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memos

✨ Featured sponsor: CodeRabbit — Industry-leading AI code reviews . Memos Your thoughts, your data, shared on your terms. Memos is a timeline for your notes, and it belongs to you. Write in Markdown, post in seconds, and choose who sees each memo: just you, the people you invite, or anyone with the link. Run with Docker · Try the live demo · Read the docs Why Memos? Write first — Save a thought without choosing a title or folder. Memos are written in Markdown and can include images and files. Find it later — Search, filter by tag, or look back through any day on the timeline. Pin what matters and save the filters you reuse as views. Yours to keep — Self-host Memos with zero telemetry , MIT-licensed source , and a full export of your memos. Share when you choose — New memos are private. Make one visible to signed-in users or public when you want to share it. Explore all features → Quick Start Run Memos with Docker: docker run -d \ --name memos \ -p 5230:5230 \ -v ~ /.memos:/var/opt/memos \ neosmemo/memos:stable Other install options are in the deployment guide . Releases use YY.MM , with optional point releases such as 26.09.1 and release candidates such as 26.09-rc.1 . Calendar release tags have no v prefix. The Docker stable tag follows stable releases; canary follows development builds. If upgrading from a release before v0.31.0, run v0.31.0 successfully. See the upgrade requirements for earlier versions. Web Clipper Save pages, selections, and images from your browser straight into Memos as source-linked Markdown. Get the Memos Web Clipper for Chrome or Firefox . Sponsors Love Memos? Sponsor the project on GitHub . Get Help Read the docs , join Discord , or ask in GitHub Discussions . Found a bug or have an idea? Open an issue . To contribute, see the contributing guide . Star History

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crawl4ai

🚀🤖 Crawl4AI: the open-source web crawler for LLMs and AI agents Latest: v0.9.4 (23 Sep 2026) · all releases → Crawl4AI turns any website into clean, LLM-ready Markdown for RAG, AI agents and data pipelines. Run the open-source web crawler and scraper yourself, free forever, or use it hosted with one key: scrape, search and extract through one API, with MCP for your agent. Two ways to use Crawl4AI 🐍 Run it yourself: open source, forever pip install -U crawl4ai crawl4ai-setup # installs the browser, once import asyncio from crawl4ai import AsyncWebCrawler async def main (): async with AsyncWebCrawler () as crawler : result = await crawler . arun ( url = "https://news.ycombinator.com" ) print ( result . markdown ) asyncio . run ( main ()) Docker server, CLI and every option: Installation · docs.crawl4ai.com ☁️ Or use the cloud: no browsers, no proxies Verify your email and free credit to start is yours. No card. Soft launch: prices can change, what you buy stays yours. Get any page as Markdown: curl -s https://api.crawl4ai.com/scrape \ -H " Authorization: Bearer $CRAWL4AI_KEY " \ -H " Content-Type: application/json " \ -d ' {"url": "https://news.ycombinator.com"} ' | jq -r .markdown The same key works for /search , /answer , /extract and many URLs at once ( /scrape/batch , /scrape/jobs ). Pay as you go: live prices . Give it to your AI agent. Claude Code shown; Codex, Cursor and OpenCode → claude mcp add --transport http crawl4ai https://api.crawl4ai.com/mcp \ --header " Authorization: Bearer $CRAWL4AI_KEY " Which one? 🐍 Library 🐳 Your own server ☁️ Crawl4AI Cloud Runs the browsers you, in your Python process you, in Docker on your machine we do JS-heavy pages and bot walls your settings, your proxies your settings, your proxies handled for you, automatically Web search – – /search and /answer Price free, forever free (your hosting) pay as you go; free credit to start 🤓 My Personal Story I grew up on an Amstrad, thanks to my dad, and never stopped building. In grad school I specialized in NLP and built crawlers for research. That’s where I learned how much extraction matters. In 2023, I needed web-to-Markdown. The “open source” option wanted an account, API token, and $16, and still under-delivered. I went turbo anger mode, built Crawl4AI in days, and it went viral. Now it’s the most-starred crawler on GitHub. I made it open source for availability , anyone can use it without a gate. Now I’m building the platform for affordability , anyone can run serious crawls without breaking the bank. If that resonates, join in, send feedback, or just crawl something amazing. That platform is live now: Crawl4AI Cloud . Why developers pick Crawl4AI LLM-ready output : smart Markdown with headings, tables, code and citation hints Fast in practice : async browser pool, caching, minimal hops Full control : sessions, proxies, cookies, user scripts, hooks Adaptive intelligence : learns site patterns, explores only what matters Deploy anywhere : no keys needed, CLI and Docker, or the hosted cloud ✨ Features 📝 Markdown generation 🧹 Clean Markdown : headings, lists, tables and code blocks, in a structure an LLM reads well. 🎯 Fit Markdown : filters remove menus, footers and boilerplate: PruningContentFilterLXML , BM25ContentFilter (for a query) and LLMContentFilter . 🔗 Citations : page links become a numbered reference list. 🛠️ Your own strategy : plug in a custom Markdown generator. ☁️ Same in the cloud: POST /scrape returns this Markdown, with no browser to run. Docs → 📊 Structured data extraction 🔎 CSS and XPath schemas : fast extraction with no LLM ( JsonCssExtractionStrategy , JsonXPathExtractionStrategy , RegexExtractionStrategy ). 🪄 Schema generator : describe what you want once; generate_schema writes a reusable schema. 🤖 LLM extraction : any LLM provider, open-source or hosted, into a typed JSON schema ( LLMExtractionStrategy ). 🧱 Chunking : topic, regex and sentence chunking for long pages. 🌌 Cosine similarity : find the chunks that match a query ( CosineStrategy ). ☁️ Same in the cloud: POST /extract , with no LLM key of your own. Docs → 🌐 Browser control 🖥️ Your own browser : persistent profiles with saved logins, cookies and settings. 🔄 Remote browsers : connect over the Chrome DevTools Protocol (CDP). 🔒 Sessions : keep a browser state across multi-step crawls. 🧩 Proxies : with authentication and rotation. 🕶️ Stealth mode : enable_stealth , and an undetected-browser adapter for sites that detect automation. ⚙️ Full control : headers, cookies, user agents, viewport. 🌍 Chromium, Firefox and WebKit . 🔎 Crawling and scraping 🕸️ Deep crawl : BFS, DFS and best-first strategies, with crash recovery ( resume_state ) for long crawls. 🧠 Adaptive crawling : AdaptiveCrawler stops when it has learned enough to answer your query. 🌱 URL discovery : AsyncUrlSeeder (sitemaps, Common Crawl) and DomainMapper ; prefetch=True finds URLs 5 to 10 times faster. 🚀 Dynamic pages : run JavaScript, wait for elements, scroll the full page ( scan_full_page ) for infinite scroll and lazy images. 📸 Screenshots and PDFs of any page. 🖼️ Media and links : images, audio, video, srcset , internal and external links, iframes, metadata. 📂 Raw HTML and local files : raw: and file:// . 🛠️ Hooks at every step of a crawl. 💾 Caching to skip repeated fetches. ⚡ Many URLs at once : arun_many with a memory-adaptive dispatcher. ☁️ Same in the cloud: up to 50 URLs in one streamed call, or 10,000 in a background job. Docs → 🐳 Self-hosting (Docker) 🔐 Secure by default : every endpoint needs your CRAWL4AI_API_TOKEN . 🧰 REST API : /md , /html , /crawl , /crawl/stream , /screenshot , /pdf , /execute_js . 🤖 MCP : connect Claude Code and other agents to your own server. 📊 Monitoring dashboard and playground , a browser pool with pre-warmed pages. 🏗️ AMD64 and ARM64 images. ☁️ Rather not run a server? The cloud is the same idea, hosted. Get a key → ☁️ What the cloud adds 🔍 Web search API : GET /search , browser-free, ranked and cleaned. Docs → 💬 Answers : GET /answer gives a direct answer to a question (experimental). Docs → 🧪 Extraction without your own LLM key : POST /extract . Docs → 🧗 JS-heavy pages and bot walls : handled automatically; you never pick an engine. Docs → 🤝 MCP for your agent : one line in Claude Code, Codex, Cursor or OpenCode. Docs → 🛠️ Installation 🐍 pip pip install -U crawl4ai crawl4ai-setup # installs and sets up the browser crawl4ai-doctor # checks the installation If the browser setup fails, install it by hand: python -m playwright install --with-deps chromium Pre-release versions: pip install crawl4ai --pre Development install , for contributors: git clone https://github.com/unclecode/crawl4ai.git cd crawl4ai pip install -e " .[all] " # or: pip install -e . (the core only) 🐳 Docker server The server needs a token. Without one it answers only inside its container. export CRAWL4AI_API_TOKEN= " $( openssl rand -hex 32 ) " docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g \ -e CRAWL4AI_API_TOKEN= " $CRAWL4AI_API_TOKEN " \ unclecode/crawl4ai:latest Test it (allow about 10 seconds for the start): curl -s http://localhost:11235/md \ -H " Authorization: Bearer $CRAWL4AI_API_TOKEN " \ -H " Content-Type: application/json " \ -d ' {"url": "https://news.ycombinator.com"} ' | jq -r .markdown The dashboard is at http://localhost:11235/dashboard , the playground at http://localhost:11235/playground . LLM keys, MCP and every setting: Self-hosting guide . ⌨️ Command line (`crwl`) # A page as Markdown crwl https://news.ycombinator.com -o markdown # Deep crawl, breadth first, at most 10 pages crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10 # Ask a question about a page (needs an LLM key: crwl config) crwl https://www.example.com/products -q " Extract all product prices " 🔬 Advanced usage examples More in docs/examples . 📝 Clean and fit Markdown import asyncio from crawl4ai import AsyncWebCrawler , BrowserConfig , CrawlerRunConfig , CacheMode from crawl4ai . content_filter_strategy import PruningContentFilterLXML from crawl4ai . markdown_generation_strategy import DefaultMarkdownGenerator async def main (): run_config = CrawlerRunConfig ( cache_mode = CacheMode . BYPASS , markdown_generator = DefaultMarkdownGenerator ( content_filter = PruningContentFilterLXML ( threshold = 0.48 , threshold_type = "fixed" , min_word_threshold = 0 ) ), ) async with AsyncWebCrawler ( config = BrowserConfig ( headless = True )) as crawler : result = await crawler . arun ( url = "https://en.wikipedia.org/wiki/Web_crawler" , config = run_config ) print ( len ( result . markdown . raw_markdown ), "characters of raw Markdown" ) print ( len ( result . markdown . fit_markdown ), "characters after the filter" ) asyncio . run ( main ()) 🖥️ A JavaScript page and structured data, without an LLM import asyncio , json from crawl4ai import AsyncWebCrawler , BrowserConfig , CrawlerRunConfig , CacheMode , JsonCssExtractionStrategy schema = { "name" : "Quotes" , "baseSelector" : "div.quote" , "fields" : [ { "name" : "text" , "selector" : "span.text" , "type" : "text" }, { "name" : "author" , "selector" : "small.author" , "type" : "text" }, { "name" : "tags" , "selector" : "a.tag" , "type" : "list" , "fields" : [{ "name" : "tag" , "type" : "text" }]}, ], } async def main (): run_config = CrawlerRunConfig ( extraction_strategy = JsonCssExtractionStrategy ( schema ), scan_full_page = True , # scroll to the end, so the page loads every quote scroll_delay = 0.5 , cache_mode = CacheMode . BYPASS , ) async with AsyncWebCrawler ( config = BrowserConfig ( headless = True )) as crawler : result = await crawler . arun ( url = "https://quotes.toscrape.com/scroll" , config = run_config ) quotes = json . loads ( result . extracted_content ) print ( f"Extracted { len ( quotes ) } quotes" ) print ( json . dumps ( quotes [ 0 ], indent = 2 )) asyncio . run ( main ()) 📚 Structured data with an LLM import os , asyncio from pydantic import BaseModel , Field from crawl4ai import AsyncWebCrawler , CrawlerRunConfig , CacheMode , LLMConfig , LLMExtractionStrategy class ModelFee ( BaseModel ): model_name : str = Field (..., description = "Name of the model." ) input_fee : str = Field (..., description = "Fee for input tokens." ) output_fee : str = Field (..., description = "Fee for output tokens." ) async def main (): run_config = CrawlerRunConfig ( cache_mode = CacheMode . BYPASS , extraction_strategy = LLMExtractionStrategy ( # any provider LiteLLM supports, e.g. "ollama/llama3.3" with api_token="no-token" llm_config = LLMConfig ( provider = "openai/gpt-4o-mini" , api_token = os . getenv ( "OPENAI_API_KEY" )), schema = ModelFee . model_json_schema (), extraction_type = "schema" , instruction = "Extract every model name with its input and output token fee." , ), ) async with AsyncWebCrawler () as crawler : result = await crawler . arun ( url = "https://openai.com/api/pricing/" , config = run_config ) print ( result . extracted_content ) asyncio . run ( main ()) 🤖 Your own browser with a saved profile import os , asyncio from pathlib import Path from crawl4ai import AsyncWebCrawler , BrowserConfig , CrawlerRunConfig , CacheMode async def main (): user_data_dir = os . path . join ( Path . home (), ".crawl4ai" , "browser_profile" ) os . makedirs ( user_data_dir , exist_ok = True ) browser_config = BrowserConfig ( headless = True , user_data_dir = user_data_dir , use_persistent_context = True ) run_config = CrawlerRunConfig ( cache_mode = CacheMode . BYPASS , magic = True ) async with AsyncWebCrawler ( config = browser_config ) as crawler : result = await crawler . arun ( url = "ADDRESS_OF_A_CHALLENGING_WEBSITE" , config = run_config ) print ( result . success , len ( result . markdown )) asyncio . run ( main ()) 📖 Documentation Library docs, guides and API reference: docs.crawl4ai.com Cloud docs: crawl4ai.com/docs Release notes: releases · Roadmap: ROADMAP.md 🤝 Contributing We welcome contributions from the open-source community. Check out our contribution guidelines for more information. 📄 License & Attribution This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the Apache 2.0 License file for details. Attribution Requirements When using Crawl4AI, you must include one of the following attribution methods: 📈 1. Badge Attribution (Recommended) Add one of these badges to your README, documentation, or website: Theme Badge Disco Theme (Animated) Night Theme (Dark with Neon) Dark Theme (Classic) Light Theme (Classic) HTML code for adding the badges: <!-- Disco Theme (Animated) --> < a href =" https://github.com/unclecode/crawl4ai " > < img src =" https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-disco.svg " alt =" Powered by Crawl4AI " width =" 200 " /> </ a > <!-- Night Theme (Dark with Neon) --> < a href =" https://github.com/unclecode/crawl4ai " > < img src =" https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-night.svg " alt =" Powered by Crawl4AI " width =" 200 " /> </ a > <!-- Dark Theme (Classic) --> < a href =" https://github.com/unclecode/crawl4ai " > < img src =" https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-dark.svg " alt =" Powered by Crawl4AI " width =" 200 " /> </ a > <!-- Light Theme (Classic) --> < a href =" https://github.com/unclecode/crawl4ai " > < img src =" https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-light.svg " alt =" Powered by Crawl4AI " width =" 200 " /> </ a > <!-- Simple Shield Badge --> < a href =" https://github.com/unclecode/crawl4ai " > < img src =" https://img.shields.io/badge/Powered%20by-Crawl4AI-blue?style=flat-square " alt =" Powered by Crawl4AI " /> </ a > 📖 2. Text Attribution Add this line to your documentation: ``` This project uses Crawl4AI ( https://github.com/unclecode/crawl4ai ) for web data extraction. ``` 📚 Citation If you use Crawl4AI in your research or project, please cite: @software { crawl4ai2024 , author = { UncleCode } , title = { Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper } , year = { 2024 } , publisher = { GitHub } , journal = { GitHub Repository } , howpublished = { \url{https://github.com/unclecode/crawl4ai} } , commit = { Please use the commit hash you're working with } } Text citation format: UncleCode. (2024). Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper [Computer software]. GitHub. https://github.com/unclecode/crawl4ai 🗾 Mission Our mission is to unlock the value of personal and enterprise data by turning digital footprints into structured, useful assets. Crawl4AI gives individuals and organizations open-source tools to extract and structure data, and a fair way to benefit from it. Full mission statement → 💖 Support Crawl4AI ⭐ Star the repo : it helps more people find it. ☁️ Use the cloud : crawl4ai.com . It funds the library. 💝 Sponsor on GitHub : github.com/sponsors/unclecode 🏢 Companies : the sponsor tiers and benefits are in SPONSORS.md . 🌟 Current Sponsors 🤝 Strategic Partners These companies provide core infrastructure and technology that power Crawl4AI’s capabilities — from web access and proxy networks to AI tooling and data pipelines. Company About Massive is a web access API backed by millions of volunteer devices in 195+ countries. AI agents, models, and data pipelines use it to reach any site on the internet, reliably, in real time, and at scale. 🏢 Enterprise Sponsors Our enterprise sponsors support Crawl4AI and help scale it to power production-grade data pipelines. Company About Sponsorship Tier Helps engineers and buyers find, compare, and source electronic & industrial parts in seconds, with specs, pricing, lead times & alternatives. 🥇 Gold Kidocode is a hybrid technology and entrepreneurship school for kids aged 5–18, offering both online and on-campus education. 🥇 Gold Singapore-based Aleph Null is Asia’s leading edtech hub, dedicated to student-centric, AI-driven education—empowering learners with the tools to thrive in a fast-changing world. 🥇 Gold 💼 Become a Strategic Partner or Sponsor Interested in partnering with Crawl4AI? Whether you’re a proxy provider, AI infrastructure company, cloud platform, or an organization looking to support the Crawl4AI ecosystem, we’d love to hear from you. 📩 Contact: [email protected] 🧑‍🤝 Individual Sponsors A heartfelt thanks to our individual supporters! Every contribution helps us keep our opensource mission alive and thriving! Want to join them? Sponsor Crawl4AI → 📧 Contact Discord · X @unclecode · GitHub @unclecode · [email protected] Building crawlers or AI agents for a living? DM me on X. I want to work with people like you, and we are hiring. From a company? We have an enterprise offer and we tailor it to your business. SOC 2 Type I is done, Type II is in progress. Write to [email protected] . Happy crawling! 🕸️🚀 Star History

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Floor Plan AI

Floor Plan AI is an AI-assisted floor plan generator for creating reviewable 2D floor plan PNG drafts. Users can start from written ideas, room dimensions, or optional JPG, PNG, and WebP reference images, then review and download the generated plan image. Generated plans require human review and are not a substitute for professional CAD, BIM, structural, permitting, or legal work.

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Puzzle Brief

Daily Puzzle Guide provides daily puzzle hints, answers, and clear explanations to help players solve challenges, learn solving techniques, and improve their puzzle skills.

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Horilla

Overview Horilla is a comprehensive open-source HR management solution delivered as a self-hosted web platform. It combines employee records, attendance tracking, leave management, payroll processing, and recruitment into a single system, making it a viable free alternative to commercial products like BambooHR. Because it is released under the GPL-3.0 license, organizations can deploy and use it without paying licensing fees. It is best suited for companies that want full control over their HR data and are comfortable managing their own infrastructure. Pros Free and open-source under the GPL-3.0 license — no licensing or subscription costs. All-in-one HR platform covering employee records, attendance, leave, payroll, and recruitment. Self-hosted deployment gives organizations full ownership and control over sensitive HR data. Runs in any modern web browser, making it usable across Windows, macOS, and Linux systems. Source code is publicly available on GitHub, offering transparency and the ability to customize the software. Cons Requires technical expertise to deploy, configure, and maintain the platform on your own servers. Support is community-driven rather than backed by a dedicated vendor, so response times may vary. As a self-hosted solution, you are responsible for security, backups, and keeping the software up to date.

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Ever Gauzy

Overview Ever Gauzy is a comprehensive open-source business management platform that integrates ERP, HRM, and CRM functionalities. Designed for small and medium enterprises, it provides a unified solution for managing operations, human resources, and customer relationships. As a free alternative to proprietary systems like NetSuite, it offers full source code access under the AGPL-3.0 license, enabling self-hosting and customization. Pros Completely free and open-source under AGPL-3.0, allowing unrestricted use and modification. Self-hosted deployment gives full control over data and infrastructure. Cross-platform support for Windows, macOS, and Linux. Comprehensive feature set covering ERP, HRM, and CRM in one system. Active community and development on GitHub. Cons Requires self-hosting and technical expertise for setup and maintenance. Community-driven support may be less responsive than commercial alternatives. Documentation and learning resources may be less extensive than paid solutions.

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VerifyWise

Overview VerifyWise is a free and open-source web-based platform designed to simplify AI governance. It helps organizations ensure their AI systems remain compliant, secure, and easy to manage. Targeting teams that need straightforward tools for AI oversight, VerifyWise is released under the AGPL-3.0 license, making it transparent and accessible for any organization. Pros Open source under AGPL-3.0 and completely free to use, with no hidden costs or licensing fees. Web-based platform accessible from any modern browser, requiring no local installation. Focused specifically on AI governance, compliance, and security, addressing key regulatory and operational needs. Aims to be simple and easy to adopt, reducing the complexity often associated with governance tools.

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Proxed AI

Overview Proxed AI is a free, open-source solution aimed at secure AI API management for iOS applications. It uses Apple’s DeviceCheck to help tie API calls to trusted devices, providing a safer way to mediate interactions between an iOS app and backend AI services. The project also includes a web component, making it useful for developers who want a management layer alongside an iOS client. Pros Free and open source, released under the AGPL-3.0 license. Integrates DeviceCheck, adding a device-verification layer to AI API access. Supports both iOS and Web, allowing for both client and management-side tooling. Focused on API management for AI services, which can help centralize control and monitoring. Cons The AGPL-3.0 license may be too restrictive for fully closed-source or commercial integrations. The directory entry does not link to a source code repository, making community inspection and contribution less immediate. The project is still tagged as “needs-ai-review,” meaning independent verification of its maturity and security posture is pending.

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openhuman

OpenHuman OpenHuman is your personal AI super intelligence: a brain that remembers everything, a fantastic orchestrator, a deep researcher. Local-first, simple, powerful. Discussions • Discord • Reddit • X/Twitter • Docs • Follow @senamakel (Creator) 🇺🇸 English | 🇨🇳 简体中文 | 🇯🇵 日本語 | 🇰🇷 한국어 | 🇩🇪 Deutsch | 🇵🇰 اردو Early Beta : Under active development. Expect rough edges. OpenHuman is not AGI. But it is a meaningful architectural step closer, with better memory, better orchestration, and better tooling. 🎉 Within one week of launch, OpenHuman became the number one trending repository on GitHub for nine days in a row. Install Download installers from tinyhumans.ai/openhuman or from the GitHub Releases page. For terminal installs (Homebrew, Debian/Ubuntu .deb , AUR, install scripts, and platform notes), see INSTALL.md . What is OpenHuman? OpenHuman is three things most assistants aren't: a brain that builds a persistent, local memory of your world; a fantastic orchestrator that runs fleets of agents on durable graphs; and a deep researcher that sweeps your data and the web before you finish asking. Every bullet links to the deeper writeup in the docs . 🧠 The brain Memory Tree + Obsidian Wiki : your data compressed into scored Markdown trees in SQLite on your machine, mirrored as an Obsidian vault you can open and edit. No vector-soup black box. 100+ OAuth integrations, 5,000+ MCP servers, 90,000+ Skills : one click into Gmail, Notion, GitHub, Slack and the rest of your stack. Auto-fetch feeds the brain every 20 minutes, so it has tomorrow's context this morning. Goals & Todos : long-term goals, durable per-thread goals, and a shared kanban board per conversation. TokenJuice : tool output compressed before it hits the model: same information, up to 80% fewer tokens. A brain this big would be unaffordable without it. 🕸️ The orchestrator Workflows : the agent proposes the automation; you review it on a canvas and save. Durable, trigger-driven, approval-gated runs on open-source tinyflows . A harness that finishes the job : checkpointed graph runs on open-source tinyagents . Stuck agents get steered, halted ones return a root cause, and every run replays with real per-call costs. A split brain, always on : a fast reflex agent triages inbound traffic while a deep reasoning core delegates to worker fleets, steered by the subconscious. 🔬 The deep researcher & doer Batteries included : managed web search , powered by Exa , is included with your OpenHuman subscription and needs no API key; bring your own Exa key to search directly on your own Exa account and billing. Plus scraper, coder toolset, a real browser , and native voice with in-process Whisper. Model routing picks the right LLM per workload on one subscription. That subscription is a default, not a lock-in: point any workload at your own provider key or a fully local Ollama model , and mix the three however you like. Image & video generation : Seedream/SeedEdit images and Seedance/Veo video, straight into your workspace on the same subscription. 17 messaging channels : Telegram, Discord, Slack, WhatsApp, Signal, iMessage… plus native email (IMAP IDLE + SMTP). Your agent reaches you where you already are. 🧍 Human, private, yours Simple, UI-first & Human : install to working agent in a few clicks, with no config files and no terminal. And it has a face : a mascot that speaks, reacts, and remembers you. Privacy & security : on-device encrypted data, approval gate, OS-keyring secrets, and opt-in sandboxing. There is also Privacy Mode : flip one switch and no inference leaves your machine, enforced in the Rust core. Themes & Theme Studio : five theme families plus a full visual editor, exportable as JSON. Context in minutes, not weeks OpenHuman is the first agent harness that gets to know you in minutes. Inspired by Karpathy's LLM Knowledgebase . Most agents start cold. Hermes learns by watching you work; OpenClaw waits for plugins to ferry context in. Either way, you spend days or weeks before the agent knows enough about your stack to be genuinely useful. OpenHuman summarizes and compresses all your documents, emails & chats; and creates a memory graph that lets your agent remember everything about you. OpenHuman skips the wait. Connect your accounts, let auto-fetch pull data locally on a 20-minute loop, and then have Memory Trees compress everything into Markdown files stored intelligently in a Karpathy-style Obsidian wiki . In just one sync pass, the agent has full (compressed) context of your inbox, your calendar, your repos, your docs, your messages. No training period. No "give it a few weeks.". It becomes you, controlled by you. Already self-host agentmemory across other coding agents? OpenHuman ships an optional Memory backend that proxies to it. Set memory.backend = "agentmemory" in config.toml and the same durable store powers OpenHuman alongside Claude Code, Cursor, Codex, and OpenCode. See the agentmemory backend page for setup. An orchestrator, not a chatbot Most agent harnesses run one agent in one loop. OpenHuman is an orchestrator : Agent-to-agent messaging runs over Signal-protocol end-to-end encryption, so you can connect anything (Claude Code, Codex, OpenClaw, Hermes) and use OpenHuman to orchestrate all of your agents and tools. Graphs, not loops : turns run as checkpointed graphs on tinyagents . They pause for a human, survive a restart, and resume mid-run. Sub-agent fleets : specialists spawn three levels deep; stuck agents become root-cause reports. Agent-to-agent, encrypted : instances orchestrate each other over Signal-protocol E2E sessions with x402 payments. No server ever sees plaintext. Workflows you can see Heavily inspired by n8n and Zapier, workflows bring the same visual, trigger-driven automation to your agent, except the agent builds them for you. Ask for an automation and it proposes one: a tinyflows graph you review on a visual canvas before saving. The agent proposes the workflow; you review it on a canvas and save it. Saved workflows are durable and trigger-driven. They fire on schedules, webhooks, or channel events, survive restarts, and gate side effects behind approvals. OpenHuman vs Other Agent Harnesses High-level comparison (products evolve, so verify against each vendor). OpenHuman is built to minimize vendor sprawl , keep workflow knowledge on-device , and give the agent a persistent memory of your data, not only chat. Claude Cowork OpenClaw Hermes Agent OpenHuman Open-source 🚫 Proprietary ✅ MIT ✅ MIT ✅ GNU Simple to start ✅ Desktop + CLI ⚠️ Terminal-first ⚠️ Terminal-first ✅ Clean UI, minutes Cost ⚠️ Sub + add-ons ⚠️ BYO models ⚠️ BYO models ✅ One sub + TokenJuice Memory ✅ Chat-scoped ⚠️ Plugin-reliant ✅ Self-learning 🚀 Memory Tree + Obsidian vault, optional agentmemory backend Integrations ⚠️ Few connectors ⚠️ BYO ⚠️ BYO 🚀 100+ OAuth · 5k+ MCP · 90k+ Skills Auto-fetch 🚫 None 🚫 None 🚫 None ✅ 20-min sync into memory Orchestration ⚠️ Sub-tasks ⚠️ Single loop ⚠️ Single loop 🚀 Agent graphs + checkpoints + E2E-encrypted A2A Workflows 🚫 None ⚠️ Scripts ⚠️ Scripts 🚀 Visual, durable, agent-proposed, approval-gated Meetings 🚫 None 🚫 None 🚫 None 🚀 Joins Meet/Zoom/Teams/Webex, speaks, live transcript Messaging channels 🚫 None ⚠️ A few ⚠️ A few ✅ 17 incl. native email (IMAP/SMTP) Local-only mode 🚫 Cloud-only ⚠️ BYO local ⚠️ BYO local ✅ One-switch enforced Privacy Mode Observability 🚫 Opaque ⚠️ Logs ⚠️ Logs ✅ Replayable run journals + per-call cost accounting API sprawl 🚫 Extra keys 🚫 BYOK 🚫 Multi-vendor ✅ One account Model routing 🚫 Single model ⚠️ Manual ⚠️ Manual ✅ Built-in Native tools ✅ Code-only ✅ Code-only ✅ Code-only ✅ Code + search + scraper + browser + voice + media gen Contributing from source New contributor? Start with CONTRIBUTING.md for the fork/PR workflow and local validation commands, or use the copy-paste AI-agent prompt in CONTRIBUTING-BEGINNERS.md . The short path is: Install Git, Node.js 24+, pnpm 10.10.0, Rust 1.93.0 ( rustfmt + clippy ), CMake, Ninja, ripgrep, and the platform desktop build prerequisites. Fork and clone the repo, then run git submodule update --init --recursive before pnpm install so the vendored Tauri/CEF sources are present. Use pnpm dev for web-only UI work, pnpm --filter openhuman-app dev:app for the desktop shell, and focused checks such as pnpm typecheck , pnpm format:check , and cargo check -p openhuman --lib before opening a PR. Deeper docs: Architecture · Getting Set Up · Cloud Deploy . Star us on GitHub Building toward AGI and artificial consciousness? Star the repo and help others find the path. Contributors Hall of Fame Show some love and end up in the hall of fame. Contributors get free merch and special access to our Discord .

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Zola

Key features AI chat companion designed for conversational interactions Versatile model support, allowing users to work with different AI backends Cross-platform desktop client for Windows, macOS, and Linux Tagged as an AI chat and multi-model tool Marked for AI review, indicating it is listed pending further evaluation Platforms: Windows, macOS, Linux Links: Pricing: Free

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Typebot

Key features Visual no-code builder for designing conversational forms, surveys, and automated chatbot flows Rich library of interactive blocks, including text, buttons, inputs, payments, and integrations with third-party services Self-hosting option for full data ownership and customization Hosted cloud version with a generous free tier for quick setup Open-source codebase available for community contributions and transparency Platforms: Web Links: Official website · Source code Pricing: Freemium. The self-hosted edition is free under the AGPL-3.0 license, and a hosted free tier is available with core features.

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Skyvern

Key features Leverages large language models and computer vision to automate browser workflows without relying on brittle selectors. Designed to work on any website, reducing the maintenance burden of traditional browser automation scripts. Open-source AI-driven alternative to conventional RPA platforms like UiPath. Built in Python, making it accessible for developers to extend and integrate. Targeted at automating complex, multi-step workflows that span multiple pages and interactions. Platforms: Linux, macOS, Windows Links Official Website Source Code Pricing Free and fully open source under the AGPL-3.0 license.

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screenpipe

screenpipe is a free, open-source alternative to Rewind AI . Screenpipe is a 24/7 local screen and audio recorder that lets you search, recall, and automate anything you've seen or heard using AI, with full data control on your device.

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Scira

Key features AI-powered search engine designed for delivering instant answers Minimalistic interface focused on simplicity and speed Free to use with no listed pricing tiers Platforms: Web-based (browser accessible). Links Official Website Pricing: Free.

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Orama

Key features Full-text and vector search engine purpose-built for JavaScript environments Lightweight, dependency-free footprint suitable for performance-sensitive use cases Runs on the edge, in the browser, on Node.js, and in any JavaScript runtime Written in TypeScript with a JavaScript-friendly API Open-source under the Apache-2.0 license Platforms: Windows, macOS, Linux. Links Official website Source code Pricing: Free. Orama is fully free and open-source under the Apache-2.0 license, with no paid tier required for core functionality.

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OpenClaw

Key features Positions itself as an agentic AI assistant focused on taking action, not just responding to queries. Designed as an open-source alternative in the AI assistant space. Aims to function as a chatbot that can execute tasks rather than only answering questions. Cross-platform availability across Windows, macOS, and Linux desktop environments. Platforms: Windows, macOS, Linux. Pricing: Free.

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Open WebUI

Key features Self-hosted AI chat interface that you can run on your own hardware, keeping conversations and data under your control. Works with both local models (for example via Ollama) and remote LLM providers from a single web UI. Extensible, community-driven project designed to be customized and adapted to different workflows. Multi-model support, letting you switch between different language models without leaving the app. Browser-based chat experience that requires no client installation beyond a modern web browser. Platforms: Windows, macOS, Linux. Links Official website Source code Pricing: Fully free and open source under the MIT license; self-hostable Docker image with no paid tier.

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Lightpanda

Key features Open-source browser engineered specifically for AI agents, automation, and web scraping tasks Lightweight architecture designed to consume far fewer resources than full browsers such as Chrome Headless browsing capabilities tailored for programmatic and automated workflows Built with a focus on speed and minimal overhead for server-side and agent-driven use cases Self-hosted deployment model that gives users full control over their automation environment Platforms: Linux, macOS Links Official website Source code Pricing Free and open-source under the MIT license; self-hosted browser for automation.

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Khoj

Key features Search across personal notes, documents, and the web from a single chat interface Automates tasks and acts as a personal research assistant Self-hosted design that keeps your data on your own infrastructure Functions as an AI "second brain" for organizing and retrieving knowledge Open-source codebase licensed under AGPL-3.0 Platforms: Windows, macOS, Linux Links Official website Source code Pricing: Freemium. A free, self-hostable open-source release under AGPL-3.0 is available, and a hosted cloud version with usage limits is also offered.

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Inbox Zero

Key features AI-powered email assistant for drafting and automating replies Automated inbox organization to surface what needs attention Tools and workflows designed to help users reach inbox zero Open-source codebase available for self-hosting and customization Web-based access with no local install required for the hosted version Platforms: Web Links Official website Source code Pricing: Freemium. An open-source, self-hostable version is available under the AGPL-3.0 license.

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