Introduction
Runtime Labs is building the temporal runtime layer for intelligent systems — where plans, memory, and agent behavior are grounded in time. Our infrastructure transforms calendars into programmable execution environments that support planning, feedback, and learning over time.
We enable agents to act in alignment with real-world constraints, commitments, and context—by embedding structured memory and scheduling directly into the interface between users and models.
To get started, head to the Quickstart Guide.
Time remains a missing primitive in modern model architectures. Runtime Labs addresses this by introducing time as a first-class interface:
- Memory traces encode events, reflections, and tasks
- Calendars serve as the medium for agent scheduling and feedback
- Agent runtimes manage planning, execution, and adaptation
We’re developing a modular stack that brings these components together into a coherent system.
Chronologue: Entry Point to the Runtime
Chronologue connects natural language interfaces to calendar-based scheduling. It allows users to prompt agents, create structured events, and reflect on past actions through:
- Memory Traces — JSON records of plans, reflections, and goals
- Calendar Integration —
.icssync with Google and Apple calendars - Promptable Interface — Interact via chat, tables, or calendar views
- Agent Control — Steerable agents that act only with user approval
Use Chronologue to:
- Generate and update calendar events with natural language
- Retrieve past memory traces to reflect or reschedule
- Sync plans and feedback into external calendars
- Maintain agency over what agents propose, execute, and learn
Principles
Runtime Labs is built on three principles:
- Temporal grounding: Intelligence must align with real-world time, commitments, and durations.
- User autonomy: Users remain in control—agents propose, users approve.
- Memory continuity: Long-term reasoning requires persistent, structured memory traces.
This philosophy informs both our UX and system architecture, aligning language models with real-world workflows.