// comparison

open-multi-agent vs VoltAgent

Both are TypeScript-native agent frameworks. VoltAgent leads with built-in observability and a supervisor/sub-agent structure; open-multi-agent leads with a lean core and goal-driven decomposition.

Enterprise support

Pick VoltAgent if

You want tracing that works out of the box; a bundled OpenTelemetry stack; and a supervisor coordinating sub-agents, with workflows, memory, and RAG included.

Pick open-multi-agent if

You want a much smaller core (three dependencies vs ~44), goal-driven decomposition, token + estimated-cost ceilings, and an optional OTel adapter instead of a bundled OTel stack.

01 at a glance

Side by side.

Dimension	open-multi-agent	VoltAgent
Language / runtime	TypeScript-native; embeds in any Node.js 20+ backend	TypeScript-native; runs on Node.js
Orchestration model	One 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 tasks	Agents plus supervisor / sub-agent networks and workflows, with memory and RAG
Runtime dependencies	3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers	~44 direct in @voltagent/core; bundles the @ai-sdk provider set and a full OpenTelemetry stack
Mixed-model teams	Yes; 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 one	Yes; per-agent model via the AI SDK providers
Run-budget control	Run-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 ceiling	No hard token cap; maxSteps limits agent steps
Observability	TraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service required	Native OpenTelemetry; the core bundles the OTel SDK and auto-instruments agents

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.

VoltAgent puts observability first: its core bundles a full OpenTelemetry stack and auto-instruments agents, and it structures work as a supervisor coordinating sub-agents, with memory, RAG, and workflows included. open-multi-agent keeps the core to three dependencies and puts its OTel mapping in the optional @open-multi-agent/otel package, which writes to an application-owned provider; TraceStore and the offline Run Viewer cover local persistence and inspection. Instead of a supervisor topology, OMA hands a coordinator a goal to decompose into a task DAG at runtime. The trade-off is still bundled batteries versus a smaller, composable core.

Where VoltAgent fits

VoltAgent fits when you want its bundled OpenTelemetry stack, supervisor and sub-agent model, memory, RAG, and workflows in one framework. That bundled surface comes with a larger dependency footprint.

VoltAgent on GitHub↗

Where open-multi-agent fits

open-multi-agent fits when you want a lean core and goal-driven orchestration: three dependencies instead of ~44, a coordinator that plans the task DAG from a goal, token or estimated-cost ceilings, and OpenTelemetry through an optional first-party adapter rather than a bundled SDK. TraceStore and the offline Run Viewer provide local query and inspection paths.

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

// Compare with another framework

open-multi-agent vs Mastra→open-multi-agent vs OpenAI Agents SDK→open-multi-agent vs CrewAI→