--- title: "graphiti vs PageIndex" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/getzep-graphiti-vs-vectifyai-pageindex" tools: ["getzep-graphiti", "vectifyai-pageindex"] --- # graphiti vs PageIndex Neutral, constraint-first comparison with live GitHub stats. | | [graphiti](/tools/getzep-graphiti.md) | [PageIndex](/tools/vectifyai-pageindex.md) | | --- | --- | --- | | Tagline | Build Real-Time Knowledge Graphs for AI Agents | Document Index for Vectorless, Reasoning-based RAG | | Stars | 28,498 | 33,874 | | Forks | 2,869 | 2,962 | | Open issues | 415 | 134 | | Language | Python | Python | | Adopt for | Graphiti is a framework for building temporal context graphs, essential for AI agents that operate in environments with rapidly evolving or frequently changing data. | PageIndex is a reasoning-based RAG system suitable for applications requiring context-aware retrieval and avoiding vector databases or chunking, specifically designed to handle professional long-form documents. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, AI Agents | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graphiti](/tools/getzep-graphiti.md) | [PageIndex](/tools/vectifyai-pageindex.md) | | --- | --- | --- | | Open issues (now) | 415 | 134 | | Security scan | No lockfile | 2 low (2 low) | | Full report | [trust report](/tools/getzep-graphiti/trust.md) | [trust report](/tools/vectifyai-pageindex/trust.md) | **Typed relationship:** graphiti _(alternative)_ PageIndex PageIndex focuses on context-aware retrieval and reasoning for RAG, whereas Graphiti is aimed at building real-time knowledge graphs which can be utilized by AI agents for improved understanding of the relational data. ## Decision facts: graphiti - **Requirements:** Min 4 GB RAM; - Python runtime is required as Graphiti is built in Python.; - Apache-2.0 licensed, meaning it's free to use but any contributions should respect the open-source nature.; - Familiarity with graph databases and temporal data management concepts can help leverage the full potential of Graphiti in AI agent development. - **Adopt for:** Graphiti is a framework for building temporal context graphs, essential for AI agents that operate in environments with rapidly evolving or frequently changing data. ## Decision facts: PageIndex - **Requirements:** PageIndex operates independently of vector databases, and it does not require Docker. However, specific resource requirements depend on the scale of documents. - **Adopt for:** PageIndex is a reasoning-based RAG system suitable for applications requiring context-aware retrieval and avoiding vector databases or chunking, specifically designed to handle professional long-form documents. ## Choose when ### Choose graphiti if… - License: graphiti is Apache-2.0, PageIndex is MIT. - Requirements: Min 4 GB RAM; - Python runtime is required as Graphiti is built in Python.; - Apache-2.0 licensed, meaning it's free to use but any contributions should respect the open-source nature.; - Familiarity with graph databases and temporal data management concepts can help leverage the full potential of Graphiti in AI agent development.. - PageIndex focuses on context-aware retrieval and reasoning for RAG, whereas Graphiti is aimed at building real-time knowledge graphs which can be utilized by AI agents for improved understanding of the relational data. - Tags unique to graphiti: llms, graph. - Also covers AI Agents. - graphiti ships Docker support for self-hosted deployment. - - When developing interactive applications where the context graph needs to evolve dynamically based on user interactions and external information. ### Choose PageIndex if… - License: PageIndex is MIT, graphiti is Apache-2.0. - Requirements: PageIndex operates independently of vector databases, and it does not require Docker. However, specific resource requirements depend on the scale of documents.. - PageIndex focuses on context-aware retrieval and reasoning for RAG, whereas Graphiti is aimed at building real-time knowledge graphs which can be utilized by AI agents for improved understanding of the relational data. - Tags unique to PageIndex: llm, reasoning, agentic-ai, information-retrieval. - - When handling long professional documents that require deep contextual understanding and multi-step reasoning where traditional similarity searches fall short. ## When NOT to use graphiti - - If your application requires only a static snapshot of the knowledge graph at a given point in time. - - For use cases where data does not change rapidly or significantly over time, making the tracking of temporal validity windows unnecessary. - - When you prefer traditional retrieval-augmented generation (RAG) methods without support for incremental updates and efficient historical queries. ## When NOT to use PageIndex - - If your application relies on quick, chunk-based indexing as PageIndex constructs a hierarchical tree index which could be slower for small documents or real-time applications. - - In scenarios where you already have an established and optimized vector database infrastructure that performs well for your retrieval needs. ## Common questions ### What is the difference between graphiti and PageIndex? graphiti: Build Real-Time Knowledge Graphs for AI Agents. PageIndex: Document Index for Vectorless, Reasoning-based RAG. See the comparison table for live GitHub stats and shared categories. ### When should I choose graphiti over PageIndex? Choose graphiti over PageIndex when License: graphiti is Apache-2.0, PageIndex is MIT; Requirements: Min 4 GB RAM; - Python runtime is required as Graphiti is built in Python.; - Apache-2.0 licensed, meaning it's free to use but any contributions should respect the open-source nature.; - Familiarity with graph databases and temporal data management concepts can help leverage the full potential of Graphiti in AI agent development.; PageIndex focuses on context-aware retrieval and reasoning for RAG, whereas Graphiti is aimed at building real-time knowledge graphs which can be utilized by AI agents for improved understanding of the relational data; Tags unique to graphiti: llms, graph; Also covers AI Agents; graphiti ships Docker support for self-hosted deployment; - When developing interactive applications where the context graph needs to evolve dynamically based on user interactions and external information. ### When should I choose PageIndex over graphiti? Choose PageIndex over graphiti when License: PageIndex is MIT, graphiti is Apache-2.0; Requirements: PageIndex operates independently of vector databases, and it does not require Docker. However, specific resource requirements depend on the scale of documents.; PageIndex focuses on context-aware retrieval and reasoning for RAG, whereas Graphiti is aimed at building real-time knowledge graphs which can be utilized by AI agents for improved understanding of the relational data; Tags unique to PageIndex: llm, reasoning, agentic-ai, information-retrieval; - When handling long professional documents that require deep contextual understanding and multi-step reasoning where traditional similarity searches fall short. ### When should I avoid graphiti? - If your application requires only a static snapshot of the knowledge graph at a given point in time. - For use cases where data does not change rapidly or significantly over time, making the tracking of temporal validity windows unnecessary. - When you prefer traditional retrieval-augmented generation (RAG) methods without support for incremental updates and efficient historical queries. ### When should I avoid PageIndex? - If your application relies on quick, chunk-based indexing as PageIndex constructs a hierarchical tree index which could be slower for small documents or real-time applications. - In scenarios where you already have an established and optimized vector database infrastructure that performs well for your retrieval needs. ### Is graphiti or PageIndex more popular on GitHub? PageIndex has more GitHub stars (33,874 vs 28,498). Stars measure visibility, not whether either tool fits your constraints. ### Are graphiti and PageIndex open source? Yes - both are open-source projects on GitHub (graphiti: Apache-2.0, PageIndex: MIT). ### Where can I find alternatives to graphiti or PageIndex? GraphCanon lists graph-backed alternatives at /tools/getzep-graphiti/alternatives and /tools/vectifyai-pageindex/alternatives (/tools/getzep-graphiti/alternatives.md, /tools/vectifyai-pageindex/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 /compare/getzep-graphiti-vs-vectifyai-pageindex.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, graphiti or PageIndex? graphiti: Very active. PageIndex: 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 graphiti and PageIndex? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: graphiti: /tools/getzep-graphiti/trust; PageIndex: /tools/vectifyai-pageindex/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=getzep-graphiti`](/api/graphcanon/graph?tool=getzep-graphiti) - 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/_