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An open protocol enabling communication and interoperability between opaque agentic applications.
One of the biggest challenges in enterprise AI adoption is getting agents built on different frameworks and vendors to work together. That’s why we created an open Agent2Agent (A2A) protocol, a collaborative way to help agents across different ecosystems communicate with each other.
The first and the best multi-agent framework. Finding the Scaling Law of Agents
Building Multi-Agent Systems for Task Automation.
CAMEL is an open-source community dedicated to finding the scaling laws of agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.
CAMEL emerges as the earliest LLM-based multi-agent framework, and is now a generic framework to build and use LLM-based agents for real-world task solving. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.
Google AI for Developers
MCP server for your browser.
Browser MCP is a Model Context Provider (MCP) server that allows AI applications to control your browser.
If you want to automate actions on a website, like repeatedly fill out a form, you normally can't do it with AI apps like Cursor or Claude because they don't have access to a web browser. With Browser MCP, you can connect AI apps to your browser so they can automate tasks on your behalf.
Instantly create a Remote MCP server for any GitHub project.
GitMCP is a free, open-source service that seamlessly transforms any GitHub project into a remote Model Context Protocol (MCP) endpoint, enabling AI assistants to access and understand the project's documentation effortlessly.
specialized MCP servers that bring AWS best practices directly to your development workflow .
A Ruby Implementation of the Model Context Protocol.
Connect AI models to your Ruby applications with ease
No complex protocols, no integration headaches, no compatibility issues – just beautiful, expressive Ruby code.
An SDK for working with LLMs and AI Agents from Apache Airflow, based on Pydantic AI.
It allows users to call LLMs and orchestrate agent calls directly within their Airflow pipelines using decorator-based tasks. The SDK leverages the familiar Airflow @task syntax with extensions like @task.llm, @task.llm_branch, and @task.agent.
Transform Al Prototypes into Enterprise-Grade Products.
Langtrace is an Open Source Observability and Evaluations Platform for Al Agents.
Related contents:
PostgreSQL Model Context Protocol (PG-MCP) Server.
A Model Context Protocol (MCP) server for PostgreSQL databases with enhanced capabilities for AI agents.
Playwright Tools for MCP.
A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. This server enables LLMs to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models.
Balance agent control with agency. Build resilient language agents as graphs.
Gain control with LangGraph to design agents that reliably handle complex tasks. Build and scale agentic applications with LangGraph Platform.
LangGraph — used by Replit, Uber, LinkedIn, GitLab and more — is a low-level orchestration framework for building controllable agents. While langchain provides integrations and composable components to streamline LLM application development, the LangGraph library enables agent orchestration — offering customizable architectures, long-term memory, and human-in-the-loop to reliably handle complex tasks.
Related contents:
This is MCP server for Claude that gives it terminal control, file system search and diff file editing capabilities.
This is server that allows Claude desktop app to execute long-running terminal commands on your computer and manage processes through Model Context Protocol (MCP) + Built on top of MCP Filesystem Server to provide additional search and replace file editing capabilities .
Fleur is the app store for Claude.
The easiest way to discover and install MCPs.
Fleur is a desktop application that serves as an app marketplace for MCPs. It allows you to discover, install, and manage apps that extend the functionality of Claude Desktop and Cursor.
All without having to use a command line. Fleur is made for non-technical users in mind, but is open-source and extensible so developers can make it their own.
Related contents:
MCP server for fetch web page content using Playwright headless browser.
Coding assistant MCP for Claude Desktop.
Make Claude Desktop a pair programming assistant by installing codemcp. With it, you can directly ask Claude to implement features, fix bugs and do refactors on a codebase on your computer; Claude will directly edit files and run tests. Say goodbye to copying code in and out of Claude's chat window!
Model Context Protocol (MCP) is a new, standardized protocol for managing context between large language models (LLMs) and external systems. In this repository, we provide an installer as well as an MCP Server for Cloudflare's API.
This lets you use Claude Desktop, or any MCP Client, to use natural language to accomplish things on your Cloudflare account
Bringing Agentic AI to cloud native.
An open-source framework for DevOps and platform engineers to run AI agents in Kubernetes, automating complex operations and troubleshooting tasks.
Evolving agents is a production-grade environment for orchestrating, evolving, and managing AI agents.
A production-grade framework for creating, managing, and evolving AI agents with intelligent agent-to-agent communication. The framework enables you to build collaborative agent ecosystems that can semantically understand requirements, evolve based on past experiences, and communicate effectively to solve complex tasks.
An in-depth book and reference on building agentic systems like Claude Code.
A deep-dive guide into architecture patterns for building responsive, reliable AI coding agents.
There's been a lot of asking about how Claude Code works under the hood. Usually, people see the prompts, but they don't see how it all comes together. This is that book. All of the systems, tools, and commands that go into building one of these.
A practical deep dive and code review into how to build a self-driving coding agent, execution engine, tools and commands. Rather than the prompts and AI engineering, this is the systems and design decisions that go into making agents that are real-time, self-corrective, and useful for productive work.