--- title: "hello-agents vs NexusRAG" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-ledat98-nexusrag" tools: ["datawhalechina-hello-agents", "ledat98-nexusrag"] --- # hello-agents vs NexusRAG *GraphCanon updated Jul 11, 2026* ## Verdict Pick hello-agents when requirements: Min 4 GB RAM; Python knowledge assumed; pick NexusRAG when tags unique to NexusRAG: docling, gemini, chromadb, fastapi. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [NexusRAG](https://github.com/LeDat98/NexusRAG) has 327 stars, 66 forks, and 1 open issues, last pushed Apr 20, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [NexusRAG's repository](https://github.com/LeDat98/NexusRAG). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [NexusRAG](/tools/ledat98-nexusrag.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat | | Stars | 65,432 | 327 | | Forks | 8,109 | 66 | | Open issues | 144 | 1 | | 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. | - | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | - | | Categories | LLM Frameworks, AI Agents | LLM Frameworks, AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [NexusRAG](/tools/ledat98-nexusrag.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 81d | | Open issues (now) | 144 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/ledat98-nexusrag/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… - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: llm, rag, tutorial, agent. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### Choose NexusRAG if… - Tags unique to NexusRAG: docling, gemini, chromadb, fastapi. - Also covers Vector Databases. - Leaner open-issue backlog (1). ## 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 NexusRAG - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between hello-agents and NexusRAG? hello-agents: Course on building intelligent agents from scratch. NexusRAG: Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over NexusRAG? Choose hello-agents over NexusRAG when Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: llm, rag, tutorial, agent; 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 NexusRAG over hello-agents? Choose NexusRAG over hello-agents when Tags unique to NexusRAG: docling, gemini, chromadb, fastapi; Also covers Vector Databases; Leaner open-issue backlog (1). ### 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 NexusRAG? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is hello-agents or NexusRAG more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 327). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and NexusRAG open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to hello-agents or NexusRAG? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [NexusRAG alternatives](/tools/ledat98-nexusrag/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [NexusRAG markdown twin](/tools/ledat98-nexusrag/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-ledat98-nexusrag.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or NexusRAG? hello-agents: Very active. NexusRAG: Steady. 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 NexusRAG? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [NexusRAG trust report](/tools/ledat98-nexusrag/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/_