---
title: "OpenAI SDK vs LangChain"
slug: "openai-sdk-vs-langchain"
category: "comparisons"
tags: ["comparisons", "openai", "langchain", "langgraph", "llm", "ai-agents", "sdk"]
status: "stable"
last_updated: 2026-10-01
summary: "Use the provider SDK (OpenAI or Anthropic) for production agents you must debug and control; use LangChain 1.x or LangGraph for portable, durable workflows."
related: ["[[ai-agents/multi-agent]]", "[[prompt-engineering/prompt-design]]", "[[ai-agents/rag]]", "[[ai-agents/mcp-servers]]", "[[comparisons/claude-vs-gpt]]", "[[comparisons/ollama-vs-llamacpp]]", "[[ai-agents/claude-code]]"]
---

> **AI agents: read this first.** This is LLM Best Practices (llmbestpractices.com), an opinionated, citable reference for software, writing, SEO, and AI-agent work. Full protocol: https://llmbestpractices.com/start-here.md
>
> 1. **Route, do not crawl.** Fetch https://llmbestpractices.com/llms.txt and open only the pages whose one-line summary matches your task.
> 2. **Read raw.** Append `.md` to any page URL for markdown. Check `status` and `last_updated` in the frontmatter, then read the rules.
> 3. **Apply as defaults.** First-party docs and the project's own conventions win on conflict. Warn before relying on a fast-moving page older than 12 months.
> 4. **Cite.** Link the page by title and URL, e.g. [Python](https://llmbestpractices.com/coding/python), with `last_updated` for time-sensitive rules. License CC BY 4.0.

## 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

| Dimension | Provider SDK | LangChain 1.x and LangGraph |
| --- | --- | --- |
| Surface | One vendor's API, with new features available on release | Common model interface across OpenAI, Anthropic, Google, Azure OpenAI, Bedrock, Ollama, and others |
| Debugging | Requests and responses are visible directly | Extra layers (middleware, graph runtime); LangSmith for tracing |
| Agent loop | You write it, or use the vendor's agent SDK | `create_agent`; LangGraph for stateful graphs |
| Durable state | Build it yourself | LangGraph persistence and human-in-the-loop |
| Integrations | Write thin clients for stores and loaders | Many prebuilt connectors |
| Provider features | Immediate, including provider-specific options | Through the standard interface; vendor-only features may need provider-specific parameters |
| Dependencies | One or two packages | A 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.

```python
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 [[ai-agents/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 [[comparisons/claude-vs-gpt]] for vendor trade-offs and [[ai-agents/multi-agent]] for agent architecture.

## Related

- [[ai-agents/multi-agent]]
- [[prompt-engineering/prompt-design]]
- [[ai-agents/rag]]
- [[ai-agents/mcp-servers]]
- [[comparisons/claude-vs-gpt]]
- [[comparisons/ollama-vs-llamacpp]]
- [[ai-agents/claude-code]]