Chat with your enterprise data using LLM vs DB-GPT

Side-by-side comparison of two AI agent tools

Short answer

  • Chat with your enterprise data using LLM has had no commit in 21 months; DB-GPT is actively maintained (80 commits in the last 90 days).
  • DB-GPT is growing faster: +267 GitHub stars in the last 30 days vs +-0 for Chat with your enterprise data using LLM.
  • Pick Chat with your enterprise data using LLM for: open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search. Pick DB-GPT for: open-source agentic AI data assistant for the next generation of AI + Data products.

From GitHub data refreshed daily.

Open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search

DB-GPTopen-source

open-source agentic AI data assistant for the next generation of AI + Data products.

Metrics

Chat with your enterprise data using LLMDB-GPT
Stars86520.1k
Star velocity /mo-0.4736842105263158266.52631578947364
Commits (90d)080
Releases (6m)02
Overall score0.121032607606905080.6359638433230541

Pros

  • +Supports multiple vector stores (Pinecone, Redis, Azure Cognitive Search) providing flexibility in deployment options
  • +Includes comprehensive evaluation framework with Prompt Flow integration and metrics like groundedness and Ada similarity
  • +Active development with regular updates and refactoring to improve core functionality and remove complexity
  • +开源免费，拥有活跃的社区支持和持续的版本更新
  • +采用代理式AI架构，能够智能理解自然语言并执行复杂数据操作
  • +专注于AI+数据融合，为下一代数据产品提供了完整的解决方案框架

Cons

  • -Designed as a sample application rather than production-ready solution, requiring additional development for enterprise deployment
  • -Specifically tied to Azure OpenAI Service, limiting flexibility in LLM provider choice
  • -Has undergone multiple refactoring cycles that removed features, suggesting potential instability in feature set
  • -作为相对新兴的AI数据工具，可能在企业级稳定性方面需要更多验证
  • -学习曲线可能较陡，需要用户具备一定的AI和数据库基础知识
  • -依赖于大语言模型的性能，可能在复杂查询场景下存在准确性挑战

Use Cases

  • •Enterprise document Q&A systems where employees need to query internal knowledge bases using natural language
  • •Internal chatbots for customer support teams to quickly access company policies and procedures
  • •Research and development teams building custom RAG applications for proprietary data analysis
  • •企业数据分析师使用自然语言查询复杂数据库，快速生成分析报告
  • •开发者构建智能数据应用，为最终用户提供对话式数据交互体验
  • •数据科学团队进行探索性数据分析，通过AI助理简化数据预处理和查询工作
View Chat with your enterprise data using LLM DetailsView DB-GPT Details

FAQ

Which is more popular, Chat with your enterprise data using LLM or DB-GPT?
DB-GPT has more GitHub stars (20,074 vs 865).
Which is more actively developed, Chat with your enterprise data using LLM or DB-GPT?
DB-GPT had more commits in the last 90 days (80 vs 0).
Should I use Chat with your enterprise data using LLM or DB-GPT?
Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.
Chat with your enterprise data using LLM vs DB-GPT (2026): GitHub Stats, Features & Which to Choose