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// 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.

Enterprise support
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 run-level token circuit breaker and a lean footprint.

01 at a glance

Side by side.

Dimensionopen-multi-agentOpenAI Agents SDK
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendPython-first; official TypeScript port (@openai/agents); both pre-1.0
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 tasksHandoffs; an agent delegates to another; agents can also be tools
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers7 direct (Python) / 2 direct + peer (JS)
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; per-agent model= (OpenAI-compatible endpoints, LiteLLM)
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 cap; max_turns counts steps; usage reported only
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredBuilt-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.

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 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, has three dependencies, and applies a run-level maxTokenBudget between model calls.

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