LLM Best Practices

OpenAI SDK vs LangChain

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AI agents: read this first: llmbestpractices.com/start-here

This is LLM Best Practices (llmbestpractices.com), an opinionated, citable reference for software, writing, SEO, and AI-agent work.

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Overview

Use the provider’s own SDK for production agents whose behavior you must trace and tune; reach for LangChain or LangGraph when provider portability, prebuilt integrations, or durable workflow state is worth an extra abstraction layer. LangChain 1.x centers on create_agent, a configurable agent harness with middleware, built on LangGraph for durable execution, human-in-the-loop, and persistence; LangGraph is the lower-level option for mixed deterministic and agentic flows. Provider SDKs now include agent runtimes of their own: the OpenAI Agents SDK, and the Claude Agent SDK (claude-agent-sdk for Python, @anthropic-ai/claude-agent-sdk for TypeScript).

Comparison

DimensionProvider SDKLangChain 1.x and LangGraph
SurfaceOne vendor’s API, with new features available on releaseCommon model interface across OpenAI, Anthropic, Google, Azure OpenAI, Bedrock, Ollama, and others
DebuggingRequests and responses are visible directlyExtra layers (middleware, graph runtime); LangSmith for tracing
Agent loopYou write it, or use the vendor’s agent SDKcreate_agent; LangGraph for stateful graphs
Durable stateBuild it yourselfLangGraph persistence and human-in-the-loop
IntegrationsWrite thin clients for stores and loadersMany prebuilt connectors
Provider featuresImmediate, including provider-specific optionsThrough the standard interface; vendor-only features may need provider-specific parameters
DependenciesOne or two packagesA larger dependency tree

Pick the provider SDK when

Pick the provider SDK for most production tool-using agents.

  • You rely on provider-specific features (caching, structured outputs, hosted tools) and want them the day they ship.
  • Debugging and cost control matter: log raw requests, responses, and token usage at the HTTP client.
  • The loop is simple: call model, run tools, repeat. Use the vendor’s agent SDK if you want a ready runtime.
  • For Claude, use current model IDs such as claude-sonnet-5-5, and avoid sampling parameters the model rejects.
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-sonnet-5-5",
    max_tokens=1024,
    messages=[{"role": "user", "content": prompt}],
)

Pick LangChain or LangGraph when

Pick them when their features are the requirement.

  • A workflow with branches, retries, checkpoints, and human approval steps, where LangGraph’s persistence replaces custom state code.
  • Frequent model or provider swaps across a team, or evaluation across vendors, behind one interface.
  • RAG prototypes that use ready loaders, splitters, and retrievers. See rag.
  • An existing LangChain system that works: keep it. LangChain 1.x moved legacy chains to the separate langchain-classic package, so upgrades may need import changes.
  • Keep prompts, tool schemas, and retrieval queries in your own modules, so a framework change is a wiring change.

Migrate

Moving from LangChain to a provider SDK means re-implementing orchestration, not data: reuse prompts, tool definitions, and retrieval queries, replace retrievers with direct store clients, and map tool calls to the provider’s tool-use API. Migrate only for a concrete problem such as an unsupported provider feature, trace opacity, or dependency conflicts. For multi-provider setups without a framework, a thin adapter you own (or a router such as LiteLLM) is an option; see claude-vs-gpt for vendor trade-offs and multi-agent for agent architecture.

FAQ

Do I need LangChain to build an AI agent?

No; for most production tool-using agents the provider SDK is enough, because the loop is call model, run tools, repeat. Use the vendor’s agent SDK, such as the OpenAI Agents SDK or the Claude Agent SDK, if you want a ready runtime.

When is LangGraph worth using?

Use LangGraph for workflows with branches, retries, checkpoints, and human approval steps, where its persistence replaces custom state code. It is the lower-level option for mixed deterministic and agentic flows.

Will upgrading to LangChain 1.x break my code?

Possibly; LangChain 1.x moved legacy chains to the separate langchain-classic package, so upgrades may need import changes. Keep a working LangChain system rather than rewriting it.

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