Best TypeScript Multi-Agent Frameworks in 2026: Choose by Workflow

There is no useful answer to “What is the best TypeScript multi-agent framework?” until you say what kind of work must survive contact with production.

A customer-support handoff, a long-running state graph, a streaming agent UI, and a goal that must be decomposed into parallel tasks are four different systems. Treating them as one leaderboard hides the decision that matters: which runtime model matches the workflow you actually own?

This guide names six strong options by their clearest fit. It is not a popularity ranking.

Method and disclosure

This comparison uses official documentation available on July 31, 2026. A project qualifies when it has a first-class JavaScript or TypeScript surface and an explicit way to compose more than one agent or agentic step.

“Best” means the clearest fit for a named operating model, not the most stars, downloads, or social mentions. We did not run a common quality, latency, or cost benchmark, so this guide makes no performance ranking.

This article is published by the Open Multi-Agent project. OMA is included, and that is a conflict readers should see before the recommendations.

Python-first frameworks such as CrewAI are outside this TypeScript shortlist. That is a scope decision, not a judgment that they are worse.

The short answer

If your system needs…	Start with…	Why
An explicit, long-running state graph	LangGraph.js	You author nodes, edges, and shared state; persistence and human intervention are core runtime concepts.
One TypeScript framework for agents, workflows, memory, evals, and operations	Mastra	It bundles a broad application surface instead of stopping at orchestration primitives.
Streaming UI and provider-neutral model/tool loops	Vercel AI SDK	Its agent and UI primitives sit close to the product interface; you can compose higher-level orchestration in application code.
Manager agents, specialist handoffs, guardrails, and built-in tracing	OpenAI Agents SDK for JS	Handoffs and agents-as-tools are first-class composition patterns.
A router-driven agent network on durable Inngest steps	AgentKit	A router selects agents around shared state, while model steps use Inngest execution semantics.
Explicit task DAGs and runtime goal-to-DAG planning in one local runtime	Open Multi-Agent	Run one agent, supply the graph, or let a coordinator generate a reviewable plan — then read back which topology ran and why.

Treat that as a shortlist. Build one representative workflow before committing.

1. LangGraph.js: best for an explicit, durable state graph

LangGraph’s JavaScript documentation describes a low-level orchestration runtime for long-running, stateful agents. You define a StateGraph, its nodes, and its edges. Deterministic code and model-driven steps can live in the same graph.

Its persistence model separates thread-scoped checkpoints from cross-thread stores. That supports interruption recovery, human-in-the-loop state changes, time travel, and longer-lived memory.

Choose it when the graph is part of your product logic and your team wants to own every transition. The cost of that control is also the boundary: LangGraph is deliberately low-level, so you are designing the topology rather than asking the runtime to invent one from a goal.

Representative fit: an underwriting or operations workflow with known stages, resumable state, and explicit human decision points.

2. Mastra: best for a batteries-included TypeScript agent application

Mastra packages agents, typed workflows, memory, a server, observability, datasets, and evaluation in one TypeScript framework. Its workflows compose steps and branches; its application surface also covers the infrastructure around those workflows.

Choose it when the team wants one opinionated stack and would rather adopt bundled memory, operations, and evaluation than assemble those parts. It is especially relevant when an agent application—not only its scheduler—is the unit you want to build and operate.

The trade-off is surface area. You still author the workflow structure, and you should validate which storage and server components your recovery requirements depend on.

Representative fit: a TypeScript product team that wants agents, workflows, memory, traces, and evals under one framework boundary.

3. Vercel AI SDK: best for streaming agent experiences

The AI SDK’s ToolLoopAgent is a reusable, multi-step tool loop that can generate or stream output, execute tools, return typed data, and pause for approval. Its broader agent APIs connect naturally to UI message streams and multiple model providers.

Choose it when the hard part of the product is the interaction layer: token streaming, tool events, typed UI messages, and provider portability. A specialist can be exposed as a tool, and application code can coordinate several loops.

That flexibility does not automatically give you a multi-agent scheduler. If the system needs a shared task DAG, dependency scheduling, or run-level recovery across several agents, you either build that orchestration or add a layer above the AI SDK.

Representative fit: a Next.js research assistant whose interface must stream intermediate tool and specialist activity.

4. OpenAI Agents SDK for JS: best for managers and handoffs

The OpenAI Agents SDK orchestration guide makes two patterns first-class:

• A manager keeps control and calls specialist agents as tools.
• A triage agent hands the conversation to a specialist, which becomes the active agent.

The SDK also provides guardrails and built-in tracing. Its JavaScript repository describes the SDK as provider-agnostic, although the default tracing experience and several platform integrations naturally align with OpenAI.

Choose it when conversation ownership is the key abstraction: one manager synthesizes, or one specialist takes over. Check the guardrail boundaries carefully; agent-level input and output guardrails apply at the ends of a chain, while tool guardrails cover custom function-tool calls.

Representative fit: a service desk where a triage agent routes a conversation to billing, refunds, or account support.

5. AgentKit: best for a routed network on Inngest

AgentKit networks combine agents, shared state, and a router. The router chooses the next agent or stops the loop; agents can use different models, and later agents can read results stored in network state.

Its agent execution documentation says inference steps run through Inngest step.ai, which adds automatic retries and cached results for durability.

Choose it when a router-driven network matches the problem and Inngest is already the execution substrate you want. Distinguish that loop from a dependency graph: the router selects what runs next, while an explicit task DAG can make independent branches and their prerequisites visible up front.

Representative fit: an event-driven enrichment workflow in an existing Inngest application, with specialists selected from shared state.

6. Open Multi-Agent: best for switching between explicit and generated task DAGs

Open Multi-Agent exposes three levels from one TypeScript runtime:

• runAgent() for one bounded agent loop.
• runTasks() when the application already knows the task DAG.
• runTeam() when a coordinator should turn a goal into a task DAG at runtime.

The distinguishing property is that moving between them is a decision the runtime records rather than a rewrite. An automatic runTeam() call resolves its topology through a documented precedence order — an explicit mode, then declared governance, then a per-run ExecutionRouter, then the orchestrator’s, then the built-in DeterministicRouter — and reports the outcome in result.routingDecision with the reasons behind it, linked to trace evidence. That is Execution Routing, and it is deliberately separate from Model Routing, which picks the model for calls inside whichever topology won.

Governance is declared rather than implied. governanceIntent with requiredRoles and requiredOrder states a role path the runtime checks against the executed topology, not against labels in agent prose, and reports governanceConclusion. An application may override a declared floor, but that returns unsatisfied with reason overridden rather than a clean success.

Approval is three boundaries, not one: onPlanReady for the generated plan, onTaskDispatch for one ready task, and onToolCall for one tool invocation — the last running after input validation and before execute, so it inspects actual arguments. Tools that cause real side effects are marked consequential: true; that classification reads tool grants only and never scans goals, prompts, or model output.

Scheduling is event-driven: a downstream task starts as soon as its own dependencies are satisfied. dependencyPayload: 'structured' passes a dependency’s validated JSON instead of its prose, and taskResults keeps every task’s unmerged result addressable by stable ID. Different agents can use different providers, including local OpenAI-compatible endpoints, and model routes support ordered fallbacks. Token and estimated-cost budgets, traces, the offline Run Viewer, versioned EvalSets, and task-grained checkpoint recovery all run without a hosted service.

Choose OMA when the key decision is not merely “one agent or many,” but who owns the plan for this run, and what evidence remains afterward. Stable support tickets can use a fixed DAG; variable escalations can use a coordinator without moving to a second framework, and the run itself records which path it took.

Two boundaries are worth stating plainly. Task-grained checkpoints are not a durable workflow service: a completed task can be reused after recovery, but an interrupted task starts again — if your primary requirement is process-independent timers, event waits, or infrastructure-managed durable execution, evaluate a workflow runtime explicitly. And the tool gate is a policy decision, not a process sandbox; it decides whether a call proceeds, not what the tool can reach once it does.

Representative fit: research, incident investigation, or operations work where the goal changes, independent investigations should run together, and a human may inspect the plan before execution.

A better selection test than a leaderboard

Take one workflow that matters and implement the smallest end-to-end slice in two candidates. Use the same input fixtures and answer these questions:

1. Who owns topology? Is it code, a router, or a planning model?
2. What is durable? Messages, graph state, completed tasks, tool calls, timers, or the whole run?
3. Where can a human intervene? Before a tool, between nodes, on a generated plan, or only around the final output?
4. What can you inspect? State transitions, task dependencies, model calls, tool calls, costs, and retries?
5. Can you change providers without changing orchestration?
6. What infrastructure must stay running for recovery to work?

Then fail a model call, stop the process mid-run, reject one action, and change one provider. The framework that makes those four events unsurprising — and leaves a record you can read afterward — is usually the better fit.

Questions 1, 3, and 4 are where the candidates differ most, and they are worth answering with an actual run rather than a docs page. “Where can a human intervene?” has a different answer depending on whether the runtime offers one hook or several at distinct boundaries, and “what can you inspect?” has a different answer depending on whether the topology decision itself is recorded or only implied by what happened.

Sources

• LangGraph.js overview and persistence
• Mastra framework overview
• Vercel AI SDK ToolLoopAgent and agent documentation
• OpenAI Agents SDK orchestration, guardrails, and tracing
• AgentKit networks and agents
• Open Multi-Agent source, architecture, and the linked framework comparison pages below