Dynamic and explicit orchestration
runTeam() builds a task DAG from a goal. runTasks() runs a graph you define, and runAgent() covers the single-agent case.
← All comparisons Enterprise support
// 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.
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 18+ backend | TypeScript-native; runs on Node.js |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | 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 can use its own cloud or local model in one team | Yes; per-agent model via the AI SDK providers |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard token cap; maxSteps limits agent steps |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | 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.
Deterministic control around agents
Inspect and approve plans, freeze and replay them as data, validate outputs with Zod, stream per agent, cancel runs, or add a proposer and judge consensus loop.
Dependency scheduling and recovery
The scheduler runs independent branches in parallel. Retries and checkpoints let an interrupted run resume without repeating completed tasks.
Production controls
Bound work with turn, token, estimated-cost, timeout, context, and loop controls. Tools are default-deny, and trace payloads redact secrets by default.
Your environment and your models
Run in your Node.js backend, locally, offline, or air-gapped. Mix cloud and local models, connect MCP tools, and bring external agents through ACP or process backends.
Inspect, trace, and evaluate
Stable run identity, TraceStore, and the offline DAG and Waterfall Viewer work without a hosted service. An optional OTel adapter and EvalSets connect runs to production telemetry and CI gates.
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.
// Compare with another framework
open-multi-agent vs LangGraph → open-multi-agent vs CrewAI → open-multi-agent vs AutoGen → open-multi-agent vs OpenAI Agents SDK → open-multi-agent vs Mastra → open-multi-agent vs Vercel AI SDK → open-multi-agent vs Inngest AgentKit → open-multi-agent vs LangChain → open-multi-agent vs LlamaIndex → open-multi-agent vs Pydantic AI → open-multi-agent vs Google ADK → open-multi-agent vs Semantic Kernel →