OpenClaw 2026.3.11 release adds local-first Ollama setup, unified OpenCode keys, and multimodal memory

Local-first Ollama becomes first-class experience

The 2026.3.11 release adds first-class Ollama setup with Local or Cloud + Local modes. The onboarding flow now includes browser-based cloud sign-in, curated model suggestions, and cloud-model handling that skips unnecessary local pulls.

What this means: You can bootstrap a local-only or hybrid Ollama agent directly from the onboarding wizard instead of hand-editing configs. The system suggests good-default models for coding, planning, and other tasks, eliminating guesswork about which models to run locally. When using cloud-only models, it skips unnecessary local pulls to keep your disk cleaner.

Use cases: Build a local-only coding assistant that runs entirely on your machine without cloud-key juggling. Create template "local-first agent" setups that others can import and reuse for privacy-heavy or cost-conscious workflows.

OpenCode Zen and Go now share one key with different roles

The release adds a new OpenCode Go provider while treating Zen and Go as one OpenCode setup in the wizard and documentation. The system stores one shared OpenCode key but keeps runtime providers split, and stops overriding built-in opencode-go routing.

What this means: You can use one OpenCode key for both Zen and Go models, then route tasks by purpose instead of splitting keys. Zen can serve as your "fast coder" model while Go handles heavier planning or long-context runs.

Use cases: Document a "Zen-for-code / Go-for-planning" pattern that others can copy-paste as a config snippet. Share OpenCode-based agent profiles that explicitly specify "use Zen for X, Go for Y" to prevent confusion for new users.

Images and audio become searchable "working memory"

The memory system now supports opt-in multimodal image and audio indexing for memorySearch.extraPaths using Gemini gemini-embedding-2-preview. Features include strict fallback gating and scope-based reindexing.

The Gemini integration specifically adds gemini-embedding-2-preview memory-search support with configurable output dimensions and automatic reindexing when dimensions change.

What this means: You can index images and audio into OpenClaw's memory, allowing agents to search them alongside text notes. The system uses gemini-embedding-2-preview with config-based dimensions and reindexing when dimensions are tweaked.

Use cases: Drop screenshots of UI errors, flow diagrams, or design comps into a folder, let OpenClaw index them, and ask questions like "What's wrong in this error?" or "Find similar past UI issues." Use recorded calls, standups, or training sessions as a searchable archive for queries like "When did we talk about feature X?" or "Summarize last month's planning meetings." Pair this with local-only models for privacy-heavy, on-device indexing instead of sending everything to the cloud.

macOS UI improvements

The macOS chat UI now includes a chat model picker, persists explicit thinking-level selections across relaunch, and hardens provider-aware session model sync for the shared chat composer.

What this means: You can pick your model directly in the macOS chat UI instead of guessing which config is active. Your chosen thinking-level (e.g., verbose/compact reasoning) persists across application relaunches.