Firecrawl

LLM-powered web scraping with graph-based extraction pipelines

ScrapeGraphAI is a Python library that uses LLMs and graph-based logic to build automated, self-healing web scraping pipelines. Developers describe desired data in natural language and ScrapeGraphAI constructs a processing graph that extracts structured information from any website. It supports multiple LLM providers, achieves 96%+ accuracy on semantic extraction benchmarks, and adapts to layout changes automatically. Over 20,000 GitHub stars.

High-performance open-source web crawler optimized for AI pipelines

Crawl4AI is an open-source Python web crawler built for AI and data-pipeline use cases. It produces LLM-ready Markdown, supports structured extraction, Playwright/browser automation, deep/adaptive crawling, proxy/security controls, anti-bot fallback patterns, and multiple output formats. With 68K+ GitHub stars and Apache-2.0 licensing, it is a strong local/self-hosted option for RAG datasets and agent data collection.

Browser automation framework turning websites into action APIs

Notte is a browser automation framework for AI agents that converts any website into a structured action API. Instead of scraping pages for text, Notte lets agents interact with sites — clicking buttons, filling forms, and navigating flows. Built with hybrid AI-plus-deterministic scripting, it includes digital personas, CAPTCHA solving, and proxy management for reliable automation at scale.

Mozilla-backed browser infrastructure for AI agents

Tabstack is Mozilla's browser infrastructure service for AI agents, providing clean markdown extraction, structured JSON data, and automated browser actions through a fast API. With two-tier fetch escalation that achieves sub-600ms latency for static pages, robots.txt compliance, and ephemeral data handling, it offers an ethical alternative to aggressive web scraping tools — complete with an MCP server for Claude and Cursor integration.

Open-source AI agent framework with persistent memory, reusable skills, tools, and messaging gateways

Hermes Agent is an open-source AI agent framework with persistent memory, reusable skills, 40+ tools, cron jobs, and messaging gateways.

Python and TypeScript framework for production multi-agent systems

BeeAI Framework is an Apache-2.0 toolkit for building production-ready AI agents and multi-agent systems in Python and TypeScript. Its docs cover agents, tools, RAG, memory, workflows, backend providers, serving, and A2A/MCP integration surfaces, making it a vendor-neutral option for teams comparing LangGraph, CrewAI, Mastra, and related agent runtimes.

Open-source Firecracker sandboxes for long-running AI agents

Superserve is an open-source sandbox infrastructure layer for AI agents that need durable computers instead of short-lived shells. It runs isolated Firecracker microVMs, supports pause, resume, snapshot, fork, preview URLs, MCP connectivity, SDK/API control, Docker workloads, and self-hosting, while the hosted service adds pay-as-you-go agent sandboxes for teams.

Official Claude Agent Skills examples, spec, and plugin marketplace for reusable agent capabilities

Anthropic Agent Skills is Anthropic's official reference repo and Claude Code plugin marketplace for reusable Skill folders. It packages example SKILL.md workflows, document skills, a Claude API skill, templates, and the Agent Skills spec so teams can turn repeatable instructions, scripts, and resources into on-demand Claude capabilities instead of copying prompts across sessions.

Cross-agent messaging for CLI coding agents

agmsg is an MIT-licensed Bash and SQLite messaging layer for CLI coding agents. It lets Claude Code, Codex, Gemini CLI, GitHub Copilot CLI, Antigravity, OpenCode, Hermes, and other terminal agents exchange messages through a shared local database instead of relying on a human copy-paste relay. It is intentionally not MCP, not a broker, and not a subagent framework.

Filesystem-first framework for durable AI agents

Eve is Vercel's filesystem-first TypeScript framework for building durable AI agents as ordinary project files. It combines Markdown instructions and skills, typed tools, channels, connections, subagents, schedules, sandboxes, and evals with Vercel's agent runtime so teams can ship deployable agents without hand-rolling orchestration. The current beta fits Vercel-native backend agent projects.