langchain-neo4j
An integration package connecting Neo4j and LangChain
Decision gist · record as of 2026-08-14
Yes, if you are building LangChain applications that need to integrate Neo4j. The package is actively maintained, has low install friction, carries no known vulnerabilities, and provides well-documented abstractions for common graph-LLM patterns. Install only if you have a Neo4j instance available and a use case that benefits from graph storage or reasoning.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a running Neo4j instance accessible at the specified URL; Python 3.10 or later.
- Low friction install with a pure Python wheel.
- Active maintenance with recent releases; last commit 2026-08-10.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
last release 2026-06-10 (65 days) · last repo commit 2026-08-10 · 42 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 252,731 downloads/mo, #8,535 on PyPI
Alternatives
Verify before relying
pip install langchain-neo4j
from langchain_neo4j import Neo4jGraph
graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")
result = graph.query("MATCH (n) RETURN n LIMIT 1;")- Whether Neo4jVector requires external embedding models (e.g., OpenAI) or supports local embeddings.
- Performance characteristics when storing large chat histories or vector indexes in Neo4j.
- Compatibility with Neo4j versions older than the driver's minimum requirement.
What it is and what it does
langchain-neo4j bridges Neo4j graph databases and LangChain's LLM framework, providing high-level abstractions for common patterns. It includes Neo4jGraph for direct Cypher queries, Neo4jChatMessageHistory for persisting conversations, Neo4jVector for semantic search over documents, and GraphCypherQAChain for translating natural language questions into Cypher queries. It also offers Neo4jSaver and AsyncNeo4jSaver as checkpoint backends for LangGraph workflows, and LLMGraphTransformer to extract knowledge graphs from unstructured text.
The package is designed for developers building AI applications that need to store, query, or reason over structured knowledge in a graph. Its main dependencies are langchain-core, langgraph, neo4j, and neo4j-graphrag—all part of the LangChain ecosystem. Installation is straightforward, but you must have a Neo4j instance running and accessible over the network.
Use it for
- Build a chatbot that recalls conversation history from Neo4j across sessions.
- Translate natural language questions into Cypher queries and retrieve graph data via an LLM.
- Store and search document embeddings in Neo4j for semantic similarity retrieval.
- Extract entities and relationships from unstructured text and populate a Neo4j knowledge graph.
- Persist LangGraph agent state and checkpoints in Neo4j for fault tolerance and replay.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building LangChain applications that need to integrate Neo4j.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and provides well-documented abstractions for common graph-LLM patterns. Install only if you have a Neo4j instance available and a use case that benefits from graph storage or reasoning.
Install
langchain-neo4j on PyPI
Before you install
Low friction install with a pure Python wheel. Active maintenance with recent releases; last commit 2026-08-10. Depends on langchain-core, langgraph, neo4j, and neo4j-graphrag, which are established LangChain ecosystem packages.
Requires a running Neo4j instance accessible at the specified URL; Python 3.10 or later.
License in practice
MIT license is permissive; you can use, modify, and distribute this package freely with minimal restrictions.
Quickstart
pip install langchain-neo4j
from langchain_neo4j import Neo4jGraph
graph = Neo4jGraph(url="bolt://localhost:7687", username="neo4j", password="password")
result = graph.query("MATCH (n) RETURN n LIMIT 1;")Verify before relying
- Whether Neo4jVector requires external embedding models (e.g., OpenAI) or supports local embeddings.
- Performance characteristics when storing large chat histories or vector indexes in Neo4j.
- Compatibility with Neo4j versions older than the driver's minimum requirement.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packageslangchain-classiclangchain-corelanggraphneo4j-graphragneo4j |
| Maintenance | Actively maintained 65 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 252,731 / month, #8,535 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: langchain_neo4j-0.10.0-py3-none-any.whl
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See also neo4j-graphrag · graphlib · ragstack-ai-knowledge-store · neomodel · langchain-postgres · braintrust-langchain · langchain-azure-ai · langgraph-checkpoint · langchain-graph-retriever · py2neo-history