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Agent Workflows are the fastest and most flexible way to build complex agentic systems, and we put them to the test. Our OSS engineer Clelia Bertelli (🦙/acc) built a powerful integration with the Agent Client Protocol (ACP) by Zed, resulting in a fully customizable agent with access to filesystem...

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16,097 görüntüleme • 5 ay önce •via X (Twitter)

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New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
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New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

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Agents & MCP are great... but how can you DEPLOY an agent with MCP tools? Here's the fastest way I've found to build and deploy with the OpenAI Agents SDK in <3 minutes.

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INSANE: Grok 3 + Lovable = BEST AI Agent Apps!!!! Grok 3 generated the code for an AI agent that manages your Gmail - send, reply, and draft effortlessly Here's how it works: - Connect with your Gmail using Composio - Use Lovable to build an extremely cool frontend for the Agent - Agentic workflow with LlamaIndex Built with LlamaIndex 🦙 To connect your agents with more tools like Gmail, use

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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!
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New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

141,952 görüntüleme • 1 yıl önce

Multi-Agent workflows are the future of AI. OpenAI released new Agent APIs today, and Box built an Agent that combines documents from Box and web search tools to generate answers. Enterprise devs can grab sample code from our GitHub repo to customize with their data.
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Multi-Agent workflows are the future of AI. OpenAI released new Agent APIs today, and Box built an Agent that combines documents from Box and web search tools to generate answers. Enterprise devs can grab sample code from our GitHub repo to customize with their data.

Aaron Levie

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Thanks to ACP (the Agent Client Protocol), JetBrains IDEs are now a fully open platform for agentic development. Junie, Claude Agent, Codex - take your pick. And Cursor, GitHub Copilot, and many more can be installed via the ACP Registry.
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Thanks to ACP (the Agent Client Protocol), JetBrains IDEs are now a fully open platform for agentic development. Junie, Claude Agent, Codex - take your pick. And Cursor, GitHub Copilot, and many more can be installed via the ACP Registry.

JetBrains

13,219 görüntüleme • 2 ay önce

We're supercharging the HYTOPIA developer experience with our in-progress Cursor IDE plugin. - Uses a bespoke HYTOPIA built MCP - Instantly test game/server & debug in the editor - Describe your game or features, the HYTOPIA agent builds it! Check out this early demo!
10:51

We're supercharging the HYTOPIA developer experience with our in-progress Cursor IDE plugin. - Uses a bespoke HYTOPIA built MCP - Instantly test game/server & debug in the editor - Describe your game or features, the HYTOPIA agent builds it! Check out this early demo!

HYTOPIA

31,941 görüntüleme • 1 yıl önce

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:
1:35

Building “RAG 2.0” is just making Claude Code running over your filesystem 🤖🗂️ To make this work well, you need to solve three things 1️⃣ Virtualize your filesystem to prevent the agent from messing stuff up. AgentFS by Turso is a nice example of how you can give the agent access to a copy of all your files without messing up your raw data. 2️⃣ Parse unstructured documents like PDFs, pptx, Word into an LLM-ready format. Agentic OCR solutions like LlamaParse can help here 3️⃣ Creating an agentic loop with human-in-the-loop. If you want to control the agent implementation instead of using Claude Code out of the box, you can use LlamaIndex 🦙 workflows to help orchestrate these long-running agent tasks. Shoutout Clelia Bertelli (🦙/acc), check it out! Blog: Repo:

Jerry Liu

55,620 görüntüleme • 6 ay önce

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.
0:36

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.

Sui_Builds

12,842 görüntüleme • 5 ay önce

Agent-in-the-browser unlocks superpowers. The magic really shines when you bring your own model, system prompts, and MCP tools into the mix. Hence, AgentBoard 🪄. Your models, keys, and settings. Integration with remote MCP servers and — this is where it gets really fun — ability to register and allow the agent to call custom ~WebMCP tools that execute in context of the page. Chrome Web Store: Code:
3:51

Agent-in-the-browser unlocks superpowers. The magic really shines when you bring your own model, system prompts, and MCP tools into the mix. Hence, AgentBoard 🪄. Your models, keys, and settings. Integration with remote MCP servers and — this is where it gets really fun — ability to register and allow the agent to call custom ~WebMCP tools that execute in context of the page. Chrome Web Store: Code:

Ilya Grigorik

22,940 görüntüleme • 8 ay önce

A 100% open-source alternative to n8n! Sim is a drag-and-drop open-source platform to build and deploy Agentic workflows. - Runs 100% locally - Works with any local LLM I used it to build a finance assistance app & connected it to Telegram in minutes. The workflow is simple: - You ask a finance question through Telegram - An Intent Classifier figures out if it's finance-related - If not, you get a polite redirect - If yes, the Finance Agent kicks in Here's what's happening under the hood: The Finance Agent uses Firecrawl for web searches and accesses stock data via Alpha Vantage's API through MCP servers. A Response Agent compiles the info and delivers it. In Sim, every tool or agent you need is available as a block. Just drag them onto the canvas and connect them. Sim agents also support integration with MCP, which is exactly what we did to connect our agent with Alpha Vantage's API. And it's simple to extend. If you want to track crypto or need portfolio analysis, you can just add another Agent. Sim allows easy feature additions without disrupting existing functionality. I have shared the link to Sim's official, open-source GitHub repo in the replies!
0:33

A 100% open-source alternative to n8n! Sim is a drag-and-drop open-source platform to build and deploy Agentic workflows. - Runs 100% locally - Works with any local LLM I used it to build a finance assistance app & connected it to Telegram in minutes. The workflow is simple: - You ask a finance question through Telegram - An Intent Classifier figures out if it's finance-related - If not, you get a polite redirect - If yes, the Finance Agent kicks in Here's what's happening under the hood: The Finance Agent uses Firecrawl for web searches and accesses stock data via Alpha Vantage's API through MCP servers. A Response Agent compiles the info and delivers it. In Sim, every tool or agent you need is available as a block. Just drag them onto the canvas and connect them. Sim agents also support integration with MCP, which is exactly what we did to connect our agent with Alpha Vantage's API. And it's simple to extend. If you want to track crypto or need portfolio analysis, you can just add another Agent. Sim allows easy feature additions without disrupting existing functionality. I have shared the link to Sim's official, open-source GitHub repo in the replies!

Avi Chawla

115,578 görüntüleme • 9 ay önce

This is Repo Prompt 2.0 A fully integrated agent, that makes it seamless to use RP's powerful MCP tools, with a built-in oracle and context builder. A first class experience showcasing how much better and efficient your agents can be with good context engineering tools.
4:35

This is Repo Prompt 2.0 A fully integrated agent, that makes it seamless to use RP's powerful MCP tools, with a built-in oracle and context builder. A first class experience showcasing how much better and efficient your agents can be with good context engineering tools.

eric provencher

38,921 görüntüleme • 4 ay önce

🤝 AI Agents That Work Together — Collaboration Made Simple! In this demo, John explores one of Agent Forge’s most powerful features: agent-to-agent collaboration. With Agent Forge, you can create agents with different roles, connect them, and watch them collaborate to solve complex tasks. Modular, scalable, and fully automated. 🔗 Try here:
1:15

🤝 AI Agents That Work Together — Collaboration Made Simple! In this demo, John explores one of Agent Forge’s most powerful features: agent-to-agent collaboration. With Agent Forge, you can create agents with different roles, connect them, and watch them collaborate to solve complex tasks. Modular, scalable, and fully automated. 🔗 Try here:

AITECH

43,206 görüntüleme • 10 ay önce

Connect your agents directly to Chrome DevTools. 🛠️ The new Chrome DevTools MCP server allows agents to inspect the DOM and debug console errors in real-time using Gemini CLI or any compatible agent. Check out the documentation on GitHub →
1:01

Connect your agents directly to Chrome DevTools. 🛠️ The new Chrome DevTools MCP server allows agents to inspect the DOM and debug console errors in real-time using Gemini CLI or any compatible agent. Check out the documentation on GitHub →

Google for Developers

22,793 görüntüleme • 6 ay önce

🖼️ Introducing Open Multi-Agent Canvas - with MCP Chat with multiple LangGraph agents and any MCP server inside of a canvas app. Powered by CopilotKit🪁, LangChain & Larinháh Here's an example of how we used it to plan our offsite. We connected an agent to the Google Maps API, had it find local spots, and then sent the itinerary directly to Slack via MCP to be reviewed by the team. Explore and extend:
1:33

🖼️ Introducing Open Multi-Agent Canvas - with MCP Chat with multiple LangGraph agents and any MCP server inside of a canvas app. Powered by CopilotKit🪁, LangChain & Larinháh Here's an example of how we used it to plan our offsite. We connected an agent to the Google Maps API, had it find local spots, and then sent the itinerary directly to Slack via MCP to be reviewed by the team. Explore and extend:

CopilotKit🪁

22,080 görüntüleme • 1 yıl önce