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 Pydantic AI
Pydantic AI applies Pydantic validation to agents and instruments them through Logfire; open-multi-agent uses task DAGs in a TypeScript runtime. The main differences are language and orchestration model.
Pick Pydantic AI if
You’re in Python and want validated agent I/O, Logfire instrumentation, and built-in usage limits.
Pick open-multi-agent if
You want TypeScript-native, goal-driven multi-agent orchestration; a coordinator that builds the task DAG from a goal; with a hard, run-aborting token budget.
01 at a glance
Side by side.
| Dimension | open-multi-agent | Pydantic AI |
|---|---|---|
| Language / runtime | TypeScript-native; embeds in any Node.js 18+ backend | Python-native (built on Pydantic); no TypeScript port |
| Orchestration model | Three modes: one agent, an explicit task DAG, or a goal decomposed by the coordinator at runtime | Type-safe, model-agnostic agents with tool calling and dependency injection; multi-agent via delegation and pydantic-graph |
| Runtime dependencies | 3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peers | A slim core (pydantic-ai-slim); model provider SDKs are optional extras |
| Mixed-model teams | Yes; each agent can use its own cloud or local model in one team | Yes; model-agnostic, per-agent model |
| Run-budget control | Token and estimated-USD ceilings through maxTokenBudget, or maxCostBudget with your estimateCost price table | Yes; UsageLimits includes total_tokens_limit, which raises before you overspend (one of the few here with a real token limit) |
| Observability | TraceRecord v2 + TraceStore, an optional first-party OTel adapter, and an offline post-run Run Viewer | Native OpenTelemetry via Pydantic Logfire (instrumentation built in) |
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.
Pydantic AI provides validated inputs and outputs, dependency injection, model-agnostic agents, and Logfire instrumentation. Its UsageLimits(total_tokens_limit=…) caps a single agent run. open-multi-agent differs in language and orchestration shape: its TypeScript coordinator builds a parallel task DAG, and maxTokenBudget caps the whole DAG run.
Where Pydantic AI fits
Pydantic AI fits Python projects that want validated agent I/O, dependency injection, Logfire tracing, and built-in usage limits. Its multi-agent patterns use delegation and pydantic-graph.
Pydantic AI on GitHub↗Where open-multi-agent fits
open-multi-agent fits when you want TypeScript-native, goal-driven multi-agent orchestration: the coordinator plans a parallel task DAG from the goal rather than you wiring delegation, and maxTokenBudget aborts the entire run at a hard ceiling. If you’re in Node rather than Python, OMA keeps you there.
// 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 LlamaIndex → open-multi-agent vs Google ADK → open-multi-agent vs Semantic Kernel →