SuperAgentX is a modular, open-source multi-agent AI framework designed for orchestrating autonomous agents that plan, act, and execute real-world workflows. It supports 100+ LLM providers (OpenAI, Azure, Gemini, Claude, Bedrock, Ollama, LiteLLM) and 10,000+ MCP tools via the Model Context Protocol. The architecture centers on `AgentXPipe` pipelines that sequence or parallelize agents, each composed of an `Engine` that wraps an LLM client and a set of tool handlers. Handlers are defined using a `@tool` decorator pattern that auto-generates LLM function schemas. The framework includes built-in human-in-the-loop approval channels (console, Slack, Teams), persistent memory with SQLite and vector stores (Chroma, OpenSearch), full observability via OpenTelemetry and SQL-based telemetry, and a CLI for project scaffolding. It also features a production-grade browser automation engine based on Playwright for tasks where APIs are unavailable.
Promnesia is a browser extension (Chrome/Firefox) paired with a Python backend that enriches browsing history with contextual metadata from personal data sources. The backend indexes visits from browser history, bookmarks, chat logs (Telegram, Signal, etc.), social media (Reddit, Twitter, Hacker News), note-taking apps (Hypothesis, Pocket, Instapaper), and plaintext files into a SQLite database. A FastAPI server exposes endpoints (`/visits`, `/search`, `/visited`) that the extension queries to annotate visited links and display a sidebar with visit context, timestamps, and highlights. The system uses URL normalization (canonical URLs) to deduplicate visits across sources and supports user-defined filtering and hooks. The architecture is split into a Python CLI (`promnesia index` / `promnesia serve`), a TypeScript browser extension built with Rollup, and Docker containers for automated indexing. While the core is a personal data application, it also serves as a data pipeline for aggregating and querying personal activity, giving it a hybrid character.
MemReduct is a Windows desktop application for real-time memory monitoring and cleaning, implemented in C with the Win32 API and the undocumented Native API. It operates as a system tray utility that can manually or automatically purge various Windows memory caches: working sets, standby list, modified page list, system file cache, registry cache (Win8.1+), and combined memory lists (Win10+). The core cleaning logic uses NtSetSystemInformation with specific information classes, supplemented by volume cache flushing via the MOUNTMGR device. The application requires administrator privileges for full functionality and provides a configuration GUI with settings for auto-clean thresholds, intervals, hotkeys, language selection, and tray icon appearance. The architecture is monolithic: a single executable built from source files (main.c, main.h, resource files) that links against a reusable utility library (the 'routine' submodule). This library abstracts common Win32 tasks such as registry/INI config management, localization via .ini files, custom listview controls, theme handling, and device context operations. The application entry point is wWinMain in main.c, which creates the main dialog and processes window messages via DlgProc. Memory status is polled every second via a timer callback, and cleaning actions are triggered either manually through the UI or automatically based on user-defined thresholds. External dependencies are limited to Windows system DLLs (kernel32, user32, ntdll, advapi32) and the two submodules: 'routine' (utility code) and 'builder' (installer packaging). The build system uses Visual Studio 2017 project files with batch scripts for x86, x64, and ARM64 targets. No network dependencies exist except an optional update check. The codebase demonstrates non-obvious skill in low-level Windows system programming, custom GDI tray icon rendering, and conditional feature compilation, but remains maintainable through its separation of concerns in the routine library.
This repository is an 'awesome list' curated by the TeleAI-UAGI team, aggregating open-source and closed-source products, papers, benchmarks, tutorials, and surveys on memory systems for LLM/MLLM agents. It does not contain application business logic or runnable services; its primary output is a meticulously structured README.md that serves as a reference catalog. The accompanying Python scripts automate two maintenance tasks: (1) checking that open-source entries are ordered by GitHub star count and respecting a 100-star boundary for emerging projects, and (2) generating self-hosted star-history SVG charts. The scripts are well-crafted, with robust parsing, GitHub API integration, and careful edge-case handling (grace bands, orphan detection), making them reusable for other awesome-list maintainers. The repository's value lies in its curated content rather than its code, but the supporting infrastructure demonstrates solid engineering principles.