--- title: "llm-app vs autoflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pathwaycom-llm-app-vs-pingcap-autoflow" tools: ["pathwaycom-llm-app", "pingcap-autoflow"] --- # llm-app vs autoflow *GraphCanon updated Aug 21, 2026* ## Verdict Pick llm-app if 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; pick autoflow if pingcap/autoflow leverages Graph RAG technology and TiDB Serverless Vector Storage, making it a specialized choice for building conversational knowledge bases in TypeScript. [llm-app](https://pathway.com/developers/templates/) reports 59k GitHub stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. [autoflow](https://tidb.ai) has 3.0k stars, 194 forks, and 74 open issues, last pushed Apr 27, 2026. Figures are from public GitHub metadata via [llm-app's repository](https://github.com/pathwaycom/llm-app) and [autoflow's repository](https://github.com/pingcap/autoflow). | | [llm-app](/tools/pathwaycom-llm-app.md) | [autoflow](/tools/pingcap-autoflow.md) | | --- | --- | --- | | Tagline | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. | Graph RAG based conversational knowledge base tool using TiDB Serverless Vector Storage | | Stars | 59,037 | 2,971 | | Forks | 1,466 | 194 | | Open issues | 8 | 74 | | Language | Jupyter Notebook | TypeScript | | 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 | pingcap/autoflow leverages Graph RAG technology and TiDB Serverless Vector Storage, making it a specialized choice for building conversational knowledge bases in TypeScript. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Data & Retrieval, LLM Frameworks, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-app](/tools/pathwaycom-llm-app.md) | [autoflow](/tools/pingcap-autoflow.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 41d | 115d | | Open issues (now) | 8 | 74 | | Stars delta | +11 (30d) | +15 (30d) | | Open issues delta | -2 (30d) | -1 (30d) | | Full report | [trust report](/tools/pathwaycom-llm-app/trust.md) | [trust report](/tools/pingcap-autoflow/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 ## Decision facts: autoflow - **Adopt for:** pingcap/autoflow leverages Graph RAG technology and TiDB Serverless Vector Storage, making it a specialized choice for building conversational knowledge bases in TypeScript. ## Choose when ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; autoflow is TypeScript. - License: llm-app is MIT, autoflow is Apache-2.0. - 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: hugging-face, llm, retrieval-augmented-generation. - Also covers LLM Frameworks. - - You need a ready-to-run solution that directly integrates with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and live APIs. ### Choose autoflow if… - autoflow is primarily TypeScript; llm-app is Jupyter Notebook. - License: autoflow is Apache-2.0, llm-app is MIT. - Tags unique to autoflow: cot, graphrag, knowledge-graph, mysql. - autoflow ships Docker support for self-hosted deployment. - - When you need to create a conversational interface that can leverage both graph-based data structures and retrieval-augmented generation techniques for context-aware responses. ## 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. ## When NOT to use autoflow - - When your application does not require a conversational knowledge base or cannot benefit from retrieval-augmented generation (RAG) techniques. - - If you are aiming for broad compatibility across different SQL-based databases, as autoflow specifically integrates with TiDB and might offer less flexibility when compared to tools that support a ## Common questions ### What is the difference between llm-app and autoflow? llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.. autoflow: Graph RAG based conversational knowledge base tool using TiDB Serverless Vector Storage. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-app over autoflow? Choose llm-app over autoflow when llm-app is primarily Jupyter Notebook; autoflow is TypeScript; License: llm-app is MIT, autoflow is Apache-2.0; 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: hugging-face, llm, retrieval-augmented-generation; Also covers LLM Frameworks; - 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 choose autoflow over llm-app? Choose autoflow over llm-app when autoflow is primarily TypeScript; llm-app is Jupyter Notebook; License: autoflow is Apache-2.0, llm-app is MIT; Tags unique to autoflow: cot, graphrag, knowledge-graph, mysql; autoflow ships Docker support for self-hosted deployment; - When you need to create a conversational interface that can leverage both graph-based data structures and retrieval-augmented generation techniques for context-aware responses. ### 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. ### When should I avoid autoflow? - When your application does not require a conversational knowledge base or cannot benefit from retrieval-augmented generation (RAG) techniques. - If you are aiming for broad compatibility across different SQL-based databases, as autoflow specifically integrates with TiDB and might offer less flexibility when compared to tools that support a ### Is llm-app or autoflow more popular on GitHub? llm-app has more GitHub stars (59,037 vs 2,971). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-app and autoflow open source? Yes - both are open-source projects on GitHub (llm-app: MIT, autoflow: Apache-2.0). ### Where can I find alternatives to llm-app or autoflow? GraphCanon lists graph-backed alternatives at [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) and [autoflow alternatives](/tools/pingcap-autoflow/alternatives) ([llm-app markdown twin](/tools/pathwaycom-llm-app/alternatives.md), [autoflow markdown twin](/tools/pingcap-autoflow/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/pathwaycom-llm-app-vs-pingcap-autoflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-app or autoflow? llm-app: Steady. autoflow: 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 llm-app and autoflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-app trust report](/tools/pathwaycom-llm-app/trust); [autoflow trust report](/tools/pingcap-autoflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pathwaycom-llm-app`](/api/graphcanon/graph?tool=pathwaycom-llm-app) - 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/_