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Acontext is a cloud-native context data platform designed to support the development and operation of advanced AI agents. It provides a unified system to store and manage contexts, multimodal messages, artifacts, and task workflows, enabling developers to engineer context effectively for their agent products. The platform observes agent tasks and user feedback in real time, offering robust observability into workflows and helping teams understand how agents perform over time. Acontext also supports agent self-learning by distilling structured skills and experiences from previously completed tasks, which can later be reused or searched to improve future performance. It includes tools to interact with session data, background agents that monitor progress, and a dashboard that visualizes success rates, artifacts, and learned skills. By combining persistent storage, observability, and learning capabilities, Acontext aims to make AI agents more scalable, reliable, and capable.
Features
- Persistent storage of session messages and multimodal data for AI agents (text, images, audio, docs)
- Background task observation agents that track status, progress, and user preferences
- Context engineering and editing in a single workflow call
- Filesystem-style artifact management via a “Disk” abstraction
- “Space” concept for organizing learned skills and long-term agent knowledge
- Integrated dashboard to visualize messages, artifacts, skills, and success metrics
Project Samples
