--- title: "hello-agents vs TradingAgents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-tauricresearch-tradingagents" tools: ["datawhalechina-hello-agents", "tauricresearch-tradingagents"] --- # hello-agents vs TradingAgents *GraphCanon updated Aug 16, 2026* ## Verdict Pick hello-agents if hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods; pick TradingAgents if use TradingAgents for projects requiring a sophisticated framework to develop and deploy AI agents in financial market transactions leveraging Large Language Models. Avoid it if you need simpler tools or frameworks thatだ. [hello-agents](https://hello-agents.datawhale.cc) reports 73k GitHub stars, 9.1k forks, and 155 open issues, last pushed Aug 14, 2026. [TradingAgents](https://arxiv.org/pdf/2412.20138) has 98k stars, 19k forks, and 364 open issues, last pushed Jul 18, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [TradingAgents's repository](https://github.com/TauricResearch/TradingAgents). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [TradingAgents](/tools/tauricresearch-tradingagents.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Multi-Agents LLM Financial Trading Framework | | Stars | 73,126 | 98,335 | | Forks | 9,108 | 18,953 | | Open issues | 155 | 364 | | Language | Python | Python | | Adopt for | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | Use TradingAgents for projects requiring a sophisticated framework to develop and deploy AI agents in financial market transactions leveraging Large Language Models. Avoid it if you need simpler tools or frameworks thatだ | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [TradingAgents](/tools/tauricresearch-tradingagents.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 28d | | Open issues (now) | 155 | 364 | | Stars delta | +6.4k (30d) | +5.0k (30d) | | Open issues delta | +8 (30d) | +62 (30d) | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/tauricresearch-tradingagents/trust.md) | ## Decision facts: hello-agents - **Requirements:** Min 4 GB RAM; Python knowledge assumed - **Adopt for:** hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. - **License detail:** hello-agents is covered under an unconventional license which may require further review before usage. ## Decision facts: TradingAgents - **Requirements:** Min 8 GB RAM; Python environment setup is required.; Deep understanding of finance and LLMs will enhance the utilization of this framework. - **Adopt for:** Use TradingAgents for projects requiring a sophisticated framework to develop and deploy AI agents in financial market transactions leveraging Large Language Models. Avoid it if you need simpler tools or frameworks thatだ ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, TradingAgents is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: rag, tutorial. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### Choose TradingAgents if… - License: TradingAgents is Apache-2.0, hello-agents is Other. - Requirements: Min 8 GB RAM; Python environment setup is required.; Deep understanding of finance and LLMs will enhance the utilization of this framework.. - Tags unique to TradingAgents: finance, multiagent, trading. - TradingAgents ships Docker support for self-hosted deployment. - When your project involves complex multi-agent interactions specifically in the finance domain, utilizing LLMs to manage trading strategies. ## When NOT to use hello-agents - Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. - Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ## When NOT to use TradingAgents - If simplicity and ease of deployment are prioritized over advanced AI capabilities; TradingAgents' complexity might introduce unnecessary overhead. - When the focus is on non-financial applications or when LLM integration isn't necessary, as this framework specializes in financial market trading with a multi-agent approach. ## Common questions ### What is the difference between hello-agents and TradingAgents? hello-agents: Course on building intelligent agents from scratch. TradingAgents: Multi-Agents LLM Financial Trading Framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over TradingAgents? Choose hello-agents over TradingAgents when License: hello-agents is Other, TradingAgents is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: rag, tutorial; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### When should I choose TradingAgents over hello-agents? Choose TradingAgents over hello-agents when License: TradingAgents is Apache-2.0, hello-agents is Other; Requirements: Min 8 GB RAM; Python environment setup is required.; Deep understanding of finance and LLMs will enhance the utilization of this framework.; Tags unique to TradingAgents: finance, multiagent, trading; TradingAgents ships Docker support for self-hosted deployment; When your project involves complex multi-agent interactions specifically in the finance domain, utilizing LLMs to manage trading strategies. ### When should I avoid hello-agents? Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ### When should I avoid TradingAgents? If simplicity and ease of deployment are prioritized over advanced AI capabilities; TradingAgents' complexity might introduce unnecessary overhead. When the focus is on non-financial applications or when LLM integration isn't necessary, as this framework specializes in financial market trading with a multi-agent approach. ### Is hello-agents or TradingAgents more popular on GitHub? TradingAgents has more GitHub stars (98,335 vs 73,126). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and TradingAgents open source? Yes - both are open-source projects on GitHub (hello-agents: Other, TradingAgents: Apache-2.0). ### Where can I find alternatives to hello-agents or TradingAgents? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [TradingAgents alternatives](/tools/tauricresearch-tradingagents/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [TradingAgents markdown twin](/tools/tauricresearch-tradingagents/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-tauricresearch-tradingagents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or TradingAgents? hello-agents: Very active. TradingAgents: 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 hello-agents and TradingAgents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [TradingAgents trust report](/tools/tauricresearch-tradingagents/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/_