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Introducing Mirage, a unified virtual filesystem for AI agents! 6 weeks. 1.1M+ lines of code. We rewrote bash from the ground up so cat, grep, head, and pipes work across heterogeneous services. S3, Google Drive, Slack, Gmail, GitHub, Linear, Notion, Postgres, MongoDB, SSH, and more, all mounted side-by-side as one filesystem. Bash that AI agents already know works on every format! cat, grep, head, and wc parse .parquet, .csv, .json, .h5, even .wav! One pipe can stitch S3, Drive, GitHub, Slack, and Linear together, same Unix semantics throughout. Workspaces are versioned too. Snapshot, clone, and roll back the whole thing with one API call. A two-layer cache turns repeated reads into local lookups, so agent loops stay fast and cheap. Drop a Workspace into FastAPI, Express, or a browser app. Wire it into OpenAI Agents SDK, Vercel AI SDK, LangChain, Mastra, or Pi. Run it alongside Claude Code and Codex. Site: GitHub: #AIAgents# #OpenSource# #AgenticAI# #Strukto# #Filesystem# #VFS#

Introducing AIO Sandbox, All-in-One Sandbox Environment for AI Agents. Unchecked AI autonomy is a ticking time bomb; it’s time to pull the plug on full system unfettered access. We can no longer afford to give AI agents the 'keys to the kingdom' without oversight. The 'wild west' of AI agents running with total system control is officially over. AIO Sandbox is an open-source project designed to solve these problems. It is everything your agent needs, out of the box. No more juggling multiple services. AIO Sandbox ships a complete, pre-wired environment in a single Docker container. The AIO (All-in-One) Sandbox is a containerized environment designed for both human developers and AI agents. Its architecture is built around a "Batteries-Included" philosophy, providing a full Linux desktop-like environment inside a single Docker container. Unified Environment: One Docker container with shared filesystem. Files downloaded in the browser are instantly accessible in Terminal and VSCode. Out of the Box: Built‑in VNC browser, VS Code, Jupyter, file manager, and terminal—accessible directly via API/SDK. Agent-Ready: Pre-configured MCP Server with Browser, File, Terminal, Markdown, Ready-to-use for AI agents. Developer Friendly: Cloud-based VSCode with persistent terminals, intelligent port forwarding, and instant frontend/backend previews. Secure Execution: Isolated Python and Node.js sandboxes. Safe code execution without system risks. Production Ready: Enterprise-grade Docker deployment. Lightweight, scalable. Calling all AI agent developers! How are you securing your builds? Let’s try running your agent in AIO Sandbox and compare notes. AIO Sandbox is open-sourced under the Apache License 2.0. Contributions welcome. GitHub: Official website: #OpenSource# #AIAgent# #Docker#

(1/2) Glad to announce our OpenMAIC! 🎉 Open-sourcing MAIC (Multi-Agent Interactive Classroom) from Tsinghua University — LLM-driven multi-agent classroom for scalable & adaptive online education. 🏗️ Core Architecture: ✅ MAIC-Craft: Read (multimodal extraction) → Plan (course components + agent generation) ✅ Adaptive Engine: Cognitive student modeling + Token-level personalization (RAG + Bloom's/ZPD/UDL) ✅ Multi-Agent Classroom: 1 Student + N Agents (Teacher, Assistant, 4 Peer Archetypes) ✅ Manager Agent: Class state receptor for turn-taking orchestration 🔗 Give it a try 👉🏻 GitHub: #AI# #EdTech# #MultiAgent# #LLM# #Research# #OpenSource# #Tsinghua#

Next week, I will be starting my certificate in Open Source Technology Management (OSTM) at @BrandeisU taught by @jimfhall, @GeorgLink, & @jredding. This is the only curriculum in the world like this! S/o to the @OpenSourceOrg for helping make this program a reality! #OpenSource#

Wanna know the best way to see if your product/system/software is 💩 or 🔥? Open source it or make it free. Is. Free. For beginner traders that wanna skip a few levels above retail trading. For pros that want a more quant approach to trading. For anyone who is tired of losing and not knowing why.

🌟Introducing🎻Violin — an Open-source Video Translation Skill. 📹Video is the dominant medium on the internet, yet most high-quality content (lecture, talk, podcast) is locked behind a single language, leaving global audiences behind. So we built Violin: a video skill that combines speech recognition, LLM translation, and speech synthesis into one seamless pipeline. 🌐 Demo: 📝 Blog: 🔗 GitHub: ✨Key Features: 🎙️High-quality multilingual ASR & Translation & TTS. 🗣️Personalize translation & voice (turn an academic talk into something children can follow). 💬Chat with the video — ask any questions grounded in the video. 🧩Support Web app, CLI, and Agent skill 🍃Fully open-source under MIT. ❤️Built with the wonderful @ShangZhu18 and advised by @james_y_zou ! All features powered by @togethercompute . Try it and let us know what you think! 🎻

Stop testing and rewriting prompts manually! Most teams run evals, look at failures, guess what's wrong, rewrite the prompt, then repeat. It's slow and you never know if your rewrite actually fixes the root issue. The better way is evolutionary optimization. Instead of manual rewrites, you use genetic algorithms to analyze eval feedback and rewrite prompts automatically. The algorithm maintains diverse prompt candidates that excel at different problem types, not just one "best" version. DeepEval does this using GEPA - Genetic Evolution with Pareto Selection. You provide a prompt template, test cases, and metrics to optimize for. The optimizer handles the rest. Here's how it works: It splits your test cases into validation and feedback sets. The validation set scores every prompt fairly. The feedback set provides training signals for mutations. Then it starts evolving. It selects a parent prompt, runs it on a minibatch of test cases, collects metric feedback on what failed, and uses an LLM to rewrite the prompt addressing those issues. If the rewritten prompt scores better, it gets added to the candidate pool. After several iterations, it returns the highest-scoring prompt. Key capabilities: • Works with 50+ built-in metrics - answer relevancy, hallucination, bias, task completion, and more. • Supports multi-objective optimization - optimize for multiple metrics simultaneously without forcing tradeoffs. • Configurable iterations and minibatch sizes - control search thoroughness and compute cost. The best part? It's 100% open source. Link to DeepEval in the comments!

Most slides AI create locked artifacts. So I built Starry Slides — an open-source tool for creating fully editable HTML slides. Built for the agent era: - HTML as source files - Fully editable with a WYSIWYG editor - No built-in templates — the input context defines the design - Works with your own agents and workflows The goal: Content creation should feel more like human–AI collaboration than one-shot generation. Quick demo ↓

Pie is now open source. It helps you turn local agents like Codex, OpenClaw, Hermes, and Pi into IM bots for Feishu/Lark, WeChat, Discord (more channels on the way). You can manage and monitor multiple agents in one desktop app: runtime lifecycle, models, skills, channel behavior, live status, usage, and logs. For each IM channel, Pie lets you control how the agent behaves: show or hide thinking/tool calls, respond only to the owner, or stay silent and just observe. GitHub: