Comparison
RAGLight vs hello-agents
Verdict
Pick RAGLight when license: RAGLight is MIT, hello-agents is Other; pick hello-agents when license: hello-agents is Other, RAGLight is MIT.
Markdown twin · RAGLight alternatives · hello-agents alternatives
GraphCanon updated today
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Trust & integrity
| Signal | RAGLight | hello-agents |
|---|---|---|
| Maintenance | Active (15d since push) As of today · github_public_v1 | Very active (0d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No MCP manifest As of today · mcp_manifest | No lockfile As of today · none |
Tagline
- RAGLight
- RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connec
- hello-agents
- Course on building intelligent agents from scratch
Stars
- RAGLight
- 668
- hello-agents
- 65k
Forks
- RAGLight
- 101
- hello-agents
- 8.1k
Open issues
- RAGLight
- 12
- hello-agents
- 144
Language
- RAGLight
- Python
- hello-agents
- Python
Adopt for
- RAGLight
- -
- hello-agents
- hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods.
Persona
- RAGLight
- -
- hello-agents
- -
Runtime
- RAGLight
- -
- hello-agents
- -
License
- RAGLight
- MIT
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
Last pushed
- RAGLight
- Jun 25, 2026
- hello-agents
- Jul 10, 2026
Categories
- RAGLight
- LLM Frameworks, Vector Databases, AI Agents
- hello-agents
- LLM Frameworks, AI Agents
Trust and health
Maintenance
- RAGLight
- Active (82%)
- hello-agents
- Very active (96%)
Days since push
- RAGLight
- 15d
- hello-agents
- 0d
Open issues (now)
- RAGLight
- 12
- hello-agents
- 144
Owner type
- RAGLight
- User
- hello-agents
- Organization
Security scan
- RAGLight
- No MCP manifest
- hello-agents
- No lockfile
Full report
- RAGLight
- Trust report
- hello-agents
- Trust report
Choose RAGLight if…
- License: RAGLight is MIT, hello-agents is Other.
- Tags unique to RAGLight: data-science, artificial-intelligence, agentic-workflow, agentic-ai.
- Also covers Vector Databases.
When NOT to use RAGLight
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
Choose hello-agents if…
- License: hello-agents is Other, RAGLight is MIT.
- 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Bessouat40/RAGLight) · observed Jul 11, 2026
- GitHub forks (Bessouat40/RAGLight) · observed Jul 11, 2026
- Last push (Bessouat40/RAGLight) · observed Jun 25, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (datawhalechina/hello-agents) · observed Jul 11, 2026
- GitHub forks (datawhalechina/hello-agents) · observed Jul 11, 2026
- Last push (datawhalechina/hello-agents) · observed Jul 10, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: RAGLight 668 · hello-agents 65k (synced Jul 11, 2026).
Common questions
- What is the difference between RAGLight and hello-agents?
- RAGLight: RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connec. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories.
- When should I choose RAGLight over hello-agents?
- Choose RAGLight over hello-agents when License: RAGLight is MIT, hello-agents is Other; Tags unique to RAGLight: data-science, artificial-intelligence, agentic-workflow, agentic-ai; Also covers Vector Databases.
- When should I choose hello-agents over RAGLight?
- Choose hello-agents over RAGLight when License: hello-agents is Other, RAGLight is MIT; 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 avoid RAGLight?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- 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.
- Is RAGLight or hello-agents more popular on GitHub?
- hello-agents has more GitHub stars (65,432 vs 668). Stars measure visibility, not whether either tool fits your constraints.
- Are RAGLight and hello-agents open source?
- Yes - both are open-source projects on GitHub (RAGLight: MIT, hello-agents: Other).
- Where can I find alternatives to RAGLight or hello-agents?
- GraphCanon lists graph-backed alternatives at RAGLight alternatives and hello-agents alternatives (RAGLight markdown twin, hello-agents markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, RAGLight or hello-agents?
- RAGLight: 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 RAGLight and hello-agents?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: RAGLight trust report; hello-agents trust report.