Integrations
Team9 is presented as a coordination layer that works alongside existing models, agents, files and internal systems. Named models include Claude Opus 4.7, GPT-5.4, Gemini 3.1 Pro, Kimi K2.5 and GLM 5.1.
Team9
Turn AI agents into an accountable execution team for real work.
Intermediate
What is Team9?
Team9 is an AI project builder tool that turns AI agents into an accountable execution team for real work. Team9 is an execution workspace where role-based AI agents are assigned scoped tasks alongside human teammates, with a shared queue, live progress, escalation of blockers, approval gates and reusable playbooks.
Team9 is a workspace built around a simple premise: agents should be given outcomes, not prompts. Instead of opening a chat window and hoping for a usable reply, you create named agents with a role, a body of context, a set of tools, a set of permissions, and a definition of done. Those agents then sit in the same work queue as your human teammates. Engineering, growth, support, research, QA and operations can each get their own specialist, and every update, blocker, decision and handoff lands on one shared timeline. The product treats long tasks as a loop rather than a single exchange. Work is planned, started, inspected, escalated and finished, with a live progress stream showing what is moving right now. When an agent runs into missing context, a broken environment or a decision it should not make alone, it raises the problem rather than inventing an answer. People can assign, review, comment, pause a run and approve sensitive steps before anything ships, which keeps final ownership with the team. Playbooks are where Team9 diverges most from chat tools. A launch checklist, bug triage routine, PR review process or customer research flow can be captured once with its instructions, examples, files, tools and decision rules, then reused by every agent and every teammate. The stated goal is compounding execution: each successful run leaves better context for the next one. Model choice is deliberately flexible. The site lists Claude Opus 4.7, GPT-5.4, Gemini 3.1 Pro, Kimi K2.5 and GLM 5.1, and encourages mixing models by role rather than using one for everything. Team9 positions itself as a coordination layer that sits alongside existing models, agents, files and internal systems, so the closest comparison is an agent orchestration or workflow platform rather than an assistant. Teams that already run coding or research agents but lack a shared queue, audit trail and approval path are the natural audience.
How do you use Team9?
1Create your workspace
Sign in with Google and set up the workspace around outcomes your team already owns, such as product work, engineering tasks, customer operations or research.
2Design your first specialist agent
Give the agent a role, context, tools, permissions and a clear definition of done so it knows what it owns and how it hands work back.3Assign work from the shared queue
Route a scoped task to the agent or teammate best suited to it, with status, dependencies and ownership visible to everyone.4Watch progress and approve checkpoints
Follow the live progress stream, review outputs and comments, and approve or pause sensitive steps before work moves forward.5Turn a good run into a playbook
Capture the instructions, examples, files, tools and decision rules from a successful run so every agent and teammate can reuse the pattern next time.
Pros and cons
Pros
- Agents carry roles, owners, permissions and a definition of done, which makes delegated work auditable rather than conversationalAI
- One timeline tracks updates, blockers, decisions and handoffs for both humans and agentsAI
- Blocker escalation is built in, so agents surface missing context instead of guessingAI
- Playbooks convert successful runs into reusable instructions, files, tools and decision rulesAI
- Model-agnostic by design, with Claude, GPT, Gemini, Kimi and GLM available and mixable by roleAI
Cons
- No pricing information is published on the homepage, so cost per seat or per compute cannot be assessed before signing upAI
- The workflow assumes a fairly mature team process; without existing task ownership and definitions, playbooks have little to encodeAI
- Value depends on steady task volume, and light users may find the queue and dashboard overhead heavier than a chat toolAI
- Observability is described in general terms (usage, latency, cost), with no detail on export formats or external monitoring hooksAI
- Homepage lists only Google sign-up and a Mac download, which suggests a narrow onboarding path at launchAI
How much does Team9 cost?
Features
Role-based agents for engineering, growth, support, research, QA and ops; a shared execution board where humans and agents pull from one queue; long-task handling across plan, start, inspect, escalate, finish and summarize; blocker escalation; a live progress stream; reusable playbooks; and a dashboard covering agents, queues, usage, latency, errors and cost.
Frequently asked questions about Team9
How do you use Team9?
The walkthrough on this page covers 5 steps: 1. Create your workspace 2. Design your first specialist agent 3. Assign work from the shared queue 4. Watch progress and approve checkpoints 5. Turn a good run into a playbook.What platforms does Team9 support?
Team9 is available on Web App and MacOS.What does Team9 integrate with?
Team9 is presented as a coordination layer that works alongside existing models, agents, files and internal systems. Named models include Claude Opus 4.7, GPT-5.4, Gemini 3.1 Pro, Kimi K2.5 and GLM 5.1.What are the limitations of Team9?
No pricing information is published on the homepage, so cost per seat or per compute cannot be assessed before signing up. The workflow assumes a fairly mature team process; without existing task ownership and definitions, playbooks have little to encode. Value depends on steady task volume, and light users may find the queue and dashboard overhead heavier than a chat tool.
Status
StatusActive
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Added10/21/2026
Platforms
Web AppMacOS
Pricing
Freemium
