--- title: "awesome-llm-apps vs docetl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/shubhamsaboo-awesome-llm-apps-vs-ucbepic-docetl" tools: ["shubhamsaboo-awesome-llm-apps", "ucbepic-docetl"] --- # awesome-llm-apps vs docetl *GraphCanon updated Jul 15, 2026* ## Verdict Pick awesome-llm-apps when license: awesome-llm-apps is Apache-2.0, docetl is MIT; pick docetl when license: docetl is MIT, awesome-llm-apps is Apache-2.0. [awesome-llm-apps](https://www.theunwindai.com) reports 120k GitHub stars, 18k forks, and 17 open issues, last pushed Jul 11, 2026. [docetl](https://docetl.org) has 3.9k stars, 414 forks, and 41 open issues, last pushed Jun 26, 2026. Figures are from public GitHub metadata via [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps) and [docetl's repository](https://github.com/ucbepic/docetl). | | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Tagline | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | A system for agentic LLM-powered data processing and ETL | | Stars | 119,936 | 3,888 | | Forks | 17,799 | 414 | | Open issues | 17 | 41 | | Language | Python | Python | | Adopt for | awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. | - | | Persona | - | - | | Runtime | - | - | | License | The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 3d | 18d | | Open issues (now) | 17 | 41 | | Owner type | User | Organization | | Full report | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | [trust report](/tools/ucbepic-docetl/trust.md) | ## Shared compatibility - **Python**: [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - Python runtime; [docetl](/tools/ucbepic-docetl.md) - Python runtime ## Decision facts: awesome-llm-apps - **Pricing:** freemium - Free with open-source licensing, but commercial exploitation is allowed. - **Adopt for:** awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. - **License detail:** The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. ## Choose when ### Choose awesome-llm-apps if… - License: awesome-llm-apps is Apache-2.0, docetl is MIT. - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: applications, customizable, deployable, llms. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ### Choose docetl if… - License: docetl is MIT, awesome-llm-apps is Apache-2.0. - Tags unique to docetl: data, data-pipelines, document-analysis, document-processing. - Also covers LLM Frameworks. - docetl ships Docker support for self-hosted deployment. ## When NOT to use awesome-llm-apps - If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. - When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ## When NOT to use docetl - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between awesome-llm-apps and docetl? awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. 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 awesome-llm-apps over docetl? Choose awesome-llm-apps over docetl when License: awesome-llm-apps is Apache-2.0, docetl is MIT; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: applications, customizable, deployable, llms; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### When should I choose docetl over awesome-llm-apps? Choose docetl over awesome-llm-apps when License: docetl is MIT, awesome-llm-apps is Apache-2.0; Tags unique to docetl: data, data-pipelines, document-analysis, document-processing; Also covers LLM Frameworks; docetl ships Docker support for self-hosted deployment. ### When should I avoid awesome-llm-apps? If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ### When should I avoid docetl? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is awesome-llm-apps or docetl more popular on GitHub? awesome-llm-apps has more GitHub stars (119,936 vs 3,888). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-llm-apps and docetl open source? Yes - both are open-source projects on GitHub (awesome-llm-apps: Apache-2.0, docetl: MIT). ### Where can I find alternatives to awesome-llm-apps or docetl? GraphCanon lists graph-backed alternatives at [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) and [docetl alternatives](/tools/ucbepic-docetl/alternatives) ([awesome-llm-apps markdown twin](/tools/shubhamsaboo-awesome-llm-apps/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/shubhamsaboo-awesome-llm-apps-vs-ucbepic-docetl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-llm-apps or docetl? awesome-llm-apps: Very active. 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 awesome-llm-apps and docetl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust); [docetl trust report](/tools/ucbepic-docetl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=shubhamsaboo-awesome-llm-apps`](/api/graphcanon/graph?tool=shubhamsaboo-awesome-llm-apps) - 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/_