Alternatives
Products that do what Latitude Agents, the first autonomous agent platform built for the MCP does
Hi HN, I'm excited to share Latitude Agents—the first autonomous agent platform built for the Model Context Protocol (MCP). With Latitude Agents, you can design, evaluate, and deploy self-improving AI agents that integrate directly with your tools and data. We've been working on agents for a while, and continue to be impressed by the things they can do. When we learned about the Model Context Protocol, we knew it was the missing piece to enable truly autonomous agents. MCP servers were first thought out as an extension for local AI tools (i.e Claude Desktop) so they aren't easily hostable in…
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I've been building computer-use tools for a while, and I quietly launched this about a month ago (122 Stars on GH). I figured it was worth sharing here. Over the last few months, a lot of computer-use agents have come out: Codex, Claude Code, CUA, and others. Most of them seem to work roughly like this: 1. Take a screenshot 2. Have the model predict pixel coordinates 3. Click x,y 4. Take another screenshot 5. Repeat That works, but it's slow, expensive in tokens, and fragile. If the UI shifts a few pixels, things break. And the model still doesn't know what any element actually is. But the…
May 2026 · github.com
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Hi HN! We’re Theodore and Louis, founders of Armature (YC P26). We reconstruct the entire session behind the MCP tool calls you receive, including what the user asked their agent to do and what the agent thought. You wrap your MCP in 3 lines of code (our SDK is available in Typescript, Python and Go) and start seeing in your dashboard: - All sessions reconstructed: it’s like reading the real conversation the user had inside Claude or ChatGPT! - A ranking of your MCP most popular use cases, built from sessions clustering - The most frequent issues your users’ agents encounter so you can fix…
Aug 2026 · armature.tech
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The fastest way to create MCP servers for AI agents
May 2026 · mcp-builder-puce.vercel.app
- 9MM
Hi HN, for about a year now I've been experimenting with AI agents and building my own home ecosystem; from the start I set out with the idea of an agent that behaves like a member of the family, not as a personal agent, and this made me clash very early first with OpenClaw's builtin memory, then I tested dozens of memory plugins without ever finding one that fit my purpose, so like any good builder I made my own. First on OpenClaw, as a plugin, then the idea matured and since the beginning of this year the memory plugin has evolved into an agent agnostic MCP server. It has been running my…
Jul 2026 · github.com
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I’ve been building and using agents heavily lately. The Model Context Protocol ecosystem is growing insanely fast, but discovering and configuring new tools is still highly manual. Every time I needed to connect an agent to a new service, I had to browse registries, figure out the transport type, identify required env vars, and manually update "mcp.json" files. So I built MCPfinder. It aggregates servers from the official MCP registry, Glama, and Smithery (around 25,000 combined entries) into a deduplicated, ranked catalog. But the real twist is the DX: MCPfinder is itself an MCP server :D…
Apr 2026 · mcpfinder.dev
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Hey HN Community, I built MCP360 after spending weeks integrating APIs for an AI agent project. Each tool needed its own setup, auth, billing, and maintenance. When any APIs changed, my integrations broke. I got tired of it. MCP360 is a single gateway giving AI agents access to 100+ tools through one config block. Search engines, web scraping, SEO, e-commerce data, maps, domain tools, and more. Real example: Instead of managing Google Search, web scraping, SERP tracking, and keyword research as 4 separate subscriptions (4 bills, 4 auth systems, 4 points of failure), you connect once. Works…
Oct 2025 · mcp360.ai
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Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
Mar 2026 · github.com
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Hello HN! Earlier this year, we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework for building agents with MCP. Since then we have tried to push the protocol to the limits, including hosting agents as long-running tools on MCP [3], and seen other creative approaches surface (mcp-ui, chatgpt apps sdk). Today, we are launching mcp-c – a cloud platform designed for hosting any kind of MCP server, including agents, ChatGPT apps, etc. We are in open beta and free to use, and would love your feedback. Here are some key choices we made:…
Oct 2025 · docs.mcp-agent.com
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Hey HN! I’m Gui from deco (decocms.com). We’ve been using this tool internally as the foundation for a few customer AI platforms, and today we’re open-sourcing it as MCP Mesh. MCP is quickly becoming the standard for agentic systems, but… once you go past a couple servers it turns into the same problems for every team: - M×N config sprawl (every client wired to every server, each with its own JSON + ports + retries) - Token + tool bloat (dumping tool definitions into every prompt doesn’t scale) - Credentials + blast radius (tokens scattered across clients, hard to audit, hard to revoke) - No…
Dec 2025 · github.com
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Hey HN! We've just open-sourced Agent, our framework for running computer-use workflows across multiple apps in isolated macOS/Linux sandboxes. After launching Computer a few weeks ago, we realized many of you wanted to run complex workflows that span multiple applications. Agent builds on Computer to make this possible. It works with local Ollama models (if you're privacy-minded) or cloud providers like OpenAI, Anthropic, and others. Why we built this: We kept hitting the same problems when building multi-app AI agents - they'd break in unpredictable ways, work inconsistently across…
2025 · github.com
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Empower AI coding agents to build APIs and MCP servers
Jun 2026 · agentback.dev
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Founder here. I built NEO, an AI agent designed specifically for AI and ML engineering workflows, after repeatedly hitting the same wall with existing tools: they work for short, linear tasks, but fall apart once workflows become long-running, stateful, and feedback-driven. In real ML work, you don’t just generate code and move on. You explore data, train models, evaluate results, adjust assumptions, rerun experiments, compare metrics, generate artifacts, and iterate; often over hours or days. Most modern coding agents already go beyond single prompts. They can plan steps, write files, run…
Jan 2026 · marketplace.visualstudio.com
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Hey HN, I'm one of the creators of joinly.ai, an open-source solution that makes browser-based video conferences accessible to AI agents, allowing you to interact with them in real time. Think of it as a connector layer that brings the functionality of your AI agents to your meetings, allowing you to essentially build your own custom meeting assistant. We didn't want to build yet another Python framework for creating agents with its own syntactic sugar that you have to learn. That's why we opted for a different approach, utilizing an MCP server. Our MCP server provides essential meeting…
2025 · github.com
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Hey HN, OpenAI released the Agents SDK yesterday, which is great because of its simplicity. I just added MCP support for it, which is currently available as a fork here: https://github.com/lastmile-ai/openai-agents-mcp (and on pypi as the openai-agents-mcp package). You can specify the names of MCP servers to give an Agent access to by setting its `mcp_servers` property. The Agent will then automatically aggregate tools from the MCP servers, as well as any `tools` specified, and create a single extended list of tools. This means you can seamlessly use MCP servers, local…
2025
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Hi HN, my name is Maria, and I’m a co-founder of Maritime. We started Maritime at MIT to build infrastructure for companies that need to run thousands of isolated AI agents for their customers. Imagine you set up an agent like OpenClaw, or a personal assistant agent with a custom framework, and want to give a separate version of it to every customer/friend. Each customer needs their own agent running in an isolated microVM, with persistent state, secrets, triggers, and sleep/wake behavior. Building such scalable and secure infra will take you months and will cost hundreds of…
18d ago · maritime.sh
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Hi HN -- Since Anthropic announced the Model Context Protocol (MCP) last week [1], I've been excited about giving Claude new capabilities through my custom servers. But while MCP is powerful, implementing the protocol correctly requires a lot of low-level boilerplate code. I found myself wanting something like FastAPI - a high-level framework that would let me focus on building features, not servers. After some hacking, I'm sharing FastMCP: a Pythonic framework for building MCP servers. FastMCP uses decorators to transform normal functions into MCP tools, resources, templates, and prompts,…
2024 · github.com
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We've built the first General AI Agent that works seamlessly across multiple AI platforms, e.g. ChatGPT, Claude, Cursor, and more. Try it today at no cost (no API credits needed), and join our waitlist for Flow, our visual designer that lets you customize it or build your own agent using just natural language—no coding required. #Why we made this We built this after experiencing firsthand the frustration of designing AI agents that require coding or the use of platforms with steep learning curves, only to find ourselves tied to these solutions. Our team spent months in stealth developing a…
2025 · orkestralai.com
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