--- title: "graphify vs kedro" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/graphify-labs-graphify-vs-kedro-org-kedro" tools: ["graphify-labs-graphify", "kedro-org-kedro"] --- # graphify vs kedro *GraphCanon updated Jul 11, 2026* ## Verdict Pick graphify when license: graphify is MIT, kedro is Apache-2.0; pick kedro when license: kedro is Apache-2.0, graphify is MIT. [graphify](https://graphifylabs.ai/) reports 82k GitHub stars, 8.1k forks, and 452 open issues, last pushed Jul 11, 2026. [kedro](https://kedro.org) has 11k stars, 1.1k forks, and 161 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [graphify's repository](https://github.com/Graphify-Labs/graphify) and [kedro's repository](https://github.com/kedro-org/kedro). | | [graphify](/tools/graphify-labs-graphify.md) | [kedro](/tools/kedro-org-kedro.md) | | --- | --- | --- | | Tagline | Turn any code or documentation into a queryable knowledge graph | Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, an | | Stars | 82,139 | 10,911 | | Forks | 8,086 | 1,050 | | Open issues | 452 | 161 | | 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 | Apache-2.0 | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graphify](/tools/graphify-labs-graphify.md) | [kedro](/tools/kedro-org-kedro.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 452 | 161 | | Full report | [trust report](/tools/graphify-labs-graphify/trust.md) | [trust report](/tools/kedro-org-kedro/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… - License: graphify is MIT, kedro is Apache-2.0. - 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: gemini, rag, codex, skills. - 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 kedro if… - License: kedro is Apache-2.0, graphify is MIT. - Tags unique to kedro: machine-learning, agentic-workflow, hacktoberfest, agentic-ai. - Leaner open-issue backlog (161). ## 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 kedro - 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. ## Common questions ### What is the difference between graphify and kedro? graphify: Turn any code or documentation into a queryable knowledge graph. kedro: Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, an. See the comparison table for live GitHub stats and shared categories. ### When should I choose graphify over kedro? Choose graphify over kedro when License: graphify is MIT, kedro is Apache-2.0; 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: gemini, rag, codex, skills; 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 kedro over graphify? Choose kedro over graphify when License: kedro is Apache-2.0, graphify is MIT; Tags unique to kedro: machine-learning, agentic-workflow, hacktoberfest, agentic-ai; Leaner open-issue backlog (161). ### 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 kedro? 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. ### Is graphify or kedro more popular on GitHub? graphify has more GitHub stars (82,139 vs 10,911). Stars measure visibility, not whether either tool fits your constraints. ### Are graphify and kedro open source? Yes - both are open-source projects on GitHub (graphify: MIT, kedro: Apache-2.0). ### Where can I find alternatives to graphify or kedro? GraphCanon lists graph-backed alternatives at [graphify alternatives](/tools/graphify-labs-graphify/alternatives) and [kedro alternatives](/tools/kedro-org-kedro/alternatives) ([graphify markdown twin](/tools/graphify-labs-graphify/alternatives.md), [kedro markdown twin](/tools/kedro-org-kedro/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-kedro-org-kedro.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, graphify or kedro? graphify: Very active. kedro: 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 graphify and kedro? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graphify trust report](/tools/graphify-labs-graphify/trust); [kedro trust report](/tools/kedro-org-kedro/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/_