Agents-OSS
EN
Submit

LangChain vs OpenAI Agents SDK

Both are catalogued under Agent Frameworks. The figures come from the GitHub API; the assessments are ours.

LangChainThe broadest set of model and tool integrations in one libraryOfficialMITOpenAI Agents SDKA deliberately small agent framework from OpenAIOfficialMIT

At a glance

At a glanceLangChainOpenAI Agents SDK
LicenseMITMIT
LanguagesPython, TypeScriptPython
DeploymentSelf-hosted / Runs locallySelf-hosted / Runs locally
MaturityEstablishedGrowing
Stars144k28.7k
Star growth over the last 7 days+23 ★+8 ★
Forks24k4.5k
Open issues41016
Last commit15 Aug 202616 Aug 2026
ActivityActiveActive

What each one does

LangChain

Its real value is coverage: whatever model, vector store or API you need, an adapter probably already exists, which shortens the distance from idea to prototype. The abstractions have a reputation for indirection, so many teams use it for the integrations and reach for something more explicit once the control flow gets complicated.

Full entry →

OpenAI Agents SDK

Few primitives — agents, handoffs, guardrails, tracing — and little else. The small surface area is the appeal: there is not much to learn and not much to fight. It is built around OpenAI's own models first, so weigh that if multi-provider portability matters to you.

Full entry →

What you can do

LangChain

  • Swap model providers in place — init_chat_model takes a "provider:model" string and returns a chat model behind one interface, so call sites keep using .invoke() when the provider changes.
  • Start from prebuilt agent patterns — Deep Agents, a higher-level package built on LangChain, ships planning, subagents and file-system use as built-in capabilities instead of patterns you assemble yourself.
  • Move to explicit orchestration — When branching and state outgrow the chain-level API, LangGraph is the sibling low-level framework for writing controllable agent workflows directly.
  • Build the same design in TypeScript — LangChain.js is the equivalent JS/TS library, distributed as the langchain npm package alongside the Python one.

OpenAI Agents SDK

  • Delegate between specialist agents — Handoffs and agents-as-tools let one Agent pass work to another, and Runner.run_sync executes the whole chain and returns final_output.
  • Give an agent a workspace — SandboxAgent takes a Manifest of entries such as GitRepo and runs commands, inspects files and applies patches through UnixLocalSandboxClient, or DockerSandboxClient on Windows.
  • Build voice agents two ways — RealtimeAgent holds a WebSocket session with gpt-realtime-2.1, while VoicePipeline chains speech-to-text, an agent workflow and text-to-speech from the optional voice extra.
  • Point it at models other than OpenAI's — The README calls the SDK provider-agnostic across the OpenAI Responses and Chat Completions APIs plus 100+ other LLMs, though every quickstart example still expects OPENAI_API_KEY in the environment.
  • Trace and resume conversations — Tracing is built in for viewing and debugging runs, and Sessions carries conversation history across separate Runner invocations without manual bookkeeping.

Other comparisons