← All comparisons
// comparison

open-multi-agent vs Claude Managed Agents

Both coordinate specialist agents in parallel. The decisive difference is ownership: Claude Managed Agents delivers orchestration as an Anthropic-operated service; open-multi-agent installs the complete, provider-neutral runtime in your backend.

Enterprise support
Pick Claude Managed Agents if

You are standardizing on Claude and want Anthropic to operate sessions, persistent threads, agent infrastructure, and Console tracing for you.

Pick open-multi-agent if

You want to own the coordinator, scheduler, plans, state, policies, and traces in your environment — while every role remains free to use a different cloud or local model.

01 at a glance

Side by side.

Dimensionopen-multi-agentClaude Managed Agents
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendManaged API with official SDKs for TypeScript, Python, Go, Java, C#, Ruby, and PHP; cloud or self-hosted Linux tool sandboxes
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 tasksA coordinator delegates to a versioned agent roster; specialists run in isolated, persistent threads over a shared sandbox and filesystem
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peersManaged platform; your application uses an Anthropic SDK or CLI, with an optional self-hosted worker for tool execution
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 onePer-agent model selection within supported Claude 4.5+ models
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 ceilingCumulative session token usage is exposed for application-side budget enforcement; platform spend and rate limits apply at organization or workspace level
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredPersistent session and thread event streams plus Claude Console timelines with token usage and tool-call details
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.

Both systems can fan work out to specialists and bring the results back together. Claude Managed Agents replaces the hand-written agent loop with an Anthropic-operated session and control plane; even when tool execution moves to a self-hosted sandbox, Anthropic operates that control plane while you operate and harden the worker. open-multi-agent ships the orchestration loop itself. Its coordinator, event-driven scheduler, execution routing, plan approval and replay, checkpoints, traces, and evaluation all run inside your application boundary. With cloud models, you choose which provider receives inference traffic; with local models, the complete run can stay offline or air-gapped. The architectural question is therefore not whether private tools are reachable, but whether the orchestration layer and model choice ultimately belong to the platform or to you.

Where Claude Managed Agents fits

Claude Managed Agents fits teams standardizing on Claude that want a managed path: versioned agent definitions, persistent sessions and threads, cloud sandboxes, optional self-hosted tool execution, event streams, and Console tracing. Anthropic operates the agent infrastructure while your application integrates through its API and SDKs.

Claude Managed Agents documentation

Where open-multi-agent fits

open-multi-agent fits when orchestration itself belongs inside your product boundary. Install the MIT-licensed TypeScript runtime in your Node.js backend and own the coordinator, scheduler, plan artifacts, checkpoints, governance hooks, traces, and evaluation data. Mix Anthropic, OpenAI, Gemini, Bedrock, OpenAI-compatible endpoints, and local models in one team; review and replay plans as data; enforce run-level token or estimated-cost ceilings; and inspect runs without a hosted control plane. Choose OMA when provider neutrality, offline or air-gapped operation, and end-to-end runtime ownership are architecture requirements.

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