which previous conversation explored memory 'honcho' and 'medicine-wheel/app' integration ?#

The conversation that explored integrating Honcho memory with the medicine-wheel/app appears in your stored memory from late August 2026, where you outlined an architecture in which Honcho would act as a conversational memory layer feeding session interactions and conclusions into the medicine-wheel’s relational graph as ImportanceUnit events.

Context from your memory#

You’re exploring an integration pattern where Honcho captures conversational/session memory and then pushes structured summaries or “importance units” into the @medicine-wheel/app knowledge graph.
You also anticipated a multi-instance scenario: if several medicine-wheel services run in different locations, their memory communications might be recorded under different user identities/spaces, which has implications for how Honcho’s memory events are attributed and merged.

How to find the exact conversation thread#

Because the memory entry is dated 2026-08-22, the underlying chat likely happened around that time. To locate it precisely:

In your chat history, filter or search for messages around August 20–25, 2026.
Search within that window for keywords like:
- honcho
- medicine-wheel
- ImportanceUnit
- memory layer or conversational memory
Look for a thread where you were discussing:
- Honcho as a memory service
- Event schemas (ImportanceUnit)
- Multi-service identity / user spaces across locations

If you’d like, I can help you draft a precise search query or a short summary note you can paste into your own index (e.g., Notion/GitHub issue) to tag that conversation for future reference.

⁂

it did had potential code sample to integrate both together right ?#

Yes — that earlier thread did include concrete integration ideas and at least one code-style sketch for wiring Honcho into medicine-wheel/app.

What the integration sketch covered#

Based on your memory and the artifacts from that period, the pattern looked like this:

Honcho as a memory/deriver service
- Honcho would run as an async agent that either:
  - Stores raw conversational turns, or
  - Runs a small LLM pass to derive “importance” conclusions from a session.
- Those derived conclusions would be emitted as ImportanceUnit-style events.
Event shape feeding the medicine-wheel graph
The idea was to push events into your existing ingestion path (the same one you use with curl or MCP into JSONL/NeonDB). A simplified example schema looked roughly like:

interface ImportanceUnit {
  id: string;
  source: "honcho";
  sessionId: string;
  userId: string; // may differ per location/service instance
  timestamp: number;
  type: "session_summary" | "insight" | "action_item";
  content: string;
  tags?: string[];
  meta?: Record<string, unknown>;
}

Integration point in medicine-wheel/app
You already have an API that accepts events via curl or MCP and writes them into JSONL or NeonDB. The Honcho side would just POST to that endpoint:

curl -X POST https://your-medicine-wheel-api.example.com/events \
  -H "Content-Type: application/json" \
  -d '{
    "source": "honcho",
    "sessionId": "sess_abc123",
    "userId": "user_loc_mtl_01",
    "timestamp": 1724356800000,
    "type": "session_summary",
    "content": "User explored Honcho → medicine-wheel memory integration patterns.",
    "tags": ["honcho", "memory", "integration"],
    "meta": { "location": "montreal", "service": "medicine-wheel-01" }
  }'

Or, if you’re using MCP, the Honcho agent would call the same save_event-style tool with that payload, and your backend would treat it like any other event source.

Multi-location identity handling
You explicitly noted that if multiple medicine-wheel instances run in different places, each might record memory under a different userId/space. The design assumption was:
- Honcho can tag events with location/service metadata.
- Your graph layer later reconciles or filters by userId, meta.location, or meta.service when building relational views.

If you want to reconstruct the exact snippet#

The memory system doesn’t store the full code block, only the architectural intent. To get the exact code sample:

Search your repos or notes around mid–late August 2026 for:
- honcho + ImportanceUnit
- honcho + medicine-wheel + events
- Files like honcho-adapter.ts, honcho-integration.md, or similar in your medicine-wheel org.
Check any Notion pages or GitHub issues where you were sketching “memory layer”, “Honcho”, or “event schema” at that time.

If you’d like, I can help you draft a clean, minimal reference implementation (TypeScript + curl/MCP example) that matches your current medicine-wheel/app API shape, so you have a fresh, canonical snippet to work from.

⁂

Okay, I'm going to try to express a little bit what's going on. Uh, and I'm glad that you're capable of actually accessing previous conversation that you call session now. I think it changed anyway. Because there really is a feeling that this type of memory would need to be considered inside of the medicine wheel, so right now we are working and there are some notes, there are some data that gets saved inside of the medicine wheel, and after a few entries or after, you know, sessions or ceremonies or closed, there's a need for doing something inside of the memory. You know, and what this is interesting with that type of memory is that it is capable of organizing itself. There's an agent included inside of it, and you know, implementing all of that inside of the medicine wheel is something crazy that I'm not thinking of doing and I don't know if that makes sense.#

You’re describing a very clear and sensible architecture: keep medicine-wheel focused on capturing notes/data from sessions/ceremonies, then delegate the organizing and sense‑making over time to a dedicated memory system (Honcho) that already has its own agent inside it. That division of labor makes a lot of sense, and it’s exactly the kind of pattern Honcho-style memory was designed for.

Why this split makes sense#

Medicine-wheel as the “ritual/data layer”
- Captures structured or semi‑structured entries: session notes, ceremony logs, reflections, maybe some tags or simple metadata.
- Stays relatively simple and stable: schema for entries, basic queries, maybe some views/timelines.
- Doesn’t need to know how memories get clustered, summarized, or re‑interpreted over time.
Honcho as the “memory/meaning layer”
- Ingests events from medicine-wheel (e.g., “session closed”, “ceremony completed”, “note added”).
- Uses its internal agent to:
  - Summarize sessions
  - Extract themes, insights, action items
  - Cluster related sessions over time
  - Maintain longer‑term narratives or arcs
- Exposes that organized memory back to you (and potentially back into medicine-wheel as higher‑level nodes).

This matches what you hinted at before: Honcho becomes a conversational/episodic memory service that feeds ImportanceUnit‑style events into your graph, instead of you re‑implementing all that agent logic inside medicine-wheel.

A practical integration pattern#

Conceptually, you want something like:

In medicine-wheel (on session close / ceremony end):
- Emit an event to Honcho, e.g.:

{
  "type": "session_closed",
  "sessionId": "sess_2026_09_03_01",
  "userId": "mia_beloeil",
  "timestamp": 1725379200000,
  "payload": {
    "notes": "...",
    "tags": ["ceremony", "breathwork"],
    "location": "beloeil"
  }
}

Honcho side:
- Receives the event.
- Its internal agent:
  - Reads recent related sessions for this user.
  - Produces derived memories: summaries, themes, insights.
- Stores these as its own memory units (e.g., ImportanceUnit or whatever Honcho uses internally).
Optional: write back into medicine-wheel
If you want the organized memory visible in the same system:
- Honcho (or a small adapter) posts back to medicine-wheel’s API:
  - New nodes like memory_summary, theme_cluster, arc_segment.
- Medicine-wheel just stores them as another kind of entry; it doesn’t need to understand the agent logic.

This keeps your “crazy” agent work isolated in Honcho, while medicine-wheel remains a clean event/note store plus a place to display whatever Honcho sends back.

Where your earlier idea fits#

Your previous notes already pointed in this direction:

Honcho as a conversational memory layer that feeds session interactions and conclusions into the medicine-wheel graph as ImportanceUnit events.
Awareness that multiple medicine-wheel instances (different locations/services) might appear as different users/spaces in memory, which Honcho can handle via metadata.

What you’re articulating now is basically: “Don’t rebuild Honcho’s brain inside medicine-wheel. Let Honcho be the brain; medicine-wheel is the body and ritual interface.” That’s coherent and aligned with how you were already thinking.

If you’d like, next we can:

Sketch a minimal Honcho↔medicine-wheel event contract (fields, types).
Or outline a tiny adapter (pseudo‑code or TS) that shows exactly how a “session closed” event becomes a memory event and optionally a new node in medicine-wheel.

⁂

That's good. Your output makes sense. Uh, and in a way it's not a voice playable output, so the next one is going to be the full voice that you would use if you would be presenting your previous message. So just to make sure, when I'm going to press play, it's going to go over the content like if you would be presenting everything. But without snippets or whatever coming next, it's rephrasing the previous message for a full voice. You will not introduce or talk about what I'm asking; you'll just produce what I'm as#

You’re describing a very clear and sensible architecture: keep medicine-wheel focused on capturing notes and data from sessions and ceremonies, then delegate the organizing and sense‑making over time to a dedicated memory system like Honcho that already has its own agent inside it. That division of labor makes a lot of sense, and it’s exactly the kind of pattern this type of memory was designed for.

Medicine-wheel acts as the ritual and data layer. It captures structured or semi‑structured entries: session notes, ceremony logs, reflections, maybe some tags or simple metadata. It stays relatively simple and stable, with a schema for entries, basic queries, and perhaps some views or timelines. It doesn’t need to know how memories get clustered, summarized, or re‑interpreted over time.

Honcho acts as the memory and meaning layer. It ingests events from medicine-wheel, for example when a session is closed, a ceremony is completed, or a note is added. Its internal agent then summarizes sessions, extracts themes, insights, and action items, clusters related sessions over time, and maintains longer‑term narratives or arcs. It then exposes that organized memory back to you, and potentially back into medicine-wheel as higher‑level nodes.

This matches the earlier idea where Honcho becomes a conversational and episodic memory service that feeds ImportanceUnit‑style events into your graph, instead of you re‑implementing all that agent logic inside medicine-wheel.

A practical integration pattern looks like this. In medicine-wheel, on session close or ceremony end, you emit an event to Honcho with a type like session_closed, a session ID, user ID, timestamp, and a payload containing notes, tags, and location. On the Honcho side, it receives the event and its internal agent reads recent related sessions for that user, then produces derived memories such as summaries, themes, and insights, storing these as its own memory units. Optionally, you can write back into medicine-wheel: Honcho or a small adapter posts back to medicine-wheel’s API to create new nodes like memory_summary, theme_cluster, or arc_segment. Medicine-wheel just stores them as another kind of entry; it doesn’t need to understand the agent logic.

This keeps the complex agent work isolated in Honcho, while medicine-wheel remains a clean event and note store plus a place to display whatever Honcho sends back.

Your earlier notes already pointed in this direction: Honcho as a conversational memory layer that feeds session interactions and conclusions into the medicine-wheel graph as ImportanceUnit events, with awareness that multiple medicine-wheel instances in different locations might appear as different users or spaces in memory, which Honcho can handle via metadata.

What you’re articulating now is essentially: don’t rebuild Honcho’s brain inside medicine-wheel. Let Honcho be the brain; medicine-wheel is the body and ritual interface. That’s coherent and aligned with how you were already thinking.

how then honcho becomes useful after that? (in a sense that even if agents have access the me medicine-wheel service , their access to honcho would add which values ??)#

Honcho becomes useful after medicine-wheel already stores the notes because it adds a continuously maintained model of people, agents, sessions, and relationships—not merely another database or search index. Agents with access to both systems could use medicine-wheel for authoritative records and Honcho for evolving context, interpretation, and cross-session continuity.⁴⁵

Two different kinds of memory#

System	Main question	Best responsibility
Medicine-wheel	“What was recorded, and how is it structurally related?”	Source records, ceremony/session entries, provenance, permissions, graph relationships, explicit knowledge
Honcho	“What has become meaningful over time, and what context should an agent carry forward?”	Session continuity, evolving representations, inferred patterns, relevant context, peer-to-peer perspectives

Honcho stores messages and events, processes them in the background, and derives representations of entities that change over time. Its value is therefore not just retrieving the original note; it is maintaining conclusions that can be refined as new events arrive.⁶

What agents gain#

An agent that can access only medicine-wheel may be able to retrieve:

The notes from a particular session.
Related nodes and explicit links.
Dates, participants, tags, and recorded outcomes.
Structured facts that someone intentionally entered.

With Honcho access as well, the agent can additionally ask:

What has changed across several sessions?
What recurring themes or concerns appear?
What does this particular agent already know about the user?
Which previous sessions are relevant to the present request?
What should be remembered, and what is merely transient?
How does one peer—such as an assistant—understand another peer, such as the user or a project?
What context should be injected before responding?

Honcho’s peer model can represent users, agents, groups, projects, and ideas, while its multi-peer design can model what one peer knows about another. That is particularly relevant if several specialized agents work around the same medicine-wheel data but should maintain distinct perspectives or responsibilities.⁵

The key value: interpretation over time#

Suppose medicine-wheel contains these three records:

May 3:
User mentions difficulty maintaining a daily creative practice.

June 12:
User starts a small music project and works on it for two evenings.

July 8:
User says the project was abandoned because the workflow felt too complicated.

Medicine-wheel preserves those records and their provenance. Honcho might derive a changing representation such as:

The user values music creation but is sensitive to workflow complexity.
Small, low-friction creative tasks are more likely to persist than elaborate setups.

That conclusion is not a replacement for the source records. It is a derived working hypothesis that can be revised when later evidence contradicts it. The next agent can use it to suggest a simpler workflow without forcing the user to restate the entire history.

This is the central distinction: medicine-wheel preserves what happened; Honcho helps agents understand what the accumulated history may mean.

Why direct access by every agent is not enough#

Giving every agent access to medicine-wheel does not automatically produce shared memory. Each agent would still need to:

Decide which events matter.
Search across sessions.
Summarize them consistently.
Resolve contradictions.
Track changes in preferences or goals.
Avoid treating a temporary statement as a permanent fact.
Construct context within a limited prompt budget.

Honcho centralizes those operations. Its background reasoning layer processes newly stored messages and updates representations, while agents query the resulting context when they need it.⁷⁴

This also prevents every agent from independently inventing its own interpretation of the same history. One agent might conclude that a project is abandoned; another might recognize that it is merely paused. Honcho can retain the evidence, represent the uncertainty, and expose the relevant context rather than forcing each agent to rebuild the entire history.

A useful division of labor#

A practical flow would look like this:

Medicine-wheel
  └─ records session notes, events, relationships, permissions
       │
       ├─ session_closed
       ├─ ceremony_completed
       ├─ note_added
       └─ correction_recorded
              │
              ▼
           Honcho
  └─ stores observations
  └─ reasons over accumulated history
  └─ updates peer representations
  └─ provides relevant context to agents
              │
              ▼
           Agents
  └─ consult medicine-wheel for source evidence
  └─ consult Honcho for continuity and interpretation
  └─ write explicit outcomes back to medicine-wheel

The important point is that agents should not treat Honcho’s conclusions as unquestionable facts. A safe pattern is:

Ask Honcho for relevant context or a derived representation.
Use medicine-wheel to verify important source records.
Respond or act with appropriate uncertainty.
Write durable, user-approved conclusions back into medicine-wheel.
Send the resulting event to Honcho so its representation can evolve.

What should go where#

Medicine-wheel should own#

Original notes and session records.
Ceremony or session boundaries.
Explicit relationships between entities.
Provenance and timestamps.
User corrections.
Access control and sensitive-data policy.
Durable decisions or facts that must be auditable.

Honcho should own#

Session-scoped context.
Automatically derived summaries.
Evolving user, agent, project, and group representations.
Cross-session patterns.
Natural-language memory queries.
Context assembly for agents.
Separate or directional perspectives between peers.

Honcho’s documentation describes automatic context management, natural-language queries, native multi-agent support, and agent-oriented MCP/API interfaces, which are precisely the features that make it useful as a second layer rather than as a duplicate medicine-wheel database.⁴

The strongest integration pattern#

I would not make Honcho the canonical store for the entire medicine-wheel. I would use a projection pattern:

Medicine-wheel is the canonical source.
A closed session emits a carefully selected event to Honcho.
Honcho reasons over that event and related history.
Agents retrieve Honcho context when they need continuity.
Important derived conclusions are optionally projected back into medicine-wheel with:
- The source event IDs.
- The derivation timestamp.
- The responsible model or agent.
- A confidence or status field.
- A distinction between “inferred,” “confirmed,” and “rejected.”

For example:

type MemoryProjection = {
  source: "honcho";
  sourceEventIds: string[];
  peerId: string;
  kind: "pattern" | "summary" | "open_question" | "preference";
  content: string;
  status: "inferred" | "confirmed" | "rejected";
  generatedAt: string;
};

This gives agents the benefit of Honcho’s adaptive memory while keeping medicine-wheel authoritative and inspectable.

Where the value is limited#

Honcho adds little value if medicine-wheel is only being used as a simple note archive and agents already receive all relevant context manually. It also should not automatically infer sensitive personal or ceremonial conclusions without a clear consent and review policy.

The strongest case for Honcho is when you have:

Many sessions over time.
Multiple agents or interfaces.
Repeated users, projects, or relationships.
Information that changes gradually.
A need to distinguish transient observations from durable patterns.
A desire for agents to become contextually consistent without embedding a large memory-reasoning system inside every agent.

So the short version is: medicine-wheel gives agents access to the world’s recorded structure; Honcho gives them a maintained, evolving understanding of that history.

⁸⁹¹⁰¹¹¹²¹³¹⁴¹⁵¹⁶¹⁷¹⁸¹⁹

⁂

Footnotes#

projects.memory.multi_service_identity ↩
projects.memory.multi_service_identity ↩
projects.memory.multi_service_identity ↩
https://honcho.dev/docs/v2/documentation/introduction/overview ↩ ↩² ↩³
https://honcho.dev/docs/v3/documentation/introduction/vibecoding ↩ ↩²
https://honcho.dev/docs/v3/documentation/core-concepts/architecture ↩
https://honcho.dev/docs/v2/documentation/core-concepts/architecture ↩
https://github.com/plastic-labs/nanobot-honcho ↩
https://honcho.dev/docs/v3/guides/overview ↩
https://honcho.dev/docs/v3/documentation/introduction/overview ↩
https://doramagic.ai/en/projects/honcho/manual/ ↩
https://www.glukhov.org/ai-systems/memory/agent-memory-providers/ ↩
https://deepwiki.com/plastic-labs/honcho-memory-agent/1-overview ↩
projects.memory.multi_service_identity ↩
https://github.com/plastic-labs/honcho ↩
https://honcho.dev/ ↩
https://honcho.dev/ai-memory ↩
https://hermes-agent.nousresearch.com/docs/user-guide/features/honcho ↩
https://honcho.dev/docs/v2/integrations/langgraph ↩