WrenAI

GenBI (Generative BI) for AI agents, an open-source, governed text-to-SQL through an open context layer that turns natural-language questions into trusted dashboards, charts, and SQL across 20+ data sources, such as BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks and more.

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Quick Overview

WrenAI is an open-source AI assistant framework designed to help developers create custom AI assistants. It provides a flexible and extensible platform for building conversational AI applications, leveraging large language models and other AI technologies.

Pros

Highly customizable and extensible architecture
Supports multiple AI models and integrations
Easy to deploy and scale
Active community and ongoing development

Cons

Limited documentation for advanced features
Steeper learning curve compared to some simpler chatbot frameworks
Requires knowledge of AI concepts and language models
May have higher computational requirements for complex assistants

Code Examples

Here are a few code examples demonstrating basic usage of WrenAI:

Creating a simple assistant:

from wrenai import Assistant

assistant = Assistant("My Assistant")
assistant.add_skill("greeting", "Say hello to the user")
assistant.add_skill("weather", "Provide weather information")

response = assistant.process("Hello, what's the weather like today?")
print(response)

Adding a custom skill:

from wrenai import Skill

class WeatherSkill(Skill):
    def execute(self, input_text):
        # Implement weather fetching logic here
        return "It's sunny and 25°C today."

assistant.add_skill(WeatherSkill())

Using a specific AI model:

from wrenai import Assistant, GPT3Model

model = GPT3Model(api_key="your-api-key")
assistant = Assistant("GPT-3 Assistant", model=model)

response = assistant.process("Explain quantum computing")
print(response)

Getting Started

To get started with WrenAI, follow these steps:

Install WrenAI:

pip install wrenai

Create a basic assistant:

from wrenai import Assistant

assistant = Assistant("My First Assistant")
assistant.add_skill("greeting", "Greet the user")
assistant.add_skill("farewell", "Say goodbye to the user")

while True:
    user_input = input("You: ")
    if user_input.lower() == "exit":
        break
    response = assistant.process(user_input)
    print(f"Assistant: {response}")

Run your assistant and start interacting with it!

For more advanced usage and customization options, refer to the WrenAI documentation.

Competitor Comparisons

whisper

110,880

Robust Speech Recognition via Large-Scale Weak Supervision

Pros of Whisper

Highly accurate speech recognition across multiple languages
Open-source with extensive documentation and community support
Robust performance on diverse audio inputs, including noisy environments

Cons of Whisper

Requires significant computational resources for real-time transcription
Large model size may be challenging for deployment on resource-constrained devices
Limited customization options for specific domain adaptations

Code Comparison

WrenAI:

from wren import Wren

wren = Wren()
result = wren.transcribe("audio.wav")
print(result.text)

Whisper:

import whisper

model = whisper.load_model("base")
result = model.transcribe("audio.wav")
print(result["text"])

Key Differences

WrenAI focuses on lightweight, efficient speech recognition, while Whisper prioritizes accuracy and multilingual support
WrenAI is designed for edge devices and real-time applications, whereas Whisper is better suited for server-side processing
Whisper offers more comprehensive language support and advanced features, while WrenAI emphasizes simplicity and ease of integration

Use Cases

Choose WrenAI for mobile apps, IoT devices, or scenarios requiring low-latency transcription
Opt for Whisper when accuracy is paramount, dealing with multiple languages, or processing large volumes of audio data

whisper.cpp

53,004

Port of OpenAI's Whisper model in C/C++

Pros of whisper.cpp

Highly optimized C++ implementation for efficient speech recognition
Supports various quantization levels for reduced memory usage
Provides both command-line and library interfaces for flexibility

Cons of whisper.cpp

Limited to Whisper model, while WrenAI supports multiple models
Requires more technical expertise to set up and use effectively
Less focus on user-friendly interfaces compared to WrenAI

Code Comparison

whisper.cpp:

#include "whisper.h"

int main(int argc, char ** argv) {
    struct whisper_context * ctx = whisper_init_from_file("ggml-base.en.bin");
    whisper_full_default(ctx, wparams, pcmf32.data(), pcmf32.size());
    whisper_print_timings(ctx);
    whisper_free(ctx);
}

WrenAI:

from wren import Wren

wren = Wren()
result = wren.transcribe("audio.mp3")
print(result.text)

The code comparison showcases the difference in complexity and ease of use between the two libraries. whisper.cpp requires more setup and C++ knowledge, while WrenAI offers a simpler Python interface for quick transcription tasks.

whisperX

23,880

WhisperX: Automatic Speech Recognition with Word-level Timestamps (& Diarization)

Pros of WhisperX

Offers advanced features like word-level timestamps and speaker diarization
Supports multiple languages and provides language detection
Actively maintained with frequent updates and improvements

Cons of WhisperX

Requires more computational resources due to its advanced features
May have a steeper learning curve for beginners
Limited to audio transcription and diarization tasks

Code Comparison

WhisperX:

import whisperx

model = whisperx.load_model("large-v2")
result = model.transcribe("audio.mp3")
print(result["segments"])

WrenAI:

from wren import Wren

wren = Wren()
response = wren.generate("Summarize this text: ...")
print(response)

Key Differences

WhisperX focuses on audio transcription and diarization, while WrenAI is a more general-purpose AI assistant. WhisperX provides detailed audio analysis, including word-level timestamps and speaker identification. WrenAI, on the other hand, offers a broader range of capabilities, including text generation, summarization, and potentially other AI-powered tasks.

The choice between these repositories depends on the specific use case. For audio-related tasks, WhisperX would be more suitable, while WrenAI might be preferable for general AI assistance and text-based tasks.

faster-whisper

25,979

Faster Whisper transcription with CTranslate2

Pros of faster-whisper

Optimized for speed, offering faster transcription than standard Whisper models
Supports multiple languages and can perform translation
Provides streaming capabilities for real-time transcription

Cons of faster-whisper

Focused solely on speech-to-text, lacking additional AI capabilities
May require more computational resources due to its optimization for speed
Limited customization options compared to more general-purpose AI frameworks

Code Comparison

faster-whisper:

from faster_whisper import WhisperModel

model = WhisperModel("large-v2", device="cuda", compute_type="float16")
segments, info = model.transcribe("audio.mp3", beam_size=5)

for segment in segments:
    print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))

WrenAI:

from wren import Wren

wren = Wren()
response = wren.chat("Transcribe the following audio file: audio.mp3")
print(response)

Summary

faster-whisper excels in speech-to-text tasks with its speed-optimized approach and multi-language support. However, it's limited to transcription tasks. WrenAI, while not specifically optimized for transcription, offers a more versatile AI platform that can handle various tasks through natural language interactions. The choice between the two depends on whether you need specialized transcription capabilities or a more general-purpose AI assistant.

Whisper

10,659

High-performance GPGPU inference of OpenAI's Whisper automatic speech recognition (ASR) model

Pros of Whisper

Optimized for performance with GPU acceleration
Supports multiple languages for transcription
Provides a C++ implementation for better integration with native applications

Cons of Whisper

Limited to speech recognition and transcription tasks
Requires more setup and configuration compared to WrenAI
May have a steeper learning curve for non-technical users

Code Comparison

WrenAI:

from wren import Wren

wren = Wren()
result = wren.transcribe("audio.mp3")
print(result.text)

Whisper:

#include "whisper.h"

whisper_context * ctx = whisper_init_from_file("model.bin");
whisper_full_params params = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
whisper_full(ctx, params, audio_data, audio_len, "en");

Summary

Whisper offers high-performance speech recognition with multi-language support and GPU acceleration, making it suitable for more advanced applications. However, it may require more technical expertise to implement and use effectively. WrenAI, on the other hand, provides a simpler interface for transcription tasks, making it more accessible for quick integration into projects, but may lack some of the advanced features and optimizations found in Whisper.

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README

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 â Â· Read the docs â Â· Join Discord â

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.

Wren AI generative BI architecture â semantic layer and AI context layer for AI agents

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 â Â· Build & deploy a GenBI app â

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	â (often wrong)	â	â	â governed
Knows your business definitions	â	partial, in-tool	â (schema only)	â + non-schema knowledge
Generates & deploys dashboards	â	â (manual, in-tool)	â	â agent-driven
Works through your agents (Claude Code, Cursor, MCPâ¦)	â	â	â	â
Open, reviewable, Git-friendly context	â	â	partial	â
Governed execution across 22+ sources	â	per-connector	â (definitions only)	â

Wren is for you ifâ¦

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
â deploy queued Â· model queryable in Wren Cloud

See how Git Sync works â Â· 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, Agentic Mode, AI-assisted context preparation
Advanced security and audit, support and SLAs, plus cloud, VPC, and air-gapped deployment

Same engine underneath. Your MDL stays in your Git either way. Read the published boundary â

Under the hood

Semantic layer (MDL)

Wren is a governed semantic layer, expressed in the Modeling Definition Language (MDL): a Git-friendly definition of what your data means, not just where it lives. Every answer and dashboard is planned against it.

Models, columns, relationships, views: the shape of your data, decoupled from any one warehouse.
Cubes and metrics: approved, reusable definitions so "revenue" means the same thing everywhere.
Context beyond the schema: enums, units, approved joins, and definitions in instructions.md and queries.yml.

What's included

Engine: Apache DataFusion based. BigQuery, Snowflake, PostgreSQL, ClickHouse, Amazon Redshift, Databricks, DuckDB, and more. Connect a database â
GenBI dashboards: agent-built, browser-side apps powered by wren-core-wasm, deployable to Vercel or Cloudflare Pages
Knowledge and memory: version-controlled instructions.md and queries.yml, plus a local LanceDB memory index with hybrid retrieval
Agent SDKs: wren-langchain (LangChain / LangGraph), wren-pydantic, and a reference Python integration for other stacks. SDK overview â
Governed execution primitives: functions, dry-plan validation, row limits, structured errors

Day-to-day commands

wren skills get onboarding         # workflow guide: set up project + first query
wren skills get enrich-context     # workflow guide: add business context
wren skills get genbi              # workflow guide: build & deploy a dashboard

wren query --sql '...'             # query through the MDL semantic layer
wren ask "<question>" --guided     # wrap a question for a weaker agent
wren ask "<question>" --direct     # wrap a question for a stronger agent

Full reference: CLI Â· MDL Â· Architecture

What's next

End-to-end correctness primitives: value profiling, rich retrieval, structured errors, golden eval runner
Agent-native distribution: first-class SDKs across major agent frameworks

Vote on what ships next in GitHub Discussions â

FAQ

What is generative BI (GenBI)?

Business intelligence produced by AI agents. Instead of a person building charts by hand, an agent generates governed SQL, deploys a dashboard, and shares it, grounded in an AI context layer so the output is trustworthy rather than merely plausible. Wren AI is the open-source GenBI engine.

Does Wren AI do text-to-SQL?

Yes, and governed: questions become SQL planned against your semantic layer (MDL) and dry-plan validated before execution. Wren then goes further, deploying dashboards and managing the context that keeps answers correct.

Is Wren AI a semantic layer?

Yes. Wren is a governed semantic layer expressed in MDL (models, metrics, relationships), paired with an AI context layer (memory, examples, unstructured knowledge) and a governed execution engine that runs those definitions across 22+ sources.

What is an AI context layer?

The reviewable, version-controlled knowledge agents need but schemas don't provide: business semantics, approved definitions, examples, memory, and governance. Read the vision: The missing context layer for AI agents over business data.

What happened to the Docker-based Wren AI GenBI app?

On 2026-05-07 Wren Engine merged into this repo under core/, and the previous Canner/wren-engine repo was archived. The earlier chat-first BI product is now Wren GenBI Classic, preserved on the legacy/v1 branch (tag v1-final) with no new features or security fixes. For a maintained, hosted version of that experience, see Wren AI Commercial. Read the announcement â

Documentation

Quickstart: from skill install to first answer
Build & deploy a GenBI app: generate a dashboard and ship it
Concepts: what context is, what MDL is, how memory works
Connect a database: Postgres, BigQuery, Snowflake, DuckDB, and more
Agent SDKs: what's shipping today, what's next
Git Sync: git push your open-source project to governed, team-wide GenBI

Community

ð¬ Discord: get unstuck fast, chat with the team and other builders
ð GitHub Discussions: design conversations, RFCs, roadmap votes
ð¦ Twitter / X: release notes and short updates
ð Blog: vision, post-mortems, deep dives

Contributing

We build in the open. Issues, PRs, connectors, SDK integrations, and docs fixes are all welcome.

New here? Pick up a good first issue.
Contributor guide: setup, conventions, and the contribution bar
Architecture map: find the right place to land your change

Project structure (click to expand)

core/
  wren-core/         Rust semantic engine (Apache DataFusion)
  wren-core-base/    Shared manifest types + MDL builder
  wren-core-py/      Python bindings (PyPI: wren-core)
  wren-core-wasm/    WebAssembly build (npm: wren-core-wasm)
  wren/              Python SDK and CLI (PyPI: wrenai)
  wren-mdl/          MDL JSON schema
sdk/
  wren-langchain/    LangChain / LangGraph integration
  wren-pydantic/     Pydantic AI integration
skills/              Agent skills for context authoring
docs/                Module documentation
examples/            Example projects

Contributors

License

Apache 2.0. See LICENSE.

If WrenAI saved you time, star the repo â. It's the fastest way to help more agent builders find it.

Get started â Â· Join Discord â Â· See Git Sync â

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Quick Overview

Pros

Cons

Code Examples

Getting Started

Competitor Comparisons

Pros of Whisper

Cons of Whisper

Code Comparison

Key Differences

Use Cases

Pros of whisper.cpp

Cons of whisper.cpp

Code Comparison

Pros of WhisperX

Cons of WhisperX

Code Comparison

Key Differences

Pros of faster-whisper

Cons of faster-whisper

Code Comparison

Summary

Pros of Whisper

Cons of Whisper

Code Comparison

Summary

Convert designs to code with AI

README

Open-source GenBI for AI agents

What WrenAI is

Quickstart

Then keep going

Why agent builders pick WrenAI

How Wren compares

Wren is for you ifâ¦

Take it to your team with git push

Open core: what's OSS, what's commercial

Under the hood

Semantic layer (MDL)

What's included

Day-to-day commands

What's next

FAQ

Documentation

Community

Contributing

Contributors

License

Top Related Projects

Convert designs to code with AI

Wren is for you ifâ¦

Take it to your team with `git push`