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 Semantic Kernel
Semantic Kernel is Microsoft’s enterprise LLM SDK for .NET, Python, and Java; open-multi-agent is a TypeScript-native task-DAG runtime. Microsoft is now converging its agent work into the Microsoft Agent Framework.
Pick Semantic Kernel if
You’re in the Microsoft / .NET / Azure ecosystem and want a first-party SDK; plugins, functions, agents; with a supported path forward via the Microsoft Agent Framework.
Pick open-multi-agent if
You’re in Node/TypeScript and want a lean, provider-neutral, goal-driven runtime with three dependencies and a hard token budget; no .NET, no Azure assumption.
Heads-up: in 2026 Microsoft began converging Semantic Kernel and AutoGen into the new Microsoft Agent Framework (Python + .NET) as the go-forward multi-agent stack. Semantic Kernel remains supported, but if you’re choosing for a new, long-lived project, weigh building on the Agent Framework directly.
01 at a glance
Side by side.
| Dimension | open-multi-agent | Semantic Kernel |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | .NET / C#-first; first-party Python and Java; no TypeScript |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Plugins / functions plus agents; multi-agent orchestration is moving to the Microsoft Agent Framework |
| Runtime dependencies | 3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers | ~22 direct in the Python package; the newer Agent Framework core is much leaner (~4) |
| Mixed-model teams | Yes; each agent can use its own cloud or local model in one team | Yes; per-agent model / service |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard token cap |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | Native OpenTelemetry |
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.
Semantic Kernel provides plugins, functions, planners, and agents. It is built C#-first with first-party Python and Java support, and integrates with Microsoft and Azure services. Its multi-agent direction is the Microsoft Agent Framework, which unifies SK and AutoGen. open-multi-agent is a TypeScript-native runtime that decomposes a goal into a task DAG at runtime, assumes no particular cloud, and carries three direct dependencies.
Where Semantic Kernel fits
Semantic Kernel or the Microsoft Agent Framework fits .NET and Azure projects that require Microsoft-supported SDKs, Azure integrations, and native OpenTelemetry.
Semantic Kernel on GitHub↗Where open-multi-agent fits
open-multi-agent fits when your stack is Node/TypeScript and you want to stay there: a lean, provider-neutral, goal-driven runtime with three dependencies and a hard maxTokenBudget, with no .NET runtime or Azure assumption. The coordinator plans the task DAG from a goal instead of you assembling plugins and planners.
// 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 VoltAgent → 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 →