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// comparison

open-multi-agent vs Vercel AI SDK

These sit at different layers. The Vercel AI SDK is a lightweight toolkit for talking to models; one agent, tools, streaming. open-multi-agent is the orchestration layer above it: describe a goal, get a multi-agent task DAG. You can even run OMA on top of the AI SDK.

Enterprise support
Pick Vercel AI SDK if

You want a lightweight, provider-neutral toolkit for a single agent; model calls, tool use, streaming; and you’ll handle any orchestration yourself.

Pick open-multi-agent if

You need orchestration above model calls, including dynamic or explicit task DAGs, dependency scheduling, approvals, recovery, budgets, and multi-agent traces.

01 at a glance

Side by side.

Dimensionopen-multi-agentVercel AI SDK
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendTypeScript-native; the leanest of the group
Orchestration modelOne agent, an explicit task DAG, or a goal the coordinator decomposes at runtime; explicit mode, governance policy, or an ExecutionRouter selects the topology, and a run can revise its not-yet-executed tasksA single-agent tool-calling loop (generateText / streamText / Agent, stopWhen); multi-agent is manual composition you build
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers3 direct (@ai-sdk/gateway, provider, provider-utils)
Mixed-model teamsYes; each agent runs its own cloud or local model in one team; model routing can send planning to a flagship model and leaves to a cheap oneYes; provider-neutral by design, one model per agent loop
Run-budget controlRun-level token and estimated-USD ceilings — maxTokenBudget, or maxCostBudget with your estimateCost table — checked between model calls and task dispatches; one in-flight model turn can cross the ceilingNo hard token cap; stopWhen / stepCountIs are step conditions
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredexperimental_telemetry emits OpenTelemetry spans
02 actual capabilities

What open-multi-agent includes.

OMA is more than goal decomposition and a small dependency count. These are current framework capabilities documented in the project README.

Dynamic, explicit, and routed orchestration

runTeam() builds a task DAG from a goal, runTasks() runs a graph you define, and runAgent() covers one agent. Explicit mode, governance declarations, or a custom ExecutionRouter choose Single or Team execution and expose a routingDecision. Opt-in hybrid routing adds one semantic assessment where the deterministic result would be Single — the policy that decides stays deterministic. Plans remain previewable, reviewable, and replayable as data.

Governance and approvals at distinct boundaries

Declare required or preferred roles, ordered review paths, and budget-aware degradation. Gate the plan with onPlanReady, one ready task with onTaskDispatch, one consequential tool call with onToolCall, and any mid-run plan revision with onPlanPatch; then inspect governanceConclusion.

Event-driven scheduling and task evidence

Ready dependents start as soon as prerequisites complete. Hard task requirements are enforced across every assignment strategy, so an unsatisfiable task is rejected instead of dispatched to an ineligible agent; taskResults preserves unmerged task outputs and structured dependency payloads carry bounded provenance. Retries and checkpoints resume from completed task boundaries, and opt-in repairable recovery can append replacement work at an outcome barrier before any original dependent starts.

Production controls

Bound each run with turn, token, estimated-cost, timeout, context, and loop limits. maxTokenBudget and maxCostBudget stop further calls after a boundary check; one in-flight model turn can cross the ceiling. Model routes support ordered fallbacks. Built-in tools are default-deny, and trace payloads redact detected secrets on a best-effort basis.

Your environment and your models

Run in your own Node.js backend — locally, offline, or air-gapped, on your own credentials, with no hosted service. Mix cloud and local models in one team, connect MCP tools, and bring external agents in through ACP or process backends.

Inspect, trace, and evaluate

Stable run identity, routing decisions, privacy-preserving execution receipts, TraceStore, and the offline Run Viewer work with no hosted service. Score quality with versioned EvalSets and GateVerdict, including a routing-stability gate, then connect runs to production telemetry through the optional OTel adapter.

03 mechanism

How they differ.

The Vercel AI SDK is primitives: a provider-neutral interface for model calls, tool use, and streaming, plus an Agent abstraction that runs a single tool-calling loop until stopWhen. Multi-agent coordination is something you compose yourself on top. open-multi-agent is that coordination layer; a coordinator decomposes a goal into a task DAG at runtime, runs independent tasks in parallel, and hands you a typed result. They’re complementary as much as competing: OMA ships an AI SDK bridge, so the SDK can be the model layer under an OMA team.

Where Vercel AI SDK fits

The Vercel AI SDK fits when you want provider-neutral model, tool, and streaming primitives and intend to own the control flow. Its Agent abstraction handles one tool-calling loop, while multi-agent coordination remains application code.

Vercel AI SDK on GitHub

Where open-multi-agent fits

open-multi-agent fits when you want the orchestration handed to you rather than hand-built: a coordinator that plans the task DAG from a goal, mixed-model teams in one run, and a run-level maxTokenBudget checked between calls. And you don’t have to choose; run OMA over the AI SDK and keep the SDK’s provider layer underneath.

Quick Start
// Enterprise

Taking this to production?

open-multi-agent is MIT-licensed and free to run yourself. When you need it delivered, integrated, or supported on a deadline, 元定义科技 (YuanASI) offers commercial delivery and support.

Enterprise support