LangChain vs OpenAI Agents SDK

What users praise

• • Provider coverage is enormous — OpenAI, Anthropic, Google, Bedrock, Ollama and more under one interface
• • LangGraph adds determinism for production agents that pure autonomous loops can't give you
• • LangSmith tracing shows every step of an agent run, which is the fastest debugging lever
• • Eval tooling with datasets and annotation queues turns agent quality into something measurable

What frustrates them

• • Version upgrades routinely break imports and force code rewrites
• • Abstraction layers obscure what's actually being sent to the model
• • LangSmith's paid tier is where the genuinely useful features live
• • Documentation quality is inconsistent across the many sub-packages

What users praise

• • Lean, low-boilerplate orchestration compared with LangChain and AutoGen, with far fewer abstractions to learn
• • Provider-agnostic via LiteLLM across 100+ LLMs, so teams aren't locked into OpenAI models
• • Sandbox harness for file inspection, command execution, and patch application ships out of the box
• • Guardrails for input and output validation are first-class, not an afterthought bolted on later

What frustrates them

• • Narrower than LangChain or AutoGen — no sprawling integration catalog when you need exotic connectors
• • Documentation is largely OpenAI-authored, so community troubleshooting resources are thinner than competitors'
• • Optional extras like voice and Redis sessions add dependency and setup complexity teams underestimate
• • Sandbox provider abstraction is unclear — reviewers can't tell if you're locked into one per run