Launched this week
BAND
Coordinate and govern multi-agent work in a single chat
242 followers
Coordinate and govern multi-agent work in a single chat
242 followers
BAND helps teams enable and govern interaction across distributed AI agents and human teams. Through a shared interaction layer, it supports real-time multi-peer collaboration with built-in governance, giving organizations a structured way to manage how agents communicate and coordinate. Unlike orchestration tools or agent frameworks, BAND governs the interaction layer itself, reducing fragmentation and making collaboration reliable at scale.
BAND
Hey Product Hunt, I'm Arick, Co-founder & CEO of Band!
The Problem
AI agents are proliferating fast — coding agents, research agents, orchestrators, specialized workers — but the infrastructure for them to work together hasn't kept up.
Most teams building multi-agent systems hit the same walls:
Point-to-point integrations – Every agent connection is custom-built, brittle, and doesn't scale across systems.
No shared context – Agents can't reliably discover, trust, or coordinate with other agents at runtime.
Visibility gaps – When something breaks in a multi-agent workflow, you have no idea where or why.
How Band is Different
Band is the interaction infrastructure for distributed multi-agent AI systems — the network layer your agents actually need to find each other, communicate, and collaborate at scale.
Agent-to-agent communication at scale – Band handles the routing, context, and coordination so your agents don't have to reinvent it every time.
Built for enterprise from day one – Security, observability, and governance baked in — not bolted on.
Works across any framework – LangGraph, CrewAI, Claude Code, custom agents, or a mix — Band connects them all.
Who is this for?
Engineering and platform teams deploying coding agents, agentic pipelines, or any multi-agent system in production — who need their agents to actually work together reliably, at scale, and in the real world.
Get started free
Band has a free tier — no commitment needed. Visit band.ai to start connecting your agents today.
A2A is the protocol. Band is the network.
the governance piece really stands out here. most multi-agent setups just throw agents together and hope for the best. curious about the real-time aspect - does BAND maintain state across all the agents so they can actually build on each other's work, or is it more about managing the conversation flow?
BAND
@piotreksedzik Great question, and you're nailing one of the core problems we set out to solve. Most multi-agent setups treat communication and context as separate concerns, if they handle context at all.
BAND does both as one system.
On the state side, each agent maintains persistent context, not just within a single conversation, but across sessions. If an agent is interrupted or needs to pick back up, it re-hydrates its full context and continues where it left off. So when agents build on each other's work, they're operating from a shared, durable understanding rather than just reacting to the last message in a thread.
On the orchestration side, BAND manages the routing, coordination, and governance so agents operate within their defined boundaries, contribute when it's appropriate, and defer when it's not. State without structure just gives you agents that know everything and step on each other's toes.
Most multi-agent frameworks give you one or the other: a message bus or a state store. We built them together because reliable agent collaboration at scale needs both. Durable awareness of what's happened, and governance over what happens next.
BAND
@joshua_herrera Yes, that's right - the state lives inside band's infrastructure.
this interaction layer approach is interesting - we've been running into coordination issues with multiple agents in our healthcare builds. how does BAND handle conflicts when agents have competing objectives? like if one agent wants to prioritize data accuracy while another is optimizing for speed?
Hi @piotr_pasierbek - great question, and one we hit early. BAND's answer is that agents coordinate the same way humans do: in the conversation. The chat room is the coordination layer.
Your accuracy-first agent and your speed-first agent are both participants - they discuss tradeoffs, mention each other, and agree on who owns what. If they can't converge, escalation is just adding another participant: a manager agent, or a human pulled into the room as the final decider. No central arbiter, no objective-weighting algorithm to tune - just the same affordances a human team uses (roles, mentions, handoffs).
For healthcare specifically that's works well because the audit trail is literally the conversation, and a clinician-in-the-loop is a first-class participant, not an out-of-band exception. Happy to dig in deeper if useful.
the "no shared context" pain point is real — every multi-agent setup I've touched ends up reinventing routing. how does Band handle agent identity/auth across frameworks like LangGraph and CrewAI in the same flow?
Hi @tijogaucher ,
Agent identity in BAND is framework-agnostic.
Every agent is a first-class identity with its own credentials, no matter what's running it - Claude, Codex, LangGraph, CrewAI, or your custom ADK. Auth is uniform: the agent's credentials resolve to an agent identity, which resolves to an owning user and tenant context, so authorization and visibility work the same no matter what's underneath. They all join the same conversation as participants and coordinate there, so the framework boundary disappears. Our Python and TypeScript SDKs ship adapters for the most used ADKs (Claude, Codex, etc.) out of the box and handle the connection plumbing for you. Happy to share docs if useful.
Portia AI
Definitely solves a pain point I've felt trying to shepherd code through to ready-to-merge! Congrats on the launch 🎉
BAND
@sam_stephens2 Thanks for the support!