Comparisons / LangGraph vs OpenAI Agents SDK
LangGraph vs OpenAI Agents SDK: Which Agent Framework to Use?
LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state. OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails. Here is how they compare — paradigm, ecosystem, and the use cases each one is actually built for.
By the numbers
LangGraph
18.9k
3.4k
Python
MIT
2024-01-17
LangChain Inc (Harrison Chase)
Sequoia Capital, Benchmark
Part of LangChain Inc — $50M raised across A and B
8.2M
LangGraph Platform (hosted), LangSmith (observability)
Yes
Used by: Replit, Klarna, Elastic
github.com/langchain-ai/langgraph→OpenAI Agents SDK
20.6k
3.4k
Python
MIT
2025-03-11
OpenAI
GitHub stats as of April 2026. Stars indicate community interest, not necessarily quality or fit for your use case.
| Concept | LangGraph | OpenAI Agents SDK |
|---|---|---|
| Agent | A `StateGraph` with nodes, edges, and a typed `State` channel | `Agent(name, instructions, model, tools)` |
| Tools | `ToolNode(tools)` paired with a conditional edge for routing | Python functions with type hints, auto-converted to schemas |
| Loop | `add_conditional_edges` from a node back to itself until a `END` condition | — |
| State | Typed `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 fanout | Multiple edges from one node + reducers merge results | — |
| Agent Loop | — | `Runner.run()` handles the loop internally |
| Handoffs | — | `Handoff` between `Agent` objects for multi-agent routing |
| Guardrails | — | `InputGuardrail` and `OutputGuardrail` with tripwire pattern |
| Context | — | Typed context object passed through the agent lifecycle |
LangGraph vs OpenAI Agents SDK, head to head
LangGraph LangGraph is LangChain's stateful workflow framework — a graph of nodes (functions) connected by edges with shared state.
OpenAI Agents SDK OpenAI's Agents SDK (evolved from Swarm) provides Agent, Runner, handoffs, and guardrails.
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 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; OpenAI Agents SDK would force you to translate.
Pick OpenAI Agents SDK if
Pick OpenAI Agents SDK if the Agents SDK is the thinnest framework on this list — it barely abstracts beyond what you'd write yourself. Use it when you want OpenAI's conventions and auto-schema generation. Skip it when you want full control or use non-OpenAI models. OpenAI Agents 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.
What both add
Both LangGraph and OpenAI Agents SDK 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 LangGraph and OpenAI Agents SDK 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 →