--- title: "rags vs docetl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/run-llama-rags-vs-ucbepic-docetl" tools: ["run-llama-rags", "ucbepic-docetl"] --- # rags vs docetl *GraphCanon updated Sep 20, 2026* ## Verdict Pick rags if decision-critical facts for 'rags':; pick docetl if docetl is an agentic system that employs large language models for data processing and ETL operations, specifically suited to handle unstructured document analysis tasks. [rags](https://github.com/run-llama/rags) reports 6.5k GitHub stars, 656 forks, and 37 open issues, last pushed Apr 5, 2024. [docetl](https://docetl.org) has 4.1k stars, 443 forks, and 45 open issues, last pushed Sep 5, 2026. Figures are from public GitHub metadata via [rags's repository](https://github.com/run-llama/rags) and [docetl's repository](https://github.com/ucbepic/docetl). | | [rags](/tools/run-llama-rags.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Tagline | Build ChatGPT over your data with natural language | A system for agentic LLM-powered data processing and ETL | | Stars | 6,549 | 4,092 | | Forks | 656 | 443 | | Open issues | 37 | 45 | | Language | Python | Python | | Adopt for | Decision-critical facts for 'rags': | Docetl is an agentic system that employs large language models for data processing and ETL operations, specifically suited to handle unstructured document analysis tasks. | | Persona | - | - | | Runtime | - | - | | License | MIT License | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [rags](/tools/run-llama-rags.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 865d | 9d | | Open issues (now) | 37 | 45 | | Stars delta | +6 (30d) | +131 (30d) | | Open issues delta | -1 (30d) | +3 (30d) | | Full report | [trust report](/tools/run-llama-rags/trust.md) | [trust report](/tools/ucbepic-docetl/trust.md) | ## Shared compatibility - **Python**: [rags](/tools/run-llama-rags.md) - Python runtime; [docetl](/tools/ucbepic-docetl.md) - Python runtime ## 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 ## Decision facts: docetl - **Adopt for:** Docetl is an agentic system that employs large language models for data processing and ETL operations, specifically suited to handle unstructured document analysis tasks. ## Choose when ### Choose rags if… - Requirements: Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment.. - Tags unique to rags: agent, chatbot, chatgpt, openai. - When leveraging natural language queries over proprietary user data using OpenAI services. ### Choose docetl if… - Tags unique to docetl: agents, data, document-analysis, etl. - docetl ships Docker support for self-hosted deployment. - When you require integration with any LLM provider through API keys like OPENAI_API_KEY. ## 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. ## When NOT to use docetl - If your project strictly requires low-latency processing for real-time applications, as Docetl's agentic approach might introduce higher latency due to backend API calls. - In scenarios where the document datasets are predominantly structured or semi-structured, making traditional ETL tools more efficient. ## Common questions ### What is the difference between rags and docetl? rags: Build ChatGPT over your data with natural language. docetl: A system for agentic LLM-powered data processing and ETL. See the comparison table for live GitHub stats and shared categories. ### When should I choose rags over docetl? Choose rags over docetl when Requirements: Installation leverages poetry for dependency management.; Setup requires configuration with OpenAI key and potentially creating a virtual environment.; Tags unique to rags: agent, chatbot, chatgpt, openai; When leveraging natural language queries over proprietary user data using OpenAI services. ### When should I choose docetl over rags? Choose docetl over rags when Tags unique to docetl: agents, data, document-analysis, etl; docetl ships Docker support for self-hosted deployment; When you require integration with any LLM provider through API keys like OPENAI_API_KEY. ### 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. ### When should I avoid docetl? If your project strictly requires low-latency processing for real-time applications, as Docetl's agentic approach might introduce higher latency due to backend API calls. In scenarios where the document datasets are predominantly structured or semi-structured, making traditional ETL tools more efficient. ### Is rags or docetl more popular on GitHub? rags has more GitHub stars (6,549 vs 4,092). Stars measure visibility, not whether either tool fits your constraints. ### Are rags and docetl open source? Yes - both are open-source projects on GitHub (rags: MIT, docetl: MIT). ### Where can I find alternatives to rags or docetl? GraphCanon lists graph-backed alternatives at [rags alternatives](/tools/run-llama-rags/alternatives) and [docetl alternatives](/tools/ucbepic-docetl/alternatives) ([rags markdown twin](/tools/run-llama-rags/alternatives.md), [docetl markdown twin](/tools/ucbepic-docetl/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/run-llama-rags-vs-ucbepic-docetl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, rags or docetl? rags: Dormant. docetl: 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 rags and docetl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [rags trust report](/tools/run-llama-rags/trust); [docetl trust report](/tools/ucbepic-docetl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=run-llama-rags`](/api/graphcanon/graph?tool=run-llama-rags) - 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/_