LangGraph vs Neo4j: Which AI Agent Framework is Right for You?

LangGraph and Neo4j both use graphs, but they solve very different problems. LangGraph is an orchestration framework for managing agents and keeping state across multi-step workflows. Neo4j is a graph database for storing and querying connected data.

If your project involves coordinating multiple agents or building workflows that rely on context, LangGraph handles that natively. If you’re mapping relationships, running queries, or building a knowledge graph, Neo4j is the tool for the job.

Let’s look at how each works, how they handle workflows, and when to use one, the other, or both together.

What is LangGraph?

LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. It was developed by the LangChain team and is designed for workflows that require persistence, human oversight, and comprehensive memory. Workflows are modeled as state graphs, where nodes represent computational steps and edges define execution flow.

LangGraph doesn’t abstract prompts or architecture. Instead, it focuses on providing infrastructure for reliable agent orchestration, with five key benefits:

• Durable execution – workflows persist through failures and automatically resume.

• Human-in-the-loop capabilities – inspect and modify agent state mid-execution.

• Comprehensive memory – supports short-term and long-term persistence.

• Debugging integration – works with LangSmith for tracing execution paths.

• Production-ready deployment – infrastructure for stateful, long-running workflows.

  
  

LangGraph is open source, available in Python and JavaScript, and can run standalone or integrate with LangChain for additional components.

What is Neo4j?

Neo4j is a high-performance graph database that stores data as nodes and relationships. First released in 2007, it provides ACID transactions and a mature infrastructure for working with flexible network structures rather than static tables.

Neo4j uses Cypher, which formed the basis for the ISO-standard Graph Query Language (GQL) in April 2024.

Neo4j is well suited for scenarios where relationships between data matter as much as the data itself. Its graph traversal queries efficiently find paths through connected data, often outperforming relational databases by a significant margin.

Key characteristics of Neo4j:

• Deployment options – standalone server or embeddable component.

• Editions – Community Edition (GPLv3) is open source; Enterprise Edition includes additional features requiring a commercial license.

• Market presence – holds 44% of the graph database market and is widely used in enterprise knowledge graph applications.

Core Features Comparison

Architecture

LangGraph models workflows as state graphs. Each node represents a computational step that reads from and writes to shared state. Edges define execution flow between nodes. The framework supports cycles, allowing workflows to loop back to previous steps based on conditions or agent decisions.

State is a first-class element in LangGraph. The framework defines state as a TypedDict that all nodes can access and modify. This architecture enables long-running workflows where agents maintain context across multiple interactions. State updates happen automatically as nodes execute.

Neo4j models data as property graphs. Nodes represent entities with properties. Relationships connect nodes with directional edges that have types and properties. The graph structure optimizes traversal operations that follow relationships between entities.

Neo4j stores and queries data but doesn't execute workflow logic. You write Cypher queries to retrieve data, then external systems decide what actions to take. The database provides the data layer while orchestration happens in application code or external frameworks.

State Management

LangGraph includes comprehensive memory with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions. State persists automatically between interactions. The framework provides thread timelines and time-travel debugging where you can inspect or roll back to previous states.

Durable execution means workflows persist through failures and resume automatically from where they stopped. This matters for long-running workflows that might span hours or days, or workflows waiting for human approval before proceeding.

Neo4j stores data persistently with ACID transaction guarantees. You model state as nodes and relationships in the graph. Cypher queries retrieve and update state, but you write the application logic that determines when and how state changes.

For agent workflows, Neo4j can store conversation history, user context, or workflow progress. The database provides reliable data storage while external orchestration tools manage workflow execution and state transitions.

Execution Models

LangGraph handles workflow execution with support for parallel node execution, conditional branching, and human-in-the-loop patterns. The framework enables seamless human oversight by allowing inspection and modification of agent state at any point during execution.

Durable execution with automatic checkpointing means workflows recover from failures by resuming from the last successful state. Error handling is configurable per node or globally. The framework integrates with LangSmith for debugging, providing visualization tools that trace execution paths, capture state transitions, and deliver detailed runtime metrics.

Neo4j executes Cypher queries and stored procedures. The database handles data operations with ACID guarantees but doesn't orchestrate multi-step workflows. You use external frameworks to coordinate workflow execution, with Neo4j serving as the data layer.

Neo4j Desktop provides a development environment for working with local databases. The database is available both as a standalone server and as an embeddable component. Neo4j can integrate with orchestration frameworks through standard database connections or specialized integrations.

LangGraph: Strengths and Use Cases

Proxy Implementation in LangGraph

LangGraph nodes can wrap any Python function, call external APIs, or integrate with LangChain components. Each node receives state as input and returns updates to that state. The framework chains these nodes together with edges that define execution flow and conditional logic.

The basic pattern involves defining a state schema using TypedDict, creating nodes as functions that process state, and connecting nodes with edges. LangGraph handles state management, persistence, and execution automatically. You write the business logic for each node without worrying about plumbing.

When to Use LangGraph

Advanced Agent Workflows

LangGraph works for workflows requiring planning, recursive agent calls, and nested logic. A research agent might generate a research plan, execute each step, evaluate results, and adjust the plan based on what it learns. This iterative process requires loops and state management.

AppFolio's Realm-X copilot saves property managers 10 hours per week by handling bulk actions like sending messages or scheduling work orders. The system needs controllable agent architecture to execute actions reliably, which LangGraph provides.

Multi-Turn Conversations

Conversations that span multiple interactions require maintaining context across turns. LangGraph's built-in memory stores conversation histories and maintains context over time. The framework supports cross-thread memory where agents recall information from previous conversation sessions.

Elastic's AI assistant migrated from LangChain to LangGraph as they added more features requiring stateful interactions. The framework's persistence layer handles the complexity of tracking context across long conversations.

Multi-Agent Collaboration

Some workflows require multiple agents with different roles collaborating to complete tasks. One agent might research information, another writes content, and a third reviews the output. LangGraph coordinates these agents, handling handoffs and state sharing between them.

The framework supports hierarchical agent structures where a supervisor agent delegates tasks to specialist agents. This pattern appears in production systems where you need both high-level planning and specialized execution.

Neo4j: Strengths and Use Cases

Proxy Implementation in Neo4j

Neo4j doesn't provide agent orchestration capabilities. The database stores and queries data through Cypher. You implement workflow logic in external application code or orchestration frameworks that query Neo4j for data.

You can extend Neo4j with stored procedures written in Java that run inside the database. These procedures can implement custom logic, but they operate within the database context rather than orchestrating external agents or services. For agent workflows, external frameworks handle orchestration while Neo4j provides data access.

When to Use Neo4j

Simple Task Processing

Neo4j works when you can encode logic as Cypher queries or stored procedures. A recommendation engine traverses relationships to find similar products. A fraud detection system identifies suspicious patterns by analyzing transaction graphs. These use cases leverage Neo4j's graph traversal performance.

The database excels at queries that would require multiple joins in relational databases. Finding the shortest paths, calculating centrality measures, or detecting communities in networks are natural operations in Neo4j. You write Cypher queries that express graph patterns, and the database efficiently finds matches.

Reusable Component Libraries

Teams building knowledge graphs benefit from Neo4j's ability to store business logic as graph structures. You model domain knowledge as entities and relationships, then write reusable queries that extract information.

GraphRAG applications use Neo4j to store context that grounds LLM responses. The database maintains accurate, explainable connections between entities. Agents retrieve relevant subgraphs using Cypher queries, then use that context to generate responses.

LangGraph vs Neo4j: Decision Guide

Choosing Based on Project Complexity

Use LangGraph when:

• Building agents with complex decision logic.

• Coordinating multiple agents with different roles.

• Maintaining conversation context across sessions.

• Implementing human-in-the-loop workflows.

• Requiring time-travel debugging and state inspection.

Use Neo4j when:

• Storing interconnected data with complex relationships.

• Performing graph traversal queries efficiently.

• Building knowledge graphs for AI applications.

• Providing context for RAG applications.

• Needing ACID transactions for data consistency.

Choosing Based on Integration Needs

LangGraph integrates with LangChain's ecosystem including LLM providers, vector databases, and tools. The framework works well in workflows where you're orchestrating calls to multiple models or services. LangSmith provides debugging and monitoring. LangGraph Studio offers visual prototyping for rapid iteration.

Neo4j integrates with various application frameworks through standard database drivers. You can use Neo4j with LangChain as a vector store or knowledge graph. The database supports extensions through stored procedures and can be embedded as a component in larger applications.

If your application centers on orchestrating complex agent workflows, LangGraph provides the foundation. If your application centers on storing and querying graph data, Neo4j is the foundation. Many production systems use both: LangGraph orchestrates workflows while Neo4j stores knowledge graphs that provide context.

Making the Right Choice

LangGraph and Neo4j address different problems and often work well together. LangGraph orchestrates agent workflows with built-in state management and multi-agent coordination. Neo4j stores and queries graph data with efficient traversal and ACID guarantees.

The decision depends on what you're building. Agent applications that require complex logic, loops, and coordination benefit from LangGraph’s orchestration model. Applications centered on interconnected data benefit from Neo4j’s graph database capabilities.

Many production systems use both. LangGraph manages agent execution flow, while Neo4j stores knowledge graphs that provide context. This separation keeps orchestration logic separate from data storage, which helps keep systems easier to reason about and maintain.

You can also reach out to us if you need help designing, building, or scaling agent systems that use LangGraph, Neo4j, or both.

Frequently Asked Questions

What is the difference between LangGraph and Neo4j?

LangGraph is an open-source agent orchestration framework built for stateful multi-agent workflows. It handles execution flow, state management, and coordination between agents. Neo4j is a graph database that stores and queries connected data using nodes and relationships. Neo4j manages data while LangGraph manages agent behavior. They solve different problems and often work together in production systems.

Is LangGraph open source?

Yes, LangGraph is open source. The framework is available in both Python and JavaScript. You can use it standalone or integrate it with LangChain, LangSmith, and other tools in the ecosystem. The codebase is available on GitHub with documentation and examples at the LangGraph documentation site.

Can I use Neo4j for AI agents?

You can use Neo4j as a data source for agents, but it doesn't orchestrate agent workflows natively. Neo4j stores knowledge graphs that agents query using Cypher. You need external orchestration tools like LangGraph or LangChain to coordinate agent execution. Recent integrations with Model Context Protocol enable agents to access Neo4j data through standardized interfaces.

Which is better for building chatbots?

LangGraph is typically better for chatbots due to built-in support for multi-turn conversations and memory management. The framework maintains conversation context automatically, handles state persistence across sessions, and supports human-in-the-loop workflows. Neo4j can store conversation history and user context, but you'd need external orchestration to manage the conversation flow.

Can LangGraph and Neo4j be used together?

Yes, many production systems use both. LangGraph orchestrates agent workflows while Neo4j stores knowledge graphs and persistent memory. LangChain provides Neo4j integration that LangGraph can use. A common pattern: agents query Neo4j using Cypher to retrieve context, then use that information to make decisions. The database provides factual memory while the framework handles execution logic.

What's the actual cost of running LangGraph vs Neo4j in production?

LangGraph is free to use but incurs costs for compute resources and LLM API calls. A workflow calling OpenAI or Anthropic APIs accumulates charges based on token usage. Hosting costs depend on your infrastructure. Neo4j offers a free tier for small databases. Production instances cost $65-$600+ monthly depending on storage, query volume, and deployment model. Neo4j AuraDB provides managed hosting with pricing based on database size and performance tier.

What happens when LangGraph agents fail or timeout?

LangGraph supports retry logic, error handling per node, and custom fallbacks within the graph structure. When agents fail, you can configure automatic retries with exponential backoff. Timeouts are configurable per node. The checkpointing system lets workflows resume from the last successful state rather than restarting completely. You can implement circuit breakers that disable failing nodes after repeated failures.

How do I migrate from Neo4j to LangGraph (or vice versa)?

Migration isn't one-to-one because they serve different purposes. From Neo4j to LangGraph: extract your orchestration logic and convert it into agent workflows using LangGraph's graph structure. Use Neo4j as a data source within LangGraph nodes. From LangGraph to Neo4j: you can't migrate orchestration logic directly, but you can store state transitions and workflow history as graph relationships in Neo4j for analysis or audit trails.

Which specific LLMs work best with each framework?

LangGraph works with OpenAI, Anthropic, Mistral, Cohere, and other providers through LangChain's unified interface. The framework is LLM-agnostic at the core. Neo4j is LLM-agnostic because it's a database, not an orchestration framework. You'd use external tools to call LLMs, then store results in Neo4j. Both tools integrate with any LLM when combined with appropriate orchestration layers.

How do I debug LangGraph agent loops?

Use LangGraph's built-in node-level logging to see what happens at each step. LangGraph Studio provides visual debugging where you can step through execution and inspect state at each node. Time-travel debugging lets you roll back to previous states and try different paths. Looping issues typically stem from missing state transitions or agents producing outputs that don't match expected formats. The framework logs full execution traces you can replay to understand behavior.

What's the learning curve timeline for each?

LangGraph has a steep initial learning curve, especially if you're new to LangChain. Experienced Python developers typically become productive in 1-2 weeks. Understanding graph-based programming paradigms and state management takes time. Neo4j is easier for developers with database experience. Cypher syntax is more intuitive than SPARQL and familiar to SQL developers. You can write basic queries within days and build sophisticated graph models within 2–3 weeks.

What are the hidden limitations nobody talks about?

LangGraph: Still early-stage with limited ecosystem maturity compared to established frameworks. Documentation sometimes lags behind code changes. Breaking changes happen between versions as the framework evolves. Debugging complex multi-agent workflows remains challenging despite tooling improvements.

Neo4j: Costs scale quickly with data volume and query complexity. The free tier has strict limits. Custom AI workflows require external orchestration, adding architectural complexity. Native vector search works but isn't as mature as specialized vector databases. GraphRAG implementations require careful schema design to perform well at scale.