Neo4j GraphRAG vs. LlamaIndex vs. LangChain Graph Transformer

Emily Winks

Data Governance Expert

Updated:08/03/2026

Published:08/03/2026

21 min read

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Key takeaways

Neo4j GraphRAG, LlamaIndex, and LangChain differ on persistence, schema control, and extraction reliability.
Neo4j's GraphRAG product and Microsoft's GraphRAG technique are different things most comparisons never disambiguate.
None of the three governs the graph once an agent queries it in production; that is a separate, unaddressed layer.

Neo4j GraphRAG vs. LlamaIndex vs. LangChain: what's the difference?

Neo4j GraphRAG, LlamaIndex's PropertyGraphIndex, and LangChain's LLMGraphTransformer are three different ways to construct a knowledge graph for an AI agent, not interchangeable names for one thing. They differ on where the graph lives, how extraction happens, and how much schema control you get. None of the three addresses who owns the graph, or who can query it, once it is running in production.

The three approaches include:

Neo4j GraphRAG: a first-party Python package for building and querying graphs on the Neo4j database
LlamaIndex PropertyGraphIndex: a pluggable, extractor-based graph abstraction with an in-memory default
LangChain LLMGraphTransformer: a single class that converts text into graph documents via an LLM

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Neo4j GraphRAG, LlamaIndex’s PropertyGraphIndex, and LangChain’s LLMGraphTransformer are three different ways to construct a knowledge graph for an AI agent, not interchangeable names for one thing. LangChain (140,570 GitHub stars) and LlamaIndex (50,531 stars) are large, fast-moving frameworks with graph construction added on top; Neo4j’s own neo4j-graphrag-python package is narrower and first-party. Neo4j’s GraphRAG product is also not Microsoft’s original GraphRAG research technique, a mix-up most comparisons never resolve. This guide treats construction as a genuine three-way decision, then covers what happens once the graph is built.

Each tool answers a different sub-question inside “how do I turn unstructured text into a knowledge graph”:

Neo4j GraphRAG asks how to store and query the result in a production graph database.
LlamaIndex asks how to make extraction pluggable across several strategies.
LangChain asks how to get graph-ready output with the fewest lines of code.

None of the three, on its own, answers who owns the schema once it drifts, or who is allowed to query the graph once an agent depends on it in production. That gap is the second half of this comparison.

Construction and governance are two different problems, and none of the three tools compared here solve the second one. That is the gap Atlan’s Enterprise Data Graph closes: it turns whatever graph gets built, whether by these tools or from existing warehouse and lake metadata, into governed, versioned, access-controlled context delivered to agents through an MCP server. The construction comparison below covers the first half of that story; who governs the result once it is live is the second, addressed later in this guide.

Dimension	Neo4j GraphRAG	LlamaIndex (PropertyGraphIndex)	LangChain (LLMGraphTransformer)
What it is	First-party Python package for graphs on Neo4j	Pluggable property-graph abstraction	Single class converting text to graph documents
Graph storage model	Persistent, durable graph database	In-memory by default, pluggable to external stores	No native storage; pairs with a backend, usually Neo4j
Extraction approach	Native construction pipeline plus Cypher retrieval	Swappable LLM path extractors	One LLM call via a fixed transformer class
Schema control	Enforced by the underlying database	Flexible: schema-optional or schema-defined modes	Prompt-defined; hints only, not enforced
Best for	Production graphs that need to persist and scale	Teams that want extractor flexibility	Fast prototyping with an existing LangChain pipeline
Community signal	1.2k stars, 457 commits (dated 2026-08-03), narrow by design	50,531 stars, +15/day (dated 2026-06-30)	140,570 stars, +46/day, highest delta tracked (dated 2026-06-30)
Complexity level	Medium (database operations)	Medium to high (extractor tuning)	Low to medium (single class, less control)

What’s the difference between Neo4j GraphRAG, LlamaIndex, and LangChain for building a knowledge graph?

All three tools construct knowledge graphs from source data, but they diverge on where the graph lives, how extraction happens, and how much schema control the builder gets. For a full definition of the underlying technique, see what GraphRAG is; this page assumes that grounding and focuses on the construction decision it leaves open. The confusion starts with the word “GraphRAG” itself, which the market now uses for at least three different things, a distinct question from how a context graph differs from a knowledge graph at the layer above.

Each tool defaults to a different storage model:

Neo4j GraphRAG is the native, persistent option: a first-party Python package that writes directly into a Neo4j graph database and queries it with Cypher. According to Suhas Bhairav’s comparison of native storage versus in-memory knowledge modeling, this is a genuine production-governance-versus-speed-of-iteration trade-off, not a cosmetic difference.
LlamaIndex takes the opposite default: its PropertyGraphIndex stores the graph in memory unless you point it at an external backend, which makes early iteration faster but adds a deployment decision teams sometimes skip until it is too late.
LangChain sits in between architecturally but narrowest in scope: LLMGraphTransformer is one class that converts documents into graph nodes and relationships, most commonly writing the result into Neo4j as the storage layer, so it depends on one of the other two tools to actually persist anything.

The term “GraphRAG” itself traces back to a specific 2024 paper, not to any of these three products. Edge et al.'s “From Local to Global: A Graph RAG Approach to Query-Focused Summarization” introduced a community-detection-and-summarization technique that Microsoft open-sourced in mid-2024 and that drew rapid open-source interest in the months after. Every graph and RAG vendor since has attached “GraphRAG” to its own, structurally different offering. The SERP’s own numbers show the resulting confusion: several ranking listicles cite LlamaIndex’s star count anywhere from 37,000 to over 118,000, all undated and unsourced, evidence the comparison space is under-verified, which is why every figure here, and in Atlan’s own knowledge graph tools comparison, carries a date.

Neo4j GraphRAG vs. Microsoft’s GraphRAG: these are not the same thing

The naming collision is worth resolving explicitly, since no top-ranking result currently does.

Name	What it actually is	Source
Microsoft GraphRAG	The original research technique: community detection plus hierarchical summarization	Edge et al., arXiv:2404.16130, 2024
Neo4j GraphRAG (`neo4j-graphrag-python`)	Neo4j’s own first-party product package, a narrower, database-specific approach	Neo4j official GitHub
`ms-graphrag-neo4j`	A community port that implements Microsoft’s actual algorithm on top of Neo4j	neo4j-contrib GitHub

A separate community project, ms-graphrag-neo4j, implements Microsoft’s original algorithm on Neo4j’s database, a different thing again from Neo4j’s own neo4j-graphrag-python package.

Neo4j, LlamaIndex, and LangChain aren’t mutually exclusive

None of this is a forced, pick-exactly-one decision. LlamaIndex’s property-graph store has an explicit Neo4j partnership: Jerry Liu, LlamaIndex’s co-founder and CEO, confirmed on X that “our property graph abstractions were created in partnership with @neo4j and give you both easy-to-use and customizable tools to construct [knowledge graphs].” That partnership makes a hybrid pattern viable: use LlamaIndex for its pluggable extraction, then write the result into Neo4j for persistence, drawing on the context graph tools landscape’s construction layer while keeping Neo4j’s storage guarantees.

What is Neo4j GraphRAG?

Neo4j GraphRAG (neo4j-graphrag-python) is Neo4j’s official Python package for constructing and querying knowledge graphs on top of the Neo4j graph database. It is a first-party, opinionated tool rather than a general-purpose framework, built and maintained directly by the database vendor rather than a broader open-source community.

That narrower scope shows up in its numbers. As of a direct GitHub scan on 2026-08-03, neo4j-graphrag-python carried roughly 1.2k stars and 457 commits, dramatically smaller than LangChain’s 140,570 or LlamaIndex’s 50,531. That gap reflects design intent rather than neglect: Neo4j maintains this package directly and, per its own documentation, positions it as a supported extension of the database rather than as a general framework competing for adoption the way LangChain and LlamaIndex do. Anyone evaluating Neo4j for agent context graphs should read the star count as a maturity signal about scope, not popularity.

The package has matured toward deeper integration with Neo4j’s own stack rather than toward general framework features. It couples construction directly to Cypher retrieval and to Neo4j’s native indexing, which is the same trade-off that shows up across the broader context graph tools compared landscape: narrower scope in exchange for tighter, vendor-supported integration.

Core components of Neo4j GraphRAG

KG construction pipeline: ingests source documents and writes extracted entities and relationships directly into a Neo4j database
Cypher-based retrieval: queries the graph using Neo4j’s native query language, distinct from the vector-similarity approach behind most vector databases
Entity resolution: reconciles duplicate or near-duplicate entities extracted from different source documents, the same reconciliation problem ontology-first AI architecture addresses at a broader scale
Native Neo4j integration: retrieval, indexing, and storage all run on the same database, with no separate backend to manage

What is LlamaIndex’s property graph index?

LlamaIndex’s PropertyGraphIndex is the framework’s abstraction for constructing and querying property graphs, built around swappable LLM path extractors rather than one fixed pipeline. By default it stores the graph in memory, but it can be pointed at Neo4j or another external graph store when persistence matters.

That extractor flexibility comes with a real reliability cost. According to a dated GitHub scan (2026-06-30), LlamaIndex had 50,531 stars and was growing at roughly 15 stars per day, evidence of an active, fast-moving community. The same scan tracked a report titled “Silent exception swallowing in Property Graph LLM Path Extractors” (issue #22194), describing exactly the kind of failure that erodes confidence in an extraction pipeline: errors during graph construction disappearing instead of surfacing, until contributor Om-Borse26 shipped a raise_on_error fix, closed via PR #22195. It is one instance of a broader pattern; a separately verified issue on the same repository, #16352, further confirms extraction reliability needed hardening, not solved on the first pass, though that issue too has since been closed.

This is the extraction-quality angle most comparisons leave scattered across GitHub threads rather than treating as a first-class evaluation axis, alongside how a graph fits into the broader knowledge graph for AI agents picture and the general question of combining knowledge graphs with LLMs. A team choosing LlamaIndex for its flexibility should budget time for extractor debugging, not assume the abstraction alone guarantees clean output.

Core components of LlamaIndex’s property graph index

LLM path extractors: swappable strategies, including SimpleLLMPathExtractor, SchemaLLMPathExtractor, and ImplicitPathExtractor, each with different accuracy and speed trade-offs
Schema-optional or schema-enforced modes: choose freeform extraction or constrain the LLM to a defined node and relationship schema
Hybrid retrieval: combines graph traversal with vector search rather than forcing a choice between the two
Pluggable graph store backends: in-memory by default, or an external store like Neo4j when the graph needs to persist

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What is LangChain’s LLMGraphTransformer?

A single LangChain class, LLMGraphTransformer, converts unstructured text into graph documents by calling an LLM’s function-calling or structured-output capability, most commonly paired with Neo4j as the storage backend through an add_graph_documents write path. LangChain’s own walkthrough of the pattern is the most-cited technical reference for this exact pairing, and for a team already running a LangChain pipeline, it is the shortest path from raw text to a queryable graph.

That speed comes from LangChain’s scale, not from a more capable extraction method. According to the same dated GitHub scan, LangChain carried 140,570 stars and gained 46 in a single day, the highest single-day delta tracked across all three tools. Tomaz Bratanic, who works on Graph ML and GenAI research at Neo4j, is direct about the ceiling on that speed: “The quality of generated graph significantly depends on the model you are using.” LLMGraphTransformer’s output is only as reliable as the underlying LLM’s ability to follow extraction instructions consistently, which is a property of the model choice, not of the transformer class itself.

For teams already orchestrating agents with LangGraph, LLMGraphTransformer is the path of least resistance, since it stays inside a stack the team already runs. That convenience is also its narrowest point: it offers the least schema control of the three, relying on prompt hints rather than enforced structure, worth weighing against what LangChain itself is built to do before defaulting to it.

Core components of LangChain’s LLMGraphTransformer

LLMGraphTransformer class: the single entry point that converts a batch of documents into graph-ready nodes and relationships
add_graph_documents: the write path on LangChain’s Neo4jGraph connector, not on LLMGraphTransformer itself, used to persist the transformer’s output directly into a Neo4j database
Node and relationship schema hints: optional prompts that steer extraction toward a target schema, without enforcing it
Custom prompt-based extraction: extraction logic lives in the prompt, which makes it easy to adjust but easy to destabilize between model versions

Neo4j GraphRAG vs. LlamaIndex vs. LangChain: head-to-head comparison

The three tools diverge most on persistence, schema enforcement, and extraction reliability, and converge on one point: all three depend entirely on the underlying LLM’s extraction quality. No amount of framework maturity fixes a graph built from a weak extraction run.

Dimension	Neo4j GraphRAG	LlamaIndex	LangChain
Primary focus	Persistent, queryable production graphs	Flexible, extractor-driven construction	Fast conversion from text to graph documents
Graph persistence	Durable, native database	In-memory by default, pluggable	None natively; depends on a backend, usually Neo4j
Schema enforcement	Strict, database-level	Flexible: optional or schema-defined	Prompt-defined hints only
Extraction approach and reliability	Native pipeline, narrower surface area	Swappable extractors; active reliability fixes in progress	Single-class extraction tied to model quality
Query interface	Cypher	LlamaIndex retrievers, hybrid graph plus vector	LangChain chains over the storage backend
Ecosystem and community size	1.2k stars, narrow by design (dated 2026-08-03)	50,531 stars, +15/day (dated 2026-06-30)	140,570 stars, +46/day (dated 2026-06-30)
Integration with agent frameworks	Backend for LangGraph, CrewAI, or custom agents	Native LlamaIndex agent workflows	Native LangGraph integration
Failure mode	Enforced schema rejects input it wasn’t defined for, by design	Silent extractor exceptions, documented in open GitHub issues	Output quality collapses with a weaker LLM, per Bratanic’s own account
What happens after construction	Not addressed	Not addressed	Not addressed

A real-world example: what happens when the schema drifts

Consider a team building an AI agent’s memory over a product-support knowledge base. Early on, the graph is small and clean, and any of the three tools handles it without issue. Then new document types start arriving: release notes, support-ticket transcripts, partner contracts. Atlan’s own competitive intelligence monitoring of Reddit’s r/AI_Agents captured this exact moment from a practitioner: after reaching just 5 documents, 17 node types, and 34 relationships, they stopped using LangChain for graph construction entirely. That is not a large graph by any measure, which is precisely the point: the tool became unmanageable well before scale was the problem. Based on how each tool is designed to handle schema, rather than on any test of this specific case, LangChain’s prompt-based hints, with no enforcement layer at all, would likely let drift like this compound silently; Neo4j’s enforced schema would likely surface it as a hard error instead; LlamaIndex’s schema-optional mode sits in between, catching some of it depending on which extractor is configured. None of the three tools has a built-in answer for who decides what a new entity type means, or whether it belongs in the graph at all, once a second team starts feeding the same one.

When does a knowledge graph beat vector-based RAG for an AI agent?

Graph and vector retrieval are not a binary choice; most production agents that reach for GraphRAG keep vector search running alongside it. Jerry Liu, LlamaIndex’s co-founder and CEO, frames the relationship directly: “GraphRAG makes sense if you model it as a superset of RAG - you can still keep vector search, but you can use graphs to augment the context the model has to make a decision.”

A knowledge graph earns its added complexity when:

a question requires connecting multiple entities across several hops
the domain has dense, explicit relationships worth traversing
an agent needs an explainable retrieval path rather than a similarity score

Gartner has projected that graph technologies would account for 80% of data and analytics innovations by 2025, up from 10% in 2021, a trend line worth reading as directional rather than as a fresh 2026 figure; Gartner’s broader take on context graphs covers where that projection sits today. For a broader view of when that trade-off makes sense, see knowledge graphs versus RAG for AI, AI memory versus RAG versus knowledge graph, and how a context graph compares to a vector database at the layer above either tool.

The honest counter-case deserves equal weight. A Hacker News commenter argued that for Microsoft-style GraphRAG, where a graph is inferred fresh per query rather than persisted, “lightweight, in-memory alternatives like NetworkX or even plain Python dictionaries are typically faster to set up… easier to integrate, and better suited to ephemeral, prompt driven graph reasoning.” That is a real, credible position, not a straw man: for ephemeral, per-query reasoning, a persistent graph database is often more infrastructure than the task needs. The decision between a vector database and a graph database for agent memory should track the actual query pattern, not a default assumption that graphs are always the more sophisticated choice.

When to prioritize graph construction now versus later

Start with a native graph database like Neo4j when relationships are dense, need to persist across sessions, and multiple teams will query the same graph.
Reach for LlamaIndex’s in-memory or pluggable mode when you are still validating whether graph structure helps at all.
Skip graph construction entirely, at least for now, when retrieval is single-hop and vector search alone already answers the question your agent gets asked.

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What goes wrong when you build a knowledge graph with an LLM?

The real stumbling block in graph construction is not which framework you pick; it is extraction quality itself, and that failure mode shows up regardless of the tool. As Tomaz Bratanic’s observation on model dependency suggests, the same extraction code produces meaningfully different graphs depending on which LLM sits behind it, which means the output quality of a construction pipeline is only as reliable as the model you point it at, not a fixed property of the code itself.

LlamaIndex’s “silent exception swallowing” issue, and the raise_on_error fix that followed it, is a concrete, dated example of a broader class of problem: extraction errors that fail quietly instead of loudly. Two related issues on the same codebase, #16033 and #17424 (both since closed), confirm this class of construction-time failure recurred more than once rather than being a solved one-off bug. Multiple Hacker News threads independently converge on the same theme: recurring discussion across item 42546890, item 40857174, and item 40921038 points at constructing the graph, not querying it, as where practitioners actually get stuck, regardless of which of the three frameworks they picked.

This connects to a second class of failure that starts only after construction succeeds: once entities and relationships exist in a database, nobody has defined what a canonical entity means across teams, or who is allowed to read what from it once an agent is querying it in production. That gap is part of why AI agents fail in production even after the graph itself looks correct, and it is a direct input into AI agent accuracy: a technically well-formed graph with no ownership model still produces answers nobody can fully trust. It is also the exact seam what is context engineering is meant to close, and the reason connecting a context graph to agent memory is a governance decision, not just a wiring one.

How Atlan governs the knowledge graph once it’s built

Every tool covered above stops at the same point: triples in a database. None of them assigns ownership of what those triples mean, tracks how the schema changes as new document types get ingested, or controls who an agent is allowed to query once the graph is live. The Reddit anecdote earlier in this guide shows the construction side of that gap, a graph becoming unmanageable at just 17 node types, well before governance even entered the picture; none of the three tools was built to answer the ownership and access questions that follow once the graph is actually running.

Atlan does not build knowledge graphs; that is exactly what Neo4j, LlamaIndex, and LangChain do, and Atlan does not compete with any of them for that job. Whatever graph gets built, by these tools or by mapping existing warehouse and lake metadata, Atlan turns it into governed, versioned, access-controlled context that AI agents consume through the MCP Server: business glossary terms, lineage, ownership, and policy rules attached directly to the graph. Atlan’s Enterprise Data Graph connects assets, lineage, and business terms into the same conceptual shape these three tools produce, but built from enterprise metadata rather than unstructured text, and continuously refreshed rather than the result of a one-time extraction run. This is the layer how to query a context graph with an AI agent addresses directly, alongside the broader agent context graph work across the context graph versus context store boundary.

For teams that already run one of these frameworks inside a LangGraph or CrewAI agent, this is not a rebuild. It is a governance layer above the graph already in production, answering the ownership question none of the three construction tools were built to answer.

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What separates knowledge graphs that ship from ones that drift

Neo4j GraphRAG, LlamaIndex, and LangChain solve the same underlying problem differently: persistence versus flexibility versus speed. Several ranking listicles on this exact query cite LlamaIndex’s star count anywhere from 37,000 to over 118,000, undated and unsourced against each other, which is a sourcing problem, not a decision framework; a reader comparing tools on that basis alone is comparing numbers nobody has verified. The right axis is which of the three trade-offs matches your actual constraints: a durable production graph, extraction flexibility while you are still iterating, or the fastest path out of an existing LangChain pipeline, and for teams whose constraints span more than one of those, the earlier point stands: these tools compose, so the choice is rarely as exclusive as a three-column table makes it look.

Construction is the easier part of this problem, not because it is simple, but because it is the part every vendor already competes to solve. Nobody in this comparison, and nobody in the top-ranking pages on this exact query, addresses what happens once an agent starts querying the finished graph in production: who owns a canonical entity’s definition, how schema drift gets caught before it compounds, and who is allowed to see what. That is a different layer, not a fourth framework to add to the pile, closer to how to build an AI agent harness and how to implement an enterprise context layer for AI than to another graph-construction library. As agent memory and context graphs move from prototypes into production, the gap between building a graph and governing one, the same gap the enterprise context layer and the semantic layer exist to close, will only get more expensive to ignore.

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FAQs about Neo4j GraphRAG, LlamaIndex, and LangChain for knowledge graphs

1. What is the difference between LangChain’s LLMGraphTransformer and LlamaIndex’s PropertyGraphIndex?

LLMGraphTransformer is a single LangChain class that converts unstructured text into graph documents through one LLM call pattern, most often written to Neo4j. PropertyGraphIndex is LlamaIndex’s broader abstraction, letting you swap between several extractor strategies and choosing an in-memory or external graph store as the backend.

2. Is Neo4j better than LlamaIndex for building production-grade knowledge graphs?

Neo4j GraphRAG is better when you need a durable, queryable graph database with Cypher access and long-term vendor support. LlamaIndex is better when you need extraction flexibility and want to defer the storage decision. LlamaIndex’s own Neo4j integration makes pairing the two, extraction with LlamaIndex, storage on Neo4j, a viable option rather than an either-or choice.

3. Can I use LangGraph and Neo4j together for AI agents?

Yes. LangGraph orchestrates an agent’s reasoning steps and tool calls, while Neo4j stores the knowledge graph one of those tools queries. They operate at different layers: LangGraph is the orchestration framework, and Neo4j is the graph database the agent reaches into for structured facts.

4. How do I convert unstructured text into a Neo4j graph using LangChain?

LangChain’s LLMGraphTransformer class takes text documents and calls an LLM to extract nodes and relationships as graph documents. A separate Neo4jGraph connector exposes the add_graph_documents method that then writes that result directly into a Neo4j database. Output quality depends heavily on the underlying LLM’s function-calling reliability, not just the transformer’s configuration.

5. Is GraphRAG more accurate than traditional vector-based RAG?

GraphRAG is more accurate for multi-hop questions that require connecting several entities, because it can traverse explicit relationships instead of relying on semantic similarity alone. It is not universally more accurate. For single-fact lookups, vector search is often faster and sufficient on its own.

6. Does building a knowledge graph with LangChain or LlamaIndex depend on the LLM’s ability to follow instructions reliably?

Yes, directly. Both tools ask an LLM to identify entities and relationships and return them in a structured format. A model with weaker function-calling or structured-output performance produces a lower-quality graph even when the surrounding code is identical, which is why extraction reliability, not framework popularity, decides output quality.

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Atlan is the Context Layer for AI, a Leader in the Gartner Magic Quadrant for D&A Governance (2026) and the Forrester Wave for Data Governance (Q3 2025). Atlan unifies your data, business knowledge, and the meaning behind your terms into one Enterprise Data Graph that gives every team and every AI agent the trusted context they need. Trusted by Mastercard, Workday, General Motors, CME Group, HubSpot, FOX, Virgin Media O2, Elastic, and 400+ enterprises representing $10T+ in market cap.

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