AI Infrastructure Tools - Tensorlake enables transformation of unstructured documents into AI-ready data [1] - Zep facilitates building human-like memory for Agents [1] - Firecrawl empowers LLM applications with clean web data [1] - Milvus provides a high-performance vector DB for scalable vector search [1]

RT Avi Chawla (@_avichawla)Build human-like memory for your Agents (open-source)!Every agentic and RAG system struggles with real-time knowledge updates and fast data retrieval.Zep solves these issues with its continuously evolving and temporally-aware Knowledge Graph.Like humans, Zep organizes an Agent's memories into episodes, extracts entities and their relationships from these episodes, and stores them in a knowledge graph:(refer to the image below as you read)1) Episode Subgraph: Captures raw data with ...

Core Functionality - Zep aims to build human-like memory for agents, addressing real-time knowledge updates and fast data retrieval challenges in agentic and RAG systems [1] - Zep organizes agent memories into episodes, extracts entities and relationships, and stores them in a knowledge graph [1] - The system features an Episode Subgraph for capturing raw, timestamped data, a Semantic Entity Subgraph for extracting and versioning entities and facts, and a Community Subgraph for grouping related entities [1][2] Performance Metrics - Zep delivers up to 1850% (18.5 times) higher accuracy with 90% lower latency compared to tools like MemGPT [2] Open Source Nature - Zep is fully open-source [2]

Problem Statement & Solution - Current memory frameworks struggle with relevance, leading to inaccurate responses or hallucinations due to the storage of arbitrary facts [3][4][5] - Semantic similarity does not equate to business relevance, as vector databases lack causal or relational understanding [7] - The industry needs domain-aware memory solutions instead of relying solely on better semantic search [8] - Zep offers a solution by enabling developers to model memory after their specific business domain, creating more cogent and capable memory [1][2] Zep's Implementation & Features - Zep allows developers to define custom entities and edges within its graph framework, tailoring memory to specific business objects [1][9] - Developers can use Pydantic, Zod, or Go structs to define business rules for these entities and their fields [9][10] - Zep's SDK allows defining entity types with descriptions and business rules for fields, enabling precise control over data stored [10] - Zep allows building tools for agents to retrieve financial snapshots by running multiple searches concurrently and filtering by specific node types [10][11] - Zep's front end provides a knowledge graph visualization, allowing users to see the relationships and fields defined for each entity [12] Demonstration & Use Case - A finance coach application demonstrates Zep's ability to store explicit business objects like financial goals, debts, and income sources [8][9] - The application captures relevant information, such as a $5,000 monthly rent, and stores it as a debt account entity with defined fields [11][12]