The AI SDK Landscape for Product Builders
Okay, which SDK do I actually use here?
Hey all,
If you’ve been building AI-powered products lately, you’ve probably opened a new project and immediately asked yourself: “Okay, which SDK do I actually use here?“
The choices have exploded. And with every major lab - OpenAI, Anthropic, Google - shipping their own agent SDK on top of the existing JS/TS ecosystem, decision fatigue is real. Let me help you cut through the noise.
The Lay of the Land
At a high level, you’re choosing between two camps: framework-level SDKs (LangChain, LlamaIndex) and provider-level SDKs (OpenAI Agents SDK, Claude Agent SDK, Google ADK), with Vercel AI SDK sitting comfortably in its own lane as the best “UI-connected” toolkit for JS/TS builders. Here’s the full picture at a glance:
The Three Decisions That Actually Matter
Before you even look at docs, ask yourself these three questions. Your answers will point you to the right tool immediately.
1. Are you building a UI or a backend agent?
If you’re building a chat interface, a workflow assistant, or anything with a streaming front-end in React or Next.js - Vercel AI SDK is the default answer.
Its useChat() hook alone cuts ~100 lines of boilerplate to ~20, handles streaming state automatically, and natively supports edge deployment. It lets you swap providers with literally one line of code, so you’re never locked in.
2. How complex is your agent logic?
For agents that need sophisticated reasoning patterns (ReAct, Plan-and-Execute), built-in RAG with vector stores, or multi-step orchestration, LangChain JS is the most mature toolbox.
For simpler agents that need to get running fast, the OpenAI Agents SDK wins on speed — clean primitives (Agents, Handoffs, Guardrails, Sessions), under 10 lines of setup, and it supports 100+ LLMs, not just GPT models.
3. Does your agent need to control a computer?
This is the Claude Agent SDK‘s unique superpower. It grew out of Claude Code - its core design principle is literally “give your agent a computer” - direct Bash execution, file system access, and spawning subagents. If you’re building developer tools, coding assistants, or anything that needs to interact with a real OS environment, nothing else comes close.
The Provider-First Trap (And 3 Other Mistakes)
Here’s the mistake I see product teams make constantly: they pick the SDK from whichever AI provider they’re using today. OpenAI API key in hand → OpenAI SDK. Anthropic fan → Claude SDK. It feels intuitive. But it’s a trap.
The raw OpenAI SDK (8.8M weekly downloads) is excellent for simple, direct API access. But the moment your product needs to A/B test models, switch providers during an outage, or run a non-OpenAI model, you’re rewriting plumbing. A provider-agnostic layer from day one costs you almost nothing and saves you a full sprint later. Here are three more anti-patterns worth naming:
Building demos, not products. AI makes it trivially easy to create impressive prototypes. But demos attract attention - products earn retention. If your agent looks magical in a five-minute Loom but breaks on the 20th real user interaction, you chose the wrong optimization target.
Chasing model quality over workflow quality. A slightly better model rarely fixes a broken product. If users are churning, the problem is almost always in the workflow - unclear user flow, missing guardrails, or poor defaults, not the underlying LLM.
The “God Agent” trap. Cramming every capability into a single monolithic agent is the 2026 equivalent of a 3,000-line spaghetti component. Design for handoffs, break agents into focused, composable units. Both OpenAI Agents SDK and Google ADK have first-class multi-agent orchestration primitives built specifically for this.
What Happens After You Ship: Observability Is Not Optional
This is the part most product builders skip - and it’s exactly where things go quietly wrong. AI systems don’t fail loudly; they drift. Outputs get subtly worse under new inputs. Costs creep up. Users complain, and by then you’ve already lost trust.
Every SDK choice should be paired with an observability layer from day one. The leading options right now are Langfuse (open-source, integrates natively with Vercel AI SDK), LangSmith (the natural companion to LangChain), and Braintrust for heavier evaluation workflows. They let you trace every agent call, score output quality automatically, and catch regressions before your users do.
Two more signals worth watching: Model Context Protocol (MCP) is becoming the standard for how agents call external tools - 72% of builders expect their MCP usage to increase this year.
And Vercel AI SDK 6 just shipped a major update with a new useChat message-parts model and improved streamText primitives. Your full production stack isn’t just the SDK you pick - it’s three layers working together:
My Quick Decision Framework
Building a chat UI in Next.js? → Vercel AI SDK, no debate.
Building complex RAG or autonomous agent workflows? → LangChain JS + LangGraph.
Rapid prototyping a multi-agent system with voice? → OpenAI Agents SDK.
Building a developer tool that touches the file system? → Claude Agent SDK.
Enterprise deployment on Google Cloud? → Google ADK.
The deeper truth is this: frameworks evolve fast, but the architectural intuitions - when to hand off vs. subagent, how to design tools that don’t bloat context, when to add a human checkpoint - those compound over time.
Add observability from day one, design for composability, and resist the “God Agent” trap. Pick the SDK that gets you shipping and learning, then optimize from there.
Until next time,
Samet Özkale, AI for Product Power