honcho

Cross-session user modeling maintains a unified user identity across conversations by generating session summaries and learning from past interactions. This approach ensures continuous context retention and improves system recall for future user interactions.

Multi-profile peer isolation ensures each AI profile maintains its own independent memory. This prevents context bleeding between profiles, improving the user experience by keeping interaction data and learned behaviors strictly separated per profile.

To set up memory management in AI systems like Hermes, run the command `hermes memory setup honcho`. This command configures cross-session user modeling and context retention capabilities directly within your existing AI framework environment.

Yes, the honcho-ai memory management library supports Linux, macOS, and Windows platforms. It requires the honcho-ai dependency to enable cross-session user modeling and peer isolation across these supported operating systems.

Observation configuration allows control over what the system learns from user interactions. This feature manages memory retention by filtering which interaction data contributes to the user modeling and dialectic reasoning processes.

Dialectic reasoning facilitates complex reasoning and decision-making processes within user modeling. By evaluating different perspectives, it enhances the AI system's ability to generate accurate session summaries and maintain robust context retention across sessions.