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.

Deep Research for Obsidian integrates with OpenAI to power an intelligent research assistant inside Obsidian. It can query academic databases and the web, ingest PDFs and reference metadata, produce concise summaries, highlight missing links in your knowledge graph, and propose an exploration path to further your study. All outputs are stored as markdown notes with citations, allowing seamless integration with your existing note-taking workflow.

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.

InLinks is a comprehensive entity-based Semantic SEO platform that leverages a proprietary semantic analyzer and knowledge graph. It helps users in optimizing content precisely for search engines by automating internal links, auditing existing content, and offering data-driven content briefs. The tool is built to demystify and optimize content, facilitating better understanding by search engines, ultimately improving site rankings.

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 Agent combines LLMs with a graph-structured memory to build autonomous agents that can remember facts, reason over relationships, and call external functions or tools when needed. Developers define memory schemas as graph nodes and edges, plug in custom tools or APIs, and orchestrate agent workflows through configurable planners and executors. This approach enhances context retention, enables knowledge-driven decision making, and supports dynamic tool invocation in diverse applications.

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.

Salieri is an innovative platform designed to streamline AI project development through multi-agent applications. By leveraging advanced AI technologies, Salieri enhances productivity and efficiency, making it easier for teams to automate workflows. Salieri's intuitive design and powerful functionalities allow users to translate detailed ideas into interactive, illustrated stories, perfect for narrative-driven projects, games, and more. Offering robust and efficient systems, Salieri integrates knowledge graphs and formal engines to improve the accuracy and cost-effectiveness of AI models.

GraphSignal is an AI-driven graph intelligence platform that seamlessly integrates vector-based embeddings and knowledge graph structures. Users can connect their data sources, automatically generate embeddings using built-in or custom models, and index nodes and edges for real-time semantic querying. The platform offers RESTful APIs and SDKs to perform advanced graph analytics, similarity searches, recommendations, and question-answering tasks across connected data. Its dynamic visualization tools help teams explore relationships and derive actionable insights from complex networks.

Tech Research Agent operates by first receiving a research query, then dispatching web searches via Google Serp API. It crawls result URLs, extracts code samples and textual content, applies natural language processing for summarization, and builds a knowledge graph of key concepts. Using OpenAI GPT, it synthesizes findings into coherent technical reports in markdown format. It supports customization of search depth, summarization granularity, and output templates. With built-in caching and parallel processing, the agent accelerates large-scale literature reviews, API explorations, and competitive analysis, enabling users to quickly identify trends, best practices, and relevant code examples for technology evaluation.