--- title: "LLMSurvey vs LLM-Agent-Paper-List" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rucaibox-llmsurvey-vs-woooodyy-llm-agent-paper-list" tools: ["rucaibox-llmsurvey", "woooodyy-llm-agent-paper-list"] --- # LLMSurvey vs LLM-Agent-Paper-List Neutral, constraint-first comparison with live GitHub stats. | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Tagline | A Survey of Large Language Models | Must-read papers for LLM-based agents. | | Stars | 12,184 | 8,158 | | Forks | 935 | 493 | | Open issues | 30 | 25 | | Language | Python | - | | Adopt for | LLMSurvey is a Python-based repository designed to collect and organize research papers concerning large language models, enabling users to stay updated with recent advancements in LLM research. | LLM-Agent-Paper-List is a curated collection of research papers focused on developing Large Language Model (LLM)-based AI agents. It links to frameworks like AgentGym-RL for reinforcement learning and tutorials for agent | | Persona | - | - | | Runtime | - | - | | License | - | The repository license and language information are currently unknown. | | Categories | LLM Frameworks, Developer Tools | AI Agents, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMSurvey](/tools/rucaibox-llmsurvey.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 484d | 300d | | Open issues (now) | 30 | 25 | | Owner type | Organization | User | | Full report | [trust report](/tools/rucaibox-llmsurvey/trust.md) | [trust report](/tools/woooodyy-llm-agent-paper-list/trust.md) | **Typed relationship:** LLMSurvey _(successor)_ LLM-Agent-Paper-List This repository could be considered an updated and more comprehensive version of a survey like LLMSurvey, specifically focusing on agent-based research. Coexists - Both repositories serve similar purposes but with different focuses; WooooDyy's list emphasizes LLM agents. ## Decision facts: LLMSurvey - **Adopt for:** LLMSurvey is a Python-based repository designed to collect and organize research papers concerning large language models, enabling users to stay updated with recent advancements in LLM research. ## Decision facts: LLM-Agent-Paper-List - **Requirements:** The tool is aimed at researchers and developers interested in the theoretical foundations of LLM-based agents. - **Adopt for:** LLM-Agent-Paper-List is a curated collection of research papers focused on developing Large Language Model (LLM)-based AI agents. It links to frameworks like AgentGym-RL for reinforcement learning and tutorials for agent - **License detail:** The repository license and language information are currently unknown. ## Choose when ### Choose LLMSurvey if… - This repository could be considered an updated and more comprehensive version of a survey like LLMSurvey, specifically focusing on agent-based research. - Tags unique to LLMSurvey: llms, chain-of-thought, instruction-tuning, natural-language-processing. - Also covers LLM Frameworks, Developer Tools. - When developing or researching projects related to large language models. ### Choose LLM-Agent-Paper-List if… - Requirements: The tool is aimed at researchers and developers interested in the theoretical foundations of LLM-based agents.. - This repository could be considered an updated and more comprehensive version of a survey like LLMSurvey, specifically focusing on agent-based research. - Tags unique to LLM-Agent-Paper-List: nlp, survey, agent. - Also covers AI Agents, Inference & Serving. - You need an overview of the latest advancements in LLM-based agents. ## When NOT to use LLMSurvey - If your focus is on practical, hands-on application rather than theoretical research or academic study. - When looking for immediate, executable LLM frameworks instead of a curated collection of papers and resources. - For projects requiring real-time data integration with specific programming tasks not covered by paper references. ## When NOT to use LLM-Agent-Paper-List - For general-purpose development tools or libraries for building AI applications, focus instead on broader, more comprehensive repositories or frameworks. - If you need hands-on code solutions without context, LLM-Agent-Paper-List might not be the immediate go-to source as it emphasizes research-driven content over practical coding examples or full-blown, ## Common questions ### What is the difference between LLMSurvey and LLM-Agent-Paper-List? LLMSurvey: A Survey of Large Language Models. LLM-Agent-Paper-List: Must-read papers for LLM-based agents.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMSurvey over LLM-Agent-Paper-List? Choose LLMSurvey over LLM-Agent-Paper-List when This repository could be considered an updated and more comprehensive version of a survey like LLMSurvey, specifically focusing on agent-based research; Tags unique to LLMSurvey: llms, chain-of-thought, instruction-tuning, natural-language-processing; Also covers LLM Frameworks, Developer Tools; When developing or researching projects related to large language models. ### When should I choose LLM-Agent-Paper-List over LLMSurvey? Choose LLM-Agent-Paper-List over LLMSurvey when Requirements: The tool is aimed at researchers and developers interested in the theoretical foundations of LLM-based agents.; This repository could be considered an updated and more comprehensive version of a survey like LLMSurvey, specifically focusing on agent-based research; Tags unique to LLM-Agent-Paper-List: nlp, survey, agent; Also covers AI Agents, Inference & Serving; You need an overview of the latest advancements in LLM-based agents. ### When should I avoid LLMSurvey? If your focus is on practical, hands-on application rather than theoretical research or academic study. When looking for immediate, executable LLM frameworks instead of a curated collection of papers and resources. For projects requiring real-time data integration with specific programming tasks not covered by paper references. ### When should I avoid LLM-Agent-Paper-List? For general-purpose development tools or libraries for building AI applications, focus instead on broader, more comprehensive repositories or frameworks. If you need hands-on code solutions without context, LLM-Agent-Paper-List might not be the immediate go-to source as it emphasizes research-driven content over practical coding examples or full-blown, ### Is LLMSurvey or LLM-Agent-Paper-List more popular on GitHub? LLMSurvey has more GitHub stars (12,184 vs 8,158). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSurvey and LLM-Agent-Paper-List open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSurvey or LLM-Agent-Paper-List? GraphCanon lists graph-backed alternatives at /tools/rucaibox-llmsurvey/alternatives and /tools/woooodyy-llm-agent-paper-list/alternatives (/tools/rucaibox-llmsurvey/alternatives.md, /tools/woooodyy-llm-agent-paper-list/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 /compare/rucaibox-llmsurvey-vs-woooodyy-llm-agent-paper-list.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMSurvey or LLM-Agent-Paper-List? LLMSurvey: Dormant. LLM-Agent-Paper-List: 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 LLMSurvey and LLM-Agent-Paper-List? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSurvey: /tools/rucaibox-llmsurvey/trust; LLM-Agent-Paper-List: /tools/woooodyy-llm-agent-paper-list/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rucaibox-llmsurvey`](/api/graphcanon/graph?tool=rucaibox-llmsurvey) - 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/_