Home/Compare/llama-hub vs awesome

Comparison

llama-hub vs awesome

Verdict

Pick llama-hub when license: llama-hub is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, llama-hub is MIT.

Markdown twin · llama-hub alternatives · awesome alternatives

GraphCanon updated today

llama-hub logo

llama-hub

run-llama/llama-hub

3.5kpushed Mar 1, 2024
vs
awesome logo

awesome

sindresorhus/awesome

484kpushed Jun 30, 2026

Trust & integrity

Signalllama-hubawesome
Maintenance
Archived (861d since push)
As of today · github_public_v1
Active (11d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Personal account
As of today · github_public_v1
Security (OSV)
121 low (121 low)
As of today · osv@v1
No lockfile
As of today · none

Tagline

llama-hub
A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain
awesome
😎 Curated list of awesome topics including hardware resources

Stars

llama-hub
3.5k
awesome
484k

Forks

llama-hub
719
awesome
36k

Open issues

llama-hub
96
awesome
92

Language

llama-hub
Jupyter Notebook
awesome
-

Adopt for

llama-hub
-
awesome
-

Persona

llama-hub
-
awesome
-

Runtime

llama-hub
-
awesome
-

License

llama-hub
MIT
awesome
CC0-1.0

Last pushed

llama-hub
Mar 1, 2024
awesome
Jun 30, 2026

Categories

llama-hub
Data & Retrieval, LLM Frameworks, Evaluation & Observability
awesome
LLM Frameworks

Trust and health

Maintenance

llama-hub
Archived (8%)
awesome
Active (82%)

Days since push

llama-hub
861d
awesome
11d

Archived on GitHub

llama-hub
Yes
awesome
No

Open issues (now)

llama-hub
96
awesome
92

Owner type

llama-hub
Organization
awesome
User

Security scan

llama-hub
121 low (121 low)
awesome
No lockfile

Full report

llama-hub
Trust report

Choose llama-hub if…

  • License: llama-hub is MIT, awesome is CC0-1.0.
  • Tags unique to llama-hub: jupyter notebook.
  • Also covers Data & Retrieval, Evaluation & Observability.

When NOT to use llama-hub

  • llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency.
  • Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
  • Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.

Choose awesome if…

  • License: awesome is CC0-1.0, llama-hub is MIT.
  • Tags unique to awesome: resources, awesome-list.
  • More GitHub stars (484k vs 3.5k) - visibility, not fit.

When NOT to use awesome

  • LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: llama-hub 3.5k · awesome 484k (synced Jul 11, 2026).

Common questions

What is the difference between llama-hub and awesome?
llama-hub: A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
When should I choose llama-hub over awesome?
Choose llama-hub over awesome when License: llama-hub is MIT, awesome is CC0-1.0; Tags unique to llama-hub: jupyter notebook; Also covers Data & Retrieval, Evaluation & Observability.
When should I choose awesome over llama-hub?
Choose awesome over llama-hub when License: awesome is CC0-1.0, llama-hub is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 3.5k) - visibility, not fit.
When should I avoid llama-hub?
llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
When should I avoid awesome?
LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Is llama-hub or awesome more popular on GitHub?
awesome has more GitHub stars (484,026 vs 3,473). Stars measure visibility, not whether either tool fits your constraints.
Are llama-hub and awesome open source?
Yes - both are open-source projects on GitHub (llama-hub: MIT, awesome: CC0-1.0).
Where can I find alternatives to llama-hub or awesome?
GraphCanon lists graph-backed alternatives at llama-hub alternatives and awesome alternatives (llama-hub markdown twin, awesome 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, llama-hub or awesome?
llama-hub: Archived. awesome: 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 llama-hub and awesome?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llama-hub trust report; awesome trust report.