Search Results for "structured storage"
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Showing 42 open source projects for "structured storage"
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Unstract
No-code LLM Platform to launch APIs and ETL Pipelines
...It integrates a visual Prompt Studio environment where users can iteratively design extraction schemas, compare outputs from different models, and monitor costs and accuracy side by side, making it easier to refine prompts and extraction logic before deploying at scale. Unstract supports deploying structured extraction as REST API endpoints or embedding it into data engineering ETL pipelines, which allows it to plug directly into data warehouses, cloud storage, or downstream analytics systems. Its platform works with a broad variety of file types — from PDFs and spreadsheets to images — and includes integrations with databases, cloud storage providers, and vector databases. -
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OpenViking
Context database designed specifically for AI Agents
...The project is implemented with performance in mind, often leveraging optimized data structures that balance fast reads and writes with minimal resource consumption. Developers can integrate OpenViking into modern AI stacks to unify context storage across services, enabling consistent session history, personalized responses, and richer search experiences. -
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GBrain
Garry's Opinionated OpenClaw/Hermes Agent Brain
GBrain is an open-source AI memory system designed to give autonomous agents persistent, structured, and scalable long-term memory across interactions and workflows. It operates by transforming large collections of markdown documents, personal notes, and external data into a searchable knowledge base backed by PostgreSQL and vector embeddings, enabling both semantic and keyword-based retrieval. The system is tightly integrated with agent frameworks such as OpenClaw and Hermes, allowing AI... -
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Memary
The Open Source Memory Layer For Autonomous Agents
Memary is a journaling and personal memory management application that helps users record and retrieve past experiences. It focuses on simplicity, ease of use, and structured data storage for personal reflections and knowledge tracking. -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
EverMemOS is an open-source memory operating system built to give AI agents long-term, structured memory. It captures conversations, transforms them into organized memory units, and enables agents to recall past interactions with context and meaning. Instead of treating each prompt independently, it builds evolving user profiles, tracks preferences, and connects related events into coherent narratives. Its architecture combines memory storage, indexing, and retrieval with agent-level reasoning, allowing AI systems to make informed decisions based on prior interactions. ... -
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Eidos
An extensible framework for Personal Data Management
Eidos is an extensible personal data management platform designed to help users organize and interact with their information using a local-first architecture. The system transforms SQLite into a flexible personal database that can store structured and unstructured information such as notes, documents, datasets, and knowledge resources. Its interface is inspired by tools like Notion, allowing users to create documents, databases, and custom views to organize personal information. Unlike cloud-based knowledge tools, Eidos runs entirely on the user’s machine, ensuring privacy and high performance through local storage. -
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OpenMemory
Local long-term memory engine for AI apps with persistent storage
OpenMemory is a self-hosted memory engine designed to provide long-term, persistent storage for AI and LLM-powered applications. It enables developers to give otherwise stateless models a structured memory layer that can store, retrieve, and manage contextual information over time. OpenMemory is built around a hierarchical memory architecture that organizes data into semantic sectors and connects them through a graph-based structure for efficient retrieval. -
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ReMe
Memory Management Kit for Agents
ReMe is a memory management kit for AI agents that gives them structured, persistent memory capabilities, enabling agents to extract, store, and reuse information across sessions, tasks, and interactions. It is designed to support long-running agent workflows where context matters and working memory alone isn’t enough, helping agents remember user preferences, task histories, and relevant past observations. -
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Beads
A memory upgrade for your coding agent
...This approach helps coding agents — and human collaborators — track which tasks depend on others, what has been done, and where workflows branch or reunify without losing important data. By leveraging Git as the storage backbone, the project ensures that memory is persistent, diffable, and sharable, with the ability to roll back, branch, or merge memory states just like source code. -
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memsearch
A Markdown-first memory system, a standalone library for any AI agent
memsearch is a markdown-first memory system designed to provide long-term memory capabilities for AI agents through structured storage and semantic retrieval. It enables agents to store, organize, and retrieve information using embeddings and hybrid search techniques, ensuring that relevant context is always available. The system supports advanced features such as reranking and progressive disclosure, which help prioritize the most useful information for a given query. ... -
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Wiseflow
Enhance any agent's browser use skill
Wiseflow is an open-source information extraction and knowledge discovery system designed to collect, filter, and organize valuable information from large volumes of online content. The platform continuously monitors specified sources such as websites, social platforms, and other digital channels to identify relevant data according to user-defined interests or topics. By combining web crawling, content parsing, and large language model analysis, the system extracts concise insights from raw... -
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Deep Research
Use any LLMs (Large Language Models) for Deep Research
Deep Research is a local-first research agent that orchestrates multiple LLMs to generate in-depth reports in minutes. It combines “thinking” and “task” model roles with live internet access to plan, search, read, and synthesize findings into structured outputs. The project emphasizes privacy: processing and storage happen locally, avoiding server-side retention of your queries and notes. A simple web UI lets you enter topics and configure models, while the backend streams progress as sources are fetched and arguments are weighed. It offers MCP server support and SSE APIs, so IDEs and agent clients can drive the same workflow programmatically. ... -
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Claude Context
Code search MCP for Claude Code
...It focuses on improving response quality by ensuring that models have access to the most relevant data when generating outputs. The system integrates with vector databases and retrieval systems, enabling efficient storage and retrieval of contextual information. It supports workflows such as retrieval-augmented generation, where external knowledge is dynamically incorporated into model responses. The project emphasizes scalability, allowing it to handle large datasets and complex queries efficiently. It also provides tools for organizing and managing context, making it easier to maintain structured knowledge bases. ... -
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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... -
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Omi
AI that sees your screen and listens to conversations
The Omi project is an open-source AI wearable ecosystem developed by Based Hardware that combines hardware, software, and cloud infrastructure to create a persistent “second brain” for capturing and processing real-world interactions. It is designed as a system that continuously listens to conversations and monitors screen activity, converting this input into structured data such as transcripts, summaries, and actionable insights in real time. The platform operates across multiple environments, including wearable devices, mobile apps, and desktop applications, ensuring seamless integration into a user’s daily workflow. At its core, omi uses a pipeline of speech-to-text systems, large language models, and memory storage services to transform raw audio and context into meaningful outputs like tasks and reminders. ... -
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Prompt Master
A Claude skill that writes the accurate prompts for any AI tool
Prompt Master is a repository focused on organizing, managing, and optimizing prompts for AI systems, particularly large language models. It provides a structured way to store and reuse prompts, making it easier to maintain consistency and improve performance across different applications. The project emphasizes clarity and organization, allowing users to categorize prompts by use case, domain, or functionality. It also supports experimentation, enabling users to refine prompts and compare... -
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camofox-browser
Headless browser automation server for AI agents to visit sites
camofox-browser is a headless browser automation server built specifically for AI agents that need to interact with websites that often block standard automation stacks. It wraps Camoufox, a Firefox fork that performs fingerprint spoofing at the C++ level, which means many browser characteristics are altered before page scripts can inspect them, rather than relying on JavaScript-layer stealth patches. The project is designed around a REST API, making it easier for agents and external tools... -
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WitNote
WitNote is a local-first AI writing companion for macOS and Windows
...For users who prefer to stay in command-line or keyboard-driven environments, WitNote offers efficient shortcuts that keep interactions nimble and fluid without forcing users into menus. Optional plug-ins or extensions expand its capabilities to include calendar integrations, reminders, or integration with external storage backends like Git repositories. -
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AI-Media2Doc
AI tool converting video/audio into structured documents instantly
AI-Media2Doc is a web-based application that uses large language models to convert video and audio content into structured, readable documents in a single workflow. It is designed to transform multimedia inputs into formats such as knowledge notes, summaries, mind maps, and social-style articles, making content easier to review and reuse. AI-Media2Doc emphasizes privacy by processing media locally in the browser using WebAssembly-based ffmpeg, ensuring that original video files are not... -
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Smart Excalidraw
A smart, powerful, and beautiful excalidraw drawing tool
Smart Excalidraw Next is an AI-powered diagramming tool that allows users to generate professional-quality visual diagrams directly from natural language descriptions, combining generative AI with the flexibility of the Excalidraw canvas. It leverages large language models to interpret user input and automatically produce structured diagrams such as flowcharts, architecture diagrams, ER diagrams, and mind maps with logical layouts and clean visual organization. One of its key innovations is... -
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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. It is built around a context engineering framework that manages the full lifecycle of data, including capture, processing, storage, retrieval, and consumption. ... -
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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... -
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Hollama
A minimal LLM chat app that runs entirely in your browser
Hollama is a lightweight open-source chat application designed to run entirely within the browser while interacting with large language model servers. The project provides a minimal but powerful user interface for communicating with local or remote LLMs, including servers powered by Ollama or OpenAI-compatible APIs. Because the application runs as a static web interface, it does not require complex backend infrastructure and can be easily deployed or self-hosted. Hollama supports both... -
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MemPalace
The highest-scoring AI memory system ever benchmarked
MemPalace is an open-source AI memory system designed to solve one of the most persistent limitations of large language models: the loss of context between sessions. Instead of relying on summarization or selective extraction like most memory tools, it takes a radically different approach by storing conversations in their entirety and making them retrievable through structured organization and semantic search. The system is inspired by the classical “memory palace” mnemonic technique,... -
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Acontext
Context data platform for building observable, self-learning AI agents
...The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable.