Wren AI
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Text-to-SQL generative BI agent for natural language data querying
Key Features
Single Agent Streaming Short-Term Memory Structured Output Self-Hosted
Community Feedback
Strengths
- Strong text-to-SQL accuracy via semantic layer
- Open-source (AGPL-3.0)
- Supports multiple databases and LLMs
- Rapid community growth
Weaknesses
- Narrowly focused on BI/analytics use cases
- Requires semantic modeling setup
- AGPL license may restrict commercial use
Wren AI Details
| Organization | Canner |
| Organization Type | Company |
| Funding | Seed |
| Category | Ready-to-Use |
| Subcategory | Single agent |
| Deployment | Self-Hosted |
| Primary Language | Python |
| Runtime | Docker |
| License | AGPL-3.0 |
| Commercial Use | Copyleft |
| GitHub Stars | 14,700 |
| GitHub Forks | 1,600 |
| Release Cadence | Biweekly |
| Maturity | Stable |
| Pricing Model | Free |
| Free Tier | Self-hosted open-source |
| Self-Hosted Free | Yes |
| Community Size | Medium (14.7k stars, rapid growth) |
| Community Activity | Very active |
| Sentiment | Positive |
| GPU Required | No |
| Research Date | 2026-03-24 |
| LLM Providers | OpenAI, Anthropic, Google Gemini, Private LLMs |
Use Cases
- Natural language querying over enterprise databases
- Self-service analytics and BI
- Text-to-SQL with semantic layer accuracy
- Multi-database analytics
When to Use
Best for: Natural language analytics over enterprise databases
Avoid when: General-purpose agent or non-analytics use case
Original data from HuggingFace, OpenCompass and various public git repos.
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Release v20260328a