Comparisons / Anthropic Agent SDK vs LangGraph

Anthropic Agent SDK vs LangGraph: Which Agent Framework to Use?

The Anthropic Agent SDK packages Claude Code's agent loop as a library. LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.

By the numbers

Anthropic Agent SDK

GitHub Stars

3.1k

Forks

582

Language

Python

License

MIT

Created

2023-01-17

Created by

Anthropic

Backed by

Google, Spark Capital

Production ready

Yes

github.com/anthropics/anthropic-sdk-python

LangGraph

GitHub Stars

18.9k

Forks

3.4k

Language

Python

License

MIT

Created

2024-01-17

Created by

LangChain Inc (Harrison Chase)

Backed by

Sequoia Capital, Benchmark

Funding

Part of LangChain Inc — $50M raised across A and B

Weekly downloads

8.2M

Cloud/SaaS

LangGraph Platform (hosted), LangSmith (observability)

Production ready

Yes

Used by: Replit, Klarna, Elastic

github.com/langchain-ai/langgraph

GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.

ConceptAnthropic Agent SDKLangGraph
AgentClaude agent with built-in tools, MCP servers, and system promptA `StateGraph` with nodes, edges, and a typed `State` channel
ToolsBuilt-in tools (`bash`, file read/write, web) + MCP server connections`ToolNode(tools)` paired with a conditional edge for routing
Agent LoopSDK's internal agentic loop with automatic tool dispatch
Sub-AgentsAgents invoke other agents as tools via the SDK
Lifecycle Hooks18 hook events: pre/post tool call, message, error, etc.
MCP IntegrationOne-line MCP server config for Playwright, Slack, GitHub, etc.
Loop`add_conditional_edges` from a node back to itself until a `END` condition
StateTyped `State` channels with reducers (`Annotated[list, add_messages]`)
Checkpointing`MemorySaver` / `PostgresSaver` persists state per `thread_id`
Human-in-loop`interrupt_before` / `interrupt_after` pauses execution for review
Parallel fanoutMultiple edges from one node + reducers merge results

Anthropic Agent SDK vs LangGraph, head to head

Anthropic Agent SDK The Anthropic Agent SDK packages Claude Code's agent loop as a library.

LangGraph LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state.

Both wrap the same underlying agent pattern — an LLM call, a tool dispatch, a loop — in different abstractions. The choice between them is mostly about which mental model and ecosystem fits the team you have, not which one is technically more capable.

Pick Anthropic Agent SDK if

Pick Anthropic Agent SDK if the Anthropic Agent SDK's real value is packaging Claude Code's battle-tested agent loop with built-in tools and MCP integration. If you want a production agent that reads files, runs commands, and connects to services, it saves significant plumbing. For understanding how agents work, the plain version is more instructive. Anthropic Agent SDK is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; LangGraph would force you to translate.

Full Anthropic Agent SDKcomparison →

Pick LangGraph if

Pick LangGraph if langGraph earns its weight when your agent is a workflow — explicit branches, checkpoints, parallel branches, or a human approval gate. For a single-agent loop, the graph machinery is overkill and a plain while loop is faster to write, debug, and ship. LangGraph is the right fit when the tradeoffs in its intro line up with how your team actually wants to work day-to-day; Anthropic Agent SDK would force you to translate.

Full LangGraphcomparison →

What both add

Both Anthropic Agent SDK and LangGraph pull in a class hierarchy and a dependency tree to wrap what is, at the core, an HTTP POST in a while loop. If your use case is straightforward — one provider, a handful of tools, a single agent — the framework cost may exceed the framework benefit. The lesson below shows the same pattern in ~60 lines without either dependency.

Or build your own in 60 lines

Both Anthropic Agent SDK and LangGraph implement the same 8 patterns. An agent is a function. Tools are a dict. The loop is a while loop. The whole thing composes in ~60 lines of Python.

No framework. No dependencies. No opinions. Just the code.

Build it from scratch →