LLM developer tooling and SDKs covers the software libraries, agent frameworks, IDE assistants, testing platforms, and marketplaces that let engineers turn large language models into reliable production services. This space spans AI code assistants (Copilot, Tabnine, JetBrains AI Assistant, Windsurf), engineering frameworks for agentic apps (LangChain with LangGraph state, AutoGPT, GPTConsole), model options for code tasks (Code Llama, Salesforce CodeT5), and platforms for hosting, data, testing and distribution (Replit, Amazon CodeWhisperer/Amazon Q Developer, AI agent marketplaces). Relevance and timing (2026-05-24): vendor updates to core models and developer APIs—including recent major releases and expanded toolkits from leading model providers—have accelerated multi‑model orchestration, stateful agent patterns, and SDKs that embed memory, testing and governance. As teams move from prototypes to production, priorities have shifted toward reproducible evaluation (GenAI test automation), private/self‑hosted and governed deployments (Tabnine, enterprise Copilot setups), integrated IDE workflows (Windsurf, JetBrains, GitHub Copilot), and standardized lifecycles for agent building and monetization (GPTConsole, agent marketplaces). What to expect: compare frameworks by abstraction (low‑level SDK vs opinionated agent runtime), by testing and CI support (automated scenario tests, regression checks), and by deployment model (cloud API, self‑hosted runtime, or hybrid). Toolchains increasingly combine code‑specialized models (Code Llama, CodeT5), in‑IDE copilots, and orchestration layers to handle memory, observability, and safety. For engineers evaluating LLM tooling, the key tradeoffs are control and governance versus speed of integration, multi‑model flexibility, and built‑in testing and lifecycle management.