Topic Hub
Agent SDK coverage: the developer libraries for building agents — OpenAI, Google, Anthropic, and the framework field (LangChain, CrewAI, AutoGen) — and how to choose between them.
What you’ll get from this hub
Understand what an agent SDK gives you, how the major options (OpenAI Agents SDK, Google ADK, Anthropic, LangChain, CrewAI, AutoGen) differ, what to weigh when choosing, and which ClawBlog analyses to read next.
Agent SDKs are where the build-your-own-agent decisions get made, and the field is consolidating from “frameworks that do everything” toward thinner, lab-blessed SDKs (OpenAI, Google, Anthropic) that bind tightly to one model provider. The choice is really a bet on portability versus first-party integration — and that bet, more than any feature list, is what you live with.
An agent SDK is the developer library you use to build an agent yourself — the primitives for the agent loop, tool calling, memory, and structured output, without writing the plumbing from scratch. It sits below a finished harness (Claude Code, OpenClaw) and above the raw model API. If you are building rather than buying, the SDK is your foundation.
The field splits two ways. The open frameworks — LangChain, CrewAI, Microsoft AutoGen and Semantic Kernel — aim to be model-agnostic and do a lot. The newer first-party SDKs — OpenAI’s Agents SDK, Google’s Agent Development Kit, Anthropic’s SDKs — are thinner and bind tightly to one provider’s model and tools. After a couple of years of "a framework that does everything," momentum is shifting toward the leaner, lab-blessed option.
The decision underneath the feature comparison is portability versus first-party integration. A model-agnostic framework lets you swap providers but carries abstraction overhead; a first-party SDK gives you the smoothest path on one provider and the tightest tool integration, at the cost of lock-in. There is no universally right answer — the careful builder picks the bet they can live with, and watches where the ecosystem is consolidating.
SpaceX's GPU rental business has annualized to roughly $28B, about twice the scale of major neocloud players. The customer it doesn't have tells you more about who controls AI's compute layer than the three it does.
OpenClaw's v2026.6.9 quietly absorbed 422 merged PRs in a single release window. That number is the story the changelog buries: a project consolidating faster than its public stability narrative can keep up.
A week of routine agent-framework releases reads like changelog noise. Read together, the patches point at one quiet structural shift: credentials are being pulled out of the place agents can see them.
A minor Langfuse release adds the ability to delete evaluators across UI, API, and MCP. The unglamorous feature signals where the agent observability market actually sits on the evolution curve.
GPT-Realtime-2, OpenAI's first voice model with GPT-5-class reasoning, is now usable in third-party tools with document context attached. It still isn't in the ChatGPT iPhone app. The sequencing tells you who OpenAI thinks matters.
OpenClaw 2026.6.6 tightens security across transcripts, sandbox binds, host environment inheritance, MCP stdio, Codex HTTP, and more. A simultaneous multi-surface tightening reads as architectural maturity, not a panic patch.
OpenAI shipped Lockdown Mode to ChatGPT this month. It doesn't stop prompt injection. It cuts the exfiltration path the injection needs to pay off, and that trust-boundary move is more honest than any detector.
Satya Nadella has shifted into a hands-on operating role at Microsoft. Read alongside the rest of this week's quiet release notes, it signals where the agent and AI infrastructure stack is consolidating, and which layer your agents will run on.
A wave of recent patches across major agent frameworks reveals an unexpected pattern: minor fixes are reshaping ecosystem trust and accelerating modular adoption.
Claude Managed Agents prices the harness at $0.08 per session-hour. The number is small. The structural shift it announces is not.
2026
First-party agent SDKs gain ground on frameworks
Lab-blessed SDKs (OpenAI Agents SDK, Google Agent Development Kit, Anthropic SDKs) drew momentum from the older model-agnostic frameworks (LangChain, CrewAI, AutoGen) as teams favored thinner, provider-native tooling.
OpenAI Agents SDK
OpenAI's first-party SDK for building agents on its models.
Google Agent Development Kit (ADK)
Google's SDK for building agents on the Gemini family.
The widely used model-agnostic agent/LLM framework.
A multi-agent orchestration framework.
Microsoft's multi-agent framework (alongside Semantic Kernel / Agent Framework).
Anthropic's official SDK for building on Claude.
Lock-in via first-party SDKs
A lab SDK is the smoothest path on one provider and the hardest to leave. Weigh portability before building deeply on it.
Framework abstraction overhead
Do-everything frameworks add layers that can obscure what is actually sent to the model — a debugging and cost-visibility cost.
Churn and breaking changes
The agent-SDK field moves fast and APIs break. Pin versions and budget for migration.
Tool / credential scope
However you build it, the agent acts with the credentials you give its tools. Scope them to the task — the SDK will not do that for you.
Choosing on hype, not fit
Star counts and launch buzz are poor proxies for fit. Match the SDK to your portability/integration bet, not the trend.
