--- title: "langchain-tutorials vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gkamradt-langchain-tutorials-vs-pathwaycom-llm-app" tools: ["gkamradt-langchain-tutorials", "pathwaycom-llm-app"] --- # langchain-tutorials vs llm-app *GraphCanon updated Jul 11, 2026* ## Verdict Pick langchain-tutorials when tags unique to langchain-tutorials: jupyter notebook; pick llm-app when requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. [langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) reports 7.5k GitHub stars, 2.0k forks, and 15 open issues, last pushed Aug 5, 2024. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.4k forks, and 10 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [langchain-tutorials's repository](https://github.com/gkamradt/langchain-tutorials) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | Overview and tutorial of the LangChain Library | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | | Stars | 7,468 | 59,068 | | Forks | 2,018 | 1,432 | | Open issues | 15 | 10 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | - | llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz | | Persona | - | - | | Runtime | - | - | | License | - | MIT | | Categories | LLM Frameworks, Vector Databases, Developer Tools | LLM Frameworks, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langchain-tutorials](/tools/gkamradt-langchain-tutorials.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 705d | 5d | | Open issues (now) | 15 | 10 | | Owner type | User | Organization | | Security scan | 174 low (174 low) | No lockfile | | Full report | [trust report](/tools/gkamradt-langchain-tutorials/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## Decision facts: llm-app - **Requirements:** Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others. - **Adopt for:** llm-app offers pre-configured cloud deployment templates designed specifically for creating AI-driven applications such as chatbots and machine learning projects leveraging Hugging Face models. It supports direct integrz ## Choose when ### Choose langchain-tutorials if… - Tags unique to langchain-tutorials: jupyter notebook. - Also covers Developer Tools. ### Choose llm-app if… - Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.. - Tags unique to llm-app: vector-database, llm, hugging-face, retrieval-augmented-generation. - Also covers Data & Retrieval. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ## When NOT to use langchain-tutorials - Last GitHub push was 705 days ago (dormant maintenance, Aug 5, 2024). Validate activity before betting a new project on langchain-tutorials. - 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. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## When NOT to use llm-app - - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ## Common questions ### What is the difference between langchain-tutorials and llm-app? langchain-tutorials: Overview and tutorial of the LangChain Library. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose langchain-tutorials over llm-app? Choose langchain-tutorials over llm-app when Tags unique to langchain-tutorials: jupyter notebook; Also covers Developer Tools. ### When should I choose llm-app over langchain-tutorials? Choose llm-app over langchain-tutorials when Requirements: Requires Docker; The tool is Docker-friendly and designed to ensure synchronization with cloud-based storage solutions among others.; Tags unique to llm-app: vector-database, llm, hugging-face, retrieval-augmented-generation; Also covers Data & Retrieval; - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### When should I avoid langchain-tutorials? Last GitHub push was 705 days ago (dormant maintenance, Aug 5, 2024). Validate activity before betting a new project on langchain-tutorials. 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. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### When should I avoid llm-app? - You require custom deployment configurations that extend beyond the pre-set cloud templates available through llm-app. - There’s a need for tightly integrated support with data sources or APIs not explicitly mentioned, such as specialized CRM systems (Salesforce), which may lack direct template support in llm-app. ### Is langchain-tutorials or llm-app more popular on GitHub? llm-app has more GitHub stars (59,068 vs 7,468). Stars measure visibility, not whether either tool fits your constraints. ### Are langchain-tutorials and llm-app open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to langchain-tutorials or llm-app? GraphCanon lists graph-backed alternatives at [langchain-tutorials alternatives](/tools/gkamradt-langchain-tutorials/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([langchain-tutorials markdown twin](/tools/gkamradt-langchain-tutorials/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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/gkamradt-langchain-tutorials-vs-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, langchain-tutorials or llm-app? langchain-tutorials: Dormant. llm-app: 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 langchain-tutorials and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [langchain-tutorials trust report](/tools/gkamradt-langchain-tutorials/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gkamradt-langchain-tutorials`](/api/graphcanon/graph?tool=gkamradt-langchain-tutorials) - 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/_