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 LangGraph
LangGraph and open-multi-agent approach multi-agent orchestration from opposite ends: LangGraph runs a graph you define; open-multi-agent decomposes a goal you describe.
Pick LangGraph if
You want a fixed graph you control node-by-node, with state history and time-travel tools built around that graph.
Pick open-multi-agent if
You want a TypeScript runtime that can generate a plan from a goal, then let you inspect, approve, freeze, replay, checkpoint, and trace it.
01 at a glance
Side by side.
| Dimension | open-multi-agent | LangGraph |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | Python-first; first-party TypeScript port (@langchain/langgraph) is GA |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Graph-first; you define nodes and edges over shared state (StateGraph) |
| Runtime dependencies | 3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers | 6 direct (Python) / 4 direct + 2 peers (JS) |
| Mixed-model teams | Yes; each agent can use its own cloud or local model in one team | Yes; bind a distinct model inside each node |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No token cap; recursion_limit counts steps, not tokens |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | First-party LangSmith tracing (near-zero-config) + OpenTelemetry export |
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.
LangGraph compiles the nodes, edges, and conditional routing of a declarative graph into an invokable you run. open-multi-agent runs a coordinator that decomposes the goal into a task DAG at runtime and auto-parallelizes independent nodes. Both checkpoint and resume. OMA snapshots completed tasks over any MemoryStore and resumes with restore(), though recovery is task-grained, so an interrupted task starts again. LangGraph additionally exposes state history and time travel over its graph.
Where LangGraph fits
LangGraph fits when the orchestration topology is known and should be authored explicitly, or when state history and time-travel debugging over that graph are requirements. Its TypeScript package is GA and it integrates with the wider LangChain stack.
LangGraph on GitHub↗Where open-multi-agent fits
open-multi-agent fits when you’d rather describe the outcome than wire the graph; the coordinator plans the task DAG at runtime, so the orchestration adapts to each goal instead of being hand-built for one. It’s TypeScript-native with three runtime dependencies, and it ships a hard maxTokenBudget cap that aborts a run before it overspends; a guardrail LangGraph doesn’t offer at the token level.
// 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 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 Google ADK → open-multi-agent vs Semantic Kernel →