docs: document conversation memory pipeline

API-Reference.md

@@ -140,6 +140,7 @@ Class-based entrypoint for inbound replies, proactive openings, and entry listin
 ### `replyToConversation(db, input)`
 
 Persists an inbound message and generates a DM-style response turn.
+When `input.memoryPipeline` is provided, the turn can also classify durable inbound/outbound messages and persist extracted IdentityDB facts back into the persona space.
 
 ```ts
 replyToConversation(db: IdentityDB, input: ReplyToConversationInput): Promise<ConversationTurnResult>
@@ -148,6 +149,7 @@ replyToConversation(db: IdentityDB, input: ReplyToConversationInput): Promise<Co
 ### `startConversation(db, input)`
 
 Generates a proactive outbound opening turn with no inbound user message.
+The same optional `memoryPipeline` can classify and extract durable proactive outbound memories.
 
 ```ts
 startConversation(db: IdentityDB, input: StartConversationInput): Promise<ConversationTurnResult>
@@ -167,6 +169,12 @@ Required model roles for turn generation:
 - `contextualMemoryModel: StructuredModelAdapter`
 - `responseModel: StructuredModelAdapter`
 
+Optional long-term memory pipeline:
+
+- `memoryPipeline.classifierModel: StructuredModelAdapter`
+- `memoryPipeline.extractorModel: StructuredModelAdapter`
+- `memoryPipeline.source?: string`
+
 ## `memory`
 
 ### `FactDraftMemoryStore`

Home.md

@@ -17,6 +17,7 @@ It is not a finished chatbot product. It is a **framework-first runtime library*
 - availability persistence with schedule/manual/tool overrides
 - availability snapshots with current and next-transition resolution
 - DM-style conversation orchestration for inbound replies and proactive openings
+- optional two-stage memory extraction for durable inbound/outbound conversation facts
 - human-like first-reply and typing delay helpers
 
 ## What is still planned