Place agents on a canvas, connect how they hand off work, and give each one the model and tools it needs. Zorg Together runs the team against a live project and streams every agent's work back into its node.
A single generalist agent has to hold the whole project in its head and re-read it on every step. A team of specialists does not.
A focused agent with a tight prompt and the right tools finishes in fewer tokens than one generalist trying to hold the entire project in context.
Agents on the canvas share the project workspace and transcript, so a handoff never restarts the conversation or re-reads the codebase.
Give the planner a strong model and the routine workers a cheap one. You choose per node, so you never overpay for simple steps.
Independent workers run at the same time instead of one long chain, cutting wall-clock time and the repeated context that serial loops accumulate.
Up to 78% lower token spend versus one generalist agent on a frontier model. Actual savings depend on your team design and task mix — a team of narrow specialists with cheap models on routine nodes saves the most. Every run reports its real usage cost.
More agents means more places for things to go wrong. Zorg Together puts a guardrail on every one of them.
Each agent gets only the MCP servers and tools you attach to it. The researcher gets search; the builder gets a browser — and nothing else.
Results are checked before the next agent acts, so a failed step stops the run instead of quietly poisoning the work downstream.
Destructive commands, privilege escalation and credential access are blocked before execution — independent of any model's judgement.
Every run is persisted with its full timeline, so refreshing the page or switching models never loses what your team did.
A global stop drains the run and closes orphaned work, and you can steer a single node without re-running the whole team.
You approve the topology, the models and the tools before anything runs. Nothing reaches your project without your design.
The canvas is the control room. Nodes light up as agents start, handoffs animate along the edges, and each agent streams its own output where you can see it.
Run a pure all-to-all swarm with the first worker as the entry point, or add a coordinator that plans, delegates and writes the final answer.
Every node shows its own reasoning and output as it works, so you always know who is doing what.
Send a creative instruction to a single agent and watch it work — without re-running the whole team.
Save a team once and reuse it across projects. Each team gets its own shareable URL.
Four steps, no orchestration code.
Drop nodes onto the canvas and give each one a role, a prompt and a model.
Draw the edges that define who passes work to whom. Choose swarm or coordinator topology.
Give each agent its own MCP servers and tool allowlist, so capabilities stay exactly where you want them.
Run the team against a live project. Nodes light up, output streams in, and the whole run is saved.
Not a diagram tool. Every node is a working agent with its own model, tools and output.
Drag agents onto a canvas, connect handoffs and see the whole team at a glance.
Run a pure all-to-all swarm, or add a coordinator that plans, delegates and writes the final answer.
Each node streams its own reasoning and output as it works, so you can see exactly who is doing what.
Attach different MCP servers to different agents. The researcher gets search; the builder gets the browser.
Send a creative instruction to a single agent and watch it work without re-running the whole team.
Runs are persisted, so refreshing the page or switching models never loses your timeline.
Most multi-agent setups are code you have to maintain. Zorg Together is a canvas you can see.
Comparison reflects the default configuration. Teams are user-scoped and reusable across projects.
A visual studio for building teams of AI agents. You place agents on a canvas, connect how they hand off work, give each one a model and its own tools, then run the team against a live project.
Focused agents can avoid spending the same tokens as one generalist holding the whole project. Choose a cost-conscious model for routine nodes and a stronger one where it matters. Our specialized neural routing network also helps select capable models for Builder tasks. Actual savings depend on the task and team configuration.
A pure swarm hands work directly between workers, all-to-all, with the first worker as the entry point. Coordinator mode adds a lead agent that plans, delegates and writes the final answer.
Yes. Each node stores its own MCP server references and tool allowlist, so the researcher can have search while the builder has a browser — and nothing else.
Nothing is lost. Runs are persisted with a durable trace, so the timeline, the active node and every agent's output are restored when you come back.
Yes. A global stop drains the run and closes any orphaned work, and you can also send a creative instruction to a single node without re-running the whole team.
Design your agent team on a canvas, give each node the model and tools it needs, and run it against a live project. Up to 78% lower token spend, with verification and a full audit trail on every run.