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 OpenAI Agents SDK
The OpenAI Agents SDK is a lightweight, handoffs-based framework with tracing enabled by default; open-multi-agent uses task DAGs and is provider-neutral. The main differences are orchestration model and provider coupling.
Pick OpenAI Agents SDK if
You’re centered on OpenAI’s platform, prefer the handoffs model, and want tracing enabled by default.
Pick open-multi-agent if
You want provider-neutral, goal-driven orchestration in TypeScript, with a hard token budget and a lean footprint.
01 at a glance
Side by side.
| Dimension | open-multi-agent | OpenAI Agents SDK |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | Python-first; official TypeScript port (@openai/agents); both pre-1.0 |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Handoffs; an agent delegates to another; agents can also be tools |
| Runtime dependencies | 3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers | 7 direct (Python) / 2 direct + peer (JS) |
| Mixed-model teams | Yes; each agent can use its own cloud or local model in one team | Yes; per-agent model= (OpenAI-compatible endpoints, LiteLLM) |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard cap; max_turns counts steps; usage reported only |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | Built-in tracing on by default (OpenAI dashboard) + 25+ external processors |
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.
The Agents SDK orchestrates through handoffs: an agent delegates to another and control passes along that chain. Agents can also be exposed as tools. open-multi-agent orchestrates through decomposition: a coordinator builds a task DAG and runs independent tasks in parallel. The SDK enables tracing by default and supports external processors. OMA exposes TraceRecord v2, TraceStore, an optional first-party OTel adapter for an application-owned provider, and an offline Run Viewer. The SDK centers OpenAI, while OMA is provider-neutral by design.
Where OpenAI Agents SDK fits
Pick the OpenAI Agents SDK if your world is OpenAI-centric, you like the handoffs model, and you want tracing that just works out of the box. It’s minimal and well-instrumented. Its TypeScript port is official, though both it and the Python package are still pre-1.0, so expect some churn.
OpenAI Agents SDK on GitHub↗Where open-multi-agent fits
open-multi-agent fits when you want to stay provider-neutral; mix Anthropic, Gemini, OpenAI, local models, or any OpenAI-compatible endpoint in one team; and prefer decomposing a goal to wiring handoffs. It’s TypeScript-native, three dependencies, and its maxTokenBudget gives a hard spend ceiling the Agents SDK doesn’t have.
// 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 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 → open-multi-agent vs Semantic Kernel →