Graphiti-RAG-Agent：基于时序知识图谱的本地优先智能RAG系统

Graphiti-RAG-Agent is a local-first intelligent RAG system that integrates Neo4j temporal knowledge graph, Pydantic AI, and local Ollama models to achieve fact-based strict retrieval and effectively eliminate AI hallucination.

Basic Information:

• Author/Maintainer: Shreyash-Gaur
• Source Platform: GitHub
• Original Link: https://github.com/Shreyash-Gaur/Graphiti-RAG-Agent
• Update Time: 2026-05-31T22:31:24Z

Key Features:

• Temporal knowledge graph support via Neo4j
• Local-first architecture using Ollama for offline/privacy protection
• Agentic design for autonomous retrieval planning
• Strict fact retrieval to minimize hallucinations

Key keywords: RAG, knowledge graph, Neo4j, temporal reasoning, local LLM, Ollama, Pydantic AI, AI hallucination

Retrieval-Augmented Generation (RAG) has become a mainstream solution to LLM hallucination by retrieving external documents. However, traditional RAG faces new challenges:

• Static knowledge representation: Hard to capture complex entity relationships with flat document collections
• Temporal information loss: Fails to handle dynamic knowledge changes over time
• Limited reasoning: Simple document retrieval can't support multi-hop complex queries

Graphiti-RAG-Agent is designed to address these issues.

The system's architecture consists of three core layers:

Neo4j Knowledge Graph Layer

• Stores entities/relations as nodes/edges for complex knowledge networks
• Supports timestamps and version control for temporal tracking
• Uses Cypher query language for efficient multi-hop reasoning

Pydantic AI Integration

• Ensures type safety via Pydantic models
• Validates and parses structured AI output
• Enables tool calling to expand agent capabilities

Ollama Local Model

• Runs open-source models (Llama, Mistral, Phi) locally
• Protects data privacy (no external API calls)
• Reduces costs and latency

Graphiti-RAG-Agent's standout feature is temporal reasoning support:

Temporal Knowledge Representation

• Records valid time (when knowledge holds), transaction time (when added), and version info in Neo4j

Temporal Query Examples

• "Who was the company's CEO in 2025?"
• "What function changes did this product have in the past three years?"
• "What's the market trend based on the latest data?"

The system returns accurate knowledge states for specific time points.

The system uses three layers to ensure factual accuracy:

1. Knowledge Graph Constraints: Structured triples (subject-relation-object) reduce ambiguity
2. Traceability: Answers can be traced to specific KG nodes/relations for verification
3. Local Model Control: Full control over model behavior (e.g., fine-tuning) to reduce hallucination tendencies

Graphiti-RAG-Agent is suitable for:

• Enterprise Knowledge Management: Build internal KG to integrate cross-departmental, time-based knowledge for accurate queries
• Historical Data Analysis: Ideal for finance, legal, medical fields needing temporal data processing
• Privacy-Sensitive Apps: Local-first architecture protects sensitive data (medical, legal, financial) from external leaks

Graphiti-RAG-Agent represents an important evolution of RAG—from document retrieval to structured knowledge graph retrieval. It combines Neo4j's graph capabilities, Pydantic's type safety, and Ollama's local models to excel in hallucination elimination, temporal reasoning, and privacy protection.

For developers and enterprises building reliable, explainable, privacy-friendly AI apps, it provides a valuable reference architecture.