Alternatives
Products that do what toq protocol does
Secure agent-to-agent communication. Open source.
- 1
- 2
- 3
- 4MA
Hey HN, I spent my xmas break building an agent framework called mcp-agent [1](https://github.com/lastmile-ai/mcp-agent) for Model Context Protocol [2]. It makes it easy to build AI apps with MCP servers, and implements every pattern from the popular Building Effective Agents blog [3] as well as OpenAI’s Swarm [4]. I’m sharing it early to get community feedback on where to take it from here, and to ask for contributions. For those who aren’t familiar with MCP, I think of it as a standardized interface to let AI communicate with software via tool calls, resources and…
2025 · github.com
- 5AJ
Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…
2025 · github.com
- 6AL
Apr 2026 · juanpabloaj.com
- 7RA
Hey HN! A few months ago we shared mcp-agent (https://github.com/lastmile-ai/mcp-agent) [1][2], a lightweight framework that implements every agent pattern from Anthropic’s Building Effective Agents blog [3] and handles MCP server/client management seamlessly. Our core bet is that connecting LLMs to tools, resources, and external systems will soon be MCP-native by default. Today we're launching a significant update: Agents as MCP servers. Currently "agentic" behavior exists only on the MCP client side – clients like Claude or Cursor use MCP servers to solve tasks.…
2025 · github.com
- 8
13,000+ MCP servers, skills & plugins for AI coding agents
Jul 2026 · codexmarketplaces.com
- 9
- 10
- 11
- 12RC
Hi HN, we built SuperHQ, an open source app that runs AI coding agents in isolated microVM sandboxes instead of directly on your machine. Each agent gets its own VM with a full Debian environment. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a diff view to accept or discard changes. API keys never enter the sandbox. We also just launched remote.superhq.ai which acts as a remote control for SuperHQ, allowing you to access your workspaces and agents from anywhere.
Apr 2026 · github.com
- 13
- 14OA
As AI agents autonomously write and deploy code, there's no standard for verifying that what they shipped actually satisfies business requirements. OQP is an attempt to define that standard. It's MCP-compatible and defines four core endpoints: - GET /capabilities — what can this agent verify? - GET /context/workflows — what are the business rules for this workflow? - POST /verification/execute — run a verification workflow - POST /verification/assess-risk — what is the risk of this change? The analogy we keep coming back to: what OpenAPI did for REST APIs,…
Apr 2026 · github.com
- 15
- 16
- 17AL
Most of the MCP servers that I’ve seen are tools implemented in standalone projects. To onboard more tools (especially agents and multi-agent workflows) to MCP, I’ve been thinking it’s important to allow AI engineers to continue to prototype in their existing agent frameworks and deploy with minimal conversion when ready. We created the automcp library, which you can add as a dependency to existing projects (CrewAI, LangGraph, Llama Index, OpenAI Agents SDK, Pydantic AI, mcp-agent currently supported but more coming soon). You just need to run a CLI command to create a run_mcp.py file, make…
2025 · github.com
- 18TP
Much of my work right now involves complex, long-running, multi-agentic teams of agents. I kept running into the same problem: “How do I keep these guys in line?” Rules weren’t cutting it, and we needed a scalable, agentic-native STANDARD I could count on. There wasn’t one. So I built one. Here are two open-source protocols that extend A2A, granting AI agents behavioral contracts and runtime integrity monitoring: - Agent Alignment Protocol (AAP): What an agent can do / has done. - Agent Integrity Protocol (AIP): What an agent is thinking about doing / is allowed to do. The problem:…
Feb 2026 · mnemom.ai
- 19NT
Today we're releasing Nanobot an open-source framework for building AI agents on top of the Model Context Protocol (MCP). MCP servers are a great way to expose structured tools, but they’re usually just that—collections of functions. Nanobot makes it simple to wrap any MCP server with reasoning, a system prompt, and orchestration so it behaves like a real agent. Even better, Nanobot fully supports MCP-UI, so agents can pass rich interactive components (forms, dashboards, even mini-apps) directly into chat. A simple example: if you had a Blackjack MCP server with tools like deal, bet, and…
Sep 2025 · nanobot.ai
- 20
- 21
- 22MS
Admittedly I work on this, but I anthropic's decision to finally embrace http, auth (at least a little), and other RFCs that are coming out is just great. With OpenAI behind it too, this ecosystem will be built around projects like this. Not saying it's MCP or not, but it'll be one of them
2025 · twitter.com
- 23HM
We built a Rust MCP server, which wraps the excellent headless terminal (HT)[1] to makes it available for agentic coding tools. Most agentic coding tools struggle with blocking, interactive terminal tools (all the boilerplate generator, infra CLI tools etc). This is because many existing CLI dev tools are built for humans and require interactive input. We built this so agentic coding tools (Claude Code, Cursor, Memex, etc) can “see” and “interact” with terminals exactly like humans do. In our demo video [2], you'll see it operating vim/emacs just as a human would - selecting options,…
2025 · github.com
- 24
Love OpenClaw? Now ship it to production. Built in Rust.
Feb 2026 · github.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →