Honcho Memory

Honcho is an AI-native memory system that gives Hermes Agent persistent, cross-session understanding of users. While Hermes has built-in memory (MEMORY.md and USER.md files), Honcho adds a deeper layer of user modeling — learning user preferences, goals, communication style, and context across conversations.

How It Complements Built-in Memory

Hermes has two memory systems that work together:

Feature	Built-in Memory	Honcho Memory
Storage	Local files (~/.hermes/memories/)	Cloud-hosted Honcho API
Scope	Agent-level notes and user profile	Deep user modeling via dialectic reasoning
Persistence	Across sessions on same machine	Across sessions, machines, and platforms
Query	Injected into system prompt automatically	On-demand via query_user_context tool
Content	Manually curated by the agent	Automatically learned from conversations

Honcho doesn't replace built-in memory — it supplements it with richer user understanding.

Setup

1. Get a Honcho API Key

Sign up at app.honcho.dev and get your API key.

2. Install the Client Library

pip install honcho-ai

3. Configure Honcho

Honcho reads its configuration from ~/.honcho/config.json (the global Honcho config shared across all Honcho-enabled applications):

{
  "apiKey": "your-honcho-api-key",
  "workspace": "hermes",
  "peerName": "your-name",
  "aiPeer": "hermes",
  "environment": "production",
  "saveMessages": true,
  "sessionStrategy": "per-directory",
  "enabled": true
}

Alternatively, set the API key as an environment variable:

# Add to ~/.hermes/.env
HONCHO_API_KEY=your-honcho-api-key

info

When an API key is present (either in ~/.honcho/config.json or as HONCHO_API_KEY), Honcho auto-enables unless explicitly set to "enabled": false in the config.

Configuration Details

Global Config (~/.honcho/config.json)

Field	Default	Description
apiKey	—	Honcho API key (required)
workspace	"hermes"	Workspace identifier
peerName	(derived)	Your identity name for user modeling
aiPeer	"hermes"	AI assistant identity name
environment	"production"	Honcho environment
saveMessages	true	Whether to sync messages to Honcho
sessionStrategy	"per-directory"	How sessions are scoped
sessionPeerPrefix	false	Prefix session names with peer name
contextTokens	(Honcho default)	Max tokens for context prefetch
sessions	{}	Manual session name overrides per directory

Host-specific Configuration

You can configure per-host settings for multi-application setups:

{
  "apiKey": "your-key",
  "hosts": {
    "hermes": {
      "workspace": "my-workspace",
      "aiPeer": "hermes-assistant",
      "linkedHosts": ["other-app"],
      "contextTokens": 2000
    }
  }
}

Host-specific fields override global fields. Resolution order:

1. Explicit host block fields
2. Global/flat fields from config root
3. Defaults (host name used as workspace/peer)

Hermes Config (~/.hermes/config.yaml)

The honcho section in Hermes config is intentionally minimal — most configuration comes from the global ~/.honcho/config.json:

honcho: {}

The query_user_context Tool

When Honcho is active, Hermes gains access to the query_user_context tool. This lets the agent proactively ask Honcho about the user during conversations:

Tool schema:

• Name: query_user_context
• Parameter: query (string) — a natural language question about the user
• Toolset: honcho

Example queries the agent might make:

"What are this user's main goals?"
"What communication style does this user prefer?"
"What topics has this user discussed recently?"
"What is this user's technical expertise level?"

The tool calls Honcho's dialectic chat API to retrieve relevant user context based on accumulated conversation history.

note

The query_user_context tool is only available when Honcho is active (API key configured and session context set). It registers in the honcho toolset and its availability is checked dynamically.

Session Management

Honcho sessions track conversation history for user modeling:

• Session creation — sessions are created or resumed automatically based on session keys (e.g., telegram:123456 or CLI session IDs)
• Message syncing — new messages are synced to Honcho incrementally (only unsynced messages)
• Peer configuration — user messages are observed for learning; assistant messages are not
• Context prefetch — before responding, Hermes can prefetch user context (representation + peer card) in a single API call
• Session rotation — when sessions reset, old data is preserved in Honcho for continued user modeling

Migration from Local Memory

When Honcho is activated on an instance that already has local conversation history:

1. Conversation history — prior messages can be uploaded to Honcho as a transcript file
2. Memory files — existing MEMORY.md and USER.md files can be uploaded for context

This ensures Honcho has the full picture even when activated mid-conversation.

Use Cases

• Personalized responses — Honcho learns how each user prefers to communicate
• Goal tracking — remembers what users are working toward across sessions
• Expertise adaptation — adjusts technical depth based on user's background
• Cross-platform memory — same user understanding across CLI, Telegram, Discord, etc.
• Multi-user support — each user (via messaging platforms) gets their own user model