{"data":{"slug":"pingcap-autoflow","name":"autoflow","tagline":"Graph RAG based conversational knowledge base tool using TiDB Serverless Vector Storage","github_url":"https://github.com/pingcap/autoflow","owner":"pingcap","repo":"autoflow","owner_avatar_url":"https://avatars.githubusercontent.com/u/11855343?v=4","primary_language":"TypeScript","stars":2971,"forks":194,"topics":["chatbot","cot","graphrag","knowledge-graph","mysql","rag","serverless","vector-database"],"archived":false,"github_pushed_at":"2026-04-27T13:55:14+00:00","maintenance_label":"Slowing","stars_delta_30d":15,"url":"https://www.graphcanon.com/tools/pingcap-autoflow","markdown_url":"https://www.graphcanon.com/tools/pingcap-autoflow.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/pingcap-autoflow","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=pingcap-autoflow","description":"pingcap/autoflow is a Graph RAG based and conversational knowledge base tool built with TiDB Serverless Vector Storage. Demo: https://tidb.ai","homepage_url":"https://tidb.ai","license":"Apache-2.0","open_issues":74,"watchers":26,"ai_summary":"pingcap/autoflow is a repository for developing a graph-based Retrieval-Augmented Generation (RAG) system intended to create conversational chatbots that can interact with extensive, structured information bases. It leverages vector databases and is architected around TiDB’s serverless functionalities.","readme_excerpt":"## License\n\nAutoFlow is open-source under the Apache License, Version 2.0. You can [find it here](https://github.com/pingcap/autoflow/blob/main/LICENSE.txt).","github_created_at":"2024-02-05T06:53:14+00:00","created_at":"2026-07-07T17:44:04.063136+00:00","updated_at":"2026-08-21T06:02:25.860384+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"vector-databases","name":"Vector Databases","url":"https://www.graphcanon.com/categories/vector-databases","markdown_url":"https://www.graphcanon.com/categories/vector-databases.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/vector-databases"}],"tags":[{"slug":"chatbot","name":"chatbot"},{"slug":"cot","name":"cot"},{"slug":"graphrag","name":"graphrag"},{"slug":"knowledge-graph","name":"knowledge-graph"},{"slug":"mysql","name":"mysql"},{"slug":"rag","name":"rag"},{"slug":"serverless","name":"serverless"},{"slug":"vector-database","name":"vector-database"}],"trust":{"provenance":{"is_fork":false,"github_id":752946440,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-21T06:02:24.805Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":115,"last_release_at":"2025-01-03T09:33:24Z","stars_delta_30d":15,"open_issues_delta_30d":-1},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:24:55.213Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-21T06:02:25.267Z"},"deploy":{"source":"dockerfile:docker-compose.yml","self_host":true,"observed_at":"2026-08-21T06:02:25.267Z","managed_saas":false},"languages":{"value":["typescript"],"source":"github.language","observed_at":"2026-08-21T06:02:25.267Z"},"has_docker":{"value":true,"source":"dockerfile:docker-compose.yml","observed_at":"2026-08-21T06:02:25.267Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-21T06:02:25.267Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- 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.","- If your project specifically requires integration with TiDB's Serverless Vector Storage, thus benefiting from its scalability and on-demand resource allocation without the overhead of managing the底层"],"when_not_to_use":["- 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多元"],"source":"enrich:decision_facts","observed_at":"2026-07-11T02:53:58.698Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"pingcap/autoflow leverages Graph RAG technology and TiDB Serverless Vector Storage, making it a specialized choice for building conversational knowledge bases in TypeScript."}]}}