Free open source alternative app list about topic "ai-agents" :

ReVa

ReVa is an open-source MCP server that lets AI coding agents inspect and decompile binaries, explain program logic, and assist with reverse-engineering workflows. It bridges LLM tooling with Ghidra-style binary analysis.

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Docker Agent

Docker Agent is an official Docker CLI plugin that lets you build, run, and share AI agents using declarative YAML configuration, with support for MCP tools and multi-agent orchestration.

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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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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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Browser Use

Key features Enables AI agents to autonomously control and automate any web browser Supports LLM-driven browser tasks including navigation, form filling, and web scraping Provides a scalable framework for orchestrating complex browser interactions Built on Playwright for reliable cross-browser automation Distributed as a Python library for easy integration into AI workflows Open-source under the MIT license, allowing flexible self-hosting and customization Platforms: Windows, macOS, Linux Links Official Website Source Code Pricing Free and open-source under the MIT license; self-hosted Python library for AI browser automation.

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Latitude

Key features End-to-end tracing for LLM applications and AI agents in production. Monitoring and analytics tools built specifically for LLM observability. Self-hosted, open-source platform with full control over your data. Designed for AI-agent workloads, covering request-level visibility and debugging. Platforms: Linux, macOS, Windows. Links Official Website Source Code Pricing: Free. Open-source self-hosted observability for AI agents, including full tracing and analytics.

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LangWatch

Key features Simulation-based testing for AI agents and LLM applications. Evaluation tools for LLM applications. Observability for AI agents and LLM applications. Open-source platform licensed under Apache-2.0. Self-hostable via Docker. Platforms: Linux, macOS, Windows Links Official website: langwatch.ai Source code: github.com/langwatch/langwatch Pricing: Free and open-source under Apache-2.0, with self-hosting via Docker.

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ByteChef

Key features Low-code platform purpose-built for creating AI agents and automating complex, multi-step workflows. Hundreds of pre-built integrations make it easy to connect your favorite tools and services. Self-hosted design means you retain full control over your data, deployment, and infrastructure. Runs on Windows, macOS, and Linux for flexible installation options. Licensed under Apache-2.0 with no limits on the number of workflows or users. Platforms Windows, macOS, and Linux. Links Official website: bytechef.io Source code: github.com/bytechefhq/bytechef Pricing Free. ByteChef is completely self-hostable and free under the Apache-2.0 open-source license, with no limits on workflows or users.

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LiveKit

Key features Full-stack platform for building real-time voice, video, and AI agent applications. Built on WebRTC technology for low-latency browser and native communication. SFU-based architecture for scalable real-time media routing. Client SDKs for Windows, macOS, Linux, Android, and iOS. Open-source core server and SDKs under the Apache-2.0 license. Self-hostable alternative to proprietary real-time communication services like Twilio. Platforms: Windows, macOS, Linux, Android, iOS Links Official website: https://livekit.io Source code: https://github.com/livekit/livekit Pricing Free. The core LiveKit server and SDKs are open source under Apache-2.0, allowing self-hosting and unrestricted use.

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Letta

Key features Open-source platform for building, deploying, and managing AI agents. Persistent memory lets agents retain context across conversations. Python framework for creating and customizing agents. Self-hostable server for full control over deployment. Licensed under Apache-2.0. Platforms: Windows, macOS, Linux. Links Official website: https://www.letta.com Source code: https://github.com/letta-ai/letta Pricing Freemium. A free, open-source self-hosted version is available under Apache-2.0, and Letta Cloud also offers a free tier.

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Langflow

Key features Visual, drag-and-drop builder for creating AI agents and RAG pipelines. Designed for LLM workflow orchestration without heavy coding. Supports building agent-based and retrieval-augmented generation applications. Platforms: Windows, macOS, Linux. Links Official website: https://www.langflow.org Source code: https://github.com/langflow-ai/langflow Pricing: Free. Open source under Apache 2.0; self-host for free.

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Mem0

Key features Provides a persistent memory layer designed for AI agents and applications. Enables storing, retrieving, and managing user memories across multiple sessions. Supports semantic memory capabilities to help agents recall relevant context over time. Offers self-hostable open-source infrastructure for developer use. Platforms: Windows, macOS, Linux Links Official website: mem0.ai Source code: github.com/mem0ai/mem0 Pricing: Completely free and open-source under Apache-2.0; self-host and use without any licensing costs.

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Tiledesk

Key features Open-source no-code platform for building AI agents and automating customer support. Combines live chat, helpdesk, and chatbot functionality in a single solution. Drag-and-drop visual builder for creating conversational flows without code. Offers self-hosted and cloud deployment options. Platforms Linux, macOS, Windows Links Official website: https://www.tiledesk.com/ Source code: https://github.com/Tiledesk/tiledesk Pricing Freemium. Free self-hosted version under GPL-3.0 with unlimited users and conversations, plus a free cloud tier with basic features.

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Botpress

Key features Open-source platform for building AI agents and chatbots powered by large language models. Drag-and-drop flow design for creating conversational workflows without heavy coding. Full source code available for customization and self-hosting. Runs on Windows, macOS, and Linux. Platforms: Windows, macOS, Linux. Links: Official website · Source code Pricing: Freemium. The self-hosted open-source version is available under the MIT license and includes all core features.

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Agno

Key features Lightweight, model-agnostic Python library for building intelligent AI agents. Built-in support for memory, knowledge, tools, and reasoning. Multimodal framework capable of handling diverse input and output types. Free and open-source core under the MPL-2.0 license, with an optional paid cloud service. Platforms: Windows, macOS, Linux Links Official website: https://agno.com Source code: https://github.com/agno-ai/agno Pricing Freemium. The core library is free and open-source under the MPL-2.0 license; optional Agno Cloud paid services are available.

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Agenta

Key features Build, evaluate, and improve AI agents and LLM applications in a single collaborative platform. Manage prompts with versioning and organization tools for LLM workflows. Run evaluations to test and compare AI agent and LLM outputs. Gain observability into LLM application behavior and performance. Support LLMOps practices for teams developing with large language models. Platforms Windows, macOS, Linux Links Official website: https://agenta.ai Source code: https://github.com/Agenta-AI/agenta Pricing Free. Free and open-source self-hosted platform under Apache-2.0 license.

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CopilotKit

CopilotKit is a free, open-source alternative to CommandBar . CopilotKit Docs · Examples · Enterprise Intelligence Platform · Discord Build agent-native applications — on any framework, on any surface. Generative UI, shared state, and human-in-the-loop workflows for React, Angular, Vue, React Native — and in Slack and Microsoft Teams. What is CopilotKit CopilotKit is a best-in-class SDK for building full-stack agentic applications, Generative UI, and chat applications. What started as a React library is now the horizontal layer between your agents and your users : the same agent can power your web app, your mobile app, and your team's Slack or Microsoft Teams workspace. We are the company behind the AG-UI Protocol - adopted by Google, LangChain, AWS, Microsoft, Mastra, PydanticAI, and more! Quick Start Up and running in under five minutes. All you need is an LLM key (OpenAI, Anthropic, Gemini, etc.). npx copilotkit@latest create Agent Skills CopilotKit ships agent skills that teach your coding agent (Claude Code, Codex, Cursor, Gemini, and others) how to set up, build with, integrate, debug, and upgrade CopilotKit. Install them into any project directory: npx copilotkit@latest skills install Run it again any time to refresh to the latest skills. Bring Your App to Life Whole.Generative.UI.v5.mp4 Add AI to your app in 1 minute Features: Chat UI – A fully customizable chat interface that supports message streaming, tool calls, and agent responses. Backend Tool Rendering – Enables agents to call backend tools that return UI components rendered directly in the client. Generative UI – Allows agents to generate and update UI components dynamically at runtime based on user intent and agent state. Shared State – A synchronized state layer that both agents and UI components can read from and write to in real time. Human-in-the-Loop – Lets agents pause execution to request user input, confirmation, or edits before continuing. Self-Learning (early access) – Agents that continuously improve from user feedback via in-context reinforcement learning (CLHF). 🧩 Works With Your Stack One agent backend. Every frontend. Platform Status Get Started ⚛️ React / Next.js ✅ GA Quickstart 🅰️ Angular ✅ Supported Source Code & Quickstart 💚 Vue ✅ Supported Source Code - Quickstart coming soon 📱 React Native ✅ Supported Quickstart 💬 Slack / Microsoft Teams ✅ Supported Channels · Quickstart 🔜 Discord / WhatsApp / Telegram / Google Chat / iMessage / SMS 🟡 Coming soon Channels Your agent logic stays the same — AG-UI handles the wire protocol, CopilotKit handles the UI layer for each framework and channel. 💬 Channels: One Agent, Every Chat App The Channels SDK takes the agent you already built and drops it into the chat apps your users live in — same tools, same shared state, same human-in-the-loop, no rewrite ( Learn more ). Slack – Agents as first-class Slack apps: threads, tool calls, and human-in-the-loop approvals right in the channel. Microsoft Teams – Bring agentic workflows to the enterprise, where your org already lives. 👉 Explore Channels → 🧠 Self-Learning Agents Improve your product by learning over time. With Continuous Learning from Human Feedback (CLHF) , part of the CopilotKit Intelligence Platform , agents improve with every interaction: In-context reinforcement learning – Agents automatically improve from user interactions, no model fine-tuning required. Automatic prompt augmentation – Agent behavior adapts based on recent interactions and outcomes. Per-user adaptation – Agents learn individual preferences and get better for each user over time. Threads & persistence – Full interaction history — generative UI, human-in-the-loop, shared state — captured across sessions. Available via CopilotKit Cloud or self-hosted. 🔒 Early access: We're onboarding teams now. 👉 Request early access → cpk-cli.mp4 What this gives you: CopilotKit installed – Core packages are fully set up in your app Provider configured – Context, state, and hooks ready to use Agent <> UI connected – Agents can stream actions and render UI immediately Deployment-ready – Your app is ready to deploy Complete getting started guide → How it works: CopilotKit connects your UI, agents, and tools into a single interaction loop. This enables: Agents that ask users for input Tools that render UI Stateful workflows across steps and sessions One agent, deployed across web, mobile, and chat platforms ⭐️ useAgent Hook The useAgent hook sits directly on AG-UI, giving you full programmatic control over the agent connection. // Programmatically access and control your agents const { agent } = useAgent ( { agentId : "my_agent" } ) ; // Render and update your agent's state return < div > < h1 > { agent . state . city } < / h1 > < button onClick = { ( ) => agent . setState ( { city : "NYC" } ) } > Set City < / b u t t o n > < / div > Check out the useAgent docs to learn more. CopilotKit.UseAgent.Graphic.Motion_2.mp4 Generative UI Generative UI is a core CopilotKit pattern that allows agents to dynamically render UI as part of their workflow. demo-generative-ui.mp4 Compare the Three Types Explore: Static (AG-UI Protocol) Declarative (A2UI) Open-Ended (MCP Apps & Open JSON) Generative UI educational repo → 🖥️ AG-UI: The Agent–User Interaction Protocol Connect agent workflows to user-facing apps, with deep partnerships and 1st-party integrations across the agentic stack—including LangChain, CrewAI, Mastra, PydanticAI, and more. npx create-ag-ui-app my-agent-app Learn more in the AG-UI README → 🤝 Community What's New Have questions or need help? Join our Discord → Read the Docs → Try the Enterprise Intelligence Platform → Stay up to date with our latest releases! Follow us on LinkedIn → Follow us on X → 🙋🏽‍♂️ Contributing Thanks for your interest in contributing to CopilotKit! 💜 We value all contributions, whether it's through code, documentation, creating demo apps, or just spreading the word. Here are a few useful resources to help you get started: For code contributions, CONTRIBUTING.md . For documentation-related contributions, check out the documentation contributions guide . Want to contribute but not sure how? Join our Discord and we'll help you out! 📄 License This repository's source code is available under the MIT License .

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langchain

langchain is a free, open-source alternative to OpenAI Assistants API . The agent engineering platform. LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves. Tip Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more. Quickstart uv add langchain from langchain . chat_models import init_chat_model model = init_chat_model ( "openai:gpt-5.5" ) result = model . invoke ( "Hello, world!" ) If you're looking for more advanced customization or agent orchestration, check out LangGraph , our framework for building controllable agent workflows. For an equivalent JS/TS library, check out LangChain.js . Tip For developing, debugging, and deploying AI agents and LLM applications, see LangSmith . LangChain ecosystem While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications. Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework Integrations — Chat & embedding models, tools & toolkits, and more LangSmith — Agent evals, observability, and debugging for LLM apps LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows Why use LangChain? LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more. Real-time data augmentation — Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more Model interoperability — Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly — LangChain's abstractions keep you moving without losing momentum Rapid prototyping — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices Vibrant community and ecosystem — Leverage a rich ecosystem of integrations, templates, and community-contributed components. Benefit from continuous improvements and stay up-to-date with the latest AI developments through an active open-source community Flexible abstraction layers — Work at the level of abstraction that suits your needs — from high-level chains for quick starts to low-level components for fine-grained control. LangChain grows with your application's complexity Resources Documentation — conceptual overviews and guides LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together API reference — complete reference for all public classes, functions, and types Discussions — community forum for technical questions, ideas, and feedback LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team Contributing Guide — how to contribute and find good first issues Code of Conduct — community guidelines and standards

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