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LangGraph vs OpenAI Agents SDK. Rows marked Differs have different values.
	Open source LangGraph Remove	Open source OpenAI Agents SDK Remove
Type	Open source	Open source
CategoriesDiffers	Orchestration & Frameworks	Orchestration & Frameworks Protocols & Interop
In one line	Low-level orchestration for controllable, stateful agents.	A lightweight, production-ready multi-agent SDK with tracing.
Stage & funding	—	—
GitHub starsDiffers	≈44k	≈30k
License	MIT	MIT
Language	Python	Python
Last pushDiffers	Oct 28, 2026	Oct 27, 2026
Works with	—	—
Built onDiffers	—	Model Context Protocol
Built on itDiffers	LangChain Skills OpenPipe (ART) VoltAgent	Agency Swarm any-agent DBOS
Similar to	—	—
People linkedDiffers	3	0
EventsDiffers	Interrupt 2026 Build AI Research Agents with LangGraph: React, Planning, and Memory	0
Pros	User reportsUsers running it in production cite checkpointing, pausing mid-graph for human input and time-travel over state as the main reasons to use it.[1][2][3] User reportsUsers find the explicit graph/state-machine model a useful way to control agent flow, even when they dislike the wider LangChain abstractions.[1][2] User reportsThe Postgres checkpointer lets stateless backends resume sessions without session affinity.[1] MetadataMIT licensed, actively released (1.2.18 on 2026-10-26), about 44k stars.[1]	User reportsVery easy start for function calling and sub-agents-as-tools; with the Responses API you don't have to store input items yourself.[1] User reportsOne long-time user says they are largely happy with its abstractions: handoffs, tools, guardrails and structured output.[1][2] DocsProvider-agnostic despite the name (100+ LLMs), with built-in tracing, sessions, sandbox agents and realtime/voice agents.[1] User reportsCommenters see it as simpler than heavier frameworks like Google ADK or LangChain, and its backlog is small (about 12 open issues).[1][2]
Cons	User reportsUsers describe a steep learning curve for people not used to graphs, and say tracing a mid-graph production failure means building your own observability.[1][2] User reportsDurability edge cases: long tool calls (about 180s or more) silently re-execute from a checkpoint on LangGraph Cloud, and sync durability doesn't enforce write ordering.[1][2] User reportsCancelling a run loses streamed state that hasn't been checkpointed yet, and the Postgres checkpointer has a long-open SSL error.[1][2] User reportsDocs lag the fast-moving API, and breaking changes are a recurring complaint.[1][2] User reportsSeveral teams outgrow it and write their own loop, keeping only the graph idea.[1]	User reportsInfra, observability and reliable scaling are left to you, and one user found those were the bigger pain.[1] User reportsOne user calls it messy internally, citing inconsistent use of its own enums.[1] User reportsSome teams used it extensively and then moved to their own framework.[1]
Best for	Long-running, stateful, resumable workflows with human approval steps where you want explicit control flow.	Python teams that want lightweight multi-agent primitives (handoffs, guardrails, tracing) with first-class OpenAI Responses and realtime support.
Watch out for	Make tool calls idempotent: a checkpoint replay can run them again.	Production concerns like durability, scaling and approvals are on you; plan that layer early.
Links	GitHub Docs	GitHub Docs

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