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// comparison

open-multi-agent vs Mastra

Both are TypeScript-native and actively developed; the real difference is surface area. Mastra is a batteries-included framework; open-multi-agent is a lean, goal-driven core.

Enterprise support
Pick Mastra if

You want an all-in-one framework and will author the workflow graph yourself and carry a larger dependency surface to get memory, RAG, evals, and a studio bundled in.

Pick open-multi-agent if

You want a small core (three dependencies) that runs in your own environment — offline or air-gapped, on your own credentials — with goal-driven decomposition instead of hand-built workflow graphs and a hard spend cap.

01 at a glance

Side by side.

Dimensionopen-multi-agentMastra
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendTypeScript-native; requires Node.js 22.13+
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 tasksAgents plus graph-based workflows you author by hand (.then / .branch / suspend); memory, RAG, and evals bundled; a separate beta Harness (AgentController) for interactive apps
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers~32 direct in @mastra/core; built on the Vercel AI SDK provider layer
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 via its "provider/model" router, layered on the Vercel AI SDK provider set
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 token cap; maxSteps limits agent steps
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredOpenTelemetry-based tracing with auto-derived metrics and the Mastra Studio dashboard
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.

Mastra bundles the whole surface; agents, graph-based workflows you wire by hand with .then()/.branch(), memory, RAG, and evals; into one framework, with a separate beta Harness (AgentController) for interactive apps. That breadth is the cost: ~32 direct dependencies in the core (atop the Vercel AI SDK provider layer), Node 22.13+, and workflow durability that leans on a storage backend and a running server to resume. open-multi-agent keeps the core to three dependencies and hands a coordinator a goal, which it decomposes into a task DAG at runtime and auto-parallelizes — so the plan is generated, reviewable data you can freeze and replay, not a graph you wire by hand. Its checkpoints resume completed tasks over any MemoryStore with no durable-execution backend, and its evaluation, tracing, and offline Run Viewer need no hosted service.

Where Mastra fits

Mastra fits when you want a batteries-included stack and are willing to author the workflow graph yourself: bundled memory, RAG, evals, and a studio, with suspend/resume durability if you run the storage and server it needs. Its dedicated Harness (still beta) targets interactive, multi-mode agent apps rather than task orchestration.

Mastra on GitHub

Where open-multi-agent fits

open-multi-agent fits when you want the plan built for you and the whole run kept in your environment. The coordinator turns a goal into a task DAG at runtime, and that plan is inspectable, replayable data (planOnlycreatePlanArtifactrunFromPlan), not a workflow graph you hand-author. It stays three dependencies on Node.js 20+, hard-caps spend with maxTokenBudget (or maxCostBudget) where Mastra has no token-level cap, and can route planning to a flagship model and leaf work to a cheap one. Evaluation, tracing, and an offline Run Viewer need no hosted service, so it runs fully offline or air-gapped, on your own credentials.

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