Awesome-LLMs-in-Graph-tasks

A curated collection of research papers exploring the utilization of LLMs for graph-related tasks.

Stars: 506

Visit

This repository is a collection of papers on leveraging Large Language Models (LLMs) in Graph Tasks. It provides a comprehensive overview of how LLMs can enhance graph-related tasks by combining them with traditional Graph Neural Networks (GNNs). The integration of LLMs with GNNs allows for capturing both structural and contextual aspects of nodes in graph data, leading to more powerful graph learning. The repository includes summaries of various models that leverage LLMs to assist in graph-related tasks, along with links to papers and code repositories for further exploration.

README:

Awesome-LLMs-in-Graph-tasks

If you like our project, please give us a star ⭐ on GitHub for the latest update.

This is a collection of papers on leveraging Large Language Models in Graph Tasks. It's based on our survey paper: A Survey of Graph Meets Large Language Model: Progress and Future Directions.

We will try to make this list updated frequently. If you found any error or any missed paper, please don't hesitate to open issues or pull requests.

Our survey has been accepted by IJCAI 2024 survey track.

How can LLMs help improve graph-related tasks?

With the help of LLMs, there has been a notable shift in the way we interact with graphs, particularly those containing nodes associated with text attributes. The integration of LLMs with traditional GNNs can be mutually beneficial and enhance graph learning. While GNNs are proficient at capturing structural information, they primarily rely on semantically constrained embeddings as node features, limiting their ability to express the full complexities of the nodes. Incorporating LLMs, GNNs can be enhanced with stronger node features that effectively capture both structural and contextual aspects. On the other hand, LLMs excel at encoding text but often struggle to capture structural information present in graph data. Combining GNNs with LLMs can leverage the robust textual understanding of LLMs while harnessing GNNs' ability to capture structural relationships, leading to more comprehensive and powerful graph learning.

Figure 1. The overview of Graph Meets LLMs.

Summarizations based on proposed taxonomy

Table 1. A summary of models that leverage LLMs to assist graph-related tasks in literature, ordered by their release time. Fine-tuning denotes whether it is necessary to fine-tune the parameters of LLMs, and ♥ indicates that models employ parameter-efficient fine-tuning (PEFT) strategies, such as LoRA and prefix tuning. Prompting indicates the use of text-formatted prompts in LLMs, done manually or automatically. Acronyms in Task: Node refers to node-level tasks; Link refers to link-level tasks; Graph refers to graph-level tasks; Reasoning refers to Graph Reasoning; Retrieval refers to Graph-Text Retrieval; Captioning refers to Graph Captioning.

Awesome-LLMs-in-Graph-tasks

LLM as Enhancer

(2022.03) [ICLR' 2022] Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction [Paper | Code]

GIANT

The framework of GIANT.
(2023.02) [ICLR' 2023] Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks [Paper | Code]

Edgeformers

The framework of Edgeformers.
(2023.05) [KDD' 2023] Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications [Paper]

GALM

The framework of GALM.
(2023.06) [KDD' 2023] Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks [Paper | Code]

Heterformer

The framework of Heterformer.
(2023.05) [ICLR' 2024] Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [Paper | Code]

TAPE

The framework of TAPE.
(2023.08) [Arxiv' 2023] Exploring the potential of large language models (llms) in learning on graphs [Paper]

KEA

The framework of KEA.
(2023.07) [Arxiv' 2023] Can Large Language Models Empower Molecular Property Prediction? [Paper | Code]

LLM4Mol

The framework of LLM4Mol.
(2023.08) [Arxiv' 2023] Simteg: A frustratingly simple approach improves textual graph learning [Paper | Code]

SimTeG

The framework of SimTeG.
(2023.09) [Arxiv' 2023] Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs [Paper]

G-Prompt

The framework of G-Prompt.
(2023.09) [Arxiv' 2023] TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning [Paper]

TouchUp-G

The framework of TouchUp-G.
(2023.09) [ICLR' 2024] One for All: Towards Training One Graph Model for All Classification Tasks [Paper | Code]

OFA

The framework of OFA.
(2023.10) [Arxiv' 2023] Learning Multiplex Embeddings on Text-rich Networks with One Text Encoder [Paper | Code]

METERN

The framework of METERN.
(2023.11) [WSDM' 2024] LLMRec: Large Language Models with Graph Augmentation for Recommendation [Paper | Code]

LLMRec

The framework of LLMRec.
(2023.11) [NeurIPS' 2023] WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding [Paper | Code]

WalkLM

The framework of WalkLM.
(2024.01) [IJCAI' 2024] Efficient Tuning and Inference for Large Language Models on Textual Graphs [Paper]

ENGINE

The framework of ENGINE.
(2024.02) [KDD' 2024] ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs [Paper]

ZeroG

The framework of ZeroG.
(2024.02) [Arxiv' 2024] UniGraph: Learning a Cross-Domain Graph Foundation Model From Natural Language [Paper]

UniGraph

The framework of UniGraph.
(2024.02) [CIKM' 2024] Distilling Large Language Models for Text-Attributed Graph Learning [Paper]

Pan, et al.

The framework of Pan, et al.

LLM as Predictor

(2023.05) [NeurIPS' 2023] Can language models solve graph problems in natural language? [Paper | Code]

NLGraph

The framework of NLGraph.
(2023.05) [Arxiv' 2023] GPT4Graph: Can Large Language Models Understand Graph Structured Data? An Empirical Evaluation and Benchmarking [Paper | Code]

GPT4Graph

The framework of GPT4Graph.
(2023.06) [NeurIPS' 2023] GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning [Paper | Code]

GIMLET

The framework of GIMLET.
(2023.07) [Arxiv' 2023] Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs [Paper | Code]

Framework

The designed prompts of Chen et al.
(2023.08) [Arxiv' 2023] GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text [Paper]

GIT-Mol

The framework of GIT-Mol.
(2023.08) [Arxiv' 2023] Natural Language is All a Graph Needs [Paper | Code]

InstructGLM

The framework of InstructGLM.
(2023.08) [Arxiv' 2023] Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis [Paper | Code]

Framework

The designed prompts of Liu et al.
(2023.09) [Arxiv' 2023] Can LLMs Effectively Leverage Graph Structural Information: When and Why [Paper | Code]

Framework

The designed prompts of Huang et al.
(2023.10) [Arxiv' 2023] GraphText: Graph Reasoning in Text Space [Paper] | Code]

GraphText

The framework of GraphText.
(2023.10) [Arxiv' 2023] Talk like a Graph: Encoding Graphs for Large Language Models [Paper]

Framework

The designed prompts of Fatemi et al.
(2023.10) [Arxiv' 2023] GraphLLM: Boosting Graph Reasoning Ability of Large Language Model [Paper | Code]

GraphLLM

The framework of GraphLLM.
(2023.10) [Arxiv' 2023] Beyond Text: A Deep Dive into Large Language Model [Paper]

Framework

The designed prompts of Hu et al.
(2023.10) [EMNLP' 2023] MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter [Paper | Code]

MolCA

The framework of MolCA.
(2023.10) [Arxiv' 2023] GraphGPT: Graph Instruction Tuning for Large Language Models [Paper | Code]

GraphGPT

The framework of GraphGPT.
(2023.10) [EMNLP' 2023] ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction [Paper | Code]

ReLM

The framework of ReLM.
(2023.10) [Arxiv' 2023] LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs? [Paper]

LLM4DyG

The framework of LLM4DyG.
(2023.10) [Arxiv' 2023] Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs [Paper]

DGTL

The framework of DGTL.
(2023.11) [Arxiv' 2023] Which Modality should I use -- Text, Motif, or Image? : Understanding Graphs with Large Language Models [Paper]

Framework

The framework of Das et al.
(2023.11) [Arxiv' 2023] InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery [Paper]

InstructMol

The framework of InstructMol.
(2023.12) [Arxiv' 2023] When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning [Paper]

Framework

The framework of Ai et al.
(2024.02) [Arxiv' 2024] Let Your Graph Do the Talking: Encoding Structured Data for LLMs [Paper]

GraphToken

The framework of GraphToken.
(2024.02) [Arxiv' 2024] Rendering Graphs for Graph Reasoning in Multimodal Large Language Models [Paper]

GITA

The framework of GITA.
(2024.02) [WWW' 2024] GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks [Paper | Code]

GraphTranslator

The framework of GraphTranslator.
(2024.02) [Arxiv' 2024] InstructGraph: Boosting Large Language Models via Graph-centric Instruction Tuning and Preference Alignment [Paper | Code]

InstructGraph

The framework of InstructGraph.
(2024.02) [Arxiv' 2024] LLaGA: Large Language and Graph Assistant [Paper | Code]

LLaGA

The framework of LLaGA.
(2024.02) [WWW' 2024] Can GNN be Good Adapter for LLMs? [Paper]

GraphAdapter

The framework of GraphAdapter.
(2024.02) [Arxiv' 2024] HiGPT: Heterogeneous Graph Language Model [Paper | Code]

HiGPT

The framework of HiGPT.
(2024.02) [Arxiv' 2024] GraphWiz: An Instruction-Following Language Model for Graph Problems [Paper | Code]

GraphWiz

The framework of GraphWiz.
(2024.03) [Arxiv' 2024] OpenGraph: Towards Open Graph Foundation Models [Paper | Code]

OpenGraph

The framework of OpenGraph.
(2024.07) [Arxiv' 2024] GOFA: A Generative One-For-All Model for Joint Graph Language Modeling [Paper | Code]

GOFA

The framework of GOFA.

GNN-LLM Alignment

(2020.08) [Arxiv' 2020] Graph-based Modeling of Online Communities for Fake News Detection [Paper | Code]

SAFER

The framework of SAFER.
(2021.05) [NeurIPS' 2021] GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [Paper | Code]

GraphFormers

The framework of GraphFormers.
(2021.11) [EMNLP' 2021] Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries [Paper | Code]

Text2Mol

The framework of Text2Mol.
(2022.07) [ACL' 2023] Hidden Schema Networks [Paper | Code]

HSN

The framework of HSN.
(2022.09) [Arxiv' 2022] A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language [Paper | Code]

MoMu

The framework of MoMu.
(2022.10) [ICLR' 2023] Learning on Large-scale Text-attributed Graphs via Variational Inference [Paper | Code]

GLEM

The framework of GLEM.
(2022.12) [NMI' 2023] Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval [Paper | Code]

MoleculeSTM

The framework of MoleculeSTM.
(2023.04) [Arxiv' 2023] Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs [Paper | Code]

GRAD

The framework of GRAD.
(2023.05) [ACL' 2023] PATTON : Language Model Pretraining on Text-Rich Networks [Paper | Code]

Patton

The framework of Patton.
(2023.05) [Arxiv' 2023] ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings [Paper | Code]

ConGraT

The framework of ConGraT.
(2023.07) [Arxiv' 2023] Prompt Tuning on Graph-augmented Low-resource Text Classification [Paper | Code]

G2P2

The framework of G2P2.
(2023.10) [EMNLP' 2023] GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs [Paper | Code]

GRENADE

The framework of GRENADE.
(2023.10) [WWW' 2024] Representation Learning with Large Language Models for Recommendation [Paper | Code]

RLMRec

The framework of RLMRec.
(2023.10) [EMNLP' 2023] Pretraining Language Models with Text-Attributed Heterogeneous Graphs [Paper | Code]

THLM

The framework of THLM.

Others

LLM as Annotator

(2023.10) [ICLR' 2024] Label-free Node Classification on Graphs with Large Language Models (LLMs) [Paper | Code]

LLM-GNN

The framework of LLM-GNN.
(2024.09) [NeurIPS' 2024] Entity Alignment with Noisy Annotations from Large Language Models [Paper | Code]

LLM4EA

The framework of LLM4EA.

LLM as Controller

(2023.10) [Arxiv' 2023] Graph Neural Architecture Search with GPT-4 [Paper]

GPT4GNAS

The framework of GPT4GNAS.

LLM as Sample Generator

(2023.10) [Arxiv' 2023] Empower Text-Attributed Graphs Learning with Large Language Models (LLMs) [Paper]

ENG

The framework of ENG.

LLM as Similarity Analyzer

(2023.11) [Arxiv' 2023] Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs [Paper]

Framework

The framework of Sun et al.

LLM for Robustness

(2024.05) [Arxiv' 2024] Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level [Paper]

Lei, et al.

The framework of Lei, et al..
(2024.08) [Arxiv' 2024] Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks? [Paper]

LLM4RGNN

The framework of LLM4RGNN.

LLM for Task Planning

(2024.05) [NeurIPS' 2024] Can Graph Learning Improve Planning in LLM-based Agents? [Paper | Code]

GNN4TaskPlan

The definition of task planning and the proposed framework.

Other Repos

We note that several repos also summarize papers on the integration of LLMs and graphs. However, we differentiate ourselves by organizing these papers leveraging a new and more granular taxonomy. We recommend researchers to explore some repositories for a comprehensive survey.

Awesome-Graph-LLM, created by Xiaoxin He from NUS.
Awesome-Large-Graph-Model, created by Ziwei Zhang from THU.
Awesome-Language-Model-on-Graphs, created by Bowen Jin from UIUC.

We highly recommend a repository that summarizes the work on Graph Prompt, which is very close to Graph-LLM.

Awesome-Graph-Prompt, created by Xixi Wu from CUHK.

Contributing

If you have come across relevant resources, feel free to open an issue or submit a pull request.

* (_time_) [conference] **paper_name** [[Paper](link) | [Code](link)]
   <details close>
   <summary>Model name</summary>
   <p align="center"><img width="75%" src="Figures/xxx.jpg" /></p>
   <p align="center"><em>The framework of model name.</em></p>
   </details>

Cite Us

Feel free to cite this work if you find it useful to you!

@article{li2023survey,
  title={A Survey of Graph Meets Large Language Model: Progress and Future Directions},
  author={Li, Yuhan and Li, Zhixun and Wang, Peisong and Li, Jia and Sun, Xiangguo and Cheng, Hong and Yu, Jeffrey Xu},
  journal={arXiv preprint arXiv:2311.12399},
  year={2023}
}

For Tasks:

Click tags to check more tools for each tasks

improve graph learning node feature extraction representation learning text-attributed graph modeling graph reasoning

For Jobs:

data scientist machine learning engineer research scientist ai engineer nlp researcher

Alternative AI tools for Awesome-LLMs-in-Graph-tasks

Similar Open Source Tools

Awesome-LLMs-in-Graph-tasks

github

: 506

prompt-in-context-learning

An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab. 📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt | ⛳ LLMs Usage Guide > **⭐️ Shining ⭐️:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness. The resources include: _🎉Papers🎉_: The latest papers about _In-Context Learning_ , _Prompt Engineering_ , _Agent_ , and _Foundation Models_. _🎉Playground🎉_: Large language models（LLMs）that enable prompt experimentation. _🎉Prompt Engineering🎉_: Prompt techniques for leveraging large language models. _🎉ChatGPT Prompt🎉_: Prompt examples that can be applied in our work and daily lives. _🎉LLMs Usage Guide🎉_: The method for quickly getting started with large language models by using LangChain. In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): - Those who enhance their abilities through the use of AIGC; - Those whose jobs are replaced by AI automation. 💎EgoAlpha: Hello! human👤, are you ready?

github

: 1.5k

chatgpt-auto-refresh

ChatGPT Auto Refresh is a userscript that keeps ChatGPT sessions fresh by eliminating network errors and Cloudflare checks. It removes the 10-minute time limit from conversations when Chat History is disabled, ensuring a seamless experience. The tool is safe, lightweight, and a time-saver, allowing users to keep their sessions alive without constant copy/paste/refresh actions. It works even in background tabs, providing convenience and efficiency for users interacting with ChatGPT. The tool relies on the chatgpt.js library and is compatible with various browsers using Tampermonkey, making it accessible to a wide range of users.

github

: 179

LLM-for-misinformation-research

LLM-for-misinformation-research is a curated paper list of misinformation research using large language models (LLMs). The repository covers methods for detection and verification, tools for fact-checking complex claims, decision-making and explanation, claim matching, post-hoc explanation generation, and other tasks related to combating misinformation. It includes papers on fake news detection, rumor detection, fact verification, and more, showcasing the application of LLMs in various aspects of misinformation research.

github

: 78

bravegpt

BraveGPT is a userscript that brings the power of ChatGPT to Brave Search. It allows users to engage with a conversational AI assistant directly within their search results, providing instant and personalized responses to their queries. BraveGPT is powered by GPT-4, the latest and most advanced language model from OpenAI, ensuring accurate and comprehensive answers. With BraveGPT, users can ask questions, get summaries, generate creative content, and more, all without leaving the Brave Search interface. The tool is easy to install and use, making it accessible to users of all levels. BraveGPT is a valuable addition to the Brave Search experience, enhancing its capabilities and providing users with a more efficient and informative search experience.

github

: 152

Awesome-Graph-LLM

Awesome-Graph-LLM is a curated collection of research papers exploring the intersection of graph-based techniques with Large Language Models (LLMs). The repository aims to bridge the gap between LLMs and graph structures prevalent in real-world applications by providing a comprehensive list of papers covering various aspects of graph reasoning, node classification, graph classification/regression, knowledge graphs, multimodal models, applications, and tools. It serves as a valuable resource for researchers and practitioners interested in leveraging LLMs for graph-related tasks.

github

: 1.7k

NewEraAI-Papers

The NewEraAI-Papers repository provides links to collections of influential and interesting research papers from top AI conferences, along with open-source code to promote reproducibility and provide detailed implementation insights beyond the scope of the article. Users can stay up to date with the latest advances in AI research by exploring this repository. Contributions to improve the completeness of the list are welcomed, and users can create pull requests, open issues, or contact the repository owner via email to enhance the repository further.

github

: 65

chatgpt.js

chatgpt.js is a powerful JavaScript library that allows for super easy interaction w/ the ChatGPT DOM. * Feature-rich * Object-oriented * Easy-to-use * Lightweight (yet optimally performant)

github

: 1.9k

awesome-LLM-AIOps

The 'awesome-LLM-AIOps' repository is a curated list of academic research and industrial materials related to Large Language Models (LLM) and Artificial Intelligence for IT Operations (AIOps). It covers various topics such as incident management, log analysis, root cause analysis, incident mitigation, and incident postmortem analysis. The repository provides a comprehensive collection of papers, projects, and tools related to the application of LLM and AI in IT operations, offering valuable insights and resources for researchers and practitioners in the field.

github

: 54

awesome-yolo-object-detection

github

: 1.2k

googlegpt

GoogleGPT is a browser extension that brings the power of ChatGPT to Google Search. With GoogleGPT, you can ask ChatGPT questions and get answers directly in your search results. You can also use GoogleGPT to generate text, translate languages, and more. GoogleGPT is compatible with all major browsers, including Chrome, Firefox, Edge, and Safari.

github

: 122

chatgpt-widescreen

ChatGPT Widescreen Mode is a browser extension that adds widescreen and fullscreen modes to ChatGPT, enhancing chat sessions by reducing scrolling and creating a more immersive viewing experience. Users can experience clearer programming code display, view multi-step instructions or long recipes on a single page, enjoy original content in a visually pleasing format, customize features like a larger chatbox and hidden header/footer, and use the tool with chat.openai.com and poe.com. The extension is compatible with various browsers and relies on code from the chatgpt.js library under the MIT license.

github

: 122

chatgpt-infinity

ChatGPT Infinity is a free and powerful add-on that makes ChatGPT generate infinite answers on any topic. It offers customizable topic selection, multilingual support, adjustable response interval, and auto-scroll feature for a seamless chat experience.

github

: 253

duckduckgpt

DuckDuckGPT brings the magic of ChatGPT to DDG (powered by GPT-4!). DuckDuckGPT is a browser extension that allows you to use ChatGPT within DuckDuckGo. This means you can ask ChatGPT questions, get help with tasks, and generate creative content, all without leaving DuckDuckGo. DuckDuckGPT is easy to use. Once you have installed the extension, simply type your question into the DuckDuckGo search bar and hit enter. ChatGPT will then generate a response that will appear below the search results. DuckDuckGPT is a powerful tool that can help you with a wide variety of tasks. Here are just a few examples of what you can use it for: * Get help with research * Write essays and other creative content * Translate languages * Get coding help * Answer trivia questions * And much more! DuckDuckGPT is still in development, but it is already a very powerful tool. As GPT-4 continues to improve, DuckDuckGPT will only get better. So if you are looking for a way to make your DuckDuckGo searches more productive, be sure to give DuckDuckGPT a try.

github

: 168

FastGPT

FastGPT is a knowledge base Q&A system based on the LLM large language model, providing out-of-the-box data processing, model calling and other capabilities. At the same time, you can use Flow to visually arrange workflows to achieve complex Q&A scenarios!

github

: 17.4k

chatgpt-auto-continue

ChatGPT Auto-Continue is a userscript that automatically continues generating ChatGPT responses when chats cut off. It relies on the powerful chatgpt.js library and is easy to install and use. Simply install Tampermonkey and ChatGPT Auto-Continue, and visit chat.openai.com as normal. Multi-reply conversations will automatically continue generating when cut-off!

github

: 146

For similar tasks

Awesome-LLMs-in-Graph-tasks

github

: 506

For similar jobs

sweep

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

github

: 7.1k

teams-ai

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

github

: 390

ai-guide

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

github

: 159

classifai

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

github

: 572

chatbot-ui

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

github

: 27.7k

BricksLLM

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

github

: 897

uAgents

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

: 951

griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.0k

Awesome-LLMs-in-Graph-tasks

README:

Awesome-LLMs-in-Graph-tasks

If you like our project, please give us a star ⭐ on GitHub for the latest update.

How can LLMs help improve graph-related tasks?

Summarizations based on proposed taxonomy

Table of Contents

LLM as Enhancer

LLM as Predictor

GNN-LLM Alignment

Others

LLM as Annotator

LLM as Controller

LLM as Sample Generator

LLM as Similarity Analyzer

LLM for Robustness

LLM for Task Planning

Other Repos

Contributing

Cite Us

For Tasks:

For Jobs:

Alternative AI tools for Awesome-LLMs-in-Graph-tasks

Similar Open Source Tools

Awesome-LLMs-in-Graph-tasks

prompt-in-context-learning

chatgpt-auto-refresh

LLM-for-misinformation-research

bravegpt

Awesome-Graph-LLM

NewEraAI-Papers

chatgpt.js

awesome-LLM-AIOps

awesome-yolo-object-detection

googlegpt

chatgpt-widescreen

chatgpt-infinity

duckduckgpt

FastGPT

chatgpt-auto-continue

For similar tasks

Awesome-LLMs-in-Graph-tasks

For similar jobs

sweep

teams-ai

ai-guide

classifai

chatbot-ui

BricksLLM

uAgents

griptape