
Documentation: 100%; README depth, plus a homepage and topics
Licensing: 100%; How freely the code can be adopted

What to know before adopting

Bus factor of 1 — one contributor dominates
Usable in commercial products with attribution.

Details

Created: last year
Last push: 18 days ago
Last release: 18 days ago
Release tag: v2.3.1
Releases in the last year: 9
Default branch: main
Size: 25k KB
Owner type: User
Owner location: Italy
Discovered via: topic:retrieval-augmented-generation
First indexed: 3 months ago
Last synced: 4 days ago

Top AI Repos — open-source AI, indexed and scored

GiovanniPasq/agentic-rag-for-dummies

From the README

Top contributors3 shown

Quality score

What to know before adopting

Categories

Details

GiovanniPasq/agentic-rag-for-dummies

From the README

Top contributors3 shown

Quality score

What to know before adopting

Categories

Details

Top AI Repos — open-source AI, indexed and scored

Trend

From the README

Top contributors3 shown

Quality score

What to know before adopting

Categories

Details

Trend

From the README

Top contributors3 shown

Quality score

What to know before adopting

Categories

Details