LangGraph vs OpenAI Agents SDK
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python framework for building long-running agents that keep state and can resume after failures.
Open source · MIT
A Python SDK for building AI agents that use tools, hand off tasks, and keep conversation history.
The short answer
Choose LangGraph if you need…
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
Choose OpenAI Agents SDK if you need…
- Python developers building multi-step AI workflows
- Teams that want agents to call tools and delegate work to other agents
- Developers building voice assistants with speech-to-text and text-to-speech
- Builders who want built-in tracing to debug agent runs
Side-by-side
| LangGraph | OpenAI Agents SDK | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 44.3k | 30.5k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 4 days ago | 5 days ago |
What each one is
LangGraph
LangGraph is a low-level framework for building workflows and AI agents that keep track of their state over time. It is designed for tasks that run for a long stretch, can pause for human review, and need to pick up where they left off after an interruption. It is produced by LangChain Inc, but the README says it can be used without LangChain.
OpenAI Agents SDK
The OpenAI Agents SDK is a Python library for building workflows where one or more LLM-powered agents work together. Each agent can be given instructions, tools, guardrails, and the ability to hand tasks to other agents. The README says it works with the OpenAI Responses and Chat Completions APIs and with 100+ other LLMs.
Best for
LangGraph
- Python developers building multi-step AI agents that must survive failures or long waits
- Teams that want fine-grained control over agent state and execution flow
- Projects that need a human to review or edit an agent's state partway through a run
- Developers who want a lower-level base instead of a higher-level agent package
OpenAI Agents SDK
- Python developers building multi-step AI workflows
- Teams that want agents to call tools and delegate work to other agents
- Developers building voice assistants with speech-to-text and text-to-speech
- Builders who want built-in tracing to debug agent runs
Strengths
LangGraph
- Built-in durable execution that resumes workflows from where they stopped, per the README
- Supports human-in-the-loop checkpoints and both short-term and long-term memory
- Open source under the MIT license with extensive documentation and a free course
OpenAI Agents SDK
- Small, focused API: an Agent plus a Runner covers the basic case, per the README examples
- Built-in features such as tools, handoffs, sessions, guardrails, and tracing are listed in the README
- Provider-agnostic design, supporting OpenAI APIs and 100+ other LLMs according to the README
Not ideal for
LangGraph
- Beginners who just want a ready-made agent: the README points to Deep Agents, a higher-level package built on LangGraph, for quicker starts
- Non-programmers, since it is a Python library that requires writing code
- JavaScript/TypeScript-only teams, who are pointed to the separate LangGraph.js project
OpenAI Agents SDK
- Non-programmers, since it is a code library with no graphical interface (reason: it is used by writing Python)
- Projects that need a non-Python stack, since the README points to a separate JavaScript/TypeScript version (reason: this repo is Python-only)
- Anyone who cannot use an OpenAI API key or another supported LLM provider (reason: the examples require OPENAI_API_KEY)
Trade-offs
LangGraph
- It is a low-level framework, so it has a steeper learning curve than higher-level tools
- Several of the most useful features (debugging, deployment) point to LangSmith, a separate product
- The README is truncated and does not show full API details, so the depth of specific features is hard to verify from it alone
OpenAI Agents SDK
- Requires an OpenAI API key or another LLM provider and usage may incur costs (the README sets OPENAI_API_KEY for examples)
- Some features depend on the platform: the sandbox example uses UnixLocalSandboxClient, which the README says is for macOS and Linux, so Windows users need Docker or a hosted client
- Contributions are limited to repository collaborators, so outside pull requests are not accepted per the README
Getting started
OpenAI Agents SDK
Create and activate a virtual environment
python -m venv .venvInstall the package with pip
pip install openai-agentsFrequently asked questions
What is the difference between LangGraph and OpenAI Agents SDK?
LangGraph: A Python framework for building long-running agents that keep state and can resume after failures. OpenAI Agents SDK: A Python SDK for building AI agents that use tools, hand off tasks, and keep conversation history. LangGraph suits python developers building multi-step ai agents that must survive failures or long waits, while OpenAI Agents SDK suits python developers building multi-step ai workflows.
Are LangGraph and OpenAI Agents SDK free?
LangGraph: Free and open source. OpenAI Agents SDK: Free and open source.
Is LangGraph or OpenAI Agents SDK more popular?
LangGraph has more GitHub stars (44.3k) than OpenAI Agents SDK (30.5k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to LangGraph and OpenAI Agents SDK?
Other options people compare them with include CrewAI, Deep Agents, PageIndex, LEANN, LlamaIndex.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.