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// 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.

Enterprise support
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.

Dimensionopen-multi-agentPydantic AI
Language / runtimeTypeScript-native; embeds in any Node.js 20+ backendPython-native (built on Pydantic); no TypeScript port
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 tasksType-safe, model-agnostic agents with tool calling and dependency injection; multi-agent via delegation and pydantic-graph
Runtime dependencies3 direct (Anthropic SDK, OpenAI SDK, Zod); extra providers and MCP are opt-in peersA slim core (pydantic-ai-slim); model provider SDKs are optional extras
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; model-agnostic, 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 ceilingYes; UsageLimits includes total_tokens_limit, which raises before you overspend (one of the few here with a real token limit)
ObservabilityTraceRecord v2 + TraceStore, stable run identity, an optional first-party OTel adapter, and an offline post-run Run Viewer — no hosted service requiredNative 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.

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.

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 stops further calls after a run-boundary check. If you’re in Node rather than Python, OMA keeps you there.

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