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 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.
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.
| Dimension | open-multi-agent | LlamaIndex |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | Python; a TypeScript port (LlamaIndex.TS) also exists |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Data / RAG-first (indexing, retrieval, query engines) plus agent workflows (AgentWorkflow, FunctionAgent) |
| Runtime dependencies | 3 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 teams | Yes; each agent can use its own cloud or local model in one team | Yes; per-agent model |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard token cap |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | An 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.
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.
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 hard maxTokenBudget. It doesn’t ship retrieval; you bring whatever RAG or tools you like; which keeps the core small and the orchestration general.
// 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 Pydantic AI → open-multi-agent vs Google ADK → open-multi-agent vs Semantic Kernel →