--- title: "dagster vs docetl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dagster-io-dagster-vs-ucbepic-docetl" tools: ["dagster-io-dagster", "ucbepic-docetl"] --- # dagster vs docetl *GraphCanon updated Sep 20, 2026* ## Verdict Pick dagster if dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows; 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. [dagster](https://dagster.io) reports 16k GitHub stars, 2.3k forks, and 2.6k open issues, last pushed Sep 11, 2026. [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 [dagster's repository](https://github.com/dagster-io/dagster) and [docetl's repository](https://github.com/ucbepic/docetl). | | [dagster](/tools/dagster-io-dagster.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Tagline | An orchestration platform for data assets | A system for agentic LLM-powered data processing and ETL | | Stars | 16,144 | 4,092 | | Forks | 2,290 | 443 | | Open issues | 2,587 | 45 | | Language | Python | Python | | Adopt for | Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. | 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 | Apache-2.0 | MIT | | Categories | Data & Retrieval, Evaluation & Observability | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dagster](/tools/dagster-io-dagster.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 2d | 9d | | Open issues (now) | 2.6k | 45 | | Stars delta | +195 (30d) | +131 (30d) | | Open issues delta | -9 (30d) | +3 (30d) | | Full report | [trust report](/tools/dagster-io-dagster/trust.md) | [trust report](/tools/ucbepic-docetl/trust.md) | ## Shared compatibility - **Python**: [dagster](/tools/dagster-io-dagster.md) - Python runtime; [docetl](/tools/ucbepic-docetl.md) - Python runtime ## Decision facts: dagster - **Adopt for:** Dagster is an open-source orchestration platform developed in Python for managing and observing data pipelines and workflows. ## 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 dagster if… - License: dagster is Apache-2.0, docetl is MIT. - Tags unique to dagster: data-engineering, data-orchestrator, mlops, workflow. - Also covers Evaluation & Observability. - When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### Choose docetl if… - License: docetl is MIT, dagster is Apache-2.0. - Tags unique to docetl: agents, data, document-analysis, llm. - Also covers AI Agents. - 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 dagster - If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements. - In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem. - For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding. - If specific features or integrations crucial to your workflow are found lacking in comparison to competitors. ## 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 dagster and docetl? dagster: An orchestration platform for data assets. 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 dagster over docetl? Choose dagster over docetl when License: dagster is Apache-2.0, docetl is MIT; Tags unique to dagster: data-engineering, data-orchestrator, mlops, workflow; Also covers Evaluation & Observability; When your project requires an Apache-2.0 licensed tool allowing broader reuse and modification of code. ### When should I choose docetl over dagster? Choose docetl over dagster when License: docetl is MIT, dagster is Apache-2.0; Tags unique to docetl: agents, data, document-analysis, llm; Also covers AI Agents; 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 dagster? If you are restricted to proprietary or non-open-source licenses, as Dagster's Apache-2.0 might not align with compliance requirements. In environments where Python is not a preferred language, considering Dagster requires good knowledge of the Python ecosystem. For teams that do not require or benefit from extensive documentation and hands-on tutorials for onboarding. If specific features or integrations crucial to your workflow are found lacking in comparison to competitors. ### 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 dagster or docetl more popular on GitHub? dagster has more GitHub stars (16,144 vs 4,092). Stars measure visibility, not whether either tool fits your constraints. ### Are dagster and docetl open source? Yes - both are open-source projects on GitHub (dagster: Apache-2.0, docetl: MIT). ### Where can I find alternatives to dagster or docetl? GraphCanon lists graph-backed alternatives at [dagster alternatives](/tools/dagster-io-dagster/alternatives) and [docetl alternatives](/tools/ucbepic-docetl/alternatives) ([dagster markdown twin](/tools/dagster-io-dagster/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/dagster-io-dagster-vs-ucbepic-docetl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dagster or docetl? dagster: 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 dagster and docetl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dagster trust report](/tools/dagster-io-dagster/trust); [docetl trust report](/tools/ucbepic-docetl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dagster-io-dagster`](/api/graphcanon/graph?tool=dagster-io-dagster) - 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/_