mcp-agent

t, allowing you to run long-running operations. This is especially critical for agents, which need to pause/resume, be fault-tolerant, and run for a long time. 3) Local to Cloud - we're fans of how easy it is to deploy a Next.js app to Vercel, so we've made it simple to take your local mcp-agent, mcp server, OpenAI app, etc. and deploy it to the cloud. We have several examples and even a handy CLI to get started quickly [4]. It's as simple as: - uvx mcp-agent init - uv init - uv add "mcp-agent[openai]" - uvx mcp-agent login # add your openai key to mcp_agent.secrets.yaml - uvx mcp-agent deploy You can also try out these hosted mcp servers to get an idea. These can be connected to any MCP client (ChatGPT, Claude Desktop/Code, Cursor, etc.) OpenAI Pizza App - https://18t536mliucyeuhkkcnjdavxtyg66pgl.deployments.mcp-age... Basic Agent Server - https://1m82g32x8nkoppinayx0k5ye12oar6vk.deployments.mcp-age... The HN community has been a big part of mcp-agent's growth, and we're super excited to share this with you as well. We would love to hear your thoughts and feedback! --- [1] - https://news.ycombinator.com/item?id=42867050 [2] - https://github.com/lastmile-ai/mcp-agent [3] - https://news.ycombinator.com/item?id=44053754 [4] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/... [5] - https://docs.mcp-agent.com/get-started/welcome [6] - https://youtu.be/9ddtaSbUJIc

t, allowing you to run long-running operations. This is especially critical for agents, which need to pause/resume, be fault-tolerant, and run for a long time. 3) Local to Cloud - we're fans of how easy it is to deploy a Next.js app to Vercel, so we've made it simple to take your local mcp-agent, mcp server, OpenAI app, etc. and deploy it to the cloud. We have several examples and even a handy CLI to get started quickly [4]. It's as simple as: - uvx mcp-agent init - uv init - uv add "mcp-agent[openai]" - uvx mcp-agent login # add your openai key to mcp_agent.secrets.yaml - uvx mcp-agent deploy You can also try out these hosted mcp servers to get an idea. These can be connected to any MCP client (ChatGPT, Claude Desktop/Code, Cursor, etc.) OpenAI Pizza App - https://18t536mliucyeuhkkcnjdavxtyg66pgl.deployments.mcp-age... Basic Agent Server - https://1m82g32x8nkoppinayx0k5ye12oar6vk.deployments.mcp-age... The HN community has been a big part of mcp-agent's growth, and we're super excited to share this with you as well. We would love to hear your thoughts and feedback! --- [1] - https://news.ycombinator.com/item?id=42867050 [2] - https://github.com/lastmile-ai/mcp-agent [3] - https://news.ycombinator.com/item?id=44053754 [4] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/... [5] - https://docs.mcp-agent.com/get-started/welcome

ticated search techniques over time.

exposes everything via API so I can use it in Streamlit, CLI, or anywhere • Memory: stores preferences and outcomes for personalization It’s modular, model-agnostic (works with GPT-4 or local models via Ollama), and shareable. Let me know what you all think!

otocol (MCP). 2. Platform Independence: Use your agents from any MCP-compatible client 3. Scalability: Run agent workflows on dedicated infrastructure, not just within client environments 4. Customization: Develop your own agent workflows and reuse them across any MCP client. How an agent server is implemented: We’ve implemented this in mcp-agent with Workflows. Each workflow is an agent application that can interact with other MCP servers (e.g. summarizing GitHub issues → Slack message). mcp-agent exposes workflows as MCP tools on an MCP Agent Server [5]: - workflows/list – list available workflows - workflows/{WorkflowName}/run – Execute the workflow (async) - workflows/{WorkflowName}/get_status – Check workflow status - workflows/{WorkflowName}/resume – Resume paused workflow (e.g. with human input) - workflows/{WorkflowName}/cancel – Terminate workflow We’ve also implemented Temporal for durable execution [6], so agent workflows can be paused, resumed and retried in production settings. This demo [7] shows Claude invoking an MCP agent server, running workflows when appropriate, and polling for status. It basically shows agentic behavior on both the MCP client and MCP server side. We're excited about the potential this unlocks—especially as more applications become MCP-compatible clients. We'd love your feedback and ideas! [1] - https://news.ycombinator.com/item?id=42867050 [2] - https://github.com/lastmile-ai/mcp-agent [3] - https://www.anthropic.com/research/building-effective-agents [4] - https://github.com/github/github-mcp-server [5] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/... [6] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples/... [7] - https://youtu.be/pLe2GAjEoYs [DEMO]

sistant with access to MCP tools.", tools=[your_other_tools], # Regular tool use for Agent SDK mcp_servers=["fetch", "filesystem"] # Names of MCP servers from your config file (see below) ) ``` The servers are configured in an `mcp_agent.config.yaml` file, very similar to how they are configured for Claude Desktop: ``` $schema: "https://raw.githubusercontent.com/lastmile-ai/mcp-agent/main..." mcp: servers: fetch: command: "uvx" args: ["mcp-server-fetch"] filesystem: command: "npx" args: ["-y", "@modelcontextprotocol/server-filesystem", "."] slack: command: "npx" args: ["-y", "@modelcontextprotocol/server-slack"] ``` I have submitted an issue and PR into the openai-agents-python repo [2], and my plan is instead of a fork, I will create an extension package for MCP support (coming later today). I was able to do this pretty quickly (got it working yesterday) because I've been building the mcp-agent library (https://github.com/lastmile-ai/mcp-agent), which makes MCP server aggregation/connection really easy. I did a Show HN about it a few weeks ago [3]. Wanted to share here to get community feedback on whether this is useful, which will help me decide if I should dedicate more time to it. [1] - https://github.com/lastmile-ai/mcp-agent [2] - https://github.com/openai/openai-agents-python/issues/23 [3] - https://news.ycombinator.com/item?id=42867050

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outer, Orchestrator-Worker, Evaluator-Optimizer, and more - Swarm The key design principles are composability and reusability – every pattern is an AugmentedLLM itself, so you can chain them into more complex workflows. Some background: I worked on LSP [5] and language servers at Microsoft, and saw firsthand how standards and protocols can revolutionize developer workflows. Before LSP every IDE had its own esoteric ways of providing language services. LSP changed all that, and arguably made every language server better, since they can focus on improving a single implementation for all clients. I think AI development is in a similar pre-LSP space right now. There are tons of frameworks [6], every model provider has its own way of handling messages, tool calls, streaming, etc. I really think we need a protocol to standardize these patterns. Pretty soon every service is going to expose an MCP interface, and mcp-agent is about letting developers orchestrate these services into applications (i.e. build “MCP apps”). This can cover any use of an AI model that needs to interact with the world around it: - RAG pipelines and Q&A chatbots - Process automation via AI workflows/async tasks - Multi-agent orchestration, with human in the loop The repo contains examples [7] to build RAG agents, streamlit apps and more. There’s a lot left to build, like streaming support, server auth and tighter integration with MCP clients. But I wanted to share early in the hopes that you can guide me: - If you find this useful, please let me know. If it’s useful to you, I will dedicate all my time to improving it. - I really welcome contributions. If you want to collaborate, please reach out on github to help take this forward. I want to help standardize AI development, so developers a few years from now can look back with horror at the pre-MCP days. [1] - https://github.com/lastmile-ai/mcp-agent [2] - https://modelcontextprotocol.io/introduction [3] - https://www.anthropic.com/research/building-effective-agents [4] - https://github.com/openai/swarm [5] - https://microsoft.github.io/language-server-protocol/ [6] - https://xkcd.com/927/ (I understand the irony) [7] - https://github.com/lastmile-ai/mcp-agent/tree/main/examples