--- title: "llm-python vs examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/onlyphantom-llm-python-vs-pinecone-io-examples" tools: ["onlyphantom-llm-python", "pinecone-io-examples"] --- # llm-python vs examples *GraphCanon updated Aug 21, 2026* ## 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. [llm-python](https://www.youtube.com/playlist?list=PLXsFtK46HZxUQERRbOmuGoqbMD-KWLkOS) reports 927 GitHub stars, 316 forks, and 0 open issues, last pushed Feb 20, 2026. [examples](https://docs.pinecone.io) has 3.0k stars, 1.1k forks, and 61 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [llm-python's repository](https://github.com/onlyphantom/llm-python) and [examples's repository](https://github.com/pinecone-io/examples). | | [llm-python](/tools/onlyphantom-llm-python.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Tagline | LLM tutorials and scripts covering langchain, openai, llamaindex, GPT, ChromaDB, Pinecone | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | | Stars | 927 | 3,036 | | Forks | 316 | 1,073 | | Open issues | 0 | 61 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | Jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone. | Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-python](/tools/onlyphantom-llm-python.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 181d | 0d | | Open issues (now) | 0 | 61 | | Stars delta | +1 (30d) | +8 (30d) | | Open issues delta | 0 (30d) | -3 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/onlyphantom-llm-python/trust.md) | [trust report](/tools/pinecone-io-examples/trust.md) | **Typed relationship:** llm-python _(integrates with)_ examples 'llm-python' contains examples and tutorials that specifically make use of Pinecone's vector databases for LLM applications, indicating an integration relationship. ## Decision facts: llm-python - **Adopt for:** Jupyter Notebook tutorials and scripts for working with LangChain, OpenAI API, llamaindex, GPT models, ChromaDB, and Pinecone. ## Decision facts: examples - **Adopt for:** Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/onlyphantom-llm-python/alternatives) and [examples alternatives](/tools/pinecone-io-examples/alternatives) ([llm-python markdown twin](/tools/onlyphantom-llm-python/alternatives.md), [examples markdown twin](/tools/pinecone-io-examples/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/onlyphantom-llm-python-vs-pinecone-io-examples.md) 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](/tools/onlyphantom-llm-python/trust); [examples trust report](/tools/pinecone-io-examples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=onlyphantom-llm-python`](/api/graphcanon/graph?tool=onlyphantom-llm-python) - 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/_