--- title: "Awesome-LLMs-ICLR-24 vs LongWriter" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-thudm-longwriter" tools: ["azminewasi-awesome-llms-iclr-24", "thudm-longwriter"] --- # Awesome-LLMs-ICLR-24 vs LongWriter *GraphCanon updated Aug 24, 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 LongWriter if longWriter specializes in exceeding the text generation limit to over 10,000 words using long-context LLMs for Python-based development. [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. [LongWriter](https://github.com/THUDM/LongWriter) has 1.9k stars, 182 forks, and 32 open issues, last pushed Jun 24, 2025. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [LongWriter's repository](https://github.com/THUDM/LongWriter). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LongWriter](/tools/thudm-longwriter.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | LongWriter enables generation of texts longer than 10,000 words using long-context LLMs | | Stars | 72 | 1,872 | | Forks | 5 | 182 | | Open issues | 0 | 32 | | 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. | LongWriter specializes in exceeding the text generation limit to over 10,000 words using long-context LLMs for Python-based development. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LongWriter](/tools/thudm-longwriter.md) | | --- | --- | --- | | Days since push | 856d | 425d | | Open issues (now) | 0 | 32 | | Stars delta | Unknown | +4 (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/thudm-longwriter/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: LongWriter - **Adopt for:** LongWriter specializes in exceeding the text generation limit to over 10,000 words using long-context LLMs for Python-based development. ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - License: Awesome-LLMs-ICLR-24 is MIT, LongWriter is Apache-2.0. - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - Also covers Developer Tools, Evaluation & Observability, Inference & Serving. - 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 LongWriter if… - License: LongWriter is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT. - Tags unique to LongWriter: fine-tuning, llm, long-context, long-text. - For projects requiring texts longer than 10,000 words with fine-tuned llm models ## 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 LongWriter - Avoid for short-form content generation where LLM context is less relevant - Not ideal when the requirement is to maintain conciseness in output texts ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and LongWriter? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. LongWriter: LongWriter enables generation of texts longer than 10,000 words using long-context LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over LongWriter? Choose Awesome-LLMs-ICLR-24 over LongWriter when License: Awesome-LLMs-ICLR-24 is MIT, LongWriter is Apache-2.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Evaluation & Observability, Inference & Serving; 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 LongWriter over Awesome-LLMs-ICLR-24? Choose LongWriter over Awesome-LLMs-ICLR-24 when License: LongWriter is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT; Tags unique to LongWriter: fine-tuning, llm, long-context, long-text; For projects requiring texts longer than 10,000 words with fine-tuned llm models. ### 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 LongWriter? Avoid for short-form content generation where LLM context is less relevant Not ideal when the requirement is to maintain conciseness in output texts ### Is Awesome-LLMs-ICLR-24 or LongWriter more popular on GitHub? LongWriter has more GitHub stars (1,872 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and LongWriter open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, LongWriter: Apache-2.0). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or LongWriter? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [LongWriter alternatives](/tools/thudm-longwriter/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [LongWriter markdown twin](/tools/thudm-longwriter/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-thudm-longwriter.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 LongWriter? Awesome-LLMs-ICLR-24: Dormant. LongWriter: Dormant. 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 LongWriter? 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); [LongWriter trust report](/tools/thudm-longwriter/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/_