---
type: entity
subtype: tool
title: "Graphiti"
description: "A knowledge-graph tool Cole names as a candidate for a future agentic-RAG-with-knowledge-graphs strategy."
resource: "https://github.com/getzep/graphiti"
tags: [knowledge-graphs, rag, memory-systems, open-source]
videos: [the-only-ai-tech-stack-you-need-in-2026, every-rag-strategy-explained-in-13-minutes-no-fluff, should-i-build-my-ai-agents-with-n8n-or-python, knowledge-graphs-in-n8n-are-finally-here, introducing-rag-20-agentic-rag-knowledge-graphs-free-template, all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants, make-rag-100x-better-with-real-time-knowledge-graphs, the-proven-solution-for-unbelievable-rag-performance-lightrag-guide]
created: 2026-07-21
updated: 2026-07-21
---

# Graphiti

Graphiti is Cole's favorite open-source library for building [knowledge graphs](../../concepts/knowledge-graphs.md), and specifically temporal-aware ones. As he introduces it, "an open- source platform with you right now called Graphiti. Graphiti is all about building temporal aware knowledge graphs" ([0:00:33]). It is the extraction-and-query layer that sits on top of a graph database: "Graphiti is the library. It's the tooling that gives us the ability to extract from raw text the entities and relationships to then store in Neo4j" ([0:06:25]). Where a plain [vector embedding](../../concepts/vector-embeddings.md) pipeline captures similarity, Graphiti captures structure, making it the backbone of Cole's [agentic RAG](../../concepts/agentic-rag.md) with knowledge graphs.

## How Cole uses it

Cole pairs Graphiti with [Neo4j](./neo4j.md) as a two-part system: Graphiti is the intelligent library, Neo4j is the underlying engine. He uses it "for the library to help us insert data into Neo4j and search our knowledge graphs here. I love using Graphiti... very intelligent entity and relationship extraction" ([0:16:16]), and calls it "my favorite library for working with knowledge graphs" ([0:05:14]). The entity and relationship extraction is LLM-driven: ingested as timestamped episodes, "the different entities and their relationships, all of this has to be defined by large language models" ([0:21:09]).

Its temporal model is the differentiator Cole keeps returning to. Because it tracks how facts change over time with "subsecond latency," "Graphiti is a lot more lightweight and scalable... you can seriously take Graphiti all the way to production environments" ([0:05:09]), and "If you're looking to build AI agents that work with constantly changing information, then Graphiti is the solution for you" ([0:10:32]). That temporal awareness is also why it underpins a real [memory system](../../concepts/memory-systems.md): "it powers the core of Zep's memory layer" ([0:09:57]).

Graphiti recurs across his stack in both code and no-code contexts. It "gives us the MCP server that makes it possible to use Graphiti with n8n" ([0:06:45]), it anchors his RAG 2.0 template "working alongside Neo4j which is the underlying knowledge graph engine" ([0:05:41]), and he names it as the path to "building out agentic RAG with knowledge graphs with something like Graphiti or LightRAG" ([0:18:51]).

> **Contradiction:** Cole calls Graphiti "a lot more lightweight and scalable" with subsecond latency and production-readiness ([0:05:09], make-rag-100x-better-with-real-time-knowledge-graphs), while elsewhere he presents [LightRAG](./lightrag.md) as the "proven solution for unbelievable RAG performance." These are framed as alternative knowledge-graph engines for the same job; he recommends evaluating both against your latency and freshness needs rather than treating either as strictly superior.

## Realizes

- [Knowledge Graphs](../../concepts/knowledge-graphs.md) - Structuring knowledge as entities and relationships (often temporal) so agents can traverse connections a vector store can't express.
- [Temporal-Aware Knowledge Graphs](../../concepts/temporal-knowledge-graphs.md) - A knowledge graph that never overwrites a changed fact but stores the new one alongside the old with valid_at/invalidated_at timestamps, so an agent knows both what is true now and how the knowledge evolved.
- [Agentic RAG](../../concepts/agentic-rag.md) - Giving an agent retrieval as a tool so it decides when and how to search a knowledge base, picking strategies and issuing follow-up queries instead of a single static lookup.
- [Memory Systems](../../concepts/memory-systems.md) - Giving agents durable short- and long-term memory so conversations, user context, and facts persist across sessions and users.

## Works with

- [Neo4j](./neo4j.md) - The graph database used to store and visualize the repository knowledge graph.
- [n8n](./n8n.md) - A no-code AI automation platform for building agents and workflows that integrate with 500+ applications.
- [Docling](./docling.md) - The open-source Python data-extraction/chunking tool that is the subject of the video summarized and integrated into Archon during the workflow demo.
- [Crawl4AI](./crawl4ai.md) - The open-source crawling library powering Cole's RAG MCP server, handling llms.txt, sitemaps, and recursive scraping.

## Contrasts with

- [LightRAG](./lightrag.md) - A knowledge-graph RAG solution Cole previously covered; suited to static document summarization but slower to build and query than Graphiti.
- [GraphRAG](./graphrag.md) - A popular knowledge-graph RAG implementation Cole groups with LightRAG as built for static, rarely-changing document summarization rather than dynamic incrementally-updated data.

## Related

- [Retrieval-Augmented Generation (RAG)](../../concepts/rag.md) - Grounding an LLM's answers by retrieving relevant documents from an external knowledge base and injecting them into the prompt at query time.
- [Traditional RAG](../../concepts/traditional-rag.md) - The classic one-shot, static RAG pipeline that embeds a query, runs a single vector similarity search, and stuffs the top chunks into the prompt.
- [Vector Embeddings](../../concepts/vector-embeddings.md) - Numeric representations of text that enable semantic similarity search, stored and managed in a vector database for retrieval.
- [Chunking](../../concepts/chunking.md) - Splitting source documents into appropriately sized, embeddable segments, the foundational preprocessing step that determines RAG retrieval quality.

## Sources

- [Make RAG 100x Better with Real-Time Knowledge Graphs](../../sources/make-rag-100x-better-with-real-time-knowledge-graphs.md) - "[0:00:33] an open- source platform with you right now called Graphiti. Graphiti is all about building temporal aware knowledge graphs."
- [Knowledge Graphs in n8n are FINALLY Here!](../../sources/knowledge-graphs-in-n8n-are-finally-here.md) - "[0:06:25] Graphiti is the library. It's the tooling that gives us the ability to extract from raw text the entities and relationships to then store in Neo4j and it gives us the MCP server that makes it possible to use Graphiti with n8n."
- [The ONLY AI Tech Stack You Need in 2026](../../sources/the-only-ai-tech-stack-you-need-in-2026.md) - "[0:16:16] for the library to help us insert data into Neo4j and search our knowledge graphs here. I love using Graphiti... very intelligent entity and relationship extraction."
- [The PROVEN Solution for Unbelievable RAG Performance (LightRAG Guide)](../../sources/the-proven-solution-for-unbelievable-rag-performance-lightrag-guide.md) - "[0:09:57] The solution to this is a powerful open-source platform for building real time knowledge graphs called Graphiti... it powers the core of Zep's memory layer."
- [Introducing RAG 2.0: Agentic RAG + Knowledge Graphs (FREE Template)](../../sources/introducing-rag-20-agentic-rag-knowledge-graphs-free-template.md) - "[0:21:09] all of these different um episodes as they're called with Graphiti... the different entities and their relationships, all of this has to be defined by large language models."
- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](../../sources/every-rag-strategy-explained-in-13-minutes-no-fluff.md) - "[0:05:14] This is the pseudo code if you want to see an example using Graphiti which is my favorite library for working with knowledge graphs."
- [All the BEST RAG Strategies in ONE MCP for AI Coding Assistants](../../sources/all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants.md) - "[0:18:51] building out agentic RAG with knowledge graphs with something like Graphiti or LightRAG."
- [Should I Build My AI Agents with n8n or Python?](../../sources/should-i-build-my-ai-agents-with-n8n-or-python.md) - "[0:13:13] All these things that I've covered on my channel like Graphiti for knowledge graphs, Docling for rag."
