--- title: "hello-agents vs RasaGPT" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-paulpierre-rasagpt" tools: ["datawhalechina-hello-agents", "paulpierre-rasagpt"] --- # hello-agents vs RasaGPT *GraphCanon updated Sep 20, 2026* ## Verdict Pick hello-agents if hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python; pick RasaGPT if rasaGPT is a pioneering headless chatbot platform that merges Rasa with technologies like FastAPI, Langchain, and LlamaIndex. [hello-agents](https://hello-agents.datawhale.cc) reports 80k GitHub stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. [RasaGPT](https://rasagpt.dev) has 2.5k stars, 250 forks, and 57 open issues, last pushed Nov 12, 2025. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [RasaGPT's repository](https://github.com/paulpierre/RasaGPT). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [RasaGPT](/tools/paulpierre-rasagpt.md) | | --- | --- | --- | | Tagline | A comprehensive guide to building intelligent agents from scratch | First headless LLM chatbot platform built on top of Rasa and Langchain | | Stars | 79,727 | 2,463 | | Forks | 9,907 | 250 | | Open issues | 82 | 57 | | Language | Python | Python | | Adopt for | Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. | RasaGPT is a pioneering headless chatbot platform that merges Rasa with technologies like FastAPI, Langchain, and LlamaIndex. | | Persona | - | - | | Runtime | - | - | | License | The license is not specified as open source, but the content is free to read and learn from. | MIT | | Categories | AI Agents, Model Training | AI Agents, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [RasaGPT](/tools/paulpierre-rasagpt.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 299d | | Open issues (now) | 82 | 57 | | Stars delta | +6.6k (30d) | -1 (30d) | | Open issues delta | -73 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/paulpierre-rasagpt/trust.md) | ## Decision facts: hello-agents - **Pricing:** freemium - The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository. - **Requirements:** Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework. - **Adopt for:** Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. - **License detail:** The license is not specified as open source, but the content is free to read and learn from. ## Decision facts: RasaGPT - **Adopt for:** RasaGPT is a pioneering headless chatbot platform that merges Rasa with technologies like FastAPI, Langchain, and LlamaIndex. ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, RasaGPT is MIT. - Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.. - Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.. - Tags unique to hello-agents: agent, llm, rag, tutorial. - When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### Choose RasaGPT if… - License: RasaGPT is MIT, hello-agents is Other. - Tags unique to RasaGPT: ai, chatbot, fastapi, langchain. - Also covers Inference & Serving. - RasaGPT ships Docker support for self-hosted deployment. - When you need a robust framework for developing conversational AI solutions and are already familiar with Rasa's ecosystem. ## When NOT to use hello-agents - If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. - If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ## When NOT to use RasaGPT - For users preferring platforms that require less technical setup, particularly without needing Docker or specific Python versions. - Avoid if development is focused on non-headless chatbot applications that don't leverage the Rasa framework's architecture. ## Common questions ### What is the difference between hello-agents and RasaGPT? hello-agents: A comprehensive guide to building intelligent agents from scratch. RasaGPT: First headless LLM chatbot platform built on top of Rasa and Langchain. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over RasaGPT? Choose hello-agents over RasaGPT when License: hello-agents is Other, RasaGPT is MIT; Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.; Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.; Tags unique to hello-agents: agent, llm, rag, tutorial; When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### When should I choose RasaGPT over hello-agents? Choose RasaGPT over hello-agents when License: RasaGPT is MIT, hello-agents is Other; Tags unique to RasaGPT: ai, chatbot, fastapi, langchain; Also covers Inference & Serving; RasaGPT ships Docker support for self-hosted deployment; When you need a robust framework for developing conversational AI solutions and are already familiar with Rasa's ecosystem. ### When should I avoid hello-agents? If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ### When should I avoid RasaGPT? For users preferring platforms that require less technical setup, particularly without needing Docker or specific Python versions. Avoid if development is focused on non-headless chatbot applications that don't leverage the Rasa framework's architecture. ### Is hello-agents or RasaGPT more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 2,463). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and RasaGPT open source? Yes - both are open-source projects on GitHub (hello-agents: Other, RasaGPT: MIT). ### Where can I find alternatives to hello-agents or RasaGPT? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [RasaGPT alternatives](/tools/paulpierre-rasagpt/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [RasaGPT markdown twin](/tools/paulpierre-rasagpt/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/datawhalechina-hello-agents-vs-paulpierre-rasagpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or RasaGPT? hello-agents: Very active. RasaGPT: 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 hello-agents and RasaGPT? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [RasaGPT trust report](/tools/paulpierre-rasagpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - 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/_