---
title: "The Knowledge Graphs, Graph Databases, and The GQL Standard"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# The Knowledge Graphs, Graph Databases, and The GQL Standard

**What Are Graph Databases?**

Graph databases are specialized database systems designed to store, manage, and query data that is best represented in the form of a graph—that is, as a collection of nodes (also called vertices) and edges (relationships or connections between nodes). Unlike traditional relational databases that use tables and fixed schemas, graph databases emphasize the relationships between data points, making them highly suitable for handling complex, interconnected data.

- **Nodes**: Represent entities or objects, such as people, places, or things.
- **Edges**: Represent relationships or connections between nodes, such as "friend of," "located at," or "purchased."
- **Properties**: Additional information or attributes associated with nodes and edges.

The adoption of graph databases has seen an increase after the rise of generative AI, although the exact correlation is multifaceted. Generative AI models like GPT-4, Llama 3, and others have driven a greater demand for data architectures that can handle complex, interconnected data in more flexible and scalable ways. This shift has led to a few key developments that have boosted the adoption of graph databases:

---

### **Key Drivers of Increased Adoption:**

1. **Need for Handling Interconnected Data:**
   - Generative AI applications, especially in fields like knowledge representation, content generation, and contextual understanding, require data that reflects real-world complexity. Graph databases, which are ideal for modeling relationships between entities (people, objects, concepts), align well with the needs of these AI models.
   - **Example:** AI models augmented with knowledge graphs can improve contextual understanding, allowing them to generate more accurate, relevant, and semantically rich outputs.

2. **Knowledge Graphs and AI Integration:**
   - **Knowledge graphs** are often stored and managed using graph databases, and these knowledge graphs have become increasingly important in enhancing the reasoning and decision-making capabilities of generative AI models.
   - Google’s Knowledge Graph, for instance, is an example of this synergy. It helps their search algorithms understand queries better by connecting data in ways that mimic human reasoning.

3. **Graph Neural Networks (GNNs) and AI Research:**
   - The rise of **Graph Neural Networks (GNNs)**, a type of neural network that operates on graph structures, has also contributed to the increased adoption of graph databases. GNNs are useful in improving AI models' ability to learn from structured graph data.
   - GNNs, combined with generative AI, can boost the understanding of relationships within data (e.g., social networks, knowledge representation), providing new possibilities in applications like recommendation systems and fraud detection.

4. **Explainability in AI (XAI):**
   - Graph databases facilitate **explainable AI (XAI)** by providing clear, traceable paths of relationships and decisions made by AI systems. This is increasingly important as regulatory bodies push for more transparency in AI decision-making.
   - In applications like finance and healthcare, graph databases are being adopted to ensure that AI systems' decision-making processes can be easily traced and understood.

---

### **Industry Adoption Examples:**

- **Healthcare:** Graph databases are increasingly being adopted in bioinformatics and healthcare applications, where AI models are used to understand complex biological pathways and relationships (e.g., drug discovery, genomic analysis).
  
- **Finance:** AI models, combined with graph databases, are helping detect complex fraud patterns by analyzing relationships between transactions, accounts, and entities.

- **Recommendation Systems:** E-commerce platforms and social media use graph databases to analyze user behavior and relationships, improving recommendation engines powered by generative AI.

- **Personalization of Learning Content:** Graph databases can play a significant role in the personalization of learning content, especially when combined with generative AI models. Their ability to model complex relationships between entities such as learners, learning materials, skills, and assessments makes them ideal for creating tailored learning experiences.

---

### **Conclusion:**

While the adoption of graph databases predates generative AI, the appearance and growth of generative AI models have accelerated the demand for systems capable of handling highly interconnected, relational data. As a result, graph databases have seen increased use in industries that leverage AI for tasks like natural language processing, recommendation systems, and fraud detection, making them essential tools in the evolving AI landscape.


## Database Ranking:

https://db-engines.com/en/ranking/graph+dbms

**Key Features:**

- **Flexible Schema**: Easily adapt to changes in data structures without the need for complex migrations.
- **Efficient Traversal**: Quickly navigate relationships, making them ideal for queries that involve multiple degrees of separation.
- **High Performance**: Optimized for querying relationships, which can be inefficient in relational databases.

**Common Use Cases:**

- **Social Networks**: Modeling and analyzing connections between users.
- **Recommendation Engines**: Suggesting products or content based on user behavior and relationships.
- **Fraud Detection**: Identifying unusual patterns and connections that may indicate fraudulent activity.
- **Knowledge Graphs**: Organizing vast amounts of information in a way that machines can understand and reason about.


**What Is the Future of Graph Databases?**

The future of graph databases quite promising, driven by several technological trends and business needs that emphasize the importance of understanding complex relationships within data.

**1. Integration with Artificial Intelligence and Machine Learning**

- **Enhanced Data Context for AI**: Graph databases provide rich context through relationships, which can improve the performance of AI models, especially in natural language understanding and recommendation systems.
- **Knowledge Graphs and LLMs**: Integrating graph databases with Large Language Models (LLMs) enhances the models' ability to provide accurate and context-aware responses by grounding them in structured, real-world data.
- **Agentic AI Applications**: For autonomous agents that need to perceive, reason, and act, graph databases offer a robust framework for knowledge representation and reasoning.

**2. Rise of Connected Data**

- **Complex Data Relationships**: As data becomes more interconnected, traditional databases struggle to efficiently handle relationships. Graph databases excel in scenarios where understanding the interplay between data points is crucial.
- **Internet of Things (IoT)**: With the proliferation of IoT devices, managing the relationships between devices, sensors, and systems becomes complex. Graph databases can efficiently model these connections.

**3. Real-Time Analytics and Decision Making**

- **Immediate Insights**: Businesses increasingly require real-time analytics to make quick decisions. Graph databases can provide instant insights into complex relational data without the overhead of complex JOIN operations in relational databases.
- **Fraud Detection and Security**: Real-time monitoring of relationships and patterns can help in promptly identifying security threats or fraudulent activities.

**4. Enhanced Personalization**

- **Recommendation Systems**: Graph databases can improve the accuracy of recommendations by better understanding user preferences and behaviors through relationship modeling.
- **Customer 360 Views**: Businesses aim to understand customers holistically. Graph databases can integrate disparate data sources to provide a unified view.

**5. Adoption of Graph Standards and Query Languages**

- **GQL**: The development of standard graph query languages facilitates wider adoption by making it easier to query and manipulate graph data.
- **Interoperability**: Standardization promotes interoperability between different systems and tools, broadening the ecosystem.

**6. Cloud Services and Scalability**

- **Managed Graph Database Services**: Cloud providers are offering managed graph database solutions, lowering the barrier to entry and allowing businesses to scale without significant infrastructure investment.
- **Distributed Graph Processing**: Advances in distributed computing enable graph databases to handle larger datasets and more complex queries efficiently.

**7. Data Governance and Explainability**

- **Transparency**: The clear structure of graph databases aids in understanding how data is connected, which is essential for compliance and ethical considerations.
- **Explainable AI**: As AI systems face scrutiny over decision-making processes, graph databases can help provide transparency into how conclusions are reached.

**Challenges to Address:**

- **Complexity**: Graph databases can be complex to design and manage, requiring specialized knowledge.
- **Performance on Large Graphs**: While they are efficient for certain types of queries, performance can degrade as the size and complexity of the graph increase, necessitating ongoing optimization.

**Conclusion**

Graph databases are poised to become increasingly vital in a data-driven world that values the insights gleaned from complex relationships. Their ability to model and query interconnected data efficiently makes them indispensable for advanced AI applications, real-time analytics, and personalized user experiences.

As technology continues to evolve, graph databases will likely see:

- **Greater Integration with AI Systems**: Enhancing the capabilities of AI through richer context and reasoning.
- **Broader Adoption Across Industries**: From healthcare to finance, as organizations recognize the value of relationship-centric data models.
- **Improved Tools and Platforms**: Making graph databases more accessible and easier to implement.

In summary, the future of graph databases is robust, with significant growth potential driven by the increasing importance of understanding and leveraging the complex interconnections within data.

**Relationship Between Graph Databases and Knowledge Graphs**

Graph databases and knowledge graphs are closely related concepts that both utilize graph structures to represent and manage data. However, they serve different purposes and operate at different levels within data management and representation systems.

---

### **What is a Knowledge Graph?**

A **knowledge graph** is a representation of real-world entities and the relationships between them, organized in a graph structure. It incorporates not just data but also the semantics—meaning and context—of the data. Knowledge graphs aim to model complex domains of knowledge in a way that is both machine-readable and semantically rich.

**Key Characteristics:**

- **Entities (Nodes):** Represent real-world objects or concepts.
- **Relationships (Edges):** Define how entities are interconnected.
- **Attributes (Properties):** Additional information about entities and relationships.
- **Ontology/Semantics:** A formal specification of how to represent the entities, relationships, and their categories.

**Use Cases:**

- **Search Engines:** Enhancing search results with rich, interconnected information (e.g., Google Knowledge Graph).
- **Natural Language Processing:** Understanding context and disambiguation in language tasks.
- **Artificial Intelligence:** Enabling reasoning over data for decision-making processes.
- **Data Integration:** Merging heterogeneous data sources into a unified model.

---

### **How Graph Databases Relate to Knowledge Graphs**

**Storage and Management:**

- **Underlying Infrastructure:** Graph databases often serve as the storage layer for knowledge graphs. They provide the necessary infrastructure to store and query graph-structured data efficiently.
- **Optimized Traversal:** Graph databases are designed for fast traversal of relationships, which is essential for navigating the complex interconnections in a knowledge graph.

**Differences:**

- **Purpose:**
  - **Graph Databases:** Focus on efficient storage, retrieval, and management of graph data.
  - **Knowledge Graphs:** Aim to represent knowledge with semantic context, enabling understanding and reasoning.
- **Semantic Layer:**
  - **Graph Databases:** Do not inherently include semantic understanding; they store data as is.
  - **Knowledge Graphs:** Incorporate ontologies and schemas that define the meaning of data, allowing for semantic queries and inference.
- **Complexity:**
  - **Graph Databases:** Handle data at a structural level without necessarily understanding the domain-specific meanings.
  - **Knowledge Graphs:** Include domain knowledge and rules that allow for more complex operations like inference and semantic reasoning.

---

### **Integration of Graph Databases in Knowledge Graphs**

**Technologies and Standards:**

- **RDF (Resource Description Framework):** A standard model for data interchange on the web, commonly used in knowledge graphs.
- **SPARQL:** A query language designed for querying RDF datasets.
- **OWL (Web Ontology Language):** Used to define ontologies, adding rich semantics to the knowledge graph.

**Implementation:**

- **Graph Database as Backend:** Knowledge graphs often use graph databases like Neo4j, Amazon Neptune, or GraphDB to store data.
- **Semantic Enhancements:** Layers of semantic information are added on top of the graph database to create a knowledge graph.

**Example:**

- **Google Knowledge Graph:** Uses a graph database to store vast amounts of data about entities and their relationships, enriched with semantic information to improve search results.

---

### **Benefits of Using Graph Databases for Knowledge Graphs**

1. **Performance and Scalability:**
   - Efficient handling of complex queries involving multiple relationships.
   - Scalable storage solutions for large datasets.

2. **Flexibility:**
   - Easy to evolve the data model as new types of entities and relationships are discovered.
   - Supports heterogeneous data integration.

3. **Advanced Querying:**
   - Enables sophisticated queries that can traverse multiple levels of relationships.
   - Supports pattern matching and pathfinding operations essential for knowledge discovery.

---

### **Conclusion**

While **graph databases** and **knowledge graphs** both utilize graph structures, they operate at different layers:

- **Graph Databases:** Provide the foundational technology for storing and querying graph-structured data efficiently.
- **Knowledge Graphs:** Build upon this foundation to represent complex knowledge domains semantically, enabling machines to interpret and reason about data.

In essence, a knowledge graph often relies on a graph database as its backbone, enriching it with semantic layers to create a powerful tool for knowledge representation and discovery.

---

**Further Reading:**

- **Ontology Development:** Learn about creating ontologies to add semantic meaning to your data.
- **Semantic Web Technologies:** Explore how RDF, OWL, and SPARQL contribute to knowledge graphs.
- **Graph Database Tutorials:** Understand how to implement and optimize graph databases for your applications.


## The Importance of Graph Databases in the age of Generative and Agentic AI

**Graph databases are quite important in the age of generative AI and agentic AI, and their significance continues to grow.**

Here's why they matter and how much:

1. **Enhanced Contextual Understanding**: Graph databases store data in a way that models relationships between entities—nodes represent entities, and edges represent relationships. This structure allows AI systems to understand and utilize complex relationships, leading to more contextually accurate and meaningful outputs.

2. **Knowledge Representation and Reasoning**: For agentic AI, which involves autonomous agents that perceive, reason, and act to achieve goals, having access to a rich knowledge base is crucial. Graph databases enable the creation of knowledge graphs that agents can query to make informed decisions, reason about consequences, and plan actions.

3. **Improved Data Integration**: Graph databases can integrate diverse data types from various sources seamlessly. This is particularly useful in AI applications that require combining structured and unstructured data, enhancing the AI's ability to generate more comprehensive and accurate results.

4. **Real-Time Performance**: They offer efficient querying capabilities, even with large and complex datasets. This efficiency is essential for AI agents that need to make quick decisions based on real-time data.

5. **Facilitating Explainability**: The transparent structure of graph databases aids in understanding how an AI system reaches a conclusion. This transparency is valuable for debugging, compliance, and building trust in AI systems.

6. **Enhancing LLM Capabilities**: Integrating Large Language Models (LLMs) with graph databases can ground the AI's responses in factual, up-to-date information. This integration helps in reducing hallucinations and increasing the reliability of generative AI outputs.

**How Much Do They Matter?**

- **Crucial for Complex Relationships**: In domains where understanding and leveraging complex relationships is key—such as healthcare, finance, recommendation systems, and social networks—graph databases are indispensable.

- **Enabling Advanced Features**: They unlock advanced functionalities like semantic search, personalized recommendations, and sophisticated reasoning, which are increasingly expected in modern AI applications.

- **Scalability for Future Growth**: As AI models become more sophisticated and require more data, graph databases provide the scalability and flexibility needed to handle growing and evolving datasets without significant redesign.

**In Summary**

Graph databases play a **significant and growing role** in the advancement of generative and agentic AI. They are not just beneficial but often essential for:

- Building AI systems that require deep understanding of interconnected data.
- Enhancing the reasoning and decision-making capabilities of AI agents.
- Improving the accuracy, reliability, and explainability of AI outputs.

While not every AI application may require a graph database, for those that do, the impact is substantial. As AI continues to evolve, the importance of effectively managing and utilizing complex relationships within data will make graph databases even more relevant.

## Graph Query Lanaguage: The ISO standard for Graphs has arrived

https://www.theregister.com/2024/04/24/gql_iso_recognition/

https://www.theregister.com/2023/03/10/great_graph_debate_friday/

https://finance.yahoo.com/news/nebulagraph-enterprise-v5-0-first-140000670.html


The Graph Query Language (GQL) is an ISO standard specifically designed for querying property graphs. Officially published as ISO/IEC 39075:2024, GQL is the first new database language standard since SQL¹².

GQL provides a declarative approach to managing and querying property graphs, which are a type of graph where nodes (vertices) and edges (relationships) can have associated properties (key-value pairs). This makes it particularly powerful for complex data structures and relationships²³.

Key features of GQL include:
- **Creation and management** of property graphs.
- **CRUD operations** (Create, Read, Update, Delete) on nodes and edges.
- **Advanced querying capabilities** for traversing and analyzing graph data⁴.

This standard aims to unify and standardize the way property graphs are handled, much like SQL did for relational databases².

Is there something specific you would like to know about GQL or its applications?

Source: Conversation with Copilot, 10/1/2024
(1) Graph Query Language GQL. https://www.gqlstandards.org/.
(2) Graph Query Language - Wikipedia. https://en.wikipedia.org/wiki/Graph_Query_Language.
(3) The Rise of GQL: A New ISO Standard in Graph Query Language - TigerGraph. https://www.tigergraph.com/blogs/gsql/the-rise-of-gql-a-new-iso-standard-in-graph-query-language/.
(4) Understanding GQL: A Comprehensive Overview of the Standard Graph Query .... https://www.nebula-graph.io/posts/An_Comprehensive_Overview_of_the_Standard_Graph_Query_Language.
(5) Property Graph Standards Update - July 2020 - JCC 
Consulting. http://www.jcc.com/resources/jcc-blogs-menu/blog-database-dreams/122-property-graph-standards-update-july-2020.
(6) undefined. https://jtc1info.org/slug/gql-database-language/.

## The Databases

 Here are some databases that currently support or are planning to support the GQL standard:

1. **Neo4j**: Known for its Cypher query language, Neo4j is aligning its support with GQL¹⁴.
2. **Amazon Neptune**: This graph database service by AWS is also moving towards GQL compatibility¹⁴.
3. **TigerGraph**: TigerGraph's GSQL is another example of a graph query language that is converging towards GQL³.
4. **NebulaGraph**: NebulaGraph Enterprise v5.0 offers native GQL support⁵.

These databases are part of a broader movement to standardize graph database queries, making it easier for users to work with graph data across different platforms.


Source: Conversation with Copilot, 10/1/2024
(1) GQL: The ISO standard for graphs has arrived | AWS Database Blog. https://aws.amazon.com/blogs/database/gql-the-iso-standard-for-graphs-has-arrived/.
(2) ISO GQL: A Defining Moment in Database Innovation - Neo4j Graph Data .... https://neo4j.com/blog/gql-international-standard/.
(3) The Rise of GQL: A New ISO Standard in Graph Query Language - TigerGraph. https://www.tigergraph.com/blogs/gsql/the-rise-of-gql-a-new-iso-standard-in-graph-query-language/.
(4) Beyond a Query Language: How GQL is Shaping the Future of Graph Databases. https://www.nebula-graph.io/posts/How_GQL_is_Shaping_the_Future_of_Graph_Databases.
(5) Graph Query Language GQL. https://www.gqlstandards.org/home.
(6) undefined. https://jtc1info.org/slug/gql-database-language/.


## Examples

https://www.nebula-graph.io/posts/An_Comprehensive_Overview_of_the_Standard_Graph_Query_Language

Here are some examples of how you can create and manage property graphs using GQL:

### Creating a Graph
To create a new graph, you can define the graph schema and then instantiate it. For example:

```gql
CREATE GRAPH TYPE socialNetworkSchema (
  Person (name STRING, age INT),
  Friend (since DATE)
);

CREATE GRAPH socialNetwork OF TYPE socialNetworkSchema;
```

### Adding Nodes and Edges
You can add nodes (vertices) and edges (relationships) to the graph:

```gql
INSERT INTO socialNetwork {
  (p1:Person {name: 'Alice', age: 30}),
  (p2:Person {name: 'Bob', age: 25}),
  (p1)-[:Friend {since: DATE '2020-01-01'}]->(p2)
};
```

### Querying the Graph
To query the graph, you can use pattern matching to find specific nodes and relationships:

```gql
MATCH (p:Person)-[f:Friend]->(q:Person)
WHERE p.name = 'Alice'
RETURN p, f, q;
```

### Updating Properties
You can update properties of nodes and edges:

```gql
MATCH (p:Person)
WHERE p.name = 'Alice'
SET p.age = 31;
```

### Deleting Nodes and Edges
To delete nodes and edges, you can use the `DETACH DELETE` command:

```gql
MATCH (p:Person)-[f:Friend]->(q:Person)
WHERE p.name = 'Alice' AND q.name = 'Bob'
DETACH DELETE p, f, q;
```

These examples illustrate some of the basic operations you can perform with GQL to manage property graphs⁶⁵. If you have any specific scenarios or more detailed queries, feel free to ask!

Source: Conversation with Copilot, 10/1/2024
(1) Understanding GQL: A Comprehensive Overview of the Standard Graph Query .... https://www.nebula-graph.io/posts/An_Comprehensive_Overview_of_the_Standard_Graph_Query_Language.
(2) Property Graph Standards Update - July 2020 - JCC Consulting. http://www.jcc.com/resources/jcc-blogs-menu/blog-database-dreams/122-property-graph-standards-update-july-2020.
(3) Graph Query Language - Wikipedia. https://en.wikipedia.org/wiki/Graph_Query_Language.
(4) GQL - initiating an industry standard property graph query language. https://db-engines.com/en/blog_post/78.
(5) Information technology — Database languages — GQL - ISO. https://www.iso.org/standard/76120.html.
(6) [2211.10962] PG-Schema: Schemas for Property Graphs - arXiv.org. https://arxiv.org/abs/2211.10962.

## GQL Relevance in the age of Generative AI

The GQL standard remains highly relevant and useful even in the age of generative AI and large language models (LLMs). Here are a few reasons why:

### Enhanced Data Management
- **Structured Data Handling**: GQL excels at managing complex, interconnected data structures, which is crucial for applications that require detailed data relationships, such as social networks, recommendation systems, and knowledge graphs.
- **Efficient Queries**: GQL's ability to perform complex queries efficiently can complement the unstructured data processing capabilities of LLMs, providing a robust backend for AI applications.

### Integration with AI
- **Knowledge Graphs**: GQL can be used to build and query knowledge graphs, which are essential for enhancing the contextual understanding of LLMs. These graphs can store vast amounts of structured information that LLMs can leverage to improve their responses.
- **Data Enrichment**: By integrating GQL with AI models, you can enrich the data stored in property graphs with insights derived from AI, leading to more intelligent and context-aware applications.

### Real-World Applications
- **Recommendation Systems**: Combining GQL with AI can enhance recommendation systems by leveraging both structured graph data and unstructured user behavior data.
- **Fraud Detection**: In financial services, GQL can help map out complex transaction networks, while AI can analyze patterns to detect fraudulent activities.

### Future-Proofing
- **Standardization**: As an ISO standard, GQL provides a consistent and reliable way to manage graph data, ensuring compatibility and interoperability across different systems and platforms.
- **Scalability**: GQL's design allows it to scale with the growing complexity and size of data, making it a future-proof choice for data management.

In summary, GQL and generative AI/LLMs can complement each other, with GQL providing a structured, efficient way to manage and query data, and AI models offering advanced data processing and analysis capabilities. This synergy can lead to more powerful and intelligent applications.

## Relevant for Developing AI Agents

GQL can be quite relevant in the development of AI agents, particularly in the following ways:

### Knowledge Representation
- **Structured Knowledge**: AI agents often need to access and manipulate structured knowledge. GQL allows for the creation and querying of knowledge graphs, which can represent complex relationships and hierarchies in a way that is easily accessible to AI agents.

### Data Integration
- **Combining Data Sources**: AI agents frequently need to integrate data from multiple sources. GQL can help by providing a unified way to query and manage this data, ensuring that the AI agent has access to comprehensive and up-to-date information.

### Contextual Understanding
- **Enhanced Context**: By leveraging property graphs, AI agents can gain a deeper understanding of the context in which they operate. This can improve decision-making and the generation of more relevant responses.

### Dynamic Querying
- **Real-Time Data Access**: AI agents can use GQL to perform real-time queries on dynamic data sets, allowing them to adapt to new information quickly and efficiently.


### Example
Imagine an AI agent designed to assist with project management. Using GQL, the agent can query a property graph to understand the relationships between tasks, team members, and deadlines. This allows the agent to provide more accurate updates, identify potential bottlenecks, and suggest optimizations.

In summary, GQL provides a robust framework for managing and querying the structured data that AI agents rely on, enhancing their capabilities and effectiveness. 

## GQL (Graph Query Language) Complement's LangGraph?

GQL can indeed complement LangGraph in developing AI agents. Here's how:

1. **Data Management**: GQL is excellent for querying and managing graph data. This capability can be leveraged within LangGraph to efficiently handle and manipulate the complex data structures that AI agents often need to work with.

2. **Enhanced Querying**: LangGraph, which is designed for building stateful, multi-actor applications with LLMs (Large Language Models), can benefit from GQL's powerful querying capabilities. This allows for more sophisticated data retrieval and manipulation, which is crucial for developing advanced AI agents⁶.

3. **Integration**: LangGraph's framework supports the integration of various tools and data sources. By incorporating GQL, developers can create more dynamic and responsive AI agents that can query and process graph data in real-time⁹.

4. **Complex Workflows**: Both GQL and LangGraph are well-suited for handling complex workflows. GQL can manage the underlying data relationships, while LangGraph can orchestrate the flow of information and decision-making processes within the AI agent⁶⁹.

Combining these two technologies can lead to the development of more robust and intelligent AI agents capable of handling intricate tasks and providing personalized experiences.

Would you like to explore a specific use case or example of how this integration might work?

Source: Conversation with Copilot, 10/1/2024
(1) langchain-ai/langgraph: Build resilient language agents as graphs. - GitHub. https://github.com/langchain-ai/langgraph.
(2) LangGraph - LangChain. https://www.langchain.com/langgraph.
(3) Using LangGraph to build better AI Agents - Geeky Gadgets. https://www.geeky-gadgets.com/using-langgraph-to-build-ai-agents/.
(4) Develop a Master AI Agent With LangGraph in Python. https://thenewstack.io/develop-a-master-ai-agent-with-langgraph-in-python/.
(5) How to build AI agents using LangGraph Llama 3 and Groq. https://www.geeky-gadgets.com/how-to-build-ai-agents/.
(6) AI Agents in LangGraph. https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/.
(7) ksm26/AI-Agents-in-LangGraph - GitHub. https://github.com/ksm26/AI-Agents-in-LangGraph.
(8) langgraph/examples/multi_agent/multi-agent-collaboration.ipynb at main .... https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb.
(9) Building Complex Multi-Agent Teams and Setups with LangGraph. https://blog.finxter.com/building-complex-multi-agent-teams-and-setups-with-langgraph/.
(10) LangGraph Tutorial: What Is LangGraph and How to Use It?. https://www.datacamp.com/tutorial/langgraph-tutorial.
(11) langgraph · PyPI. https://pypi.org/project/langgraph/.
(12) langgraph/README.md at main · langchain-ai/langgraph - GitHub. https://github.com/langchain-ai/langgraph/blob/main/README.md.
