honcho skill

Honcho Memory for Hermes

Honcho provides AI-native cross-session user modeling. It learns who the user is across conversations and gives every Hermes profile its own peer identity while sharing a unified view of the user.

When to Use

• Setting up Honcho (cloud or self-hosted)
• Troubleshooting memory not working / peers not syncing
• Creating multi-profile setups where each agent has its own Honcho peer
• Tuning observation, recall, dialectic depth, or write frequency settings
• Understanding what the 5 Honcho tools do and when to use them
• Configuring context budgets and session summary injection

Setup

Honcho is a plugin-catalog memory provider maintained by Plastic Labs. Install it once per machine (homes upgraded from a release that bundled Honcho get it automatically):

hermes plugins install honcho

Cloud (app.honcho.dev)

hermes memory setup honcho
# select "cloud", paste API key from https://app.honcho.dev

Self-hosted

hermes memory setup honcho
# select "local", enter base URL (e.g. http://localhost:8000)

See: https://docs.honcho.dev/v3/guides/integrations/hermes#running-honcho-locally-with-hermes

Verify

hermes honcho status    # shows resolved config, connection test, peer info

Architecture

Base Context Injection

When Honcho injects context into the system prompt (in hybrid or context recall modes), it assembles the base context block in this order:

1. Session summary -- a short digest of the current session so far (placed first so the model has immediate conversational continuity)
2. User representation -- Honcho's accumulated model of the user (preferences, facts, patterns)
3. AI peer card -- the identity card for this Hermes profile's AI peer

The session summary is generated automatically by Honcho at the start of each turn (when a prior session exists). It gives the model a warm start without replaying full history.

Cold / Warm Prompt Selection

Honcho automatically selects between two prompt strategies:

You do not need to configure this -- it is automatic based on session state.

Peers

Honcho models conversations as interactions between peers. Hermes creates two peers per session:

• User peer (peerName): represents the human. Honcho builds a user representation from observed messages.
• AI peer (aiPeer): represents this Hermes instance. Each profile gets its own AI peer so agents develop independent views.

Observation

Each peer has two observation toggles that control what Honcho learns from:

Default: all four toggles on (full bidirectional observation).

Configure per-peer in honcho.json:

{
  "observation": {
    "user": { "observeMe": true, "observeOthers": true },
    "ai":   { "observeMe": true, "observeOthers": true }
  }
}

Or use the shorthand presets:

Settings changed in the Honcho dashboard are synced back on session init -- server-side config wins over local defaults.

Sessions

Honcho sessions scope where messages and observations land. Strategy options:

Manual override: hermes honcho map my-project-name

Recall Modes

How the agent accesses Honcho memory:

Three Orthogonal Knobs

Honcho's dialectic behavior is controlled by three independent dimensions. Each can be tuned without affecting the others:

Cadence (when)

Controls how often dialectic and context calls happen.

Higher cadence values fire the dialectic LLM less often. dialecticCadence: 2 means the engine fires every other turn. Setting it to 1 fires every turn.

Depth (how many)

Controls how many rounds of dialectic reasoning Honcho performs per query.

dialecticDepth: 2 means Honcho runs two rounds of dialectic synthesis. The first round produces an initial answer; the second refines it.

dialecticDepthLevels lets you set the reasoning level for each round independently:

{
  "dialecticDepth": 3,
  "dialecticDepthLevels": ["low", "medium", "high"]
}

If dialecticDepthLevels is omitted, rounds use proportional levels derived from dialecticReasoningLevel (the base):

This keeps earlier passes cheap while using full depth on the final synthesis.

Depth at session start. The session-start prewarm runs the full configured dialecticDepth in the background before turn 1. A single-pass prewarm on a cold peer often returns thin output — multi-pass depth runs the audit/reconcile cycle before the user ever speaks. Turn 1 consumes the prewarm result directly; if prewarm hasn't landed in time, turn 1 falls back to a synchronous call with a bounded timeout.

Level (how hard)

Controls the intensity of each dialectic reasoning round.

Higher levels produce richer synthesis but cost more tokens on Honcho's backend.

Multi-Profile Setup

Each Hermes profile gets its own Honcho AI peer while sharing the same workspace (user context). This means:

• All profiles see the same user representation
• Each profile builds its own AI identity and observations
• Conclusions written by one profile are visible to others via the shared workspace

Create a profile with Honcho peer

hermes profile create coder --clone
# creates host block hermes.coder, AI peer "coder", inherits config from default

What --clone does for Honcho:

1. Creates a hermes.coder host block in honcho.json
2. Sets aiPeer: "coder" (the profile name)
3. Inherits workspace, peerName, writeFrequency, recallMode, etc. from default
4. Eagerly creates the peer in Honcho so it exists before first message