--- title: "graphify vs docetl" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/graphify-labs-graphify-vs-ucbepic-docetl" tools: ["graphify-labs-graphify", "ucbepic-docetl"] --- # graphify vs docetl *GraphCanon updated Jul 15, 2026* ## Verdict Pick graphify when requirements: Ensure to install from the correct PyPI package named `graphifyy` (with double 'y') and not other similar-named packages which are unaffiliated.; Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed.; pick docetl when tags unique to docetl: agents, data, data-pipelines, document-analysis. [graphify](https://graphifylabs.ai/) reports 82k GitHub stars, 8.1k forks, and 452 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 [graphify's repository](https://github.com/Graphify-Labs/graphify) and [docetl's repository](https://github.com/ucbepic/docetl). | | [graphify](/tools/graphify-labs-graphify.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Tagline | Turn any code or documentation into a queryable knowledge graph | A system for agentic LLM-powered data processing and ETL | | Stars | 82,139 | 3,888 | | Forks | 8,086 | 414 | | Open issues | 452 | 41 | | Language | Python | Python | | Adopt for | Graphify transforms a variety of inputs into a unified knowledge graph, ideal for creating searchable insights from mixed content types. | - | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graphify](/tools/graphify-labs-graphify.md) | [docetl](/tools/ucbepic-docetl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 18d | | Open issues (now) | 452 | 41 | | Full report | [trust report](/tools/graphify-labs-graphify/trust.md) | [trust report](/tools/ucbepic-docetl/trust.md) | ## Decision facts: graphify - **Requirements:** Ensure to install from the correct PyPI package named `graphifyy` (with double 'y') and not other similar-named packages which are unaffiliated.; Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed. - **Adopt for:** Graphify transforms a variety of inputs into a unified knowledge graph, ideal for creating searchable insights from mixed content types. ## Choose when ### Choose graphify if… - Requirements: Ensure to install from the correct PyPI package named `graphifyy` (with double 'y') and not other similar-named packages which are unaffiliated.; Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed.. - Tags unique to graphify: claude code, codex, gemini, knowledge-graph. - When you need to turn diverse file types (code, SQL schemas, documents, images) into a single queryable data structure that can be searched and analyzed together. ### Choose docetl if… - Tags unique to docetl: agents, data, data-pipelines, document-analysis. - Also covers LLM Frameworks. - docetl ships Docker support for self-hosted deployment. ## When NOT to use graphify - If the project exclusively involves text-based content without requiring integration or querying across different file types (e.g., plain documents with no need for cross-referencing). - Avoid using Graphify if you are looking for a tool that focuses solely on visual graph representation without the depth of semantic querying capabilities. ## 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 graphify and docetl? graphify: Turn any code or documentation into a queryable knowledge graph. 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 graphify over docetl? Choose graphify over docetl when Requirements: Ensure to install from the correct PyPI package named `graphifyy` (with double 'y') and not other similar-named packages which are unaffiliated.; Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed.; Tags unique to graphify: claude code, codex, gemini, knowledge-graph; When you need to turn diverse file types (code, SQL schemas, documents, images) into a single queryable data structure that can be searched and analyzed together. ### When should I choose docetl over graphify? Choose docetl over graphify when Tags unique to docetl: agents, data, data-pipelines, document-analysis; Also covers LLM Frameworks; docetl ships Docker support for self-hosted deployment. ### When should I avoid graphify? If the project exclusively involves text-based content without requiring integration or querying across different file types (e.g., plain documents with no need for cross-referencing). Avoid using Graphify if you are looking for a tool that focuses solely on visual graph representation without the depth of semantic querying capabilities. ### 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 graphify or docetl more popular on GitHub? graphify has more GitHub stars (82,139 vs 3,888). Stars measure visibility, not whether either tool fits your constraints. ### Are graphify and docetl open source? Yes - both are open-source projects on GitHub (graphify: MIT, docetl: MIT). ### Where can I find alternatives to graphify or docetl? GraphCanon lists graph-backed alternatives at [graphify alternatives](/tools/graphify-labs-graphify/alternatives) and [docetl alternatives](/tools/ucbepic-docetl/alternatives) ([graphify markdown twin](/tools/graphify-labs-graphify/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/graphify-labs-graphify-vs-ucbepic-docetl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, graphify or docetl? graphify: 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 graphify and docetl? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graphify trust report](/tools/graphify-labs-graphify/trust); [docetl trust report](/tools/ucbepic-docetl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=graphify-labs-graphify`](/api/graphcanon/graph?tool=graphify-labs-graphify) - 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/_