---
title: "agentic-rag-for-dummies vs langgraph"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/giovannipasq-agentic-rag-for-dummies-vs-langchain-ai-langgraph"
tools: ["giovannipasq-agentic-rag-for-dummies", "langchain-ai-langgraph"]
---

# agentic-rag-for-dummies vs langgraph

*GraphCanon updated Sep 20, 2026*

## Verdict

Pick agentic-rag-for-dummies if agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models; pick langgraph if langGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents, offering durable execution, human-in-the-loop oversight, and comprehensive memory management.

[agentic-rag-for-dummies](https://github.com/GiovanniPasq/agentic-rag-for-dummies) reports 4.2k GitHub stars, 552 forks, and 0 open issues, last pushed Aug 30, 2026. [langgraph](https://docs.langchain.com/oss/python/langgraph/) has 42k stars, 7.1k forks, and 794 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [agentic-rag-for-dummies's repository](https://github.com/GiovanniPasq/agentic-rag-for-dummies) and [langgraph's repository](https://github.com/langchain-ai/langgraph).

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [langgraph](/tools/langchain-ai-langgraph.md) |
| --- | --- | --- |
| Tagline | A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents | Low-level orchestration framework for building stateful agents |
| Stars | 4,188 | 41,866 |
| Forks | 552 | 7,073 |
| Open issues | 0 | 794 |
| Language | Jupyter Notebook | Python |
| Adopt for | Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models. | LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents, offering durable execution, human-in-the-loop oversight, and comprehensive memory management. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | AI Agents, Data & Retrieval | AI Agents |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) | [langgraph](/tools/langchain-ai-langgraph.md) |
| --- | --- | --- |
| Maintenance | Active (82%) | Very active (96%) |
| Days since push | 20d | 0d |
| Open issues (now) | 0 | 794 |
| Stars delta | +295 (30d) | +3.5k (30d) |
| Open issues delta | 0 (30d) | +147 (30d) |
| Owner type | User | Organization |
| Full report | [trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust.md) | [trust report](/tools/langchain-ai-langgraph/trust.md) |

## Shared compatibility

- **Python**: [agentic-rag-for-dummies](/tools/giovannipasq-agentic-rag-for-dummies.md) - Python runtime; [langgraph](/tools/langchain-ai-langgraph.md) - Python runtime

## Decision facts: agentic-rag-for-dummies

- **Adopt for:** Agentic RAG for Dummies simplifies the setup of retrieval-augmented generation agents using LangGraph and Ollama models.

## Decision facts: langgraph

- **Adopt for:** LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents, offering durable execution, human-in-the-loop oversight, and comprehensive memory management.

## Choose when

### Choose agentic-rag-for-dummies if…

- agentic-rag-for-dummies is primarily Jupyter Notebook; langgraph is Python.
- Tags unique to agentic-rag-for-dummies: agent, agentic-ai, bm25, gradio.
- Also covers Data & Retrieval.
- When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph.

### Choose langgraph if…

- langgraph is primarily Python; agentic-rag-for-dummies is Jupyter Notebook.
- Tags unique to langgraph: agents, ai-agents, framework, langgraph.
- When you need to build agents that can persist through failures and resume from where they left off, ensuring durable execution.

## When NOT to use agentic-rag-for-dummies

- 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.

## When NOT to use langgraph

- If you are looking for a higher-level package that simplifies the process of building agents with planning and subagent capabilities, consider Deep Agents instead.
- For projects that do not require long-running, stateful workflows, LangGraph's features may be overkill and a simpler framework might be more appropriate.
- If your project is primarily focused on JavaScript or TypeScript, LangGraph.js might be a better fit, as LangGraph is primarily designed for Python environments.

## Common questions

### What is the difference between agentic-rag-for-dummies and langgraph?

agentic-rag-for-dummies: A modular Agentic RAG built with LangGraph for learning Retrieval-Augmented Generation Agents. langgraph: Low-level orchestration framework for building stateful agents. See the comparison table for live GitHub stats and shared categories.

### When should I choose agentic-rag-for-dummies over langgraph?

Choose agentic-rag-for-dummies over langgraph when agentic-rag-for-dummies is primarily Jupyter Notebook; langgraph is Python; Tags unique to agentic-rag-for-dummies: agent, agentic-ai, bm25, gradio; Also covers Data & Retrieval; When aiming to quickly develop a retrieval-augmented generation agent, thanks to its streamlined setup with LangGraph.

### When should I choose langgraph over agentic-rag-for-dummies?

Choose langgraph over agentic-rag-for-dummies when langgraph is primarily Python; agentic-rag-for-dummies is Jupyter Notebook; Tags unique to langgraph: agents, ai-agents, framework, langgraph; When you need to build agents that can persist through failures and resume from where they left off, ensuring durable execution.

### When should I avoid agentic-rag-for-dummies?

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.

### When should I avoid langgraph?

If you are looking for a higher-level package that simplifies the process of building agents with planning and subagent capabilities, consider Deep Agents instead. For projects that do not require long-running, stateful workflows, LangGraph's features may be overkill and a simpler framework might be more appropriate. If your project is primarily focused on JavaScript or TypeScript, LangGraph.js might be a better fit, as LangGraph is primarily designed for Python environments.

### Is agentic-rag-for-dummies or langgraph more popular on GitHub?

langgraph has more GitHub stars (41,866 vs 4,188). Stars measure visibility, not whether either tool fits your constraints.

### Are agentic-rag-for-dummies and langgraph open source?

Yes - both are open-source projects on GitHub (agentic-rag-for-dummies: MIT, langgraph: MIT).

### Where can I find alternatives to agentic-rag-for-dummies or langgraph?

GraphCanon lists graph-backed alternatives at [agentic-rag-for-dummies alternatives](/tools/giovannipasq-agentic-rag-for-dummies/alternatives) and [langgraph alternatives](/tools/langchain-ai-langgraph/alternatives) ([agentic-rag-for-dummies markdown twin](/tools/giovannipasq-agentic-rag-for-dummies/alternatives.md), [langgraph markdown twin](/tools/langchain-ai-langgraph/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/giovannipasq-agentic-rag-for-dummies-vs-langchain-ai-langgraph.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, agentic-rag-for-dummies or langgraph?

agentic-rag-for-dummies: Active. langgraph: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for agentic-rag-for-dummies and langgraph?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentic-rag-for-dummies trust report](/tools/giovannipasq-agentic-rag-for-dummies/trust); [langgraph trust report](/tools/langchain-ai-langgraph/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies`](/api/graphcanon/graph?tool=giovannipasq-agentic-rag-for-dummies)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_