WrenAI用自然语言查询业务数据

GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language quest…

Open-source GenBI for AI agents Your agents generate governed SQL, deploy dashboards, and keep business definitions in Git, across 22+ data sources. Get started in 3 commands ↓ · Read the docs → · Join Discord → What WrenAI is WrenAI is the open-source generative BI (GenBI) engine. It gives the AI agents you already use (Claude Code, Cursor, MCP clients, LangChain) a governed semantic layer and an AI context layer, so they turn business questions into correct SQL, ship the answer as a shareable dashboard, and stay inside your guardrails. Schemas tell an agent where data lives. Wren tells it what the data means: approved metric definitions, enums, units, joins, worked examples, and the tribal knowledge buried in docs and chat threads. All of it lives as reviewable YAML and Markdown in a repo you own. Quickstart Three commands, then your agent does the rest. Works with Claude Code, Cursor, Cline, Codex, and 50+ other agents. 1. Install the CLI pip install wrenai Add connector extras as you need them, for example pip install "wrenai[postgres,memory]". 2. Teach your agent about Wren npx skills add Canner/WrenAI This installs a ~50-line discovery stub. Your agent fetches full workflow guides from the CLI on demand, so instructions always match the installed version. 3. Open your agent in a project folder and ask "Use Wren to set up my Postgres database." The agent checks your environment, creates a connection profile, scaffolds the project, and runs a first query. No database handy? Say "Use Wren with the bundled jaffle_shop sample" and run the same flow against a real sample warehouse. Slow pip install from mainland China? pip install wrenai -i https://pypi.tuna.tsinghua.edu.cn/simple If HuggingFace model downloads time out, run export HF_ENDPOINT=https://hf-mirror.com before using the CLI. Then keep going Once you're connected, these three prompts cover the whole GenBI loop: Beat Ask your agent What happens Know "Enrich my Wren project with the business context in raw/." Runs wren skills get enrich-context. Writes definitions, examples, and memory as reviewable files. Generate "Who are our top 10 customers by sales this quarter?" Recalls MDL context and past queries, writes governed SQL, executes via wren query. Deploy "Turn that into a dashboard I can filter and share, deployed to Vercel." Runs wren skills get genbi. Builds a browser-side app and returns a live URL on your Vercel or Cloudflare Pages account. Follow the full quickstart → · Build & deploy a GenBI app → Why agent builders pick WrenAI Answers and dashboards, not just SQL. Generate a governed answer, deploy it as a dashboard, share the URL. One agent-driven loop, end to end. Correct instead of confidently wrong. Schema-aware retrieval, MDL planning, dry-plan validation, row limits, value profiling, and structured errors with hints keep agent SQL inside guardrails. Business meaning lives in Git. Metric definitions, enums, approved joins, and proven examples are versioned files. Review them in a pull request. Diff them. Take them with you. Works through the agents you already run. Claude Code, Cursor, Cline, Codex, MCP clients, LangChain. Wren is a layer, not another chat UI to adopt. Sits on your existing stack. Warehouse, transformation pipelines, existing semantic models. 22+ sources on one Apache DataFusion engine. Open by default. Core engine, SDK, and skills under Apache-2.0. It runs without us. How Wren compares A raw LLM agent A traditional BI tool A bare semantic layer WrenAI Writes SQL for you ✅ (often wrong) ❌ ❌ ✅ governed Knows your business definitions ❌ partial, in-tool ✅ (schema only) ✅ + non-schema knowledge Generates & deploys dashboards ❌ ✅ (manual, in-tool) ❌ ✅ agent-driven Works through your agents (Claude Code, Cursor, MCP…) ✅ ❌ ❌ ✅ Open, reviewable, Git-friendly context ❌ ❌ partial ✅ Governed execution across 22+ sources ❌ per-connector ✅ (definitions only) ✅ Wren is for you if… You want AI agents to produce trustworthy BI, answers and dashboards, not just plausible SQL. Your business logic (definitions, enums, units, approved joins) lives outside the database and your agents keep getting it wrong. You want a context layer that is open, reviewable, and version-controlled, usable by every agent and every person, not gated behind one vendor's UI. Skip Wren if you only need a one-off chart from a single CSV, or you're happy letting an agent guess at SQL with no governance. Take it to your team with git push Everything Wren writes on your laptop is plain YAML and Markdown in a repo you own. Git Sync turns that same repo into a governed, team-wide GenBI deployment in Wren Cloud or a self-hosted installation, including air-gapped. Nothing to export, nothing to re-model. No new CLI to learn. Bind a directory once with wren cloud create or wren cloud link. After that, git push and git pull are the entire interface. Metrics reviewed like code. A change to net_revenue shows up as a readable diff in a pull request on GitHub, GitLab, Bitbucket, or your own remote. Run CI on it. Promote staging to production like application code. You keep the repo, always. Clone it, back it up, feed it to another tool, or leave with it, any time. No reusable credential on disk. Every push authenticates with a fresh token that expires in 600 seconds. The durable key stays in ~/.wren/cloud.yml (mode 0600) and is never handed to git. $ git push To cloud.getwren.ai/acme/wren-analytics.git 9f2c1a4..b71e0d3 main -> main ✔ deploy queued · model queryable in Wren Cloud See how Git Sync works → · CLI reference: wren cloud Open core: what's OSS, what's commercial The engine in this repo (MDL semantic layer, governed text-to-SQL, MCP server, CLI, 22+ connectors) is Apache-2.0, free forever, and self-hostable. The following are commercial, delivered as Wren AI Cloud or self-hosted Enterprise Plus: Row- and column-level security and access control with users and groups GenBI UI, dashboards, embedded and APIs Scenario AI harnesses: GenBI Apps, Agenti