Comparison
hello-agents vs FedML
Verdict
Pick hello-agents when license: hello-agents is Other, FedML is Apache-2.0; pick FedML when license: FedML is Apache-2.0, hello-agents is Other.
Markdown twin · hello-agents alternatives · FedML alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | hello-agents | FedML |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1d · github_public_v1 | Slowing (256d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | 88 low (88 low) As of today · osv@v1 |
Tagline
- hello-agents
- Course on building intelligent agents from scratch
- FedML
- FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on a
Stars
- hello-agents
- 65k
- FedML
- 4.1k
Forks
- hello-agents
- 8.1k
- FedML
- 765
Open issues
- hello-agents
- 144
- FedML
- 147
Language
- hello-agents
- Python
- FedML
- Python
Adopt for
- hello-agents
- hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods.
- FedML
- -
Persona
- hello-agents
- -
- FedML
- -
Runtime
- hello-agents
- -
- FedML
- -
License
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
- FedML
- Apache-2.0
Last pushed
- hello-agents
- Jul 10, 2026
- FedML
- Oct 28, 2025
Categories
- hello-agents
- AI Agents, LLM Frameworks
- FedML
- AI Agents, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- hello-agents
- Very active (96%)
- FedML
- Slowing (36%)
Days since push
- hello-agents
- 0d
- FedML
- 256d
Open issues (now)
- hello-agents
- 144
- FedML
- 147
Security scan
- hello-agents
- No lockfile
- FedML
- 88 low (88 low)
Full report
- hello-agents
- Trust report
- FedML
- Trust report
Choose hello-agents if…
- License: hello-agents is Other, FedML is Apache-2.0.
- 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 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.
Choose FedML if…
- License: FedML is Apache-2.0, hello-agents is Other.
- Tags unique to FedML: ai-agent, deep-learning, distributed-training, edge-ai.
- Also covers Vector Databases.
When NOT to use FedML
- Last GitHub push was 256 days ago (slowing maintenance, Oct 28, 2025). Validate activity before betting a new project on FedML.
- 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.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (FedML-AI/FedML) · observed Jul 11, 2026
- GitHub forks (FedML-AI/FedML) · observed Jul 11, 2026
- Last push (FedML-AI/FedML) · observed Oct 28, 2025
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: hello-agents 65k · FedML 4.1k (synced Jul 11, 2026).
Common questions
- What is the difference between hello-agents and FedML?
- hello-agents: Course on building intelligent agents from scratch. FedML: FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on a. See the comparison table for live GitHub stats and shared categories.
- When should I choose hello-agents over FedML?
- Choose hello-agents over FedML when License: hello-agents is Other, FedML is Apache-2.0; 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 FedML over hello-agents?
- Choose FedML over hello-agents when License: FedML is Apache-2.0, hello-agents is Other; Tags unique to FedML: ai-agent, deep-learning, distributed-training, edge-ai; Also covers Vector Databases.
- 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 FedML?
- Last GitHub push was 256 days ago (slowing maintenance, Oct 28, 2025). Validate activity before betting a new project on FedML. 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. 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 FedML more popular on GitHub?
- hello-agents has more GitHub stars (65,432 vs 4,051). Stars measure visibility, not whether either tool fits your constraints.
- Are hello-agents and FedML open source?
- Yes - both are open-source projects on GitHub (hello-agents: Other, FedML: Apache-2.0).
- Where can I find alternatives to hello-agents or FedML?
- GraphCanon lists graph-backed alternatives at hello-agents alternatives and FedML alternatives (hello-agents markdown twin, FedML 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, hello-agents or FedML?
- hello-agents: Very active. FedML: Slowing. 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 FedML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hello-agents trust report; FedML trust report.