RAG vs Agentic RAG vs GraphRAG: Which One Fits Your Business Data?

By Rajesh Kannan, Co-Founder & CTO, Gramosoft · Last updated: November 8, 2026 · Product and library facts checked on each vendor's docs, repository or package page on November 8, 2026

Your team wants an AI assistant that answers from company data. Then the questions start: should it be plain RAG, agentic RAG or GraphRAG? Each name sounds more advanced than the last, and each costs more to build and run. The right answer depends on your data and the questions people ask, not on which is newest.

We build enterprise AI search at Gramosoft, so this guide starts with where we fit. Then it explains the three approaches in plain words, shows the same question answered by each in a small demo, and gives a decision table for common business data: policies, contracts, invoices, tickets and ERP records.

Where Gramosoft fits in RAG, agentic RAG and GraphRAG

Gramosoft is a Chennai software company that builds AI products and custom software. Our own enterprise search product, GsearchAI, is a GraphRAG system: it stores entities and links in Neo4j and text in pgvector, so one assistant can answer both "what does the policy say" and "which suppliers are linked to this contract". We use the lightest approach that works.

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"Most teams that ask us for GraphRAG first need better chunks and a test set of real questions. Once the easy questions are answered well, the hard ones show you exactly where a graph or an agent is worth the cost."

Rajesh Kannan, Co-Founder & CTO at Gramosoft

RAG vs agentic RAG vs GraphRAG: the short answer

Start from the questions your people actually ask, not from the architecture. Collect 50 real questions, sort them by type, and the right approach usually becomes obvious. The table below maps common business situations to a starting point; most companies end up running plain RAG for most questions and something heavier for a few.

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How the three approaches compare

All three connect a language model to your data, but they differ in when the work happens. Plain RAG does little work at indexing and one search per question. Agentic RAG does more work at question time. GraphRAG does most of its work up front, when it builds the graph. That one difference drives cost, speed and upkeep.

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What plain RAG does well, and how to fix it first

Plain retrieval-augmented generation turns your documents into chunks, finds the chunks closest to the question, and gives them to the language model as context. It is fast, cheap and easy to explain. When it fails, the cause is usually the retrieval, not the architecture, so fix that before you add anything heavier.

• Chunk by structure: split on headings and sections, and keep tables whole.
• Use hybrid search: combine keyword search for exact terms like invoice numbers with vector search for meaning.
• Add a reranker: re-score the top results before they reach the model.
• Filter by metadata: department, date, region or document type.
• Clean the input: scanned PDFs need good OCR first; GdoczAI does this step.

Agentic RAG: when the question needs several steps

Agentic RAG puts a planner in front of retrieval. The model breaks a question into sub-questions, decides which source or tool to use for each, reads the results and searches again if something is missing. That makes it strong on multi-part questions, and slower and costlier, because one question can trigger several model and search calls.

Cloud platforms now offer this as a managed feature. Agentic retrieval in Azure AI Search uses a language model to split a query into subqueries, runs them in parallel as keyword, vector or hybrid searches and reranks the results. Microsoft notes it adds latency compared with a single query, and its LLM query planning is still in preview. For your own builds, LangGraph is a common way to define the loop.

• Good fit: "Check our policy, compare it with the new rule and list what must change"; questions that need SQL or an API call.
• Set limits: cap the number of steps, log every tool call, and stop when an answer is good enough.
• Measure: task success, tool-selection accuracy, number of calls and end-to-end time, not only answer quality.

GraphRAG: when the answer is in the connections

GraphRAG first uses a language model to pull entities and relationships out of your documents, such as suppliers, plants, products, contracts and owners. It stores them as a graph. At question time it finds the starting entities and follows the links, so it can answer questions whose facts sit in different documents that never mention each other directly.

At question time GraphRAG runs in one of two modes. Local search suits specific questions: it matches the entities in the question, walks their links and answers from the connected facts. Global search suits broad questions about the whole collection: it reads the pre-written community summaries in batches, keeps the highest-rated points and combines them into one answer.

The idea comes from Microsoft's GraphRAG project and its 2024 research paper, which added community summaries for questions about a whole corpus. Microsoft now describes the repository as a research project that is largely in maintenance mode, and warns that indexing can be expensive, so start small. Production options include:

• Amazon Bedrock Knowledge Bases with Neptune Analytics: combines vector search with graph traversal; data sources are Amazon S3 only, up to 1,000 files per source by default.
• Neo4j GraphRAG for Python: Neo4j's official package, version 1.23.0 on November 8, 2026, Apache 2.0.
• LightRAG: an open-source, MIT-licensed graph-based RAG framework with multimodal document support.
• GsearchAI: Gramosoft's product on Neo4j and pgvector, built for business documents and spreadsheets.

The same question, answered three ways

To see the difference, here is one business question run through all three approaches on five short documents: an invoice on hold, the supplier, the plant it supplies, the product the plant builds and where that product ships. No document mentions the invoice and the dealers together, which is exactly where plain RAG struggles.

three_rags.py

# three_rags.py: the same question answered by plain RAG, agentic RAG and GraphRAG
# A toy demo with no libraries. Word overlap stands in for vector search.
import re

DOCS = {
    "d1": "Invoice INV-204 from Sri Ganesh Traders was put on hold by the finance team.",
    "d2": "Sri Ganesh Traders supplies steel brackets to the Chennai plant.",
    "d3": "The Chennai plant builds the K-series dealer kits.",
    "d4": "Supplier invoices above 5 lakh need a second approval under policy FIN-7.",
    "d5": "The K-series dealer kits ship to all Tamil Nadu dealers every Monday.",
}
# Entity links a GraphRAG index would extract from the same five documents
EDGES = [("INV-204", "Sri Ganesh Traders", "d1"), ("Sri Ganesh Traders", "Chennai plant", "d2"),
         ("Chennai plant", "K-series dealer kits", "d3"), ("K-series dealer kits", "Tamil Nadu dealers", "d5")]
QUESTION = "Invoice INV-204 is on hold. Who downstream is affected?"

def words(text):
    return set(re.findall(r"[a-z0-9-]+", text.lower())) - {"the", "is", "to", "on", "by", "which", "could", "be", "a"}

def search(query, k=2, skip=()):
    pool = [d for d in DOCS if d not in skip]
    return sorted(pool, key=lambda d: len(words(query) & words(DOCS[d])), reverse=True)[:k]

def plain_rag(q):
    return search(q)                                # one search, top 2 chunks, then answer

def agentic_rag(q, max_steps=5):
    found, query = [], q
    for _ in range(max_steps):                      # search, read, decide the next search
        hit = search(query, k=1, skip=found)
        if not hit:
            break
        found += hit
        if "dealers" in DOCS[hit[0]]:               # the agent judges it has the answer
            break
        query = DOCS[hit[0]]                        # follow what it just learned
    return found

def graph_rag(start):
    found, node = [], start
    for a, b, doc in EDGES * len(EDGES):            # walk the links from the invoice outwards
        if a == node and doc not in found:
            found.append(doc); node = b
    return found

if __name__ == "__main__":
    print("Q:", QUESTION)
    for name, docs in (("Plain RAG", plain_rag(QUESTION)), ("Agentic RAG", agentic_rag(QUESTION)), ("GraphRAG", graph_rag("INV-204"))):
        answer = "Tamil Nadu dealers" if "d5" in docs else "not found"
        print(f"{name:12} {len(docs)} chunks {', '.join(docs):16} -> {answer}")

It prints:

demo_output.txt

Q: Invoice INV-204 is on hold. Who downstream is affected?
Plain RAG    2 chunks d1, d2           -> not found
Agentic RAG  4 chunks d1, d2, d3, d5   -> Tamil Nadu dealers
GraphRAG     4 chunks d1, d2, d3, d5   -> Tamil Nadu dealers

Plain RAG finds the invoice and the supplier but never reaches the dealers. Agentic RAG gets there by searching four times, each step following what it just learned; in a real system, each step is another model call. GraphRAG walks the same chain in one pass, because the links were built at indexing time. This is a logic demo with word overlap standing in for vector search, not a benchmark.

Which approach fits your business data

The shape of your data decides more than its size. Text that answers questions on its own suits plain RAG. Records that point at each other, such as invoices, suppliers, assets and contracts, suit a graph. Questions that need live numbers or several systems suit an agent. Many businesses need two of the three, routed by question type.

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Cost, speed and upkeep in production

The architecture choice is mostly a choice about where you pay. Plain RAG is cheap at both ends. Agentic RAG pays at question time, with a cost that varies by question. GraphRAG pays up front and again whenever the data changes. Budget for the pattern you pick, and measure it from the first pilot rather than guessing.

• Plain RAG: embedding cost at indexing, one search and one model call per question.
• Agentic RAG: several model and search calls per question; cap steps and cache common sub-questions.
• GraphRAG: a model reads every document to extract entities; plan for re-extraction when documents change, and check graph quality.
• All three: track answer accuracy on a fixed question set, cost per question and response time every week.

Access control and data protection

Any RAG system can leak what a user shouldn't see, and graphs add a new risk: a relationship can reveal something sensitive even when the documents are protected. Check permissions at retrieval time, not only at login, and keep personal data handling in line with India's Digital Personal Data Protection Act.

• Filter by user rights before chunks or graph paths reach the model.
• Log every answer with the sources it used, for audit.
• Limit agent tools to read-only access unless an action is approved.

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