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// comparison

open-multi-agent vs Inngest AgentKit

Both build multi-agent systems in TypeScript. AgentKit routes a network of agents with deterministic, state-based logic on top of Inngest; open-multi-agent decomposes a goal into a task DAG at runtime.

Enterprise support
Pick Inngest AgentKit if

You want deterministic, inspectable routing you control, and durable, replayable execution; and you’re happy to run on Inngest.

Pick open-multi-agent if

You want the plan built at runtime instead of hand-authored routing, no Inngest dependency, and a run-level token circuit breaker.

01 at a glance

Side by side.

Dimensionopen-multi-agentInngest AgentKit
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendTypeScript-native; pre-1.0 (0.13)
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 tasksMulti-agent networks with deterministic, state-based routing; a router (code or model) picks the next agent
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers6 direct; runs on Inngest for durable, replayable execution
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 @inngest/ai adapters
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; maxIter caps router iterations
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredRun traces via the Inngest platform it runs on
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.

AgentKit models work as a network of agents sharing state, with a router; code you write or a model you delegate to; deciding which agent runs next, capped by maxIter. It runs on Inngest, so execution is durable and replayable. open-multi-agent doesn’t ask you to author the routing: a coordinator decomposes the goal into a task DAG at runtime and parallelizes the independent nodes. AgentKit gives you explicit, deterministic control flow (and Inngest’s durability); OMA gives you a plan generated per goal and no orchestration service to run.

Where Inngest AgentKit fits

Choose AgentKit when you want deterministic, inspectable routing you author yourself and Inngest’s durable, replayable execution underneath; valuable when a run must survive restarts and every routing decision should be explicit and reproducible. It’s pre-1.0, so expect some churn, and it assumes Inngest in your stack.

Inngest AgentKit on GitHub

Where open-multi-agent fits

open-multi-agent fits when you’d rather describe the goal than author the routing, and you want to stay dependency-light: the coordinator plans the task DAG at runtime, there’s no orchestration service to stand up, and maxTokenBudget stops further calls at run boundaries. Checkpoint/restore covers crash recovery at completed task boundaries over any MemoryStore, without a separate durable-execution backend.

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