Configure and troubleshoot Honcho memory for Mibyan.

Skill metadata


Source	Optional — install with `mibyan skills install official/autonomous-ai-agents/honcho`
Path	`optional-skills/autonomous-ai-agents/honcho`
Version	`2.0.0`
Author	Mibyan
License	MIT
Platforms	linux, macos, windows
Tags	`Honcho`, `Memory`, `Profiles`, `Observation`, `Dialectic`, `User-Modeling`, `Session-Summary`
Related skills	`mibyan-agent`

Reference: full SKILL.md

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

Honcho Memory for Mibyan

Honcho provides AI-native cross-session user modeling. It learns who the user is across conversations and gives every Mibyan 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

Cloud (app.honcho.dev)

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

Self-hosted

mibyan 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

mibyan 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:

Session summary — a short digest of the current session so far (placed first so the model has immediate conversational continuity)
User representation — Honcho’s accumulated model of the user (preferences, facts, patterns)
AI peer card — the identity card for this Mibyan 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:

Condition	Strategy	What happens
No prior session or empty representation	Cold start	Lightweight intro prompt; skips summary injection; encourages the model to learn about the user
Existing representation and/or session history	Warm start	Full base context injection (summary → representation → card); richer system prompt

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

Peers

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

User peer (peerName): represents the human. Honcho builds a user representation from observed messages.
AI peer (aiPeer): represents this Mibyan 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:

Toggle	What it does
`observeMe`	Peer’s own messages are observed (builds self-representation)
`observeOthers`	Other peers’ messages are observed (builds cross-peer understanding)

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:

Preset	User	AI	Use case
`"directional"` (default)	me:on, others:on	me:on, others:on	Multi-agent, full memory
`"unified"`	me:on, others:off	me:off, others:on	Single agent, user-only modeling

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:

Strategy	Behavior
`per-directory` (default)	One session per working directory
`per-repo`	One session per git repository root
`per-session`	New Honcho session each Mibyan run
`global`	Single session across all directories

Manual override: mibyan honcho map my-project-name

Recall Modes

How the agent accesses Honcho memory:

Mode	Auto-inject context?	Tools available?	Use case
`hybrid` (default)	Yes	Yes	Agent decides when to use tools vs auto context
`context`	Yes	No (hidden)	Minimal token cost, no tool calls
`tools`	No	Yes	Agent controls all memory access explicitly

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.

Key	Default	Description
`contextCadence`	`1`	Min turns between context API calls
`dialecticCadence`	`2`	Min turns between dialectic API calls. Recommended 1–5
`injectionFrequency`	`every-turn`	`every-turn` or `first-turn` for base context injection

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.

Key	Default	Range	Description
`dialecticDepth`	`1`	1-3	Number of dialectic reasoning rounds per query
`dialecticDepthLevels`	—	array	Optional per-depth-round level overrides (see below)

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):

Depth	Pass levels
1	[base]
2	[minimal, base]
3	[minimal, base, low]

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.

Key	Default	Description
`dialecticReasoningLevel`	`low`	`minimal`, `low`, `medium`, `high`, `max`
`dialecticDynamic`	`true`	When `true`, the model can pass `reasoning_level` to `honcho_reasoning` to override the default per-call. `false` = always use `dialecticReasoningLevel`, model overrides ignored

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

Multi-Profile Setup

Each Mibyan 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

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

What --clone does for Honcho:

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

Backfill existing profiles

mibyan 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": {
    "mibyan.coder": {
      "aiPeer": "coder",
      "recallMode": "tools",
      "dialecticDepth": 2,
      "observation": {
        "user": { "observeMe": true, "observeOthers": false },
        "ai": { "observeMe": true, "observeOthers": true }
      }
    }
  }
}

Tools

The agent has 5 bidirectional Honcho tools (hidden in context recall mode):

Tool	LLM call?	Cost	Use when
`honcho_profile`	No	minimal	Quick factual snapshot at conversation start or for fast name/role/pref lookups
`honcho_search`	No	low	Fetch specific past facts to reason over yourself — raw excerpts, no synthesis
`honcho_context`	No	low	Full session context snapshot: summary, representation, card, recent messages
`honcho_reasoning`	Yes	medium–high	Natural language question synthesized by Honcho’s dialectic engine
`honcho_conclude`	No	minimal	Write or delete a persistent fact; pass `peer: "ai"` for AI self-knowledge

`honcho_profile`

Read or update a peer card — curated key facts (name, role, preferences, communication style). Pass card: [...] to update; omit to read. No LLM call.

`honcho_search`

Semantic search over stored context for a specific peer. Returns raw excerpts ranked by relevance, no synthesis. Default 800 tokens, max 2000. Good when you need specific past facts to reason over yourself rather than a synthesized answer.

`honcho_context`

Full session context snapshot from Honcho — session summary, peer representation, peer card, and recent messages. No LLM call. Use when you want to see everything Honcho knows about the current session and peer in one shot.

`honcho_reasoning`

Natural language question answered by Honcho’s dialectic reasoning engine (LLM call on Honcho’s backend). Higher cost, higher quality. Pass reasoning_level to control depth: minimal (fast/cheap) → low → medium → high → max (thorough). Omit to use the configured default (low). Use for synthesized understanding of the user’s patterns, goals, or current state.

`honcho_conclude`

Write or delete a persistent conclusion about a peer. Pass conclusion: "..." to create. Pass delete_id: "..." to remove a conclusion (for PII removal — Honcho self-heals incorrect conclusions over time, so deletion is only needed for PII). You MUST pass exactly one of the two.

Bidirectional peer targeting

All 5 tools accept an optional peer parameter:

peer: "user" (default) — operates on the user peer
peer: "ai" — operates on this profile’s AI peer
peer: "<explicit-id>" — any peer ID in the workspace

Examples:

honcho_profile                        # read user's card
honcho_profile peer="ai"              # read AI peer's card
honcho_reasoning query="What does this user care about most?"
honcho_reasoning query="What are my interaction patterns?" peer="ai" reasoning_level="medium"
honcho_conclude conclusion="Prefers terse answers"
honcho_conclude conclusion="I tend to over-explain code" peer="ai"
honcho_conclude delete_id="abc123"    # PII removal

Agent Usage Patterns

Guidelines for Mibyan when Honcho memory is active.

On conversation start

honcho_profile                  → fast warmup, no LLM cost
If context looks thin → honcho_context  (full snapshot, still no LLM)
If deep synthesis needed → honcho_reasoning  (LLM call, use sparingly)

Do NOT call honcho_reasoning on every turn. Auto-injection already handles ongoing context refresh. Use the reasoning tool only when you genuinely need synthesized insight the base context doesn’t provide.

When the user shares something to remember

honcho_conclude conclusion="<specific, actionable fact>"

Good conclusions: “Prefers code examples over prose explanations”, “Working on a Rust async project through April 2026” Bad conclusions: “User said something about Rust” (too vague), “User seems technical” (already in representation)

When the user asks about past context / you need to recall specifics

honcho_search query="<topic>"       → fast, no LLM, good for specific facts
honcho_context                       → full snapshot with summary + messages
honcho_reasoning query="<question>"  → synthesized answer, use when search isn't enough

When to use `peer: "ai"`

Use AI peer targeting to build and query the agent’s own self-knowledge:

honcho_conclude conclusion="I tend to be verbose when explaining architecture" peer="ai" — self-correction
honcho_reasoning query="How do I typically handle ambiguous requests?" peer="ai" — self-audit
honcho_profile peer="ai" — review own identity card

When NOT to call tools

In hybrid and context modes, base context (user representation + card + session summary) is auto-injected before every turn. Do not re-fetch what was already injected. Call tools only when:

You need something the injected context doesn’t have
The user explicitly asks you to recall or check memory
You’re writing a conclusion about something new

Cadence awareness

honcho_reasoning on the tool side shares the same cost as auto-injection dialectic. After an explicit tool call, the auto-injection cadence resets — avoiding double-charging the same turn.

Config Reference

Config file: $mibyan_HOME/honcho.json (profile-local) or ~/.honcho/config.json (global).

Key settings

Key	Default	Description
`apiKey`	—	API key (get one)
`baseUrl`	—	Base URL for self-hosted Honcho
`peerName`	—	User peer identity
`aiPeer`	host key	AI peer identity
`workspace`	host key	Shared workspace ID
`recallMode`	`hybrid`	`hybrid`, `context`, or `tools`
`observation`	all on	Per-peer `observeMe`/`observeOthers` booleans
`writeFrequency`	`async`	`async`, `turn`, `session`, or integer N
`sessionStrategy`	`per-directory`	`per-directory`, `per-repo`, `per-session`, `global`
`messageMaxChars`	`25000`	Max chars per message (chunked if exceeded)

Dialectic settings

Key	Default	Description
`dialecticReasoningLevel`	`low`	`minimal`, `low`, `medium`, `high`, `max`
`dialecticDynamic`	`true`	Auto-bump reasoning by query complexity. `false` = fixed level
`dialecticDepth`	`1`	Number of dialectic rounds per query (1-3)
`dialecticDepthLevels`	—	Optional array of per-round levels, e.g. `["low", "high"]`
`dialecticMaxInputChars`	`10000`	Max chars for dialectic query input

Context budget and injection

Key	Default	Description
`contextTokens`	uncapped	Max tokens for the combined base context injection (summary + representation + card). Opt-in cap — omit to leave uncapped, set to an integer to bound injection size.
`injectionFrequency`	`every-turn`	`every-turn` or `first-turn`
`contextCadence`	`1`	Min turns between context API calls
`dialecticCadence`	`2`	Min turns between dialectic LLM calls (recommended 1–5)

The contextTokens budget is enforced at injection time. If the session summary + representation + card exceed the budget, Honcho trims the summary first, then the representation, preserving the card. This prevents context blowup in long sessions.

Memory-context sanitization

Honcho sanitizes the memory-context block before injection to prevent prompt injection and malformed content:

Strips XML/HTML tags from user-authored conclusions
Normalizes whitespace and control characters
Truncates individual conclusions that exceed messageMaxChars
Escapes delimiter sequences that could break the system prompt structure

This fix addresses edge cases where raw user conclusions containing markup or special characters could corrupt the injected context block.

Troubleshooting

”Honcho not configured”

Run mibyan honcho setup. Ensure memory.provider: honcho is in ~/.mibyan/config.yaml.

Memory not persisting across sessions

Check mibyan honcho status — verify saveMessages: true and writeFrequency isn’t session (which only writes on exit).

Profile not getting its own peer

Use --clone when creating: mibyan profile create <name> --clone. For existing profiles: mibyan 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 (default 25k) are automatically chunked with [continued] markers. If you’re hitting this often, check if tool results or skill content is inflating message size.

Context injection too large

If you see warnings about context budget exceeded, lower contextTokens or reduce dialecticDepth. The session summary is trimmed first when the budget is tight.

Session summary missing

Session summary requires at least one prior turn in the current Honcho session. On cold start (new session, no history), the summary is omitted and Honcho uses the cold-start prompt strategy instead.

CLI Commands

Command	Description
`mibyan honcho setup`	Interactive setup wizard (cloud/local, identity, observation, recall, sessions)
`mibyan honcho status`	Show resolved config, connection test, peer info for active profile
`mibyan honcho enable`	Enable Honcho for the active profile (creates host block if needed)
`mibyan honcho disable`	Disable Honcho for the active profile
`mibyan honcho peer`	Show or update peer names (`--user <name>`, `--ai <name>`, `--reasoning <level>`)
`mibyan honcho peers`	Show peer identities across all profiles
`mibyan honcho mode`	Show or set recall mode (`hybrid`, `context`, `tools`)
`mibyan honcho tokens`	Show or set token budgets (`--context <N>`, `--dialectic <N>`)
`mibyan honcho sessions`	List known directory-to-session-name mappings
`mibyan honcho map <name>`	Map current working directory to a Honcho session name
`mibyan honcho identity`	Seed AI peer identity or show both peer representations
`mibyan honcho sync`	Create host blocks for all Mibyan profiles that don’t have one yet
`mibyan honcho migrate`	Step-by-step migration guide from OpenClaw native memory to Mibyan + Honcho
`mibyan memory setup`	Generic memory provider picker (selecting “honcho” runs the same wizard)
`mibyan memory status`	Show active memory provider and config
`mibyan memory off`	Disable external memory provider

​Skill metadata

​Reference: full SKILL.md

​Honcho Memory for Mibyan

​When to Use

​Setup

​Cloud (app.honcho.dev)

​Self-hosted

​Verify

​Architecture

​Base Context Injection

​Cold / Warm Prompt Selection

​Peers

​Observation

​Sessions

​Recall Modes

​Three Orthogonal Knobs

​Cadence (when)

​Depth (how many)

​Level (how hard)

​Multi-Profile Setup

​Create a profile with Honcho peer

​Backfill existing profiles

​Per-profile config

​Tools

​honcho_profile

​honcho_search

​honcho_context

​honcho_reasoning

​honcho_conclude

​Bidirectional peer targeting

​Agent Usage Patterns

​On conversation start

​When the user shares something to remember

​When the user asks about past context / you need to recall specifics

​When to use peer: "ai"

​When NOT to call tools

​Cadence awareness

​Config Reference

​Key settings

​Dialectic settings

​Context budget and injection

​Memory-context sanitization

​Troubleshooting

​”Honcho not configured”

​Memory not persisting across sessions

​Profile not getting its own peer

​Observation changes in dashboard not reflected

​Messages truncated

​Context injection too large

​Session summary missing

​CLI Commands

Skill metadata

Reference: full SKILL.md

Honcho Memory for Mibyan

When to Use

Setup

Cloud (app.honcho.dev)

Self-hosted

Verify

Architecture

Base Context Injection

Cold / Warm Prompt Selection

Peers

Observation

Sessions

Recall Modes

Three Orthogonal Knobs

Cadence (when)

Depth (how many)

Level (how hard)

Multi-Profile Setup

Create a profile with Honcho peer

Backfill existing profiles

Per-profile config

Tools

`honcho_profile`

`honcho_search`

`honcho_context`

`honcho_reasoning`

`honcho_conclude`

Bidirectional peer targeting

Agent Usage Patterns

On conversation start

When the user shares something to remember

When the user asks about past context / you need to recall specifics

When to use `peer: "ai"`

When NOT to call tools

Cadence awareness

Config Reference

Key settings

Dialectic settings

Context budget and injection

Memory-context sanitization

Troubleshooting

”Honcho not configured”

Memory not persisting across sessions

Profile not getting its own peer

Observation changes in dashboard not reflected

Messages truncated

Context injection too large

Session summary missing

CLI Commands