--- title: "RD-Agent vs LLM-Agent-Paper-List" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-rd-agent-vs-woooodyy-llm-agent-paper-list" tools: ["microsoft-rd-agent", "woooodyy-llm-agent-paper-list"] --- # RD-Agent vs LLM-Agent-Paper-List *GraphCanon updated Sep 20, 2026* ## Verdict Pick RD-Agent if rD-Agent is an automation tool for AI-driven R&D processes, focusing on data and model development using Python with support from Docker installations; pick LLM-Agent-Paper-List if lLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. [RD-Agent](https://rdagent.azurewebsites.net/) reports 14k GitHub stars, 1.8k forks, and 198 open issues, last pushed Aug 4, 2026. [LLM-Agent-Paper-List](https://arxiv.org/abs/2309.07864) has 8.2k stars, 499 forks, and 32 open issues, last pushed Sep 12, 2025. Figures are from public GitHub metadata via [RD-Agent's repository](https://github.com/microsoft/RD-Agent) and [LLM-Agent-Paper-List's repository](https://github.com/WooooDyy/LLM-Agent-Paper-List). | | [RD-Agent](/tools/microsoft-rd-agent.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Tagline | Automating high-value R&D processes through AI-driven data science and model development. | Must-read papers for LLM-based agents. | | Stars | 14,275 | 8,222 | | Forks | 1,834 | 499 | | Open issues | 198 | 32 | | Language | Python | - | | Adopt for | RD-Agent is an automation tool for AI-driven R&D processes, focusing on data and model development using Python with support from Docker installations. | LLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | AI Agents, Data & Retrieval, Model Training | AI Agents, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RD-Agent](/tools/microsoft-rd-agent.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 14d | 371d | | Open issues (now) | 198 | 32 | | Stars delta | +332 (30d) | +50 (30d) | | Open issues delta | +5 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/microsoft-rd-agent/trust.md) | [trust report](/tools/woooodyy-llm-agent-paper-list/trust.md) | ## Decision facts: RD-Agent - **Pricing:** freemium - RD-Agent operates under the MIT license allowing free use of the tool. Its framework for R&D automation can be expanded with premium services for enterprise-level support and integration if needed. - **Requirements:** Requires Docker; Ensure Docker is installed beforehand and accessible without `sudo` by the current user for RD-Agent operation.; Supports Python installations via PyPI or development setup from source, which also requires installation of dependencies as per the documentation. - **Adopt for:** RD-Agent is an automation tool for AI-driven R&D processes, focusing on data and model development using Python with support from Docker installations. ## Decision facts: LLM-Agent-Paper-List - **Adopt for:** LLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. ## Choose when ### Choose RD-Agent if… - Pricing: RD-Agent operates under the MIT license allowing free use of the tool. Its framework for R&D automation can be expanded with premium services for enterprise-level support and integration if needed.. - Requirements: Requires Docker; Ensure Docker is installed beforehand and accessible without `sudo` by the current user for RD-Agent operation.; Supports Python installations via PyPI or development setup from source, which also requires installation of dependencies as per the documentation.. - Tags unique to RD-Agent: ai, automation, data-mining, data-science. - Also covers Data & Retrieval. - When you require automation in high-value R&D tasks that revolve around data science and model development within the AI domain. ### Choose LLM-Agent-Paper-List if… - Tags unique to LLM-Agent-Paper-List: evaluation, large-language-models, nlp, reinforcement learning. - Also covers Evaluation & Observability. - When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through reinforcement learning ## When NOT to use RD-Agent - When the need arises to work in an environment where Python cannot be used or there is a requirement for another programming language framework that complements existing infrastructure better. - If your development team lacks expertise with Docker and is not willing or able to adopt it, as most scenarios within RD-Agent require a solid Docker setup. ## When NOT to use LLM-Agent-Paper-List - If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents - When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications - If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper ## Common questions ### What is the difference between RD-Agent and LLM-Agent-Paper-List? RD-Agent: Automating high-value R&D processes through AI-driven data science and model development.. 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 RD-Agent over LLM-Agent-Paper-List? Choose RD-Agent over LLM-Agent-Paper-List when Pricing: RD-Agent operates under the MIT license allowing free use of the tool. Its framework for R&D automation can be expanded with premium services for enterprise-level support and integration if needed.; Requirements: Requires Docker; Ensure Docker is installed beforehand and accessible without `sudo` by the current user for RD-Agent operation.; Supports Python installations via PyPI or development setup from source, which also requires installation of dependencies as per the documentation.; Tags unique to RD-Agent: ai, automation, data-mining, data-science; Also covers Data & Retrieval; When you require automation in high-value R&D tasks that revolve around data science and model development within the AI domain. ### When should I choose LLM-Agent-Paper-List over RD-Agent? Choose LLM-Agent-Paper-List over RD-Agent when Tags unique to LLM-Agent-Paper-List: evaluation, large-language-models, nlp, reinforcement learning; Also covers Evaluation & Observability; When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through reinforcement learning. ### When should I avoid RD-Agent? When the need arises to work in an environment where Python cannot be used or there is a requirement for another programming language framework that complements existing infrastructure better. If your development team lacks expertise with Docker and is not willing or able to adopt it, as most scenarios within RD-Agent require a solid Docker setup. ### When should I avoid LLM-Agent-Paper-List? If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper ### Is RD-Agent or LLM-Agent-Paper-List more popular on GitHub? RD-Agent has more GitHub stars (14,275 vs 8,222). Stars measure visibility, not whether either tool fits your constraints. ### Are RD-Agent and LLM-Agent-Paper-List open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to RD-Agent or LLM-Agent-Paper-List? GraphCanon lists graph-backed alternatives at [RD-Agent alternatives](/tools/microsoft-rd-agent/alternatives) and [LLM-Agent-Paper-List alternatives](/tools/woooodyy-llm-agent-paper-list/alternatives) ([RD-Agent markdown twin](/tools/microsoft-rd-agent/alternatives.md), [LLM-Agent-Paper-List markdown twin](/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 [this comparison](/compare/microsoft-rd-agent-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, RD-Agent or LLM-Agent-Paper-List? RD-Agent: Active. LLM-Agent-Paper-List: 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 RD-Agent and LLM-Agent-Paper-List? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RD-Agent trust report](/tools/microsoft-rd-agent/trust); [LLM-Agent-Paper-List trust report](/tools/woooodyy-llm-agent-paper-list/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=microsoft-rd-agent`](/api/graphcanon/graph?tool=microsoft-rd-agent) - 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/_