--- title: "hello-agents vs langgraph4j" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-langgraph4j-langgraph4j" tools: ["datawhalechina-hello-agents", "langgraph4j-langgraph4j"] --- # hello-agents vs langgraph4j *GraphCanon updated Jul 15, 2026* ## Verdict Pick hello-agents when hello-agents is primarily Python; langgraph4j is Java; pick langgraph4j when langgraph4j is primarily Java; hello-agents is Python. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [langgraph4j](https://langgraph4j.github.io/langgraph4j/) has 1.8k stars, 244 forks, and 22 open issues, last pushed Jul 14, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [langgraph4j's repository](https://github.com/langgraph4j/langgraph4j). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [langgraph4j](/tools/langgraph4j-langgraph4j.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | 🚀 LangGraph for Java. A library for develop AI Agentic Architectures in the Java ecosystem. Designed to work seamlessly with both Langchain4j and Spring AI. | | Stars | 65,432 | 1,816 | | Forks | 8,109 | 244 | | Open issues | 144 | 22 | | Language | Python | Java | | Adopt for | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | - | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | MIT | | 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) | [langgraph4j](/tools/langgraph4j-langgraph4j.md) | | --- | --- | --- | | Open issues (now) | 144 | 22 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/langgraph4j-langgraph4j/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. ## Choose when ### Choose hello-agents if… - hello-agents is primarily Python; langgraph4j is Java. - License: hello-agents is Other, langgraph4j is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, 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 langgraph4j if… - langgraph4j is primarily Java; hello-agents is Python. - License: langgraph4j is MIT, hello-agents is Other. - Tags unique to langgraph4j: agents, ai, java, langchain4j. ## 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 langgraph4j - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between hello-agents and langgraph4j? hello-agents: Course on building intelligent agents from scratch. langgraph4j: 🚀 LangGraph for Java. A library for develop AI Agentic Architectures in the Java ecosystem. Designed to work seamlessly with both Langchain4j and Spring AI.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over langgraph4j? Choose hello-agents over langgraph4j when hello-agents is primarily Python; langgraph4j is Java; License: hello-agents is Other, langgraph4j is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, 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 langgraph4j over hello-agents? Choose langgraph4j over hello-agents when langgraph4j is primarily Java; hello-agents is Python; License: langgraph4j is MIT, hello-agents is Other; Tags unique to langgraph4j: agents, ai, java, langchain4j. ### 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 langgraph4j? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is hello-agents or langgraph4j more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 1,816). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and langgraph4j open source? Yes - both are open-source projects on GitHub (hello-agents: Other, langgraph4j: MIT). ### Where can I find alternatives to hello-agents or langgraph4j? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [langgraph4j alternatives](/tools/langgraph4j-langgraph4j/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [langgraph4j markdown twin](/tools/langgraph4j-langgraph4j/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-langgraph4j-langgraph4j.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or langgraph4j? hello-agents: Very active. langgraph4j: 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 hello-agents and langgraph4j? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [langgraph4j trust report](/tools/langgraph4j-langgraph4j/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/_