Cortexon transforms enterprise data into intelligent, context-aware AI agents. The platform ingests documents from multiple sources—such as PDFs, Word files, and databases—using advanced embedding and semantic indexing techniques. It constructs a knowledge graph that powers a natural language interface, enabling seamless question answering and decision support. Users can customize conversation flows, define response templates, and integrate the agent into websites, chat applications, or internal tools via REST APIs and SDKs. Cortexon also offers real-time analytics to monitor user interactions and optimize performance. Its secure, scalable infrastructure ensures data privacy and compliance, making it suitable for customer support automation, internal knowledge management, sales enablement, and research acceleration across various industries.

Graph_RAG is a Python-based framework designed to build and query knowledge graphs for retrieval-augmented generation (RAG). It supports ingestion of unstructured documents, automated extraction of entities and relationships using LLMs or NLP tools, and storage in graph databases such as Neo4j. With Graph_RAG, developers can construct connected knowledge graphs, execute semantic graph queries to identify relevant nodes and paths, and feed the retrieved context into LLM prompts. The framework provides modular pipelines, configurable components, and integration examples to facilitate end-to-end RAG applications, improving answer accuracy and interpretability through structured knowledge representation.

Knowledge-Discovery-Agents provides a modular set of pre-built and customizable AI agents designed to extract structured insights from PDFs, CSVs, websites, and other sources. It integrates with LangChain to manage tool usage, supports chaining of tasks like web scraping, embedding generation, semantic search, and knowledge graph creation. Users can define agent workflows, incorporate new data loaders, and deploy QA bots or analytics pipelines. With minimal boilerplate code, it accelerates prototyping, data exploration, and automated report generation in research and enterprise contexts.

LangGraph-Chatchat functions as a memory management plugin for the ChatChat conversational framework, utilizing LangGraph’s graph database model to store and retrieve conversation context. During runtime, user inputs and agent responses are converted into semantic nodes with relationships, forming a comprehensive knowledge graph. This structure allows efficient querying of past interactions based on similarity metrics, keywords, or custom filters. The plugin supports configuration of memory persistence, node merging, and TTL policies, ensuring relevant context retention without bloat. With built-in serializers and adapters, LangGraph-Chatchat seamlessly integrates into ChatChat deployments, providing developers a robust solution for building AI agents capable of maintaining long-term memory, improving response relevance, and handling complex dialog flows.

MemU is an agentic memory layer built to function as an intelligent and autonomous file system for AI companions, transforming memory management by organizing, linking, and continuously improving stored data. It integrates with major LLMs like OpenAI and Anthropic, enhancing the AI's ability to memorize and recall conversations and knowledge efficiently, thus optimizing AI agent performance and user experience.

Mindcore Labs provides a no-code and developer-friendly environment to design and launch AI agents. It features a knowledge graph memory system that retains context over time, supports ingestion of documents and data sources, and integrates with external APIs and plugins. Users can configure agents via an intuitive UI or CLI, test them in real time, and deploy to production endpoints. Built-in monitoring and analytics help track performance and optimize agent behaviors.