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// 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.

Enterprise support
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.

Dimensionopen-multi-agentLangGraph
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendPython-first; first-party TypeScript port (@langchain/langgraph) is GA
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 tasksGraph-first; you define nodes and edges over shared state (StateGraph)
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers6 direct (Python) / 4 direct + 2 peers (JS)
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; bind a distinct model inside each node
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 token cap; recursion_limit counts steps, not tokens
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredFirst-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.

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.

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 maxTokenBudget stops further model calls after cumulative usage crosses the run ceiling; checks occur between calls, not mid-generation.

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