# Hermes Agent Features and Practical Usage 🚀 More than remembering facts, this memory continuously infers who you are from conversation. The result is an agent that understands you, getting more on-target the longer you use it. 📌 Title and Feature URL Title: Honcho Memory URL: 📝 Overview Honcho is an AI-native memory backend that goes beyond simple key-value storage. After each turn it performs "dialectic reasoning" to automatically derive insights about your preferences, communication style, goals, and behavioral patterns, building a user model that deepens over time. 🔧 How It Works ・Dialectic reasoning is multi-pass analysis: Pass 0 produces an initial assessment, Pass 1 self-audits for gaps, and Pass 2 reconciles contradictions into a final synthesis (depth 1-3). ・New users get cold-start queries probing general preferences and goals; returning users get warm-session queries prioritizing current context. ・Where built-in memory is manually curated static facts, Honcho maintains server-side profiles with automatic reasoning, enabling semantic search over conclusions and multi-agent peer isolation. ・Five tools integrate: honcho_profile (read/update peer identity cards), honcho_search (semantic search over memory and conclusions), honcho_context (retrieve session context with summaries), honcho_reasoning (synthesized reasoning at a chosen depth), and honcho_conclude (create/delete conclusions, useful for PII management). 🛠 Practical Usage ・Run `hermes memory setup honcho` for guided setup; config is written to `~/.honcho/config.json` (global) or `$HERMES_HOME/honcho.json` (per-profile). ・Key config keys: `contextCadence` (base-context refresh interval), `dialecticCadence` (LLM reasoning interval), `dialecticDepth` (multi-pass depth), `recallMode` (hybrid / context / tools), `writeFrequency` (async / turn / session), plus `apiKey` / `peerName` / `aiPeer` / `workspace`. ・In the default hybrid mode, base context and dialectic supplements inject into the system prompt automatically while tools stay available. ・Once Honcho is active, subcommands like `hermes honcho status` become available. 🎯 Use Cases ・As a long-term assistant, track a user's working preferences across months. ・Keep a coding assistant and a personal assistant talking to the same user with independent models, preventing context bleed. ・Reduce repetitive topic recaps and raise suggestion accuracy via session-scoped injection. ⚠️ Caveats ・Cost scales with reasoning depth (depth 2-3 means more LLM calls; balance via cadence settings). ・New peers need a background prewarm; if it doesn't complete in time, a bounded synchronous fallback applies. ・In recallMode, tools mode gives the agent control but needs explicit reasoning calls, while context mode hides tools and reduces flexibility. State is server-side, so migrating from file memory requires a data export. #HermesAgent# #Memory#