--- title: "RD-Agent vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/microsoft-rd-agent-vs-shubhamsaboo-awesome-llm-apps" tools: ["microsoft-rd-agent", "shubhamsaboo-awesome-llm-apps"] --- # RD-Agent vs awesome-llm-apps *GraphCanon updated Aug 19, 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 awesome-llm-apps if awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. [RD-Agent](https://rdagent.azurewebsites.net/) reports 14k GitHub stars, 1.8k forks, and 198 open issues, last pushed Aug 4, 2026. [awesome-llm-apps](https://www.theunwindai.com) has 131k stars, 19k forks, and 13 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [RD-Agent's repository](https://github.com/microsoft/RD-Agent) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [RD-Agent](/tools/microsoft-rd-agent.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | Automating high-value R&D processes through AI-driven data science and model development. | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | | Stars | 14,275 | 131,230 | | Forks | 1,834 | 19,346 | | Open issues | 198 | 13 | | Language | Python | 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. | awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. | | Persona | - | - | | Runtime | - | - | | License | MIT | The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. | | Categories | AI Agents, Data & Retrieval, Model Training | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [RD-Agent](/tools/microsoft-rd-agent.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 14d | 4d | | Open issues (now) | 198 | 13 | | Stars delta | +332 (30d) | +14k (30d) | | Open issues delta | +5 (30d) | +6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/microsoft-rd-agent/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | ## Shared compatibility - **Python**: [RD-Agent](/tools/microsoft-rd-agent.md) - Python runtime; [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - Python runtime ## 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: awesome-llm-apps - **Pricing:** freemium - Free with open-source licensing, but commercial exploitation is allowed. - **Adopt for:** awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. - **License detail:** The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. ## Choose when ### Choose RD-Agent if… - License: RD-Agent is MIT, awesome-llm-apps is Apache-2.0. - 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: agent, ai, automation, data-mining. - Also covers Model Training. - When you require automation in high-value R&D tasks that revolve around data science and model development within the AI domain. ### Choose awesome-llm-apps if… - License: awesome-llm-apps is Apache-2.0, RD-Agent is MIT. - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: agents, applications, customizable, deployable. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ## 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 awesome-llm-apps - If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. - When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ## Common questions ### What is the difference between RD-Agent and awesome-llm-apps? RD-Agent: Automating high-value R&D processes through AI-driven data science and model development.. awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. See the comparison table for live GitHub stats and shared categories. ### When should I choose RD-Agent over awesome-llm-apps? Choose RD-Agent over awesome-llm-apps when License: RD-Agent is MIT, awesome-llm-apps is Apache-2.0; 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: agent, ai, automation, data-mining; Also covers Model Training; 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 awesome-llm-apps over RD-Agent? Choose awesome-llm-apps over RD-Agent when License: awesome-llm-apps is Apache-2.0, RD-Agent is MIT; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: agents, applications, customizable, deployable; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### 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 awesome-llm-apps? If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ### Is RD-Agent or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (131,230 vs 14,275). Stars measure visibility, not whether either tool fits your constraints. ### Are RD-Agent and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (RD-Agent: MIT, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to RD-Agent or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [RD-Agent alternatives](/tools/microsoft-rd-agent/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([RD-Agent markdown twin](/tools/microsoft-rd-agent/alternatives.md), [awesome-llm-apps markdown twin](/tools/shubhamsaboo-awesome-llm-apps/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-shubhamsaboo-awesome-llm-apps.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, RD-Agent or awesome-llm-apps? RD-Agent: Active. awesome-llm-apps: Very active. 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 awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [RD-Agent trust report](/tools/microsoft-rd-agent/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/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/_