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honcho

Manage and configure Honcho memory for AI agents using Hermes.

Updated May 11, 2026
Search Tags:#AI#memory management#Hermes#user modeling#Honcho
One-click install
npx skills add https://github.com/richardnguyen0715/keep-it-real --skill honcho-richardnguyen0715
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: honcho
Source: https://github.com/richardnguyen0715/keep-it-real/tree/main/refer-projects/hermes-agent/optional-skills/autonomous-ai-agents/honcho
Command: npx skills add https://github.com/richardnguyen0715/keep-it-real --skill honcho-richardnguyen0715

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires honcho-ai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of managing complex AI agent memory and profiling using Honcho with Hermes, providing tools for cross-session user modeling, multi-profile peer isolation, and advanced reasoning capabilities.

Core Features & Use Cases

  • Cross-Session User Modeling: Leverage Honcho for AI-native cross-session user modeling, maintaining a unified view of the user across conversations.
  • Multi-Profile Setup: Create and manage multi-profile setups where each agent has its own Honcho peer, with independent views and shared user context.
  • Observation & Recall: Control observation settings and recall modes for AI peers, optimizing memory access and context injection.
  • Dialectic Reasoning: Utilize Honcho's dialectic reasoning for synthesized understanding of user patterns and goals.
  • Tools & Configuration: Access a suite of Honcho tools and configuration options for profiling, including peer identity, observation, and context budgets.

Quick Start

Run the command 'hermes honcho setup' to configure Honcho memory for your Hermes agent.

Frequently Asked Questions about honcho

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up cross-session user modeling for AI agents?▼

Cross-session user modeling is configured by running 'hermes honcho setup' to initialize Honcho memory, which maintains a unified view of the user across multiple conversations using Hermes.

Can I manage multiple AI agent profiles with isolated memory contexts?▼

Yes, you can create multi-profile setups where each AI agent operates as an independent Honcho peer, maintaining isolated views while sharing a unified user context for targeted memory recall.

How does dialectic reasoning work for AI agent profiling?▼

Dialectic reasoning in Honcho synthesizes user patterns and goals by processing cross-session observations, enabling AI agents to form a deeper understanding of user behavior through Hermes integration.

Do I need the honcho-ai package to use Honcho memory with Hermes?▼

Yes, the honcho-ai dependency and its required Python packages must be installed to enable API integration for Honcho memory management and Hermes agent configuration.

What are the limitations of controlling observation settings for AI peers?▼

Observation settings and recall modes are bounded by configured context budgets, meaning memory access and context injection for AI peers are optimized based on the specific limits defined during Honcho setup.

What is the best way to configure peer identity and context budgets in Honcho?▼

The best approach is using the suite of Honcho tools provided via the Hermes integration, allowing you to define peer identity, toggle observation settings, and allocate context budgets for each agent profile.

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