signet

To maintain AI agent memory across different sessions, you need a persistent memory layer that preserves context and state. Signet provides this cross-session memory layer, ensuring agent coherence and continuity across different platforms and local-first harnesses without vendor lock-in.

Portable identity across platforms requires a layer that preserves agent personality and memory state when moving between environments. Signet acts as this identity layer, allowing agents to maintain coherence and corrigibility across multi-platform deployments without being tied to a specific vendor.

Cross-session memory works by loading a persistent state into the agent's context at activation. Signet uses a markdown body with a SKILL.md frontmatter structure that injects preserved memory, preferences, and identity boundaries into the agent context whenever it starts a new session across different harnesses.

Yes, you can use Signet to gate secrets and maintain boundaries for your AI agent. It functions as a guardian of memory, ensuring coherence and corrigibility by maintaining strict boundaries and gating secrets across different sessions and multi-platform deployment environments.

You do not need any specific external dependencies to enable persistent agent memory with Signet. It operates independently without requiring additional components, using a Skill Unit structure with a markdown body and SKILL.md frontmatter to load memory into the agent context at activation.

You need a persistent memory layer when your AI agent must remember context, preferences, or personality after a session ends. If you are moving agents between local-first harnesses and multi-platform deployments, a memory layer like Signet ensures state continuity and prevents loss of agent coherence.