Traditional vs agentic RAG: diagrams, agentic search, GraphRAG, LangGraph tutorial, tools

Traditional RAG runs one fixed pipeline: retrieve passages once, then generate an answer. Agentic RAG puts a language model agent in control: it decides whether to retrieve, which source or tool to use, rewrites queries, checks results and retrieves again until it has enough evidence. I built RAG.NextUpgrad with hybrid retrieval, fusion, reranking and a confidence gate, so this page compares traditional and agentic RAG, agentic search, GraphRAG, a LangGraph tutorial, examples, frameworks, models, courses and code.

Traditional vs agentic RAG: what is the difference?

Traditional RAG is a fixed sequence: embed the question, retrieve top passages, generate. Agentic RAG makes retrieval a tool the model chooses to use in a loop, planning queries, selecting sources, evaluating results and iterating before answering.

Traditional RAG is fast, cheap, predictable and easy to evaluate, which suits single-hop questions over one knowledge base. Agentic RAG handles multi-step questions, such as comparing policies across years, combining documents with database queries, or recovering when the first search returns nothing useful. The costs are latency, more model calls, harder debugging and new failure modes, like loops or irrelevant tool use. Start traditional, measure failures, and add agentic steps only where they fix real problems.

What does a RAG vs agentic RAG diagram look like?

A traditional RAG diagram is a straight line: question, retriever, top passages, prompt, language model, answer. An agentic RAG diagram is a loop: question, agent, decision, tool calls to retrievers or APIs, observation, decision again, and finally an answer.

In the agentic version, the agent box connects to several tools, such as a vector search over documents, a web search, a SQL database and a calculator, with arrows back to the agent after each call. Many diagrams also show a grader step that checks whether retrieved documents are relevant, and a rewrite step that reformulates the question if not. Drawing your own diagram before building helps decide where loops are truly needed and where a straight pipeline is enough.

RAG vs agentic search: how do they differ?

RAG retrieves from a prepared index of your documents to answer one question. Agentic search uses an agent that issues many searches, often on the web or across several sources, reads results, follows leads and synthesises a report, like a research assistant.

Agentic search powers deep research features in assistants, which may run dozens of searches and browse many pages for one request. RAG is better for fast, cited answers from known, trusted content; agentic search is better for open-ended research where the right sources are unknown. They converge in agentic RAG, where an agent searches internal indexes iteratively. Both need source quality checks, since agents can collect unreliable pages, and both should cite sources so users can verify.

Agentic RAG vs GraphRAG: what is the difference?

GraphRAG builds a knowledge graph from documents, extracting entities and relationships and often summarising communities of related entities, then retrieves over that structure. Agentic RAG adds an agent that decides how and where to retrieve. They address different problems and can be combined.

GraphRAG, popularised by Microsoft Research, helps with global questions such as the main themes across a large corpus, and with multi-hop relationships between entities, which chunk search handles poorly. Building the graph costs significant indexing compute and needs updating as documents change. Agentic RAG improves flexibility at query time instead. An agent can use a graph as one of its tools, querying relationships when needed and falling back to vector search for detailed passages.

How do you build agentic RAG with LangGraph?

In LangGraph, define a state with the question, messages and retrieved documents; add nodes for an agent that decides to call a retriever tool, a grader that checks relevance, a rewriter that reformulates queries, and a generator; then connect them with conditional edges.

Also asked as: agentic rag tutorial langgraph

The flow: the agent node receives the question and either answers directly or calls the retriever tool; the grader node scores retrieved documents; if relevant, the generate node writes a cited answer; if not, the rewrite node improves the query and loops back, with a maximum iteration count to prevent endless loops. Compile the graph, run it with streaming to watch each step, and trace runs in LangSmith. LangGraph's documentation includes an agentic RAG tutorial with this structure.

What are some examples of agentic RAG?

Examples include a support agent that searches documentation, checks order data and opens a ticket; a research assistant that runs multiple searches and compares findings; a legal assistant that retrieves clauses across contracts and compares them; and a coding agent searching a codebase.

Other examples: a financial analyst agent that pulls figures from reports with retrieval and computes ratios with a calculator tool; an HR assistant that routes questions to the right policy collection; and a study assistant that searches course notes, then quizzes the student. In each, the agent decides which source to query and whether results are sufficient. The common thread is multi-step reasoning where a single retrieval pass would miss evidence or tools.

What is the best agentic RAG framework?

Popular choices are LangGraph for explicit, stateful control flows, LlamaIndex for data-centric agents with strong retrieval components, Haystack for production pipelines with agents, and lighter options such as smolagents, PydanticAI or provider agent SDKs.

Choose by control and team skills. LangGraph suits complex flows with branching, retries and human approval. LlamaIndex offers many retrievers, query engines and agent workflows out of the box. Haystack provides modular pipelines with clear components. Provider SDKs, such as OpenAI's Agents SDK or Google's ADK, are simple for tool-calling agents. For many teams, plain Python with a tool-calling model and a well-built retriever is enough, and easier to debug than a heavy framework.

What is the best LLM for agentic RAG?

Agentic RAG needs models that call tools reliably, follow instructions over many steps and judge relevance well. Frontier models from Anthropic, OpenAI and Google lead on these abilities, while strong open models such as the Llama, Qwen and Mistral families work well for self-hosting.

Benchmark on your own tasks: measure tool-call accuracy, answer faithfulness, number of steps and latency. A practical pattern is model routing: a fast, cheap model for query rewriting and relevance grading, and a stronger model for planning and final answers. Models with long context windows help when many passages are retrieved, but good reranking keeps context small. For hosted open models, providers such as Groq offer fast inference, which matters when agents make many sequential calls.

What is the best agentic RAG course?

Good options include DeepLearning.AI short courses on agentic RAG with LlamaIndex and on LangGraph agents, LangChain Academy's free LangGraph course, Hugging Face's Agents Course, and the official tutorials in the LlamaIndex and LangGraph documentation.

Before agentic RAG, make sure you understand basic RAG well: chunking, embeddings, hybrid retrieval, reranking and evaluation, since agents amplify retrieval problems rather than hiding them. Then follow a course that builds a working agent with a retriever tool, a relevance grader and query rewriting. Finish by adding evaluation: compare agentic and traditional pipelines on the same questions, measuring accuracy, latency and cost, to learn when agents actually help.

What are the best agentic RAG GitHub repositories?

Useful repositories include the LangGraph examples with agentic RAG notebooks, LlamaIndex's agent and workflow examples, Microsoft's GraphRAG, Haystack's tutorials, and curated awesome-RAG lists that link to many implementations.

When evaluating a repository, check recent activity, issues, licence, and whether it includes evaluation, not just a demo. Prefer examples that log every tool call and show failure handling, such as retries and iteration limits. Clone one, run it on your own documents, and break it deliberately: ask multi-hop questions, questions with no answer in the data, and ambiguous follow-ups. Studying how the agent fails teaches more than reading code that works on curated examples.

Pranjul Rathour

GenAI Engineer · India

GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).

Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.

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