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 AutoGen
AutoGen models multi-agent work as conversations over a Python runtime; open-multi-agent uses task DAGs in a TypeScript runtime.
Pick AutoGen if
You’re in Python, prefer a conversational or actor-model mental model, and want native OpenTelemetry; and you’ve accounted for AutoGen’s maintenance status.
Pick open-multi-agent if
You want an actively-developed, TypeScript-native runtime with goal-driven decomposition, token + estimated-cost ceilings, and an optional first-party OTel adapter.
Heads-up: in 2026 Microsoft merged AutoGen and Semantic Kernel into the new Microsoft Agent Framework, its supported successor. AutoGen still gets fixes but is effectively in maintenance mode (latest release 0.7.5, September 2025). Worth weighing if you’re choosing a framework for a new, long-lived project.
01 at a glance
Side by side.
| Dimension | open-multi-agent | AutoGen |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | Python (autogen-core / autogen-agentchat); .NET in preview; no TypeScript |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Conversation / group-chat (v0.2) over an event-driven actor runtime (v0.4) |
| Runtime dependencies | 3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers | 6 direct (autogen-core) |
| Mixed-model teams | Yes; each agent can use its own cloud or local model in one team | Yes; per-agent model_client via autogen-ext |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | No hard cap; soft, self-reported TokenUsageTermination between turns |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | Native OpenTelemetry; runtimes auto-emit spans |
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.
AutoGen models multi-agent work as a conversation: agents exchange messages in a group chat and coordination emerges from that dialogue (its v0.4 core adds an event-driven, actor-model runtime underneath). open-multi-agent is goal-driven: you hand the coordinator an outcome and it decomposes it into a task DAG with explicit dependencies, running independents in parallel. Both now have a first-party OpenTelemetry path. AutoGen auto-emits OTel spans; OMA keeps OTel out of its three-dependency core and maps TraceRecord v2 through the optional @open-multi-agent/otel adapter to a provider your application owns, alongside TraceStore and the offline Run Viewer.
Where AutoGen fits
AutoGen fits Python systems built around conversation or actor-model coordination and native OpenTelemetry. Microsoft now directs new multi-agent work to the Agent Framework, so AutoGen is primarily relevant to existing systems that already use it.
AutoGen on GitHub↗Where open-multi-agent fits
open-multi-agent fits when you want a TypeScript-native runtime under active development, a goal-first model instead of a conversation you have to steer, and run-level ceilings through maxTokenBudget or maxCostBudget + estimateCost. Its optional OTel adapter preserves the lean core while the offline Run Viewer gives each completed run a local inspection path. Starting fresh in Node.js, OMA avoids both a Python dependency and a framework in transition.
// 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 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 →