Synap is a context management and memory layer built for AI agents. It helps agents retain, retrieve, and manage relevant context across conversations and workflows, enabling more accurate and personalized interactions over time.

Designed for production-grade AI systems, Synap delivers low-latency memory retrieval while handling complex challenges such as entity resolution, temporal relevance, contradiction detection, and intelligent forgetting. This ensures agents remain contextually aware without accumulating outdated or conflicting information.

Synap integrates with 18 leading agent frameworks and adapts its memory architecture to different use cases, including voice AI, customer support, copilots, and autonomous agents. By abstracting away the complexity of memory engineering, Synap allows teams to build and deploy reliable AI agents faster, without spending months designing retrieval pipelines and context management systems.