Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
OpenChatBI is an intelligent chat-based BI tool powered by large language models, designed to help users query, analyze, and visualize data through natural language conversations. It uses LangGraph and LangChain to build chat agent and workflows that support natural language to SQL conversion and data analysis.
| Date | Stars |
|---|---|
| 2026-07-31 | 611 |
| 2026-08-01 | 612 |
| 2026-08-02 | 612 |
| 2026-08-03 | 613 |
| 2026-08-04 | 614 |
| 2026-08-06 | 614 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
15.0
growth rate 0.00%/day
# OpenChatBI OpenChatBI is an open source, chat-based intelligent BI tool powered by large language models, designed to help users query, analyze, and visualize data through natural language conversations. Built on LangGraph and LangChain ecosystem, it provides chat agents and workflows that support natural language to SQL conversion and streamlined data analysis. Join the Slack channel to discuss: [Invitation Link To Join](https://join.slack.com/t/openchatbicommunity/shared_invite/zt-40o5ncq8k-fMU61eUg87m8JhrCIBOV8A) <img src="https://github.com/zhongyu09/openchatbi/raw/main/example/demo.gif" alt="Demo" width="800"> ## Core Features 1. **Natural Language Interaction**: Get data analysis results by asking questions in natural language 2. **Automatic SQL Generation**: Convert natural language queries into SQL statements using advanced text2sql workflows with schema linking and well organized prompt engineering 3. **Data Visualization**: Generate intuitive data visualizations (via plotly) 4. **Data Catalog Management**: Automatically discovers and indexes database table structures, supports flexible catalog storage backends with vector-based or BM25-based retrieval, and easily maintains business explanations for tables and columns as well as optimizes Prompts. 5. **Time Series Forecasting**: In-house forecasting models, accessed through the data analysis agent (see feature 12) 6. **Code Execution**: Execute Python code for data analysis and visualization 7. **Interactive Problem-Solving**: Proactively ask users for more context when information is incomplete 8. **Persistent Memory**: Conversation management and user characteristic memory based on LangGraph checkpointing 9. **MCP Support**: Integration with MCP tools by configuration 10. **Knowledge Base Integration**: Answer complex questions by combining catalog based knowledge retrival and external knowledge base retrival (via MCP tools) 11. **Web UI Interface**: Provide 2 sample UI: simple and streaming web interfaces using Gradio and Streamlit, easy to integrate with other web applications 12. **Data Analysis Agent**: A specialized sub-agent (built on [deepagents](https://github.com/langchain-ai/deepagents)) that the main agent delegates complex analysis to. It orchestrates text2sql, time series forecasting, anomaly detection, multi-dimensional drill-down (Adtributor) and Python execution to cover trend forecasting, anomaly detection, anomaly root-cause drill-down, multi-metric correlation and business combination analysis. Optionally uses a dedicated `analysis_llm`. See [`openchatbi/analysis/README.md`](openchatbi/analysis/README.md) for the agent and the underlying anomaly detection / Adtributor algorithms. ## Roadmap 1. **Anomaly Detection Algorithm**: Time series anomaly detection — initial version available via the data analysis agent; actively being refined toward production readiness. 2. **Root Cause Analysis Algorithm**: Multi-dimensional drill-down for anomaly investigation — initial Adtributor-based drill-down tool available; actively being refined toward production readiness. 3. **Data Analysis Agent**: End-to-end analysis orchestration — initial version available; iterating on robustness, data hand-off between tools, and overall quality to reach production readiness. # Getting started ## Installation & Setup ### Prerequisites - Python 3.11 or higher - Access to a supported LLM provider (OpenAI, Anthropic, etc.) - Data Warehouse (Database) credentials (like Presto, PostgreSQL, MySQL, etc.) - (Optional) Embedding model for vector-based retrieval - if not available, BM25-based retrieval will be used - (Optional) Docker - required only for `docker` executor mode **Note on Chinese Text Segmentation**: For better Chinese text retrieval, `jieba` is used for word segmentation. However, `jieba` is not compatible with Python 3.12+. On Python 3.12 and higher, the system automatically falls back to simple punctuation-based
Excerpt of 36,536 characters
Read on GitHub
104
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9bd0f3b4034960e9, topic:llm, topic:gpt
matched fp:9bd0f3b4034960e9, topic:nlp
matched fp:9bd0f3b4034960e9, topic:langgraph