Spacebar - 2026 Pricing, Features, Reviews & Alternatives

Spacebar is a cloud-based, full-duplex multiplayer runtime substrate that places AI agents and human participants on a single synchronized surface spanning canvas, audio, vision, live browser, and an immutable event stream. Agents perceive audio, visual context, and canvas state simultaneously and continuously rather than in discrete prompt-response turns, and they participate over WebRTC as first-class members of a Space alongside humans. Chrome and Firefox extensions deliver the agent as a sidebar within the user's existing browser profile, preserving authenticated sessions and cookies without requiring an environment switch. Bring-your-own model configurations are supported, with on-premises model weights remaining on the operator's hardware.

The platform offers two agent modes: real-time agents that join live sessions and always-on agents that operate between sessions on triggers, returning to a Space with work already laid out. Full-duplex audio processing separates per-participant streams and transcribes speech in real time through a pluggable provider, while canvas board images are sampled at speaking-activity cadence and piped into every model iteration. The shared browser gives agents continuous access to the live DOM along with behavioral signals such as hover, scroll, and hesitation patterns rather than static screenshots. A two-tier persistent session memory, comprising a canvas, event log, and memory store, survives session boundaries to support long-horizon task continuity. Every create, update, and delete action is captured in an immutable mutation log, making the platform suited to enterprise and regulated industry use cases requiring auditable event streams.

Spacebar targets teams running ongoing, multi-party, browser-grounded, or visually structured AI workflows, including enterprise and regulated organizations and AI research labs requiring high-quality multimodal human-agent training data. The shared browser captures natural interaction patterns, including voice, canvas activity, browser behavior, and tool use, producing high-realism trajectory data for frontier model training programs. All agent tool calls route through the same authorization layer as human actions, bounding agent permissions to those of the user the agent represents.