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A multi-agent system can help when one general-purpose agent is being asked to do distinct jobs—for example, gather source material, check it, and assemble a response. The key design decision is not simply how many agents to create: it is who controls the workflow, who owns the final response, and how you inspect what happened. Multiple agents add coordination and operational complexity; the sources below do not establish that they automatically improve quality, speed, or cost.
What does a multi-agent system mean in Node.js?
Here, “monolith” is a useful metaphor for one general-purpose agent that handles every responsibility, not a formal system category. A multi-agent design assigns focused responsibilities to separate agents and defines how they work together.
For instance, a coordinator might ask one specialist to gather material and another to check it, then assemble their results. That decomposition is worthwhile only when the responsibilities are genuinely separable. A fixed sequence of deterministic operations may be clearer as ordinary application code, with an agent used only where language understanding or generation is needed.
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How do agents hand off work?
Every agent workflow has an orchestration policy: application code chooses what runs next, a model chooses, or the two share control. OpenAI’s Agent Orchestration guide describes these as code-directed and LLM-directed patterns and notes, “You can mix and match these patterns.” That makes the choice a design spectrum rather than an all-or-nothing decision.
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Code-directed steps
Use code when the order is known: run a research step, pass its output to a checking step, then synthesize. Loops can repeat a step until an application-defined condition is met. For independent work, JavaScript’s Promise.all can run calls concurrently; use this only when the tasks do not depend on one another’s results.
Agents as tools
A manager can invoke a specialist as a tool and remain responsible for the final response. This is useful when the specialist’s result is an input to the manager’s answer, rather than a replacement for it.
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Handoffs
In a handoff, the manager routes the interaction to a selected specialist, which becomes the active agent for the next part of the interaction. This fits open-ended routing when the appropriate specialist depends on what the user says. It also means the design must make clear which agent currently owns the response.
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Should you use multiple agents or one agent with tools?
Choose the least complex design that gives each responsibility clear ownership. A single agent with tools is often a better fit when one agent can decide which capability to call and then explain the result. Multiple specialists are more appropriate when responsibilities need distinct instructions, independent work, or explicit review and synthesis.
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| Design question | Code-directed workflow | Model-directed workflow |
|---|---|---|
| Who chooses the next step? | Application code follows defined steps or routing rules. | The model selects a tool or specialist based on the interaction. |
| Best fit | Known sequences, loops with explicit conditions, or independent tasks. | Open-ended requests where the appropriate specialist is not known in advance. |
| Final-response ownership | Set by the application’s workflow. | An agents-as-tools manager can retain it; after a handoff, the specialist is active. |
| Main trade-off | More explicit control, but the application must encode the flow. | More flexible routing, but the model’s decisions need monitoring and evaluation. |
These patterns can be combined: application code can enforce the broad sequence while a model chooses among specialists at a particular step. Do not assume that adding agents improves outcomes; evaluate the design against the task and its operational overhead.
How to build a multi-agent system in Node.js
The OpenAI Agents SDK for JavaScript and TypeScript provides one concrete implementation path. Its official quickstart walks through creating an npm project, installing the SDK and Zod, defining agents and tools, configuring handoffs, and invoking the runner.
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- Start an npm project. Initialize a Node.js project using your team’s usual package-manager workflow.
- Install the documented packages. Add
@openai/agentsandzod, as shown in the quickstart. Check the current quickstart for exact commands and package requirements, which can change. - Define focused agents. Give each agent a bounded responsibility, such as gathering material, checking it, or synthesizing results. Define tools for actions the agent needs to take.
- Choose the coordination pattern. Have a manager call specialists as tools if it should own the final response; configure handoffs if a specialist should take over. For a predetermined sequence, let application code direct the steps.
- Run and inspect the workflow. Invoke the SDK runner and review traces to examine operations, tool calls, and handoffs. Use those observations to evaluate behavior and improve instructions or routing.
Tracing helps make a run inspectable; it does not by itself establish that the output is correct. Define task-specific checks for the results that matter, and monitor behavior as the workflow changes.
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The official documentation for both OpenAI’s Agents SDK and Google’s ADK for TypeScript describes implementation options, not an independent head-to-head benchmark. Compare them by workflow fit, runtime target, and operational responsibility rather than assuming that a feature list proves one is better.
| Option | Documented fit and workflow details | Runtime responsibility |
|---|---|---|
| OpenAI Agents SDK for JavaScript/TypeScript | The official quickstart covers agents, tools, handoffs, running workflows, and traces. | The SDK runs in your application; the application controls deployment, tools, state storage, and approval decisions, according to OpenAI’s runtime documentation. |
| Google ADK for TypeScript | The repository README describes Node.js and browser support, ESM and CommonJS, and sequential, parallel, loop, routed, and A2A workflows. It lists Node.js 20.19 or newer as a prerequisite. | Review the framework’s own deployment and operational documentation for the setup you intend to use; the README’s workflow list is not a comparative quality assessment. |
| Anthropic managed agents | The cited managed-agent documentation describes a managed session model rather than the same application-owned SDK arrangement. | The documentation identifies the feature as beta under the dated header managed-agents-2026-04-01. It describes separate persistent session threads and per-agent configuration, alongside a shared sandbox, filesystem, and vault credentials. See Anthropic’s managed-agent documentation. |
What should your application own?
Framework choice does not remove the need to decide how the system operates. With the OpenAI Agents SDK, the application owns deployment, tools, state storage, and approvals. A managed harness represents a different responsibility boundary; Anthropic’s cited managed-agent documentation describes its own session and shared-resource model, so do not generalize those details to other frameworks.
- State: Decide what context persists between steps and where it is stored.
- Tools: Specify which agent can invoke each tool and what inputs it accepts.
- Approvals: Determine which actions need human or application-level approval.
- Isolation: Understand which sessions, files, credentials, and resources are separate or shared in the chosen runtime.
- Observability and evaluation: Inspect calls, handoffs, and outputs, then test results against task-specific expectations.
These are framework- and runtime-specific choices. Read the documentation for the exact version and deployment model you plan to use; package requirements and beta behavior can change.
How to tell whether the design is working
Instrument runs so you can inspect which agents ran, what tools they called, and where handoffs occurred. Then evaluate whether each specialist performed its assigned task and whether the coordinator produced an acceptable result. A trace makes the workflow easier to investigate, but it is not a correctness guarantee.
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Keep a single-agent or code-only baseline for comparison if you need to decide whether the added coordination is justified. The cited implementation documentation does not supply a general benchmark for accuracy, speed, adoption, or savings, so make that decision using results from your own workload rather than an assumed benefit.
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