Mercury – No-code orchestration for human and agent teams
Hey HN, I'm Naveen, one of three co-founders building Mercury (mercury.build). We spent the last year in deploying AI agents for teams in large enterprises. The agents themselves worked fine. The problem was managing them. You've got Claude Code in a terminal, a research agent in a browser tab, a Slack bot somewhere else, a scheduling assistant in yet another window. It's chaotic. There's no single place to see what's running, who's doing what, or how things connect. As the number of agents grows, this gets unmanageable fast. Mercury is a canvas where you bring your agents and humans…
Mercury – No-code orchestration for human and agent teams launched on April 13, 2026 with 6 votes, #1,677 of 21,248 launches that month and more than 75% of that year's launches.
What it does
Oasis HQ is a workspace where people and AI agents work together with shared context, memory, policies, and approvals.
A room is a group chat between humans and agents. Built so your whole team can work with the same agents in one place. Get work done together in the same file. Humans and agents edit one doc, spreadsheet, or codebase side by side, live in the room. Build context that compounds, and that you own. Oasis holds living memory of every decision, task, and lesson learned so every new agent starts sharp. Swap an agent, upgrade a model, or bring in a new one; it picks up exactly where the last left off. Oasis learns how you work and brings the right agents into each task. Agents can be created and shared with teammates, so everyone works with the same agent. Build a fleet of specialized agents in…from mercury.build
In the maker’s words, at launch
Hey HN, I'm Naveen, one of three co-founders building Mercury (mercury.build). We spent the last year in deploying AI agents for teams in large enterprises. The agents themselves worked fine. The problem was managing them. You've got Claude Code in a terminal, a research agent in a browser tab, a Slack bot somewhere…
Does a similar job
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Aviator Agents2025 · aviator.co · ▲97When we asked developers from large engineering teams, almost everyone is using Claude Code, Cursor, or Copilot. But adoption is still inconsistent. Some of us would have 15 agents running in Claude Code at the same time; some would still refuse to use any of them and write code manually. There's a fragmented AI development problem: - Five developers building similar features, and all of them start from scratch - No way to share "here's how we prompt AI to follow our architecture" - No trail of how the AI changes were generated - People leave the company, so does the their knowledge Aviator…
Zen Agents (by Zencoder)2025 · docs.zencoder.ai · ▲287Build AI agents. Share org-wide. 100+ Tools&MCP
Cosmic AI AgentsDec 2025 · cosmicjs.com · ▲145Hey HN, We're Cosmic (YC W19), a headless CMS. Today we're launching Team Agents and the Cosmic Agent. Team Agents are AI-powered team members that live in Slack, WhatsApp, and Telegram. They have names, roles, persistent memory, and real capabilities. Not chatbots. Not one-off prompts. Persistent agents that work alongside your team. Here's what they can do: CONTENT AGENTS write blog posts, landing pages, and marketing copy. They generate images, add SEO metadata, and publish directly to the CMS. Message them in Slack, get a finished post back. CODE AGENTS read your codebase, create…
ReplicasJun 2026 · replicas.dev · ▲239Hey HN! I'm Connor, creator of Replicas. I'm a heavy Codex/Claude Code user, and I wanted to hand off tasks to agents like I would to an engineer - kick something off before work, let it scaffold while I'm at lunch, even come back to a ready PR. The problem: existing alternatives use workspace VMs that are black boxes. I have no access to any of them - they can't run tests, read logs, or use tools like the Playwright MCP (which I use to verify agents actually fixed what they claim to fix). Replicas gives agents real cloud workspaces where they can: - Run your test suite and read the…
20+ Claude Code agents coordinating on real work (open source)Feb 2026 · github.com · ▲53Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
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