AgentCenter for OpenClaw

AgentCenter for OpenClaw

AgentCenter · Productivity

AgentCenter for OpenClaw is a mission control dashboard that pulls every one of your OpenClaw AI agents into a single control plane. You connect agents running on laptops, cloud VMs, or on-prem servers, assign them tasks and workflows, then watch progress, logs, and deliverables come back in real time. It's built for people running more than one agent and getting tired of checking each machine by hand.

Interface preview of AgentCenter for OpenClaw

About AgentCenter for OpenClaw

What Is AgentCenter for OpenClaw

AgentCenter is a hosted web dashboard, not an agent framework. Your OpenClaw agents keep running wherever they already live, and AgentCenter becomes the layer on top that shows you what all of them are doing at once. The pitch is simple: one screen instead of five terminals.

The problem it targets is fragmentation. Once you run a handful of agents across different machines, you lose track of which task is stuck, which one finished overnight, and which deliverable needs review. Sound familiar? AgentCenter collects all of that into tasks, a Kanban view, live activity feeds, and approval workflows.

The biggest limitation is scope. AgentCenter manages agents; it doesn't provide the AI model behind them. You still need an API key or subscription for the provider your agents use, such as Anthropic Claude, OpenAI, or Google Gemini. It's also a single-user product today, so team accounts aren't there yet.

Getting Started

  1. Create an AgentCenter account and start the 7-day free trial on any monthly plan.
  2. Connect your agents by adding them through the API or a framework connector, whatever stack or language they run on.
  3. Create a workflow and assign tasks to specific agents, setting priorities, deadlines, and dependencies.
  4. Watch execution in the live activity stream, then open a deliverable to review and approve it.
  5. Adjust your plan from the billing portal once you know how many agents you actually keep active.

Product Information

A quick look at AgentCenter for OpenClaw's pricing, supported platforms, and performance.

Free PlanNo
Paid Plans$14 - $79/mo
PlatformWeb
DeveloperAgentCenter
CategoryProductivity
Release DateSep 2025
Latest UpdatedSep 2025
Website Visits8.4K
Website Global Rank2.4M
API AvailabilityYes

Best for

The users, tasks, and scenarios where this tool fits best.

Users

  • Solo developers running several OpenClaw agents
  • Automation hobbyists with agents spread across a home server and a cheap cloud VM
  • Small operators who lose track of overnight jobs

Tasks

  • Assigning and sequencing multi-step work
  • Reviewing AI deliverables before they ship
  • Tracking agent progress in real time
  • Coordinating agents that specialize

Scenarios

  • Morning check-ins on a fleet of agents
  • Running parallel projects with different agent teams
  • Scaling past one machine

Key features

Centralized Control Plane

AgentCenter connects agents across any infrastructure. Your agents can run on a local Mac, an AWS EC2 instance, a GCP VM, a Raspberry Pi, or an on-prem server. They stay put and report back through the API. So you avoid installing a management tool on every machine. The practical result is one login that sees your whole fleet. That's the whole idea behind AI agent mission control.

Task and Workflow Management

You create workflows and hand tasks to specific agents, then set priorities, deadlines, and dependencies. Dependencies matter more than they sound. They let one agent's output trigger another's work without you babysitting the sequence. It turns a pile of one-off prompts into real agent task management.

Real-Time Monitoring

A live activity stream shows tasks started, deliverables submitted, code pushed, and reports filed as they happen. Instead of polling each agent, you watch a single feed. The live status view also flags which agents are active right now. It's the fastest way to spot one that has quietly stalled. That's multi-agent coordination in plain sight.

Deliverable Review

Finished work lands in a review queue rather than disappearing into a chat log. You open the deliverable, check it, then approve it before it moves on. This matters if you run agents that produce things you actually use, like reports or code. You don't want to trust them blindly.

Kanban and Analytics

A Kanban board gives tasks a visual state, from assigned to done. You see the shape of your backlog at a glance. Analytics add a usage layer on top. Useful once you're paying per agent and want to know where the effort goes.

Channels and Notifications

Agents connect and communicate through channels, and notifications push status changes to you. Approval workflows are included too, so sensitive steps can wait for a human before they proceed. All of it ships on every plan, which is unusual for tools that usually gate basics behind tiers.

Approval Workflows

Tasks that need a human decision can pause at a checkpoint until you sign off. That's the difference between an agent that acts and an agent that waits when it matters. Handy when an autonomous step could touch something you'd rather review first.

Pros and cons

Pros

  • Brings agents from different machines and providers into one dashboard without moving them.
  • Live activity feed and status view make stalled agents easy to catch.
  • Every plan includes the full product, so there are no hidden feature gates.
  • Pricing stays low, from $14/mo on the Starter plan with a 7-day free trial.
  • Connects through a secure API with encrypted communication and cloud-hosted data.

Cons

  • It's a management layer only, so you still pay separately for your AI provider's API or subscription.
  • Single-user for now, meaning teams can't share one workspace.
  • Agent limits are tied to plans (5 on Starter, 15 on Pro, 50 on Scale), so big fleets need the top tier.
  • Recurring tasks are limited to Pro and above, and Cloud VM provisioning is Scale-only.

Frequently asked questions

It's a dashboard for managing OpenClaw AI agents. You connect agents running on different machines, assign them tasks and workflows, and monitor progress, logs, and deliverables from one place.