Graphiti – An open-source AI framework for dynamic knowledge graph generation

What is Graphiti

Graphiti is an AI knowledge graph generation framework designed for dynamic environments. It enables AI agents to build queryable, time-aware knowledge networks. Graphiti can ingest and process both structured and unstructured data in real time, supporting dynamic data integration, state reasoning and automation, as well as hybrid retrieval. It is suitable for use in AI memory layers, real-time interaction systems, and enterprise knowledge management, providing powerful dynamic memory and reasoning capabilities to enhance the intelligence and efficiency of AI applications.

Key Features of Graphiti

• Dynamic Data Integration: Ingests structured and unstructured data in real time, supporting incremental updates.

• Time Awareness: Tracks the evolution of facts and relationships over time, enabling time-point queries.

• Hybrid Retrieval: Combines semantic search, keyword search, and graph traversal to enable low-latency queries.

• State Reasoning and Automation: Allows agents to automate tasks and perform reasoning based on the graph.

• Scalability: Designed to handle large-scale datasets with support for parallel processing.

• Custom Entity Types: Supports defining domain-specific entity types for more accurate knowledge representation.

• Multimodal Search: Supports complex queries across semantics, keywords, and graph structures.

Technical Principles of Graphiti

• Time-Aware Graphs: Adds temporal metadata to graph edges to record the lifecycle of relationships. Each relationship in the graph has a timestamp to track its full history from creation to termination.

• Event-Driven Architecture: Utilizes an event-driven architecture to ingest data as discrete events. Each event contains data source and timestamp information, supporting incremental processing.

• Hybrid Retrieval Engine: Combines semantic embeddings, keyword search, and graph traversal to enable low-latency, complex queries. Semantic embeddings understand text in vector space; keyword search quickly locates relevant text fragments; graph traversal enables relational reasoning. Query results are re-ranked based on their distance to the central node to further enhance relevance.

• Parallel Processing: Graphiti supports parallel data processing, enabling the handling of multiple data streams simultaneously to improve efficiency.

Project Links

• Official Website: https://help.getzep.com/graphiti/graphiti

• GitHub Repository: https://github.com/getzep/graphiti/

Application Scenarios for Graphiti

• AI Agent Memory Layer: Provides long-term memory for AI assistants to support personalized services.

• Real-Time Interaction Systems: Supports dynamic updates and historical queries, ideal for smart homes, intelligent customer service, etc.

• Enterprise Knowledge Management: Efficiently manages enterprise data, supporting complex queries and decision-making.

• Healthcare: Manages medical records and treatment data to assist AI in diagnosis and medical recommendations.

• Financial Analysis: Continuously updates market data to support risk assessment and decision optimization.