dify
Build agentic workflows and RAG pipelines in one open-source workspace.
About dify
dify is an open-source platform for building agentic workflows and RAG pipelines with rich model and tool support, all in one collaborative workspace. It combines visual building with the escape hatch of custom code.
You can deploy it on cloud, in a VPC, or fully self-hosted, so teams move from prototype to production without rebuilding the stack. If you are assembling agents with real tool use and retrieval, it is one of the most complete open options.
More Github
browser-use
browser-use makes websites accessible to AI agents — it hands your agent a real browser so it can navigate, click, fill forms, and complete tasks online instead of being limited to APIs that may not exist. Built on Playwright with a clean Python interface, it has become one of the most popular ways to give agents hands on the open web. Ideal for automating research, data entry, and any workflow that lives inside a browser.
agent-skills
agent-skills is Addy Osmani’s collection of production-grade engineering skills for AI coding agents — reusable, opinionated instructions that teach your agent how a senior engineer actually approaches a task, not just how to autocomplete. It works across Claude Code, Codex, Cursor and Antigravity, so you can drop battle-tested skills into whatever harness you already use. A strong starting point if you want your agent to write code that fits real engineering standards.
chrome-devtools-mcp
chrome-devtools-mcp is an official MCP server that hands coding agents the full Chrome DevTools protocol — inspect the DOM, read console errors, trace network requests, and drive the page like a real browser session. It turns "the button doesn’t work" into something your agent can actually debug, live, instead of guessing from source. If you build web apps with an agent in the loop, this is one of the highest-leverage MCP servers to add.
serena
serena is a powerful MCP toolkit that gives coding agents semantic retrieval and editing — essentially an IDE for your agent. Instead of blindly grepping and pasting whole files, the agent navigates code by symbols and structure. It plugs into Claude Code, Codex, and JetBrains via a language server, making edits more precise and token-efficient on large codebases. A strong pick for anyone whose agent struggles once a repo gets big.
headroom
headroom compresses tool outputs, logs, files, and RAG chunks before they ever reach the LLM. The claim: 20% fewer tokens for coding agents and 60–95% fewer for JSON — with the same answers. It ships as a library, a proxy, and an MCP server, so you can drop it into an existing agent stack without rewriting your pipeline. For anyone burning context window on noisy tool results, it is a cheap efficiency win.