--- title: "oasis vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/camel-ai-oasis-vs-datawhalechina-hello-agents" tools: ["camel-ai-oasis", "datawhalechina-hello-agents"] --- # oasis vs hello-agents *GraphCanon updated Sep 20, 2026* ## Verdict Pick oasis if oASIS is tailored for simulating large-scale AI societies using deep learning, LLMs, and NLP; 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. [oasis](https://docs.oasis.camel-ai.org/) reports 5.0k GitHub stars, 618 forks, and 56 open issues, last pushed Aug 14, 2026. [hello-agents](https://hello-agents.datawhale.cc) has 80k stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [oasis's repository](https://github.com/camel-ai/oasis) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [oasis](/tools/camel-ai-oasis.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | OASIS: Open Agent Social Interaction Simulations with One Million Agents | A comprehensive guide to building intelligent agents from scratch | | Stars | 5,031 | 79,727 | | Forks | 618 | 9,907 | | Open issues | 56 | 82 | | Language | Python | Python | | Adopt for | OASIS is tailored for simulating large-scale AI societies using deep learning, LLMs, and NLP. | 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. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The license is not specified as open source, but the content is free to read and learn from. | | Categories | AI Agents, Model Training | AI Agents, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [oasis](/tools/camel-ai-oasis.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 56 | 82 | | Stars delta | +93 (30d) | +6.6k (30d) | | Open issues delta | -1 (30d) | -73 (30d) | | Full report | [trust report](/tools/camel-ai-oasis/trust.md) | [trust report](/tools/datawhalechina-hello-agents/trust.md) | ## Decision facts: oasis - **Adopt for:** OASIS is tailored for simulating large-scale AI societies using deep learning, LLMs, and NLP. ## 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. ## Choose when ### Choose oasis if… - License: oasis is Apache-2.0, hello-agents is Other. - Tags unique to oasis: agent-based-framework, agent-based-simulation, ai-societies, deep-learning. - When modeling complex interactions among millions of agents is necessary ### Choose hello-agents if… - License: hello-agents is Other, oasis is Apache-2.0. - 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 NOT to use oasis - If you require customization beyond what LLMs and NLP provide within the framework - In scenarios where real-time agent interaction response speed is critical, as simulation may introduce lag ## 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. ## Common questions ### What is the difference between oasis and hello-agents? oasis: OASIS: Open Agent Social Interaction Simulations with One Million Agents. hello-agents: A comprehensive guide to building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose oasis over hello-agents? Choose oasis over hello-agents when License: oasis is Apache-2.0, hello-agents is Other; Tags unique to oasis: agent-based-framework, agent-based-simulation, ai-societies, deep-learning; When modeling complex interactions among millions of agents is necessary. ### When should I choose hello-agents over oasis? Choose hello-agents over oasis when License: hello-agents is Other, oasis is Apache-2.0; 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 avoid oasis? If you require customization beyond what LLMs and NLP provide within the framework In scenarios where real-time agent interaction response speed is critical, as simulation may introduce lag ### 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. ### Is oasis or hello-agents more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 5,031). Stars measure visibility, not whether either tool fits your constraints. ### Are oasis and hello-agents open source? Yes - both are open-source projects on GitHub (oasis: Apache-2.0, hello-agents: Other). ### Where can I find alternatives to oasis or hello-agents? GraphCanon lists graph-backed alternatives at [oasis alternatives](/tools/camel-ai-oasis/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([oasis markdown twin](/tools/camel-ai-oasis/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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/camel-ai-oasis-vs-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, oasis or hello-agents? oasis: Very active. hello-agents: 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 oasis and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [oasis trust report](/tools/camel-ai-oasis/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=camel-ai-oasis`](/api/graphcanon/graph?tool=camel-ai-oasis) - 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/_