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// 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.

Enterprise support
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.

Dimensionopen-multi-agentAutoGen
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendPython (autogen-core / autogen-agentchat); .NET in preview; no TypeScript
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 tasksConversation / group-chat (v0.2) over an event-driven actor runtime (v0.4)
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers6 direct (autogen-core)
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_client via autogen-ext
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 cap; soft, self-reported TokenUsageTermination between turns
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredNative 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.

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.

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.

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