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 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.
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 hard token budget.
01 at a glance
Side by side.
| Dimension | open-multi-agent | Google ADK |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | Python-first (a Java port exists); no TypeScript |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Code-first agents: an LlmAgent plus explicit workflow agents (SequentialAgent, ParallelAgent, LoopAgent) and multi-agent hierarchies |
| Runtime dependencies | 3 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 teams | Yes; each agent can use its own cloud or local model in one team | Yes; Gemini-first, other providers via LiteLLM |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard token cap; LoopAgent bounds iterations, not tokens |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | OpenTelemetry, 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.
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.
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 gives a hard spend ceiling.
// 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 OpenAI Agents SDK → 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 Semantic Kernel →