openchatbi: Intelligent chat-based BI tool powered by large language models, designed to help users query, analyze

by zhongyu09 · Agent Tool · ★ 676

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install openchatbi

pip install openchatbi

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 69/100

Flagged: sudo usage. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

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Quick Facts

Stars676
Forks94
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score69/100
Open Issues2
Last Updated2026-11-03
Created2025-09-09
Platformspython
Est. Tokens~22k

About openchatbi

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.

Compatible Skills

These tools work well together with openchatbi for enhanced workflows:

  • langchain-mcp-adapters — semantic(0.18)+complementary+shared_fw(langchain)+same_lang+similar_pop+shared_platform (59%)
  • langchain-agent-skills — semantic(0.16)+complementary+shared_fw(langchain)+same_lang+similar_pop+shared_platform (59%)
  • full-stack-ai-agent-template — semantic(0.15)+complementary+shared_fw(langchain)+same_lang+similar_pop+shared_platform (58%)
  • langchain_data_agent — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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