Which AI agent framework should I use in production in June 2026?

Pick by language, deployment target, and provider commitment. Mastra is the TypeScript-native pick — strong on serverless deployment (Vercel, Cloudflare, Netlify), built-in observational memory, and multi-provider routing. LangGraph is the Python-native pick — best graph-based orchestration primitives, deepest enterprise observability, mature deployments. OpenAI Agents SDK is the lowest-friction choice if you are already on OpenAI — handoffs, native tool use, biggest community. Claude Agent SDK is best for Claude-committed teams and MCP-heavy workflows. For multi-agent Python with explicit role-based crews, CrewAI is still a viable fifth choice but loses ground in 2026 to LangGraph's flexibility.

What does Mastra do better than LangGraph?

Three things. First, TypeScript-native ergonomics — Mastra was built TS-first; LangGraph's TS port lags Python parity. Second, serverless deployment — Mastra officially supports @mastra/deployer-vercel, Cloudflare Workers, and Netlify; LangGraph cannot scale to zero on those platforms. Third, automatic context compression — Mastra's Observational Memory handles conversation compression by default. LangGraph wins on graph orchestration primitives, Python ecosystem, and enterprise observability features (LangSmith integration).

Is OpenAI Agents SDK production-ready?

Yes, in June 2026. The OpenAI Agents SDK shipped in October 2024 and has matured significantly through 2025–2026. It now supports handoffs, native function calling, MCP servers, computer use, and Realtime API integration. The biggest concern is provider lock-in — porting an Agents SDK-built system to Claude or Gemini requires meaningful rewrites. For OpenAI-committed shops, it is the lowest-friction choice and has the largest community. For multi-provider teams, Mastra or LangGraph offers cleaner abstractions.

When should I pick Claude Agent SDK over the others?

When you are committed to Claude and want first-class MCP, computer use, and subagent primitives. Claude Agent SDK powers Claude Code and is purpose-built for Anthropic's tool-use, computer-use, and orchestrator-subagent patterns. It is the lowest-friction way to ship Claude-based agents with [Dynamic Workflows](/answers/dynamic-workflows-1000-subagents-cap-explained-june-2026/) and full MCP server support. Trade-off: it is single-vendor — switching to GPT-5.5 or Gemini means a rewrite. For multi-provider future-proofing, Mastra or LangGraph stay ahead.

Quick Answer

Mastra vs LangGraph vs OpenAI Agents SDK vs Claude Agent SDK (June 2026)

Published: June 11, 2026

Mastra vs LangGraph vs OpenAI Agents SDK vs Claude Agent SDK

Four production-ready agent frameworks in June 2026. Each has a clear winning use case. Here is the honest matchup.

Last verified: June 11, 2026

TL;DR

Framework	Language	Provider model	Best for
Mastra	TypeScript-first	Multi-provider	TS-native serverless agents
LangGraph	Python-first (TS port)	Multi-provider	Complex graph orchestration, enterprise observability
OpenAI Agents SDK	Python + TS	OpenAI primary	OpenAI-committed shops, fast iteration
Claude Agent SDK	Python + TS	Anthropic only	Claude-committed shops, MCP + subagents

Head-to-head

Property	Mastra	LangGraph	OpenAI Agents SDK	Claude Agent SDK
Languages	TS (primary), Python (alpha)	Python (primary), TS (lagging)	Python + TS	Python + TS
Provider model	Multi-provider	Multi-provider	OpenAI primary, others via wrappers	Anthropic only
Orchestration model	Workflows + steps	Directed graph + conditional edges	Handoffs	Subagents + handoffs
MCP support	Native	Native	Native (added late 2025)	Best (native)
Computer use	Via Anthropic/OpenAI tools	Via provider tools	Native (limited)	Native (best)
Memory	Observational Memory (auto compression)	LangChain memory primitives	Threads (Realtime API)	Conversation history
Observability	Built-in tracing	LangSmith (deepest)	OpenAI Dashboard	Anthropic console
Serverless deployment	Native (Vercel, CF Workers, Netlify)	Limited (needs Docker)	Limited	Limited
Streaming	Yes	Yes	Yes	Yes
First public release	2024	2023	Oct 2024	March 2026
License	Apache 2.0 (open)	MIT (open)	OpenAI SDK license	Anthropic SDK + open
Community size	Growing fast (TS)	Largest (Python)	Very large	Growing

Pick by scenario

Building a Next.js app with an embedded agent

→ Mastra. TypeScript-native, deploys cleanly to Vercel, includes Observational Memory and tool primitives without LangChain’s surface area.

Complex multi-agent Python backend with deep observability

→ LangGraph + LangSmith. Best graph orchestration primitives in 2026. Conditional edges, state management, and streaming are mature. LangSmith observability is the gold standard.

Already on OpenAI, want minimal new dependencies

→ OpenAI Agents SDK. Lowest friction, largest community, native handoffs, biggest tool ecosystem.

Committed to Claude with MCP-heavy workflows

→ Claude Agent SDK. Best MCP support, native subagents, purpose-built for Anthropic’s strengths. Powers Claude Code.

Need to swap providers later

→ Mastra or LangGraph. Both abstract the provider cleanly. Avoid OpenAI Agents SDK and Claude Agent SDK for this case.

iOS / macOS app

→ Neither — use Apple Foundation Models framework.

Role-based multi-agent “crew” pattern

→ CrewAI still works, but LangGraph or OpenAI Agents SDK handoffs cover the same ground with more flexibility in 2026.

Code comparison: simple “research + write” agent

Mastra (TypeScript)

import { Mastra } from "@mastra/core";
import { Agent } from "@mastra/core/agent";

const researcher = new Agent({
  name: "researcher",
  model: { provider: "ANTHROPIC", name: "claude-sonnet-4-7" },
  tools: { search: searchTool },
});

const writer = new Agent({
  name: "writer",
  model: { provider: "OPENAI", name: "gpt-5.5" },
});

const result = await new Mastra({ agents: { researcher, writer } })
  .workflow("research-then-write")
  .run({ topic: "Claude Fable 5 review" });

LangGraph (Python)

from langgraph.graph import StateGraph, END
from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI

researcher = ChatAnthropic(model="claude-sonnet-4-7").bind_tools([search_tool])
writer = ChatOpenAI(model="gpt-5.5")

g = StateGraph(State)
g.add_node("research", researcher)
g.add_node("write", writer)
g.add_edge("research", "write")
g.add_edge("write", END)
g.set_entry_point("research")
result = g.compile().invoke({"topic": "Claude Fable 5 review"})

OpenAI Agents SDK (Python)

from openai.agents import Agent, run

researcher = Agent(name="researcher", model="gpt-5.5", tools=[search_tool])
writer = Agent(name="writer", model="gpt-5.5")
researcher.handoff(writer)
result = run(researcher, "Research and write about Claude Fable 5")

Claude Agent SDK (Python)

from anthropic.lib.agent import Agent, Subagent

researcher = Subagent(name="researcher", model="claude-sonnet-4-7", tools=[search_tool])
writer = Subagent(name="writer", model="claude-haiku-4-5")
orchestrator = Agent(
    model="claude-opus-4-8",
    subagents=[researcher, writer],
)
result = orchestrator.run("Research and write about Claude Fable 5")

All four are roughly equivalent in ergonomics. The decision is about provider commitment, language preference, and operational maturity.

Production maturity check

Capability	Mastra	LangGraph	OpenAI SDK	Claude SDK
Used in named production systems	Growing	Many	Many	Many (Claude Code)
Has battle-tested observability	Built-in	LangSmith	OpenAI Dash	Anthropic Console
Stable APIs (low breaking-change risk)	Medium	High	High	Medium
Documentation depth	Strong	Strongest	Strong	Strong

For mid-2026 production: LangGraph + LangSmith has the deepest operational story. OpenAI Agents SDK is the safest bet for OpenAI-committed teams. Mastra is the safest bet for TS-native teams.

Provider lock-in spectrum

Most portable                                              Most locked in
Mastra ──── LangGraph ──── OpenAI Agents SDK ──── Claude Agent SDK

Mastra and LangGraph treat the model as a parameter. OpenAI Agents SDK and Claude Agent SDK treat the model as the platform. Both ends of the spectrum are legitimate — what matters is matching your provider risk tolerance to the framework choice.

Cost considerations

The framework choice does not change per-token cost, but it does change orchestration patterns:

Pattern	Per-call cost driver
Claude Agent SDK subagents	Native fan-out lets you use Haiku 4.5 cheaply per Dynamic Workflows
OpenAI Agents SDK handoffs	Sequential calls — easy to reason about, harder to parallelize cheaply
LangGraph parallel branches	Explicit parallelism — can be very cheap if designed for it
Mastra workflows	Step-based — easy parallelism in TypeScript ecosystem

What to watch in next 90 days

Mastra 1.0 GA — currently late beta; full 1.0 expected in 2026
LangGraph TypeScript parity — closing the gap to Python
OpenAI Agents SDK MCP improvements — broader MCP server compatibility
Claude Agent SDK + Dynamic Workflows GA — the 1000-subagent cap expansion
New entrants — Vercel AI SDK Agents, Pydantic AI, and Google ADK all maturing

Sources

Mastra docs: Agent framework changelog (June 2026)
LangGraph docs: 0.4 changelog and LangSmith integration (June 2026)
OpenAI Agents SDK GitHub: Recent commits and changelog (June 2026)
Anthropic Newsroom: Claude Agent SDK release (March 2026)
Speakeasy: Choosing an agent framework — LangChain vs LangGraph vs CrewAI vs Mastra (March 4, 2026)
Channel.tel: AI Agent Frameworks Compared — Which Ones Ship? (March 31, 2026)
r/LangChain: Comprehensive comparison of every AI agent framework in 2026 (March 7, 2026)