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docs(honcho): wizard cadence default 2, prewarm/depth + observation + multi-peer
- cli: setup wizard pre-fills dialecticCadence=2 (code default stays 1 so unset → every turn) - honcho.md: fix stale dialecticCadence default in tables, add Session-Start Prewarm subsection (depth runs at init), add Query-Adaptive Reasoning Level subsection, expand Observation section with directional vs unified semantics and per-peer patterns - memory-providers.md: fix stale default, rename Multi-agent/Profiles to Multi-peer setup, add concrete walkthrough for new profiles and sync, document observation toggles + presets, link to honcho.md - SKILL.md: fix stale defaults, add Depth at session start callout
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Erosika
kshitij
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4 changed files with 103 additions and 17 deletions
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@ -77,7 +77,7 @@ Cost and depth are controlled by three independent knobs:
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| Knob | Controls | Default |
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|------|----------|---------|
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| `contextCadence` | Turns between `context()` API calls (base layer refresh) | `1` |
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| `dialecticCadence` | Turns between `peer.chat()` LLM calls (dialectic layer refresh) | `3` |
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| `dialecticCadence` | Turns between `peer.chat()` LLM calls (dialectic layer refresh) | `1` (code default) / `2` (setup wizard default) |
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| `dialecticDepth` | Number of `.chat()` passes per dialectic invocation (1–3) | `1` |
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These are orthogonal — you can have frequent context refreshes with infrequent dialectic, or deep multi-pass dialectic at low frequency. Example: `contextCadence: 1, dialecticCadence: 5, dialecticDepth: 2` refreshes base context every turn, runs dialectic every 5 turns, and each dialectic run makes 2 passes.
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@ -94,6 +94,14 @@ Each pass uses a proportional reasoning level (lighter early passes, base level
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Passes bail out early if the prior pass returned strong signal (long, structured output), so depth 3 doesn't always mean 3 LLM calls.
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### Session-Start Prewarm
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On session init, Honcho fires a dialectic call in the background at the full configured `dialecticDepth` and hands the result directly to turn 1's context assembly. 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. If prewarm hasn't landed by turn 1, turn 1 falls back to a synchronous call with a bounded timeout.
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### Query-Adaptive Reasoning Level
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The auto-injected dialectic scales `dialecticReasoningLevel` by query length: +1 level at ≥120 chars, +2 at ≥400, clamped at `reasoningLevelCap` (default `"high"`). Disable with `reasoningHeuristic: false` to pin every auto call to `dialecticReasoningLevel`. `"max"` is reserved for explicit tool-path selection via `honcho_reasoning`.
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## Configuration Options
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Honcho is configured in `~/.honcho/config.json` (global) or `$HERMES_HOME/honcho.json` (profile-local). The setup wizard handles this for you.
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@ -104,7 +112,7 @@ Honcho is configured in `~/.honcho/config.json` (global) or `$HERMES_HOME/honcho
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|-----|---------|-------------|
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| `contextTokens` | `null` (uncapped) | Token budget for auto-injected context per turn. Set to an integer (e.g. 1200) to cap. Truncates at word boundaries |
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| `contextCadence` | `1` | Minimum turns between `context()` API calls (base layer refresh) |
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| `dialecticCadence` | `3` | Minimum turns between `peer.chat()` LLM calls (dialectic layer). In `tools` mode, irrelevant — model calls explicitly |
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| `dialecticCadence` | `1` (wizard sets `2`) | Minimum turns between `peer.chat()` LLM calls (dialectic layer). Code default fires every turn when the key is unset; the setup wizard pre-fills `2`. In `tools` mode, irrelevant — model calls explicitly |
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| `dialecticDepth` | `1` | Number of `.chat()` passes per dialectic invocation. Clamped to 1–3 |
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| `dialecticDepthLevels` | `null` | Optional array of reasoning levels per pass, e.g. `["minimal", "low", "medium"]`. Overrides proportional defaults |
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| `dialecticReasoningLevel` | `'low'` | Base reasoning level: `minimal`, `low`, `medium`, `high`, `max` |
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@ -142,6 +150,41 @@ Honcho is configured in `~/.honcho/config.json` (global) or `$HERMES_HOME/honcho
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In `tools` mode, the model is fully in control — it calls `honcho_reasoning` when it wants, at whatever `reasoning_level` it picks. Cadence and budget settings only apply to modes with auto-injection (`hybrid` and `context`).
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## Observation (Directional vs. Unified)
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Honcho models a conversation as peers exchanging messages. Each peer has two observation toggles that map 1:1 to Honcho's `SessionPeerConfig`:
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| Toggle | Effect |
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|--------|--------|
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| `observeMe` | Honcho builds a representation of this peer from its own messages |
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| `observeOthers` | This peer observes the other peer's messages (feeds cross-peer reasoning) |
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Two peers × two toggles = four flags. `observationMode` is a shorthand preset:
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| Preset | User flags | AI flags | Semantics |
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|--------|-----------|----------|-----------|
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| `"directional"` (default) | me: on, others: on | me: on, others: on | Full mutual observation. Enables cross-peer dialectic — "what does the AI know about the user, based on what the user said and the AI replied." |
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| `"unified"` | me: on, others: off | me: off, others: on | Shared-pool semantics — the AI observes the user's messages only, the user peer only self-models. Single-observer pool. |
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Override the preset with an explicit `observation` block for per-peer control:
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```json
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"observation": {
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"user": { "observeMe": true, "observeOthers": true },
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"ai": { "observeMe": true, "observeOthers": false }
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}
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```
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Common patterns:
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| Intent | Config |
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|--------|--------|
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| Full observation (most users) | `"observationMode": "directional"` |
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| AI shouldn't re-model the user from its own replies | `"ai": {"observeMe": true, "observeOthers": false}` |
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| Strong persona the AI peer shouldn't update from self-observation | `"ai": {"observeMe": false, "observeOthers": true}` |
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Server-side toggles set via the Honcho dashboard win over local defaults — Hermes syncs them back at session init.
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## Tools
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When Honcho is active as the memory provider, five tools become available:
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@ -82,7 +82,7 @@ hermes memory setup # select "honcho"
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| `workspace` | host key | Shared workspace ID |
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| `contextTokens` | `null` (uncapped) | Token budget for auto-injected context per turn. Truncates at word boundaries |
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| `contextCadence` | `1` | Minimum turns between `context()` API calls (base layer refresh) |
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| `dialecticCadence` | `3` | Minimum turns between `peer.chat()` LLM calls. Only applies to `hybrid`/`context` modes |
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| `dialecticCadence` | `1` (wizard sets `2`) | Minimum turns between `peer.chat()` LLM calls. Unset → every turn; wizard pre-fills `2`. Only applies to `hybrid`/`context` modes |
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| `dialecticDepth` | `1` | Number of `.chat()` passes per dialectic invocation. Clamped 1–3. Pass 0: cold/warm prompt, pass 1: self-audit, pass 2: reconciliation |
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| `dialecticDepthLevels` | `null` | Optional array of reasoning levels per pass, e.g. `["minimal", "low", "medium"]`. Overrides proportional defaults |
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| `dialecticReasoningLevel` | `'low'` | Base reasoning level: `minimal`, `low`, `medium`, `high`, `max` |
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@ -140,23 +140,64 @@ hermes memory setup # select "honcho"
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If you previously used `hermes honcho setup`, your config and all server-side data are intact. Just re-enable through the setup wizard again or manually set `memory.provider: honcho` to reactivate via the new system.
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:::
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**Multi-agent / Profiles:**
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**Multi-peer setup:**
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Each Hermes profile gets its own Honcho AI peer while sharing the same workspace -- all profiles see the same user representation, but each agent builds its own identity and observations.
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Honcho models conversations as peers exchanging messages — one user peer plus one AI peer per Hermes profile, all sharing a workspace. The workspace is the shared environment: the user peer is global across profiles, each AI peer is its own identity. Every AI peer builds an independent representation / card from its own observations, so a `coder` profile stays code-oriented while a `writer` profile stays editorial against the same user.
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The mapping:
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| Concept | What it is |
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|---------|-----------|
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| **Workspace** | Shared environment. All Hermes profiles under one workspace see the same user identity. |
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| **User peer** (`peerName`) | The human. Shared across profiles in the workspace. |
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| **AI peer** (`aiPeer`) | One per Hermes profile. Host key `hermes` → default; `hermes.<profile>` for others. |
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| **Observation** | Per-peer toggles controlling what Honcho models from whose messages. `directional` (default, all four on) or `unified` (single-observer pool). |
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### New profile, fresh Honcho peer
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```bash
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hermes profile create coder --clone # creates honcho peer "coder", inherits config from default
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hermes profile create coder --clone
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```
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What `--clone` does: creates a `hermes.coder` host block in `honcho.json` with `aiPeer: "coder"`, shared `workspace`, inherited `peerName`, `recallMode`, `writeFrequency`, `observation`, etc. The peer is eagerly created in Honcho so it exists before first message.
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`--clone` creates a `hermes.coder` host block in `honcho.json` with `aiPeer: "coder"`, shared `workspace`, inherited `peerName`, `recallMode`, `writeFrequency`, `observation`, etc. The AI peer is eagerly created in Honcho so it exists before the first message.
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For profiles created before Honcho was set up:
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### Existing profiles, backfill Honcho peers
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```bash
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hermes honcho sync # scans all profiles, creates host blocks for any missing ones
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hermes honcho sync
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```
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This inherits settings from the default `hermes` host block and creates new AI peers for each profile. Idempotent -- skips profiles that already have a host block.
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Scans every Hermes profile, creates host blocks for any profile without one, inherits settings from the default `hermes` block, and creates the new AI peers eagerly. Idempotent — skips profiles that already have a host block.
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### Per-profile observation
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Each host block can override the observation config independently. Example: a code-focused profile where the AI peer observes the user but doesn't self-model:
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```json
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"hermes.coder": {
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"aiPeer": "coder",
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"observation": {
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"user": { "observeMe": true, "observeOthers": true },
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"ai": { "observeMe": false, "observeOthers": true }
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}
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}
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```
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**Observation toggles (one set per peer):**
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| Toggle | Effect |
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|--------|--------|
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| `observeMe` | Honcho builds a representation of this peer from its own messages |
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| `observeOthers` | This peer observes the other peer's messages (feeds cross-peer reasoning) |
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Presets via `observationMode`:
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- **`"directional"`** (default) — all four flags on. Full mutual observation; enables cross-peer dialectic.
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- **`"unified"`** — user `observeMe: true`, AI `observeOthers: true`, rest false. Single-observer pool; AI models the user but not itself, user peer only self-models.
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Server-side toggles set via the [Honcho dashboard](https://app.honcho.dev) win over local defaults — synced back at session init.
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See the [Honcho page](./honcho.md#observation-directional-vs-unified) for the full observation reference.
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<details>
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<summary>Full honcho.json example (multi-profile)</summary>
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@ -181,7 +222,7 @@ This inherits settings from the default `hermes` host block and creates new AI p
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},
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"dialecticReasoningLevel": "low",
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"dialecticDynamic": true,
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"dialecticCadence": 3,
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"dialecticCadence": 2,
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"dialecticDepth": 1,
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"dialecticMaxChars": 600,
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"contextCadence": 1,
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