--- title: "deeplake vs raglite" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-deeplake-vs-superlinear-ai-raglite" tools: ["activeloopai-deeplake", "superlinear-ai-raglite"] --- # deeplake vs raglite *GraphCanon updated Jul 12, 2026* ## Verdict Pick deeplake if deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities; pick raglite if rAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. [deeplake](https://deeplake.ai) reports 9.2k GitHub stars, 721 forks, and 69 open issues, last pushed May 21, 2026. [raglite](https://github.com/superlinear-ai/raglite) has 1.2k stars, 108 forks, and 13 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [deeplake's repository](https://github.com/activeloopai/deeplake) and [raglite's repository](https://github.com/superlinear-ai/raglite). | | [deeplake](/tools/activeloopai-deeplake.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Tagline | AI Data Runtime for Agents with scalable retrieval and training features | Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL | | Stars | 9,202 | 1,194 | | Forks | 721 | 108 | | Open issues | 69 | 13 | | Language | C++ | Python | | Adopt for | Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities. | RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. | | Persona | - | - | | Runtime | - | - | | License | Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution. | MPL-2.0 | | Categories | Data & Retrieval, Model Training, Vector Databases | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deeplake](/tools/activeloopai-deeplake.md) | [raglite](/tools/superlinear-ai-raglite.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 50d | 2d | | Open issues (now) | 69 | 13 | | Full report | [trust report](/tools/activeloopai-deeplake/trust.md) | [trust report](/tools/superlinear-ai-raglite/trust.md) | ## Decision facts: deeplake - **Pricing:** unknown - Pricing details are not specified for Deeplake's public repository. - **Requirements:** Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`. - **Adopt for:** Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities. - **License detail:** Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution. ## Decision facts: raglite - **Adopt for:** RAGLite offers specialized capabilities for integrating Retrieval-Augmented Generation (RAG) models with DuckDB or PostgreSQL. ## Choose when ### Choose deeplake if… - deeplake is primarily C++; raglite is Python. - License: deeplake is Apache-2.0, raglite is MPL-2.0. - Pricing: Pricing details are not specified for Deeplake's public repository.. - Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`.. - Tags unique to deeplake: agent, agentic-rag, ai, computer-vision. - Also covers Vector Databases. - When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### Choose raglite if… - raglite is primarily Python; deeplake is C++. - License: raglite is MPL-2.0, deeplake is Apache-2.0. - Tags unique to raglite: chainlit, colbert, duckdb, evals. - raglite ships Docker support for self-hosted deployment. - - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ## When NOT to use deeplake - If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. - When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training. ## When NOT to use raglite - - The project demands integration with RAG systems that natively support database backends other than DuckDB and PostgreSQL, as RAGLite is limited to these two options. - - You are looking for a more generalized framework that supports multiple vector search engines besides those compatible with DuckDB or PostgreSQL. ## Common questions ### What is the difference between deeplake and raglite? deeplake: AI Data Runtime for Agents with scalable retrieval and training features. raglite: Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL. See the comparison table for live GitHub stats and shared categories. ### When should I choose deeplake over raglite? Choose deeplake over raglite when deeplake is primarily C++; raglite is Python; License: deeplake is Apache-2.0, raglite is MPL-2.0; Pricing: Pricing details are not specified for Deeplake's public repository.; Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`.; Tags unique to deeplake: agent, agentic-rag, ai, computer-vision; Also covers Vector Databases; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### When should I choose raglite over deeplake? Choose raglite over deeplake when raglite is primarily Python; deeplake is C++; License: raglite is MPL-2.0, deeplake is Apache-2.0; Tags unique to raglite: chainlit, colbert, duckdb, evals; raglite ships Docker support for self-hosted deployment; - You need to leverage Retriever-Reader architectures specifically optimized for either DuckDB or PostgreSQL backend databases. ### When should I avoid deeplake? If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training. ### When should I avoid raglite? - The project demands integration with RAG systems that natively support database backends other than DuckDB and PostgreSQL, as RAGLite is limited to these two options. - You are looking for a more generalized framework that supports multiple vector search engines besides those compatible with DuckDB or PostgreSQL. ### Is deeplake or raglite more popular on GitHub? deeplake has more GitHub stars (9,202 vs 1,194). Stars measure visibility, not whether either tool fits your constraints. ### Are deeplake and raglite open source? Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, raglite: MPL-2.0). ### Where can I find alternatives to deeplake or raglite? GraphCanon lists graph-backed alternatives at [deeplake alternatives](/tools/activeloopai-deeplake/alternatives) and [raglite alternatives](/tools/superlinear-ai-raglite/alternatives) ([deeplake markdown twin](/tools/activeloopai-deeplake/alternatives.md), [raglite markdown twin](/tools/superlinear-ai-raglite/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/activeloopai-deeplake-vs-superlinear-ai-raglite.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deeplake or raglite? deeplake: Steady. raglite: Very active. 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 deeplake and raglite? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deeplake trust report](/tools/activeloopai-deeplake/trust); [raglite trust report](/tools/superlinear-ai-raglite/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=activeloopai-deeplake`](/api/graphcanon/graph?tool=activeloopai-deeplake) - 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/_