Choosing an Agent SDK in 2026: Claude, LangGraph, AutoGen

The Five Contenders, Briefly

• Claude Agent SDK (Anthropic). Python and TypeScript. Closely tied to the Claude API surface. Strong on production tool-use loops, native MCP integration, ergonomic for single-agent workflows that scale up.
• LangGraph (LangChain). Python and TypeScript. Graph-based; agents are nodes, control flow is edges. Strong on complex multi-agent orchestration with explicit state management.
• AutoGen (Microsoft Research). Python primarily. Multi-agent first; conversation-based agent interaction. Strong research roots, broad community.
• Microsoft Agent Framework (Microsoft). C# and Python. Newer, enterprise-positioned successor to Semantic Kernel and AutoGen for production. Native Azure integration; strongest path for .NET shops.
• Mastra. TypeScript-only. Workflow-engine framing for agent systems. Strong on developer experience, deployment simplicity for Node-first stacks.

Claude Agent SDK

Anthropic ships the Claude Agent SDK as the production-grade primitive for building agents on Claude. It abstracts the loop, tool execution, MCP server integration, and prompt-caching breakpoints. The mental model is closest to what an engineer would write by hand against the Claude messages API, with the boilerplate automated.

• Strengths. Tight integration with Claude features: prompt caching, extended thinking, computer use, MCP. Idiomatic Python and TypeScript. Lower learning curve than graph frameworks.
• Tradeoffs. Single-vendor by design; portability to other models requires writing parallel code paths. Less explicit state management than LangGraph for complex multi-agent orchestration.
• Best fit. Teams committed to Claude as the primary model. Production agents that need MCP server integration. Single-agent or planner-worker patterns.

// Claude Agent SDK, conceptual TypeScript shape
import { ClaudeAgent, tool } from '@anthropic-ai/agent-sdk';

const searchCrm = tool({
  name: 'search_crm',
  description: 'Search the CRM by customer name or email.',
  input_schema: { /* ... */ },
  handler: async ({ query }) => {
    return await crm.search(query);
  },
});

const agent = new ClaudeAgent({
  model: 'claude-opus-4-7',
  system: SYSTEM_PROMPT,
  tools: [searchCrm /* ... */],
  promptCache: true,
  maxSteps: 10,
});

const result = await agent.run({ task: userTask });

LangGraph

LangGraph models the agent system as an explicit state graph. Nodes are agents or tools; edges are control-flow transitions; state is shared via typed dictionaries. The mental model is closer to a workflow engine than to "wrap an LLM call in a loop."

• Strengths. Best-in-class for complex multi-agent orchestration. Explicit state and control flow make systems easier to reason about at scale. Strong observability via LangSmith. Model-agnostic.
• Tradeoffs. Higher learning curve. The graph DSL is power but also verbosity for simple cases. Heavyweight for single-agent workloads.
• Best fit. Multi-agent systems with non-trivial control flow. Teams already using LangChain. Workloads where explicit state management catches bugs that hidden state would not.

AutoGen

AutoGen pioneered the multi-agent conversation pattern. The architecture: agents talk to each other through a conversation manager. Each agent has its own role, system prompt, and tool set. The framework coordinates turns and termination conditions.

• Strengths. Mature multi-agent abstractions. Active research community pushes the framework forward. Strong examples for common patterns.
• Tradeoffs. The conversation metaphor obscures control flow. Production deployments often hit the limits of conversation-based coordination and add structure on top. Microsoft has positioned the newer Agent Framework as the path forward.
• Best fit. Research and prototyping. Teams already invested. New production projects have stronger options.

Microsoft Agent Framework

Microsoft Agent Framework consolidates the lessons from AutoGen and Semantic Kernel into a production-positioned SDK. C# and Python support, native integration with Azure AI Foundry, identity propagation through Entra Agent ID, telemetry through Application Insights GenAI.

• Strengths. Strongest enterprise integration on Azure. First-class .NET support. Native handling of identity, observability, and deployment for Microsoft-platform organisations.
• Tradeoffs. Younger ecosystem than LangGraph or AutoGen. Some abstractions still evolving. Best in Microsoft-native environments; less of a fit if you are not on Azure.
• Best fit. .NET shops shipping agents on Azure. Teams using Azure AI Foundry. Workloads where Entra-backed identity propagation is required.

Mastra

Mastra is a TypeScript-first framework that frames agents as workflows. The mental model is closer to Temporal or Inngest than to LangGraph: an agent is a long-running workflow with steps, retries, and persistent state. Integrates with Vercel-style deployment patterns.

• Strengths. Clean TypeScript ergonomics. Workflow patterns map well to long-running agents. Easy deployment on Node-first platforms.
• Tradeoffs. Newer; smaller community than LangGraph. TypeScript-only means it does not fit Python-native data and ML teams.
• Best fit. Next.js or other Node-first stacks. Teams that want the workflow-engine mental model. Long-running agents that benefit from durable execution patterns.

Decision Matrix

• If you are committed to Claude: Claude Agent SDK first, LangGraph if you need its multi-agent depth.
• If you are model-agnostic and want best multi-agent orchestration: LangGraph.
• If you are .NET-first or Azure-native: Microsoft Agent Framework.
• If you are TypeScript-first and want workflow-engine semantics: Mastra.
• If you have AutoGen in production already: Stay until the next major refactor; AutoGen still works.

Migration Costs

Switching SDKs is rarely cheap. The investment is in the framework's mental model, the prompt patterns that work with its abstractions, and the observability and evaluation tools that integrate with it. Realistic migration cost for a non-trivial agent system: 4–8 engineering weeks.

Migrating is justified when the current framework is genuinely blocking work. "Switching from LangGraph to Mastra to be more idiomatic in TypeScript" usually does not pay back. "Switching from AutoGen to Microsoft Agent Framework because we are committing to Azure-native enterprise deployment" often does.

Cross-Cutting Concerns

Three concerns matter regardless of SDK choice:

• Observability. All five emit OpenTelemetry; choose the export target (Langfuse, App Insights, custom) based on your platform standard.
• Evaluation. Build a labelled evaluation set independent of the SDK. DeepEval, Promptfoo, and LangSmith all work alongside any SDK.
• Identity. Per-agent identity belongs in your platform layer (Entra Agent ID for Microsoft shops, equivalent for others), not in the SDK.

A Pragmatic Order of Investigation

1. List your platform constraints: language, cloud, model preference, .NET vs Python vs Node.
2. Eliminate SDKs that fail the constraints. Usually leaves two or three.
3. Build the same simple agent (search a CRM, summarise findings, take a templated action) in each remaining SDK over a half-day.
4. Pick on developer experience and observability fit. Performance and capability differences at this scale are noise.

The right SDK is the one your team can iterate on without fighting. Frameworks evolve; the mental model and team familiarity persist longer. Pick well at the start, accept the migration as a possibility a year later, and ship the agents that matter in the meantime.