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// comparison

open-multi-agent vs LlamaIndex

LlamaIndex started as a data/RAG framework and grew agent workflows on top; open-multi-agent starts from orchestration. If your problem is retrieval over your data, they lead from opposite ends.

Enterprise support
Pick LlamaIndex if

Your core problem is RAG over your own data; indexing, retrieval, query engines; and you want agents that build on that, in Python.

Pick open-multi-agent if

Your core problem is coordinating several agents, dependencies, approvals, and recovery steps in TypeScript. You can connect the retrieval layer you already use.

01 at a glance

Side by side.

Dimensionopen-multi-agentLlamaIndex
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendPython; a TypeScript port (LlamaIndex.TS) also exists
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 tasksData / RAG-first (indexing, retrieval, query engines) plus agent workflows (AgentWorkflow, FunctionAgent)
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers~29 direct in llama-index-core (RAG-oriented: numpy, nltk, tiktoken, networkx, …)
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
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
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredAn instrumentation module plus integrations (Arize, Langfuse, and others)
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.

LlamaIndex is retrieval-first: its center of gravity is indexing your data and querying it, with agent workflows layered on top. open-multi-agent is orchestration-first: it decomposes a goal into a task DAG and coordinates agents, and leaves retrieval to you. They overlap only at the edges; a RAG-heavy application leans toward LlamaIndex; a multi-agent coordination problem leans toward OMA. LlamaIndex carries ~29 core dependencies for all that data tooling; OMA carries three.

Where LlamaIndex fits

LlamaIndex fits when retrieval over your own data is the main problem and you want its loaders, indexes, retrievers, query engines, and agent workflows in the same stack.

LlamaIndex on GitHub

Where open-multi-agent fits

open-multi-agent fits when orchestration is the heart of the problem: a coordinator that decomposes a goal into a parallel task DAG, TypeScript-native, three dependencies, and a boundary-checked maxTokenBudget circuit breaker. It doesn’t ship retrieval; you bring whatever RAG or tools you like; which keeps the core small and the orchestration general.

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