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, or write frequency settings
• Understanding what the 4 Honcho tools do and when to use them

Setup

Cloud (app.honcho.dev)

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

Self-hosted

hermes honcho setup
# 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

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:

observeMe

observeOthers

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:

"directional"

"unified"

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:

per-directory

per-repo

per-session

global

Manual override:

hermes honcho map my-project-name

Recall Modes

How the agent accesses Honcho memory:

hybrid

context

tools

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

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

Backfill existing profiles

hermes honcho sync    # creates host blocks for all profiles that don't have one yet

Per-profile config

Override any setting in the host block:

{
  "hosts": {
    "hermes.coder": {
      "aiPeer": "coder",
      "recallMode": "tools",
      "observation": {
        "user": { "observeMe": true, "observeOthers": false },
        "ai": { "observeMe": true, "observeOthers": true }
      }
    }
  }
}

Tools

The agent has 4 Honcho tools (hidden in

context

honcho_profile

Quick factual snapshot of the user -- name, role, preferences, patterns. No LLM call, minimal cost. Use at conversation start or for fast lookups.

honcho_search

Semantic search over stored context. Returns raw excerpts ranked by relevance, no LLM synthesis. Default 800 tokens, max 2000. Use when you want specific past facts to reason over yourself.

honcho_context

Natural language question answered by Honcho's dialectic reasoning (LLM call on Honcho's backend). Higher cost, higher quality. Can query about user (default) or the AI peer.

honcho_conclude

Write a persistent fact about the user. Conclusions build the user's profile over time. Use when the user states a preference, corrects you, or shares something to remember.

Config Reference

Config file:

$HERMES_HOME/honcho.json

~/.honcho/config.json

Key settings

apiKey

baseUrl

peerName

aiPeer

workspace

recallMode

hybrid

hybrid

context

tools

observation

observeMe

observeOthers

writeFrequency

async

async

turn

session

sessionStrategy

per-directory

per-directory

per-repo

per-session

global

dialecticReasoningLevel

low

minimal

low

medium

high

max

dialecticDynamic

true

false

messageMaxChars

25000

dialecticMaxInputChars

10000

Cost-awareness (advanced, root config only)

injectionFrequency

every-turn

every-turn

first-turn

contextCadence

1

dialecticCadence

1

Troubleshooting

"Honcho not configured"

Run

hermes honcho setup

memory.provider: honcho

~/.hermes/config.yaml

Memory not persisting across sessions

Check

hermes honcho status

saveMessages: true

writeFrequency

session

Profile not getting its own peer

Use

--clone

hermes profile create <name> --clone

hermes honcho sync

Observation changes in dashboard not reflected

Observation config is synced from the server on each session init. Start a new session after changing settings in the Honcho UI.

Messages truncated

Messages over

messageMaxChars

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CLI Commands

hermes honcho setup

hermes honcho status

hermes honcho enable

hermes honcho disable

hermes honcho peer

--user <name>

--ai <name>

--reasoning <level>

hermes honcho peers

hermes honcho mode

hybrid

context

tools

hermes honcho tokens

--context <N>

--dialectic <N>

hermes honcho sessions

hermes honcho map <name>

hermes honcho identity

hermes honcho sync

hermes honcho migrate

hermes memory setup

hermes memory status

hermes memory off