--- title: "WrenAI vs rags" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/canner-wrenai-vs-run-llama-rags" tools: ["canner-wrenai", "run-llama-rags"] --- # WrenAI vs rags *GraphCanon updated Aug 18, 2026* ## Verdict Pick WrenAI if wrenAI transforms natural-language queries into SQL and produces visual reports from over twenty supported data sources using an open context layer; pick rags if decision-critical facts for 'rags':. [WrenAI](https://www.getwren.ai/en/open-core) reports 17k GitHub stars, 2.0k forks, and 327 open issues, last pushed Aug 18, 2026. [rags](https://github.com/run-llama/rags) has 6.5k stars, 656 forks, and 37 open issues, last pushed Apr 5, 2024. Figures are from public GitHub metadata via [WrenAI's repository](https://github.com/Canner/WrenAI) and [rags's repository](https://github.com/run-llama/rags). | | [WrenAI](/tools/canner-wrenai.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Tagline | GenBI for AI agents, turns natural-language questions into trusted dashboards and SQL | Build ChatGPT over your data with natural language | | Stars | 17,295 | 6,549 | | Forks | 1,955 | 656 | | Open issues | 327 | 37 | | Language | Python | Python | | Adopt for | WrenAI transforms natural-language queries into SQL and produces visual reports from over twenty supported data sources using an open context layer. | Decision-critical facts for 'rags': | | Persona | - | - | | Runtime | - | - | | License | WrenAI is distributed under an open-source Apache 2.0 license, allowing for broad use, modification, and distribution. | MIT License | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [WrenAI](/tools/canner-wrenai.md) | [rags](/tools/run-llama-rags.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 865d | | Open issues (now) | 327 | 37 | | Stars delta | +1.3k (30d) | +6 (30d) | | Open issues delta | -41 (30d) | -1 (30d) | | Full report | [trust report](/tools/canner-wrenai/trust.md) | [trust report](/tools/run-llama-rags/trust.md) | ## Shared compatibility - **Python**: [WrenAI](/tools/canner-wrenai.md) - Python runtime; [rags](/tools/run-llama-rags.md) - Python runtime ## Decision facts: WrenAI - **Pricing:** freemium - Free to use with self-hosting capabilities; commercial support available. - **Requirements:** Install the CLI using pip (e.g., `pip install wrenai`). Consider regional mirrors like Tsinghua for faster installation in mainland China. - **Adopt for:** WrenAI transforms natural-language queries into SQL and produces visual reports from over twenty supported data sources using an open context layer. - **License detail:** WrenAI is distributed under an open-source Apache 2.0 license, allowing for broad use, modification, and distribution. ## Decision facts: rags - **Requirements:** Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment. - **Adopt for:** Decision-critical facts for 'rags': - **License detail:** MIT License ## Choose when ### Choose WrenAI if… - License: WrenAI is Other, rags is MIT. - Pricing: Free to use with self-hosting capabilities; commercial support available.. - Requirements: Install the CLI using pip (e.g., `pip install wrenai`). Consider regional mirrors like Tsinghua for faster installation in mainland China.. - Tags unique to WrenAI: bigquery, context-engineering, duckdb, genbi. - When you require a tool that can generate trusted dashboards, charts, and SQL queries directly from natural language inputs. ### Choose rags if… - License: rags is MIT, WrenAI is Other. - Requirements: Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment.. - Tags unique to rags: chatbot, chatgpt, llm, openai. - When leveraging natural language queries over proprietary user data using OpenAI services. ## When NOT to use WrenAI - For environments where the specific installation instructions for regions like mainland China might be problematic or where direct dependency on certain mirrors could cause operational challenges. - When a project strictly requires proprietary tools or has licensing constraints that do not align with the Apache 2.0 license of WrenAI. ## When NOT to use rags - Not suitable if you seek solutions not dependent on OpenAI's services as the underlying framework is tightly coupled with OpenAI APIs. - Avoid using rags if your project involves sensitive or highly confidential data since it requires integrating API keys, potentially posing security concerns. - If your team does not have familiarity or access to Streamlit for app development, you might find setting up and deploying a conversational agent more challenging. ## Common questions ### What is the difference between WrenAI and rags? WrenAI: GenBI for AI agents, turns natural-language questions into trusted dashboards and SQL. rags: Build ChatGPT over your data with natural language. See the comparison table for live GitHub stats and shared categories. ### When should I choose WrenAI over rags? Choose WrenAI over rags when License: WrenAI is Other, rags is MIT; Pricing: Free to use with self-hosting capabilities; commercial support available.; Requirements: Install the CLI using pip (e.g., `pip install wrenai`). Consider regional mirrors like Tsinghua for faster installation in mainland China.; Tags unique to WrenAI: bigquery, context-engineering, duckdb, genbi; When you require a tool that can generate trusted dashboards, charts, and SQL queries directly from natural language inputs. ### When should I choose rags over WrenAI? Choose rags over WrenAI when License: rags is MIT, WrenAI is Other; Requirements: Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment.; Tags unique to rags: chatbot, chatgpt, llm, openai; When leveraging natural language queries over proprietary user data using OpenAI services. ### When should I avoid WrenAI? For environments where the specific installation instructions for regions like mainland China might be problematic or where direct dependency on certain mirrors could cause operational challenges. When a project strictly requires proprietary tools or has licensing constraints that do not align with the Apache 2.0 license of WrenAI. ### When should I avoid rags? Not suitable if you seek solutions not dependent on OpenAI's services as the underlying framework is tightly coupled with OpenAI APIs. Avoid using rags if your project involves sensitive or highly confidential data since it requires integrating API keys, potentially posing security concerns. If your team does not have familiarity or access to Streamlit for app development, you might find setting up and deploying a conversational agent more challenging. ### Is WrenAI or rags more popular on GitHub? WrenAI has more GitHub stars (17,295 vs 6,549). Stars measure visibility, not whether either tool fits your constraints. ### Are WrenAI and rags open source? Yes - both are open-source projects on GitHub (WrenAI: Other, rags: MIT). ### Where can I find alternatives to WrenAI or rags? GraphCanon lists graph-backed alternatives at [WrenAI alternatives](/tools/canner-wrenai/alternatives) and [rags alternatives](/tools/run-llama-rags/alternatives) ([WrenAI markdown twin](/tools/canner-wrenai/alternatives.md), [rags markdown twin](/tools/run-llama-rags/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/canner-wrenai-vs-run-llama-rags.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, WrenAI or rags? WrenAI: Very active. rags: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for WrenAI and rags? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [WrenAI trust report](/tools/canner-wrenai/trust); [rags trust report](/tools/run-llama-rags/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=canner-wrenai`](/api/graphcanon/graph?tool=canner-wrenai) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_