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3tears-conversations

Canonical owner of the per-agent conversations table plus its entity and three-tier collection.

Purpose

Tracks one row per user-facing conversation an agent is engaged in: ownership scope (agent_id, customer_id, user_id), external reference (channel_type, conversation_ref), lifecycle (status, summary), timestamps (date_created, date_updated, date_last_message).

This table is owned here because multiple packages key off conversation_id (memory, agent-tools context items, workspace bindings) and no single consumer is the natural owner of the shape. Owning the table here lets each downstream package depend on conversations without pulling in memory's extraction pipeline.

Public API

Exports (see src/threetears/conversations/__init__.py):

  • Conversation -- BaseEntity subclass for a single row.
  • ConversationsCollection -- three-tier (L1 SQLite / L2 NATS KV / L3 postgres) collection with the usual get / save / subscript semantics.
  • register(runner) -- agent-scope migration entry point. Contributes the conversations table creation to a canonical threetears.core.data.migrations.MigrationRunner. No depends_on edges; this is the root of the agent-schema dependency graph, so memory / workspace / tools migrations that reference conversations.id sort after it.

Minimal usage

from threetears.conversations import Conversation, ConversationsCollection, register
from threetears.core.data.migrations import MigrationRunner

# 1. register the package's migrations on the runner
runner = MigrationRunner(store)
register(runner)
await store.run_migrations(runner)

# 2. use the collection like any other 3tears collection
collection = ConversationsCollection(registry=registry)
conv = await collection.get(conversation_id)
conv.summary = "follow-up on last week's analysis"
await conv.save()

Reference

  • Downstream consumers: threetears.agent.memory, threetears.agent.tools (context items), threetears.agent.workspace (bindings)

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