Configure and use Honcho memory with Mangaba -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation, recall, and dialectic settings.
Configure and use Honcho memory with Mangaba -- cross-session user modeling, multi-profile peer isolation, observation config, dialectic reasoning, session summaries, and context budget enforcement. Use when setting up Honcho, troubleshooting memory, managing profiles with Honcho peers, or tuning observation, recall, and dialectic settings.
Honcho provides AI-native cross-session user modeling. It learns who the user is across conversations and gives every Mangaba 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)
mangaba honcho setup
# select "cloud", paste API key from https://app.honcho.dev
Self-hosted
mangaba honcho setup
# select "local", enter base URL (e.g. http://localhost:8000)
mangaba 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 Mangaba 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. Mangaba creates two peers per session:
User peer (peerName): represents the human. Honcho builds a user representation from observed messages.
AI peer (aiPeer): represents this Mangaba 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).
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:
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 Mangaba 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
mangaba profile create coder --clone# creates host block mangaba.coder, AI peer "coder", inherits config from default
What --clone does for Honcho:
Creates a mangaba.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
mangaba honcho sync# creates host blocks for all profiles that don't have one yet
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 Mangaba when Honcho memory is active.
On conversation start
1. honcho_profile → fast warmup, no LLM cost
2. If context looks thin → honcho_context (full snapshot, still no LLM)
3. 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.
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: $MANGABA_HOME/honcho.json (profile-local) or ~/.honcho/config.json (global).
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 mangaba honcho setup. Ensure memory.provider: honcho is in ~/.mangaba/config.yaml.
Memory not persisting across sessions
Check mangaba 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: mangaba profile create <name> --clone. For existing profiles: mangaba 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.
