LLMs with local computer access and tool‑use capabilities are systems that let language models perform actions on a user’s machine or via connected tools—reading and writing files, running code, invoking IDE commands, or orchestrating external services—rather than only returning text. This topic covers agent frameworks, marketplaces for agents and tools, self‑hosted/edge deployments, and in‑IDE assistants that combine model reasoning with deterministic tool calls. Relevance in 2026 stems from three converging trends: wider adoption of agentic workflows in developer and enterprise contexts; demand for local‑first and self‑hosted deployments to reduce data exposure; and maturation of tool ecosystems and governance controls. Frameworks such as LangChain provide engineering primitives and stateful orchestration (e.g., LangGraph) for building, testing, and deploying reliable agents. No‑code/low‑code platforms like StackAI target enterprise automation and governance, while infrastructure vendors (Xilos, Mistral AI, IBM watsonx Assistant) emphasize visibility, model governance, and privacy for production use. Developer‑focused tools show how local access is applied: Tabby is an open‑source, self‑hosted coding assistant with IDE extensions and model serving; Windsurf (formerly Codeium) and JetBrains AI Assistant embed agentic features into IDEs for in‑context edits and live previews. Open code models—StarCoder, Code Llama, Salesforce CodeT5, and instruction families such as WizardLM—power local or hosted tool chains for code generation, completion, and instruction following. Expect ongoing tradeoffs: local performance and privacy versus centralized management and updates; multi‑model orchestration and tool marketplaces versus tighter governance requirements. The ecosystem now centers on composable agent frameworks, model choices tuned for code or instruction following, and deployment options that balance usability, security, and regulatory compliance.