Search Results for "system information"
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Showing 120 open source projects for "system information"
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MiroFish
A Simple and Universal Swarm Intelligence Engine
MiroFish is a next-generation artificial intelligence prediction engine that leverages multi-agent technology and swarm-intelligence simulation to model, simulate, and forecast complex real-world scenarios. The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. Users can inject variables or conditions into this simulated environment from a “god’s eye view,” enabling iterative prediction of future trends under different assumptions, which can be useful for decision support, scenario planning, or creative exploration. ... -
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docext
An on-premises, OCR-free unstructured data extraction
...This allows the system to detect and extract structured elements such as tables, signatures, key fields, and layout information while maintaining semantic understanding of the document content. The toolkit can also convert complex documents into structured markdown representations that preserve formatting and contextual relationships. -
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Papermerge
Open Source Document Management System for Digital Archives
Papermerge is an open source document management system (DMS) primarily designed for archiving and retrieving your digital documents. Instead of having piles of paper documents all over your desk, office or drawers - you can quickly scan them and configure your scanner to directly upload to Papermerge DMS. Store, organize and index scanned documents in PDF, JPEG and TIFF formats. Instantly find relevant information using full text, tags and metadata-based search. -
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RAPTOR
The official implementation of RAPTOR
...Traditional RAG systems typically retrieve small text chunks independently, which can limit a model’s ability to understand broader document context. RAPTOR addresses this limitation by recursively embedding, clustering, and summarizing documents to create a tree-structured hierarchy of information. Each level of the tree represents summaries at different levels of abstraction, allowing retrieval to operate at both detailed and high-level conceptual layers. During inference, the system can navigate this hierarchical representation to retrieve information that best matches the user’s query while preserving broader contextual understanding. ... -
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MemoryOS
MemoryOS is designed to provide a memory operating system
MemoryOS is an open-source framework designed to provide a structured memory management system for AI agents and large language model applications. The project addresses one of the major limitations of modern language models: their inability to maintain long-term context beyond the limits of their prompt window. MemoryOS introduces a hierarchical memory architecture inspired by operating system memory management principles, allowing agents to store, update, retrieve, and generate information from multiple layers of memory. ... -
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Pal
A personal context-agent that learns how you work
Pal is an open-source AI personal agent built within the Agno ecosystem that functions as an intelligent digital assistant designed to learn from user activity over time. The system acts as an AI-powered “second brain” capable of capturing, organizing, and retrieving personal knowledge such as notes, bookmarks, research findings, people, and meeting information. Instead of acting as a simple chatbot, Pal continuously builds a structured database of a user’s knowledge and context so it can answer questions, recall information, and assist with future tasks more effectively. ... -
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MESHROOM
3D reconstruction software
...Take advantage of motorized-head file. Easy to integrate in your Renderfarm System. Add specific rules to select the most suitable machines regarding CPU, RAM, GPU requirements of each Node. -
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WebGLM
An Efficient Web-enhanced Question Answering System
WebGLM is a web-enhanced question-answering system that combines a large language model with web search and retrieval capabilities to produce more accurate answers. The system is based on the General Language Model architecture and was designed to enable language models to interact directly with web information during the question-answering process. Instead of relying solely on knowledge stored in the model’s training data, the system retrieves relevant web content and integrates it into the reasoning process. ... -
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PyKEEN
A Python library for learning and evaluating knowledge graph embedding
PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information). PyKEEN is a Python package for reproducible, facile knowledge graph embeddings. PyKEEN has a function pykeen.env() that magically prints relevant version information about PyTorch, CUDA, and your operating system that can be used for debugging. If you’re in a Jupyter Notebook, it will be pretty-printed as an HTML table. -
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MaiBot
Maimaibot, a (more focused) multi-platform intelligent agent
...The project focuses on creating a more human-like interactive experience by combining large language models with behavioral planning and contextual awareness. Instead of functioning as a traditional command-driven chatbot, the system attempts to simulate natural social participation within group conversations. It can generate responses that imitate human speech patterns, learn slang or expressions from chat participants, and adapt its conversational style based on previous interactions. The architecture includes a memory system that stores conversation history and contextual information, allowing the bot to recall previous events and maintain continuity in discussions. -
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MiroThinker
MiroThinker is an open source deep research agent
MiroThinker is an open-source deep research AI agent designed to perform complex reasoning, information gathering, and predictive analysis tasks. The system focuses on enabling long-horizon research workflows by allowing the agent to interact repeatedly with external tools, search systems, and data sources while refining its reasoning through iterative steps. Rather than simply generating responses from a single prompt, the agent performs structured multi-step reasoning processes that involve searching for information, analyzing evidence, and synthesizing conclusions. ... -
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ROSA
I Agent designed to interact with ROS1- and ROS2-based robotics system
ROSA, short for Robot Operating System Agent, is an AI-powered software assistant developed by NASA’s Jet Propulsion Laboratory to simplify interaction with robotic systems that use the Robot Operating System (ROS). The project provides a natural language interface that allows developers and operators to interact with robots by issuing commands or queries in conversational language. Built on top of frameworks such as LangChain and modern large language models, ROSA translates user... -
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MineContext
MineContext is your proactive context-aware AI partner
MineContext is an open-source, proactive AI assistant designed to capture, understand, and leverage a user’s digital context in order to provide meaningful insights, summaries, and productivity support. The system continuously collects contextual data from sources such as screenshots and user activity, then processes and organizes this information into structured knowledge that can be reused later. Unlike traditional chat-based assistants, MineContext operates in the background and delivers proactive outputs such as daily summaries, task suggestions, and contextual reminders without requiring explicit prompts. ... -
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OpenRecall
OpenRecall is a fully open-source, privacy-first alternative
OpenRecall is an open-source, privacy-first system designed to capture, index, and make searchable a user’s entire digital activity history, effectively acting as a personal memory layer for computing environments. It works by taking periodic screenshots of a user’s screen and applying local AI processing, including OCR and semantic analysis, to extract and structure information from both text and images. -
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Streamer-Sales
LLM Large Model of Selling Anchor
...The system integrates multiple AI technologies including retrieval-augmented generation to incorporate product knowledge, speech synthesis to convert generated scripts into voice output, and digital human generation to create virtual hosts. It also supports automatic speech recognition and agent-based tools that can retrieve additional information such as logistics or product details during live sessions. -
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Hallucination Leaderboard
Leaderboard Comparing LLM Performance at Producing Hallucinations
Hallucination Leaderboard is an open research project that tracks and compares the tendency of large language models to produce hallucinated or inaccurate information when generating summaries. The project provides a standardized benchmark that evaluates different models using a dedicated hallucination detection system known as the Hallucination Evaluation Model. Each model is tested on document summarization tasks to measure how often generated responses introduce information that is not supported by the original source material. ... -
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BettaFish
Public opinion analysis system
...With a design that lets users pose questions in natural language and receive structured reports, charts, and visualizations, the system aims to break information cocoons and provide comprehensive views of trends and public sentiment. Unlike simpler analytics tools, BettaFish employs agent collaboration and a “forum” style internal mechanism to combine diverse model outputs, making the analysis richer and more robust. It also integrates multimodal processing, enabling it to parse images and video alongside text. -
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PySyft
Data science on data without acquiring a copy
...The Syft ecosystem seeks to change this system, allowing you to write software which can compute over information you do not own on machines you do not have (total) control over. This not only includes servers in the cloud, but also personal desktops, laptops, mobile phones, websites, and edge devices. Wherever your data wants to live in your ownership, the Syft ecosystem exists to help keep it there while allowing it to be used privately. -
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Local File Organizer
An AI-powered file management tool that ensures privacy
...The system scans directories, extracts relevant information from files, and restructures folder hierarchies to make content easier to locate and manage. Through AI-driven analysis, the software can detect themes, topics, and metadata in files, allowing it to organize information in ways that traditional rule-based file managers cannot achieve. -
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AI Powered Knowledge Graph Generator
AI Powered Knowledge Graph Generator
...These capabilities make knowledge graph platforms particularly useful for applications such as recommendation engines, enterprise knowledge management, and research data exploration. The system emphasizes structured data modeling and graph-based queries that allow users to explore relationships that would be difficult to identify using traditional relational databases. -
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SGR Agent Core
Schema-Guided Reasoning (SGR) has agentic system design
...The framework provides a core library that allows developers to design autonomous agents capable of structured reasoning and complex task execution. Instead of relying solely on free-form prompts, the system organizes reasoning processes around schemas that guide how agents analyze problems, gather information, and generate outputs. This architecture enables agents to follow structured reasoning workflows while still benefiting from the flexibility of large language models. The framework includes a BaseAgent interface and a two-phase architecture that separates reasoning planning from execution, allowing developers to implement custom agent behaviors and research pipelines. -
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WeKnora
LLM framework for document understanding and semantic retrieval
WeKnora is an open source framework developed for deep document understanding and semantic information retrieval using large language models. It focuses on analyzing complex and heterogeneous documents by combining multiple processing stages such as multimodal document parsing, vector indexing, and intelligent retrieval. It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. ... -
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Paper2Slides
From Paper to Presentation in One Click
...It is designed to replace the repetitive work of turning dense technical documents into presentation-friendly structure by extracting key points, figures, and data into a coherent visual narrative. The system supports multiple input formats, so you can process PDFs and common office documents rather than being locked to a single file type. It uses an extraction approach intended to capture critical insights comprehensively, including important visuals and data points that often get missed in naive summarization. A major focus is traceability: generated slide content is designed to remain linked back to the source material so you can verify accuracy and reduce information drift. ... -
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Conversational Health Agents (CHA)
A Personalized LLM-powered Agent Frameworks
CHA, or Conversational Health Agents, is an open-source framework designed to build intelligent healthcare assistants powered by large language models and external data sources. The system enables developers to create personalized AI agents that can interact with users through natural language while performing multi-step reasoning and task execution. It integrates orchestration capabilities that allow the agent to gather information from APIs, knowledge bases, and external services in order to generate more accurate and context-aware responses. ... -
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uqlm
Uncertainty Quantification for Language Models, is a Python package
UQLM is a Python library developed to detect hallucinations and quantify uncertainty in the outputs of large language models. The system implements a variety of uncertainty quantification techniques that assign confidence scores to model responses. These scores help developers determine how likely a generated answer is to contain errors or fabricated information. The library includes both black-box and white-box approaches to uncertainty estimation. Black-box methods evaluate model outputs through multiple generations or comparative analysis, while white-box methods rely on token probabilities produced during inference. ...
