Acontext

Detailed Introduction

Acontext is a context data platform for self-learning agents that centralizes session context, task observations, and artifacts. It captures agent task traces and user feedback, distills experiences into long-term memory, and provides a local dashboard and CLI for developers to build an observation-and-learning loop. See the official documentation at Acontext Docs .

Main Features

• Structured context storage: hierarchical Session, Space, and Artifact models for easy retrieval and management.
• Observability & metrics: task traces, success-rate dashboards, and diagnostic views for debugging agent behaviour.
• Experience distillation: converts SOPs and task outcomes into reusable skills and memories.
• Local and cloud deployment: acontext CLI, Docker presets and templates to speed up proofs-of-concept.

Use Cases

• Agent products: provide centralized context and memory storage to improve multi-agent coordination and success rates.
• R&D and testing: reproduce task flows locally, analyse failures, and iterate strategies quickly.
• Enterprise deployment: run in controlled networks to meet compliance and data governance requirements.
• Education & prototyping: serve as a foundation for building agent demos and teaching examples.

Technical Features

• Multi-language SDKs and templates: support for Go, Python, TypeScript integration templates.
• Extensible storage backends: disk and external object storage support for artifacts.
• Developer-friendly: example repositories, scaffolding templates, and comprehensive docs for integration.
• Open-source license: Apache-2.0 licensed for community adoption and contribution.