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AgentsMesh – AI agent fleet command center

by zyf1994·Mar 4, 2026·3 points·3 comments

AI Analysis

●●●BangerBold BetShip It

Kanban-driven multi-agent fleet orchestration beats single-chat-per-agent tools.

Strengths
  • Pod-binding + Kanban design solves real visibility gap in multi-agent workflows.
  • BYOK (bring your own keys) + self-hosted runners keep costs and code sovereignty.
  • Multi-git + SSO + RBAC addresses real team/enterprise deployment needs cleanly.
Weaknesses
  • Hosted SaaS requirement for quick start; unclear how DIY self-host compares.
  • Early docs suggest feature-rich but potentially steep learning curve vs simpler alternatives.
Target Audience

Teams running multiple AI coding agents, enterprise dev teams needing agent coordination

Similar To

Continue · Cursor · GitHub Copilot Workspace

Post Description

Hi HN — I built AgentsMesh, a “fleet command center” that has all componenets you need to harness agentic engineering

Most tools assume one agent, one chat. In practice, I wanted something closer to “a small team”: multiple agents working on different parts of a project, with clear task ownership, visibility, and an easy way to supervise and coordinate them.

What it does

- Spin up multiple remote dev sessions (e.g., Claude Code / Codex / Gemini CLI / OpenCode) and access them from the browser (desktop + mobile). - Assign work via a Kanban board: each ticket maps to an agent/session so work stays scoped and trackable. - Agent collaboration channel: see activity and share context across agents while they work. - Scheduled/repetitive agent jobs (e.g., “run nightly dependency updates”, “daily bug triage”). - Self-host / bring-your-own-infra option with basic runner/health visibility.

Why I made it When I tried running 3–5 agents at once, the biggest problems weren’t “prompting” — they were coordination and supervision:

- keeping tasks from overlapping - tracking what each agent changed - sharing the right context without dumping everything - knowing when an agent is stuck or doing something risky

AgentsMesh is my attempt to make multi-agent work feel more like managing a small engineering team.

How it works (high level)

- Each agent runs in its own isolated session/environment (often backed by separate worktrees/branches). - Tickets are the unit of work; sessions attach to tickets. - The UI focuses on observability: what each agent is doing, what changed, and where attention is needed.

Links

- Website: https://agentsmesh.com - Docs: https://agentsmesh.com/docs - Repo (if open source): https://github.com/AgentsMesh/AgentsMesh - Demo video: https://www.youtube.com/watch?v=FZrUO0tim0U

I’d love feedback on: 1. Where this breaks down in real workflows 2. Security/isolation expectations when supervising agents 3. What features are “must have” for coordinating multiple coding agents

Maker here — happy to answer questions.

Similar Projects

AI/MLMid

Multi-agent orchestration layer for experimentation

File-system driven agent config is clean, but OpenClaw and LangGraph already solve this.

Ship It
theo-kirby
1017d ago
Developer Tools●●Solid

Supervisor IDE – Command center for coding agents in complex projects

It wires practical, product-focused features together—scoped agent permissions, automatic context injection from apps, persistent background agent sessions, and Kanban-driven ticket pickup that spawns branches/worktrees and auto-opens PRs. Useful, pragmatic feature set for teams that want to run many LLM workers without chaos, but its ultimate value will hinge on integrations, security/permission guarantees, and how well it handles real-world scale and noise.

Bold BetSlick
nexroo
103mo ago