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Comparison

llm-python vs examples

Verdict

Pick llm-python if jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone; pick examples if examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance.

Markdown twin · llm-python alternatives · examples alternatives

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llm-python logo

llm-python

onlyphantom/llm-python

★ 927pushed Feb 20, 2026
vs
examples logo

examples

pinecone-io/examples

★ 3.0kpushed Aug 14, 2026

Trust & integrity

Signalllm-pythonexamples
Maintenance
Slowing (181d since push)
As of today · github_public_v1
Very active (0d since push)
As of 6d · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Organization account
As of 6d · github_public_v1
OSV dependency advisories
Published findings
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
llm-python trust reportexamples trust reportTrust methodology

Tagline

llm-python
LLM tutorials and scripts covering langchain, openai, llamaindex, GPT, ChromaDB, Pinecone
examples
Jupyter Notebooks to help you get hands-on with Pinecone vector databases

Stars

llm-python
927
examples
3.0k

Forks

llm-python
316
examples
1.1k

Open issues

llm-python
0
examples
61

Language

llm-python
Jupyter Notebook
examples
Jupyter Notebook

Adopt for

llm-python
Jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone.
examples
Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance.

Persona

llm-python
-
examples
-

Runtime

llm-python
-
examples
-

License

llm-python
MIT
examples
MIT

Last pushed

llm-python
Feb 20, 2026
examples
Aug 14, 2026

Categories

llm-python
LLM Frameworks, Vector Databases
examples
Data & Retrieval, Vector Databases

Trust and health

Maintenance

llm-python
Slowing (36%)
examples
Very active (96%)

Days since push

llm-python
181d
examples
0d

Open issues (now)

llm-python
0
examples
61

Stars delta

llm-python
+1 (30d)
examples
+8 (30d)

Open issues delta

llm-python
0 (30d)
examples
-3 (30d)

Owner type

llm-python
User
examples
Organization

OSV dependency advisories

llm-python
Published findings
examples
No lockfile (source not queried)

Full report

llm-python
Trust report
examples
Trust report

Typed relationship

llm-python integrates examples'llm-python' contains examples and tutorials that specifically make use of Pinecone's vector databases for LLM applications, indicating an integration relationship.

Choose llm-python if…

  • 'llm-python' contains examples and tutorials that specifically make use of Pinecone's vector databases for LLM applications, indicating an integration relationship.
  • Tags unique to llm-python: chromadb, gpt-3, langchain, llama-index.
  • Also covers LLM Frameworks.
  • When you want comprehensive Jupyter-based tutorials on integrating multiple LLM tools including OpenAI and LangChain.

When NOT to use llm-python

  • Avoid if you require a purely code-library without tutorial-like content in Jupyter Notebooks.
  • Not suitable if your project strictly demands proprietary or closed-access LLM tools not covered in the repo, like those beyond OpenAI and LangChain.
GitHubDetails

Choose examples if…

  • 'llm-python' contains examples and tutorials that specifically make use of Pinecone's vector databases for LLM applications, indicating an integration relationship.
  • Tags unique to examples: ai, jupyter-notebook, llm, python.
  • Also covers Data & Retrieval.
  • When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks.

When NOT to use examples

  • Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone.
  • Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab.
GitHubDetails

Explore

llm-python trust report →examples trust report →LLM Frameworks category →Vector Databases category →Data & Retrieval category →All comparisonsStack workflowsTrending tools

Sources

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

GitHub stars on cards: llm-python 927 · examples 3.0k (synced Aug 21, 2026).

Common questions

What is the difference between llm-python and examples?
llm-python: LLM tutorials and scripts covering langchain, openai, llamaindex, GPT, ChromaDB, Pinecone. examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. See the comparison table for live GitHub stats and shared categories.
When should I choose llm-python over examples?
Choose llm-python over examples when 'llm-python' contains examples and tutorials that specifically make use of Pinecone's vector databases for LLM applications, indicating an integration relationship; Tags unique to llm-python: chromadb, gpt-3, langchain, llama-index; Also covers LLM Frameworks; When you want comprehensive Jupyter-based tutorials on integrating multiple LLM tools including OpenAI and LangChain.
When should I choose examples over llm-python?
Choose examples over llm-python when 'llm-python' contains examples and tutorials that specifically make use of Pinecone's vector databases for LLM applications, indicating an integration relationship; Tags unique to examples: ai, jupyter-notebook, llm, python; Also covers Data & Retrieval; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks.
When should I avoid llm-python?
Avoid if you require a purely code-library without tutorial-like content in Jupyter Notebooks. Not suitable if your project strictly demands proprietary or closed-access LLM tools not covered in the repo, like those beyond OpenAI and LangChain.
When should I avoid examples?
Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone. Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab.
Is llm-python or examples more popular on GitHub?
examples has more GitHub stars (3,036 vs 927). Stars measure visibility, not whether either tool fits your constraints.
Are llm-python and examples open source?
Yes - both are open-source projects on GitHub (llm-python: MIT, examples: MIT).
Where can I find alternatives to llm-python or examples?
GraphCanon lists graph-backed alternatives at llm-python alternatives and examples alternatives (llm-python markdown twin, examples 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, llm-python or examples?
llm-python: Slowing. examples: 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 llm-python and examples?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llm-python trust report; examples trust report.

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