Report: Mastra vs Vercel AI SDK vs LangGraph
9 min read
11/15/2025
Overview
This report compares Mastra, Vercel AI SDK ("AI SDK"), and LangGraph as building blocks for AI applications and agentic systems.
- Mastra is a TypeScript-native agent framework for building AI-powered applications and agents.
- Vercel AI SDK is a TypeScript toolkit for integrating LLMs into applications with unified provider APIs, streaming, and tool-calling support.
- LangGraph is a graph- and state-based orchestration framework for long-running, stateful, multi-agent workflows, primarily in Python.
High-level positioning
| Dimension | Mastra | Vercel AI SDK (AI SDK) | LangGraph |
|---|---|---|---|
| Primary role | Full TypeScript agent framework (agents, workflows, RAG, memory, evals, MCP) | LLM integration toolkit for TS/JS (providers, streaming, tools, UI hooks) | Agent orchestration runtime (graph/state machine for long-running, multi-agent workflows) |
| Main language / runtime | TypeScript / Node | TypeScript / JS (Node, Next.js, React, etc.) | Python (core SDK) |
| Abstraction level | High – "from prototype to production AI app/agent" | Low/medium – LLM plumbing and UI integration; orchestration mostly up to you | High – orchestration, state, control flows; leaves model/provider choice to you |
| Typical use cases | In-product copilots, support agents, data/analytics agents, JS/TS startup workflows | Chat, completion, tool calling in web apps; type-safe model integration | Deep research agents, complex ops workflows, multi-agent systems, long-running backends with human checkpoints |
Feature comparison matrix
Language, runtime, and ecosystem
| Feature | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Primary language | TypeScript | TypeScript / JavaScript | Python |
| First-class TS typing | Yes – framework designed around TS types for agents, tools, workflows, context | Yes – strong typing for providers, models, structured outputs | Partial/indirect (Python type hints); not TS-focused |
| Runtime focus | Node, edge/runtime-friendly JS stacks | Node, Next.js/React, other JS runtimes | Python servers, notebooks, backends |
| Ecosystem maturity | Newer but growing (case studies with SoftBank, Index, Cedar, Replit Agent 3) | Very widely used in the Next.js / TS ecosystem | Mature and rapidly growing; many production case studies with enterprises |
Core abstraction level
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Main abstraction | Agents, Workflows, Tools, Memory, RAG, Evals, Model Router | Providers, Models, Chat/Completion APIs, Tools, Structured Outputs, Streaming | StateGraph (nodes, edges, shared state, human checkpoints), multi-agent orchestration |
| Agent concept | Built-in first-class "Agent" abstraction | No "agent" primitive – you build loops/logic yourself or via higher-level libs | Agents modelled as nodes or sub-graphs within a stateful workflow |
| Workflow modelling | Explicit Workflows with steps, tools, memory, and branching | DIY; you orchestrate via code, queues, or other frameworks | Graph-based workflows: nodes (steps), edges (routing), shared typed state |
State, memory, and long-running behavior
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Built-in conversation / task state | Yes – agent context and memory abstractions | No – you manage state externally (DB, cache) | Yes – state is first-class; shared typed state object passed between nodes |
| Long-running processes | Supported via workflows, but primarily framed as app-level design | Not directly; you need custom infra (queues, workers, schedulers) | Designed for long-running, resumable workflows with persistence and checkpoints |
| Multi-step reasoning and backtracking | Workflows with tool calls and memory; backtracking patterns possible but less formalized | Fully manual | Native via graph edges, checkpoints, and branching control flow |
Multi-agent and orchestration capabilities
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Multi-agent support | Supports multi-agent patterns at the framework level in TS | No explicit multi-agent runtime; each "agent" is an independent use of the SDK | Native support for single/multi/hierarchical agents within one StateGraph |
| Orchestration complexity | Moderate – opinionated TS abstractions hide some complexity; better for product agents than research-scale multi-agent systems | Low-level; you must orchestrate via your own code or external orchestrator | High – can express sophisticated flows, branches, and agent roles in one graph |
Model and provider routing
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Provider integration | Uses Vercel AI SDK and other clients under the hood; integrates with many providers | Unified provider API for OpenAI, Anthropic, Hugging Face, Vercel, etc. | Integrates with providers mostly via LangChain or direct client libraries |
| Model routing / switching | Mastra Model Router with 600+ models, type-complete TS autocompletion, and dynamic model selection (A/B, user selection) | Provider-agnostic but no global router; routing/fallbacks are DIY or via Vercel AI Gateway | No native router; you design routing logic within the graph or external services |
Tools, RAG, and evaluations
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Tool-calling | Built-in tools abstraction; integrates with external APIs and MCP servers | Tool-calling primitives (functions, streaming tools) for providers that support them | Typically via LangChain tools; LangGraph orchestrates when/how they are called |
| RAG support | First-class RAG primitives for chunking, embedding, retrieval, and RAG workflows | No direct high-level RAG API; you build or import your own RAG layer | Frequently combined with LangChain RAG; RAG is orchestrated as part of the graph |
| Evals / quality | Includes evaluation features to assess agent quality and accuracy | No native eval framework; you integrate external eval tools | No built-in eval; you integrate external evaluation frameworks around LangGraph workflows |
UI and product integration
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Integration with web UI | Natural fit with React/Next.js and TS frontends; you wire API endpoints or server-side handlers | Strong: hooks like useChat, streaming data protocol, React/Next.js examples, PDF chat, computer use | Not UI-focused; you expose APIs end-points for separate UIs to talk to the LangGraph backend |
| Streaming to UI | Via AI SDK and/or underlying providers | First-class streaming (tokens, tool calls, event streams) | First-class streaming in the runtime; still needs integration glue to frontends |
Human-in-the-loop and governance
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Human approval / review patterns | Possible at app level (eg. workflow steps requiring user confirmation) | DIY in application code | Built-in concept of checkpoints and human review; workflows can pause, wait for approval, and resume |
| Observability of agent behavior | Good for a TS framework; logs, structured outputs, and integration patterns documented | Mostly your responsibility; the SDK provides primitives, not a full observability stack | Strong – explicit state and node execution paths make debugging complex flows easier compared to "black box" loops |
Error handling, resilience, and limitations
| Aspect | Mastra | AI SDK | LangGraph |
|---|---|---|---|
| Error handling | Benefits from AI SDK capabilities plus Mastra's framework-level patterns; still subject to LLM/tool failure modes | Error handling can be brittle: validation errors in tools often raise exceptions with limited visibility unless explicitly handled; streaming can be cut off without good feedback if not wired correctly | Orchestration-level errors and retries are clearer due to explicit nodes and state; still inherits LLM limitations like hallucinations, tool misuse, and causal reasoning gaps |
| Resilience to provider downtime | Model Router supports switching between providers/models, but you must design policies | No automatic resilience; you must implement fallbacks, circuit breakers, and multi-provider strategies yourself or via Vercel AI Gateway | No built-in cross-provider resilience; you design fallbacks as separate nodes/branches or external services |
| Fundamental limitations | Shares all LLM/agentic issues: hallucinations, data-quality dependence, safety/ethics challenges; still a young ecosystem compared to Python frameworks | Not a framework for correctness; thin LLM layer, so all higher-level concerns (hallucinations, safety, evaluation) are on you | Does not fix LLM limits; multi-agent and graph complexity can amplify issues if data quality, evaluation, and safety are not carefully managed |
When to use which
Mastra
Use Mastra when:
- Your team is JavaScript/TypeScript-first.
- You want a batteries-included framework for agents, workflows, RAG, and model routing.
- You are building productized copilots or agents inside web/SaaS apps and want opinionated structure rather than assembling pieces yourself.
Mastra is a strong choice for JS/TS product teams that want to move from prototype to production without designing a custom agent framework from scratch.
Vercel AI SDK (AI SDK)
Use AI SDK when:
- You primarily need a clean, ergonomic way to call LLMs and tools in TypeScript.
- You want strong UI integration (React hooks, streaming) and will own orchestration and state.
- You may plug this into higher-level frameworks (Mastra) or your own bespoke agent logic.
AI SDK is ideal as the LLM plumbing layer in TS/JS apps, especially where UI/UX around streaming and structured outputs matters.
LangGraph
Use LangGraph when:
- You need serious orchestration of long-running, stateful workflows and multi-agent systems.
- Your core team and infrastructure are Python-friendly.
- You care about explicit state, branching, and human checkpoints for complex flows (e.g., research, operations, regulated domains).
LangGraph excels as the backbone of complex AI backends: it brings clarity and control where simple agent loops break down.
Combined patterns
These technologies can be combined:
- Mastra + AI SDK: already the common pattern; Mastra handles agents/workflows/model routing; AI SDK handles model calls and UI integration.
- LangGraph + AI SDK: LangGraph runs as a Python orchestrator backend; AI SDK powers the frontend's chat, tools, and streaming UX.
For a TypeScript-only stack, starting with Mastra (on top of AI SDK) is typically more efficient. For mixed Python+TS environments with complex orchestration requirements, LangGraph for backend orchestration + AI SDK for frontend integration is a strong architecture.