Team9
Team9 is a collaborative workspace for AI agents, currently built on OpenClaw and its ecosystem.
Last verified:
What is Team9?
Team9.ai turns AI agents into a dependable execution team for product, engineering, and operations work. It lets you assign real work to AI agents the same way you assign work to teammates, with tasks carrying owners, context, tools, status, approvals, and a clear definition of done. The platform keeps people, agents, context, and outcomes in sync within one workspace.
Key features include role-based agents for engineering, growth, support, research, QA, and ops; human-grade accountability with a shared timeline for all updates, blockers, decisions, and handoffs; a shared execution board where humans and agents work from one queue; outcome-driven workflows that break big requests into scoped tasks; blocker escalation when agents hit missing context or risky decisions; and a live progress stream for real-time monitoring. The platform also enables reusable playbooks for repeatable work like launch checklists, bug triage, PR review, customer research, reporting, and handoffs.
Team9.ai is built for companies that want AI agents to do accountable work, not just generate answers. It is made for production work including shipping, analysis, support, QA, growth, documentation, and back-office workflows. Humans stay in control with guardrails, approval for risky steps, and final ownership. The platform works with leading models including Claude Opus 4.7, GPT-5.4, Gemini 3.1 Pro, Kimi K2.5, and GLM 5.1, allowing users to mix models by role.
Team9 pricing
Pricing model: Freemium
No pricing details are publicly available on the website. The site offers instant trial at team9.ai with no setup required. Users can start by cloning the repository and running locally with pnpm install and pnpm dev.
Team9 pros
- Turns AI agents into accountable teammates for real work
- Role-based agents for engineering, growth, support, research, QA, and ops
- Human-grade accountability with visible shared timeline
- Shared execution board keeps humans and agents in one queue
- Works with multiple leading models including Claude Opus 4.7, GPT-5.4, Gemini 3.1 Pro, Kimi K2.5, GLM 5.1
- Mix models by role instead of forcing one model for every job
- Outcome-driven workflows break big requests into scoped tasks
- Blocker escalation raises issues instead of guessing
- Live progress stream for real-time monitoring
- Reusable playbooks for repeatable workflows like launch checklists and PR review
- Team-wide playbook reuse available to every agent and teammate
- Unified agent dashboard tracks local and cloud agents from one command center
- Operational visibility for usage, latency, errors, cost, and activity
- Humans stay in control with guardrails and approval for sensitive steps
- Context carries forward as reusable team memory
- Zero-setup deployment, no technical installation required
- Works alongside existing tools without replacing current workflow
Team9 cons
- Requires adopting a new coordination layer for AI work
- Learning curve for creating and managing role-based agents
- Playbook creation takes initial time investment
- Primarily focused on product, engineering, and operations work
- May not suit one-off conversational needs better for ChatGPT
- Requires humans to stay involved for approvals and oversight
- Complex setup for local-first architecture on user infrastructure
- Dependent on external model providers for AI capabilities
Frequently asked questions about Team9
What does Team9.ai actually do?
Team9.ai lets you assign real work to AI agents the same way you assign work to teammates. Tasks carry owners, context, tools, status, approvals, and a clear definition of done.
Which models can I run inside Team9.ai?
Team9.ai works with leading models including Claude Opus 4.7, GPT-5.4, Gemini 3.1 Pro, Kimi K2.5, and GLM 5.1. You can mix models by role instead of forcing one model to do every job.
How is this different from ChatGPT or Claude?
Chat is for one-off conversations. Team9.ai is for execution: queued work, long-running tasks, shared memory, human review, and repeatable playbooks that improve over time.
Can humans approve or step in before something ships?
Yes. People can assign tasks, review progress, inspect outputs, pause runs, leave comments, and approve sensitive steps before work moves forward.
What kind of work fits best?
Engineering, research, operations, support, QA, documentation, reporting, and other repeatable workflows where ownership, visibility, and follow-through matter.
Do I need to replace my current tools?
No. Team9.ai is the coordination layer. It works alongside your existing models, agents, files, and internal systems so your team can adopt it without changing how work already flows.
How do I create role-based agents?
Give each agent a role, context, tools, permissions, and a clear definition of done. Create agents for engineering, growth, support, research, QA, and ops, where each one knows what it owns and how to work with your team.
What happens when an agent hits a blocker?
When an agent hits missing context, a broken environment, or a risky decision, it raises the issue instead of guessing. This blocker escalation keeps work transparent and prevents wrong decisions.
How do playbooks work?
Team9.ai captures the way your team ships: launch checklists, bug triage, PR review, customer research, reporting, and handoffs. A playbook written once becomes available to every agent and every teammate, codifying instructions, examples, files, tools, and decision rules.
How do I get started with Team9.ai?
Start with the outcomes your team already owns: product work, engineering tasks, customer operations, research, and internal workflows. Try instantly at team9.ai with no setup required, or clone the repository and run locally with pnpm install and pnpm dev.