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// comparison

open-multi-agent vs Google ADK

Google’s ADK is a code-first Python toolkit with explicit workflow agents and a path to Vertex AI deployment; open-multi-agent is a TypeScript-native, provider-neutral runtime that plans the workflow from a goal.

Enterprise support
Pick Google ADK if

You’re on Google Cloud / Gemini, want explicit workflow agents (sequential, parallel, loop) you compose, and a managed deploy target (Vertex Agent Engine).

Pick open-multi-agent if

You want a TypeScript-native, provider-neutral runtime that decomposes a goal at runtime; no web-server or cloud stack pulled in; with a lean core and a run-level token circuit breaker.

01 at a glance

Side by side.

Dimensionopen-multi-agentGoogle ADK
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendPython-first (a Java port exists); no TypeScript
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 tasksCode-first agents: an LlmAgent plus explicit workflow agents (SequentialAgent, ParallelAgent, LoopAgent) and multi-agent hierarchies
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers~24 direct; includes a FastAPI/Uvicorn web stack, google-genai, google-auth, and OpenTelemetry
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; Gemini-first, other providers via 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 token cap; LoopAgent bounds iterations, not tokens
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredOpenTelemetry, with Google Cloud Trace integration
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.

ADK is code-first and explicit: you compose an LlmAgent with workflow agents; SequentialAgent, ParallelAgent, LoopAgent; into a hierarchy, and it carries a FastAPI-based serving and Google Cloud deploy story. open-multi-agent doesn’t ask you to lay out the workflow: a coordinator decomposes the goal into a task DAG at runtime and parallelizes it. ADK is Gemini-first (other models via LiteLLM) and pulls a web-server stack into its ~24 dependencies; OMA is provider-neutral, three dependencies, and ships no server.

Where Google ADK fits

ADK fits Google Cloud projects that want explicit sequential, parallel, and loop agents, Gemini integration, evaluation tooling, and a first-party deployment path to Vertex AI.

Google ADK on GitHub

Where open-multi-agent fits

open-multi-agent fits when you’d rather describe the goal than assemble workflow agents, want to stay provider-neutral and TypeScript-native, and don’t want a web-server or cloud stack in your dependencies. The coordinator plans the task DAG at runtime, the core is three dependencies, and maxTokenBudget applies a run-level ceiling between 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