--- title: "Awesome-LLMs-ICLR-24 vs ArXivChatGuru" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-redis-developer-arxivchatguru" tools: ["azminewasi-awesome-llms-iclr-24", "redis-developer-arxivchatguru"] --- # Awesome-LLMs-ICLR-24 vs ArXivChatGuru *GraphCanon updated Aug 22, 2026* ## Verdict Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; pick ArXivChatGuru if arXivChatGuru uses LangChain and OpenAI for question-answering over ArXiv research papers with Redis as the vector database. [Awesome-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) reports 72 GitHub stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. [ArXivChatGuru](https://github.com/redis-developer/ArXivChatGuru) has 561 stars, 75 forks, and 7 open issues, last pushed Mar 18, 2026. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [ArXivChatGuru's repository](https://github.com/redis-developer/ArXivChatGuru). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [ArXivChatGuru](/tools/redis-developer-arxivchatguru.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | An application to interrogate research papers using AI | | Stars | 72 | 561 | | Forks | 5 | 75 | | Open issues | 0 | 7 | | Language | - | Python | | Adopt for | Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. | ArXivChatGuru uses LangChain and OpenAI for question-answering over ArXiv research papers with Redis as the vector database. | | Persona | - | - | | Runtime | - | - | | License | MIT | ArXivChatGuru is covered under the MIT License, allowing free use and modification with attribution. | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [ArXivChatGuru](/tools/redis-developer-arxivchatguru.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 856d | 156d | | Open issues (now) | 0 | 7 | | Stars delta | Unknown | -1 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/redis-developer-arxivchatguru/trust.md) | ## Decision facts: Awesome-LLMs-ICLR-24 - **Adopt for:** Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. ## Decision facts: ArXivChatGuru - **Pricing:** freemium - Free to use but might incur costs for OpenAI API calls, depending on usage intensity. - **Requirements:** Python knowledge is required for setting up ArXivChatGuru locally.; Integration expertise with LangChain and Redis is beneficial for optimizing the retrieval system. - **Adopt for:** ArXivChatGuru uses LangChain and OpenAI for question-answering over ArXiv research papers with Redis as the vector database. - **License detail:** ArXivChatGuru is covered under the MIT License, allowing free use and modification with attribution. ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - Also covers Developer Tools, Inference & Serving, LLM Frameworks, Model Training. - If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### Choose ArXivChatGuru if… - Pricing: Free to use but might incur costs for OpenAI API calls, depending on usage intensity.. - Requirements: Python knowledge is required for setting up ArXivChatGuru locally.; Integration expertise with LangChain and Redis is beneficial for optimizing the retrieval system.. - Tags unique to ArXivChatGuru: ai, arxiv, langchain, machine-learning. - Also covers Vector Databases. - ArXivChatGuru ships Docker support for self-hosted deployment. - You need to derive insights from complex academic papers on ArXiv where a conversational AI interface could help in understanding dense content. ## When NOT to use Awesome-LLMs-ICLR-24 - If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. - For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ## When NOT to use ArXivChatGuru - The focus is on real-time data processing that requires updates more frequent than daily, as ArXivChatGuru's primary strength lies in static research paper analysis. - You require comprehensive coverage of a domain beyond ArXiv, since the tool is specifically tailored for ArXiv-hosted papers and does not cover external academic databases. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and ArXivChatGuru? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. ArXivChatGuru: An application to interrogate research papers using AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over ArXivChatGuru? Choose Awesome-LLMs-ICLR-24 over ArXivChatGuru when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Inference & Serving, LLM Frameworks, Model Training; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### When should I choose ArXivChatGuru over Awesome-LLMs-ICLR-24? Choose ArXivChatGuru over Awesome-LLMs-ICLR-24 when Pricing: Free to use but might incur costs for OpenAI API calls, depending on usage intensity.; Requirements: Python knowledge is required for setting up ArXivChatGuru locally.; Integration expertise with LangChain and Redis is beneficial for optimizing the retrieval system.; Tags unique to ArXivChatGuru: ai, arxiv, langchain, machine-learning; Also covers Vector Databases; ArXivChatGuru ships Docker support for self-hosted deployment; You need to derive insights from complex academic papers on ArXiv where a conversational AI interface could help in understanding dense content. ### When should I avoid Awesome-LLMs-ICLR-24? If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ### When should I avoid ArXivChatGuru? The focus is on real-time data processing that requires updates more frequent than daily, as ArXivChatGuru's primary strength lies in static research paper analysis. You require comprehensive coverage of a domain beyond ArXiv, since the tool is specifically tailored for ArXiv-hosted papers and does not cover external academic databases. ### Is Awesome-LLMs-ICLR-24 or ArXivChatGuru more popular on GitHub? ArXivChatGuru has more GitHub stars (561 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and ArXivChatGuru open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, ArXivChatGuru: MIT). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or ArXivChatGuru? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [ArXivChatGuru alternatives](/tools/redis-developer-arxivchatguru/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [ArXivChatGuru markdown twin](/tools/redis-developer-arxivchatguru/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/azminewasi-awesome-llms-iclr-24-vs-redis-developer-arxivchatguru.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMs-ICLR-24 or ArXivChatGuru? Awesome-LLMs-ICLR-24: Dormant. ArXivChatGuru: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Awesome-LLMs-ICLR-24 and ArXivChatGuru? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMs-ICLR-24 trust report](/tools/azminewasi-awesome-llms-iclr-24/trust); [ArXivChatGuru trust report](/tools/redis-developer-arxivchatguru/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24`](/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_