Feat/memory maintenance

README.md

@@ -22,6 +22,7 @@ An older version of this project may be found here https://github.com/GilesStron
 
 - High-level backend/frontend architecture and flows: [`docs/developer-architecture-guide.md`](docs/developer-architecture-guide.md)
 - Dedicated deck-building flow guide: [`docs/deck-building.md`](docs/deck-building.md)
+- AI memory system architecture, maintenance, and operations: [`docs/memory-system.md`](docs/memory-system.md)
 
 ## Fresh Clone Setup (Docker)
 
@@ -131,26 +132,27 @@ If you run Ollama in a separate compose project, attach it to `llm_net` using a
 
 ```yaml
 services:
-	ollama:
-		image: ollama/ollama:latest
-		container_name: ollama
-		restart: unless-stopped
-		volumes:
-			- ~/infra/ollama:/root/.ollama
-		networks:
-			- llm_net
-			- 127.0.0.1:11434:11434
-		deploy:
-			resources:
-				reservations:
-					devices:
-						- driver: nvidia
-							count: all
-							capabilities: [gpu]
+  ollama:
+    image: ollama/ollama:latest
+    container_name: ollama
+    restart: unless-stopped
+    volumes:
+      - ~/infra/ollama:/root/.ollama
+    ports:
+      - "127.0.0.1:11434:11434"
+    networks:
+      - llm_net
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
 
 networks:
-	llm_net:
-		name: llm_net
+  llm_net:
+    name: llm_net
 ```
 
 After starting Ollama, pull the models referenced by your `.env` (`EMBEDDING_MODEL`), for example:
@@ -210,6 +212,25 @@ Run frontend tests:
 docker compose exec frontend bun run test --run
 ```
 
+## Memory System Operations
+
+The AI memory system is integrated into deck generation and automatically maintained by Celery beat.
+
+- Full guide: [`docs/memory-system.md`](docs/memory-system.md)
+- Scheduled maintenance task: `appai.tasks.memory_maintenance.run_memory_maintenance_task`
+
+Run maintenance manually (direct graph execution):
+
+```bash
+docker compose exec web python app/manage.py shell -c "import asyncio; from appai.services.graphs.memory_maintenance import run_memory_maintenance; asyncio.run(run_memory_maintenance())"
+```
+
+Queue maintenance via Celery (requires `celery_llm_worker` running):
+
+```bash
+docker compose exec web python app/manage.py shell -c "from appai.tasks.memory_maintenance import run_memory_maintenance_task; run_memory_maintenance_task.delay()"
+```
+
 ## Production Server Commands
 
 ```bash
@@ -318,7 +339,7 @@ docker compose \
   --project-name deepmtg_2_prod \
   --env-file .env.prod \
   -f docker-compose.prod.yml \
-  run --rm web python manage.py migrate
+  run --rm web python app/manage.py migrate
 
 docker compose \
   --project-name deepmtg_2_prod \

app/app/settings.py

@@ -225,6 +225,10 @@
         "task": "appai.tasks.daily_theme.make_daily_theme",
         "schedule": crontab(minute="*/30"),  # every 30 minutes, but only completes once a day due to early exit
     },
+    "daily-memory-maintenance-job": {
+        "task": "appai.tasks.memory_maintenance.run_memory_maintenance_task",
+        "schedule": crontab(minute="*/30"),  # every 30 minutes, but only completes once a day due to early exit
+    },
     "cleanup-old-deck-build-tasks": {
         "task": "appai.tasks.cleanup.cleanup_old_deck_build_tasks",
         "schedule": crontab(minute="*/30"),  # every 30 minutes

app/appai/dataclasses/memory.py

@@ -0,0 +1,37 @@
+# Copyright 2026 Giles Strong
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from uuid import UUID
+
+from pydantic import BaseModel, Field
+
+
+class Memory(BaseModel):
+    name: str = Field(
+        max_length=64, description="The name of the memory, which can be used to reference it in future queries."
+    )
+    text: str = Field(description="The content of the memory, which can be any text that the agent wants to remember.")
+    related_card_uuids: set[UUID] = Field(
+        default_factory=set,
+        max_length=10,
+        description="A list of UUIDs of cards that are related to this memory, which can be used to link the memory to specific cards and retrieve it later based on those cards. Up to 10 related card UUIDs can be included.",
+    )
+
+
+class ExistingMemory(Memory):
+    id: UUID = Field(
+        description="The unique identifier of the memory in the database, used for referencing and updating the memory."
+    )
+    created_at: str = Field(
+        description="The timestamp of when the memory was created in the database, used for tracking the age of the memory and determining if it may be obsolete or in need of consolidation with newer memories."
+    )