Inside ModelCrew
How ModelCrew works
Build an AI crew with distinct roles, choose the models behind each teammate, and coordinate work inside one project.
How collaborative tasks flow
You
Set the goal
Project Lead
Plans & delegates
AI teammates
Work on tasks
Review
Synthesis & approval
Illustrative workflow · Execution and approvals depend on project configuration.
01 · Create your crew
Define the job, not just the prompt.
Give each teammate a specific role, working instructions and collaboration boundaries. Rather than rebuilding that setup in every new chat, maintain it in the workspace.
How roles remain consistentThe teammate is the role. The model is interchangeable.
Sarah · Research teammate
Assigned model
Changing models preserves configured teammate settings, not identical model behavior. This is a conceptual illustration.
02 · Assign models
Choose an available API model for each teammate, with freedom to change assignments when your priorities shift.
Learn more03 · Work together
A Project Lead can delegate work, gather specialist contributions and synthesize the result while you remain involved.
Learn more04 · Stay in control
Connect your provider API credentials, review collaboration, and apply human approval where your workflow requires it.
Learn moreExplore the system
Deeper explanations of the ideas and mechanics behind ModelCrew.
How it works
AI Models vs AI Agents vs AI Teammates
Model = the engine. Agent = engine + instructions + tools. Teammate = the persistent role you actually work with. Here's how the three layers fit together — and why the role doesn't have to be the model.
How it works
How Do AI Agents Keep Consistent Roles?
AI agents stay in role through system instructions, persistent role definitions, project context, and collaboration rules — not magic. Here's the machinery, why it's better than retyping “you are my marketing expert…”, and why consistency still isn't perfect.
How it works
Why Make the AI Model Behind an Agent Interchangeable?
Your AI coworker shouldn't disappear because you changed the engine powering them. Here's why teams swap the model behind an agent — cost, speed, quality, availability — and the honest caveat: swapping engines changes behavior.
How it works
What Is AI Agent Orchestration?
AI agent orchestration is the layer that decides which agent works on what, in which order, with what context — and who reviews the result. Learn the core components, common patterns, and how it works inside a multi-model workspace.
How it works
How to Build a Team of AI Agents
A concrete 9-step framework for building a team of AI agents: define the work, assign roles, set collaboration rules, choose models, add human approvals, and iterate. With a full marketing-crew example.
How it works
How to Use Multiple AI Models on the Same Project
A practical pattern for running several AI models on one project: research, critique, technical analysis, and synthesis — each on the model that fits. Includes when model diversity helps, when it doesn't, and the cost/latency tradeoffs.
Ready to see it in action?
Explore AI teammates, model choice, and collaboration in one workspace.