In this tutorial, we build a self-organized memory system for an agent that goes beyond storing raw conversation history and instead structures conversations into persistent, meaningful knowledge units. We design systems so that logic and memory management are clearly separated, allowing a dedicated component to extract, compress, and organize information. Whereas the main agent focuses on responding to the user. We use structured storage with SQLite, view-based grouping, and summary aggregation, and we show how an agent can maintain useful context over long horizons without relying solely on opaque vector retrieval.
import sqlite3
import json
import re
from datetime import datetime
from typing import List, Dict
from getpass import getpass
from openai import OpenAI
OPENAI_API_KEY = getpass("Enter your OpenAI API key: ").strip()
client = OpenAI(api_key=OPENAI_API_KEY)
def llm(prompt, temperature=0.1, max_tokens=500):
return client.chat.completions.create(
model="gpt-4o-mini",
messages=({"role": "user", "content": prompt}),
temperature=temperature,
max_tokens=max_tokens
).choices(0).message.content.strip()We set up the core runtime by importing all required libraries and securely collecting the API key at execution time. We initialize the language model client and define a single helper function that standardizes all model calls. We ensure that every downstream component relies on this shared interface for consistent generation behavior.
class MemoryDB:
def __init__(self):
self.db = sqlite3.connect(":memory:")
self.db.row_factory = sqlite3.Row
self._init_schema()
def _init_schema(self):
self.db.execute("""
CREATE TABLE mem_cells (
id INTEGER PRIMARY KEY,
scene TEXT,
cell_type TEXT,
salience REAL,
content TEXT,
created_at TEXT
)
""")
self.db.execute("""
CREATE TABLE mem_scenes (
scene TEXT PRIMARY KEY,
summary TEXT,
updated_at TEXT
)
""")
self.db.execute("""
CREATE VIRTUAL TABLE mem_cells_fts
USING fts5(content, scene, cell_type)
""")
def insert_cell(self, cell):
self.db.execute(
"INSERT INTO mem_cells VALUES(NULL,?,?,?,?,?)",
(
cell("scene"),
cell("cell_type"),
cell("salience"),
json.dumps(cell("content")),
datetime.utcnow().isoformat()
)
)
self.db.execute(
"INSERT INTO mem_cells_fts VALUES(?,?,?)",
(
json.dumps(cell("content")),
cell("scene"),
cell("cell_type")
)
)
self.db.commit()We define a structured memory database that maintains information across interactions. We create tables for atomic memory units, high-level views, and a full-text search index to enable symbolic retrieval. We also implement logic to insert new memory entries in a normalized and queryable form.
def get_scene(self, scene):
return self.db.execute(
"SELECT * FROM mem_scenes WHERE scene=?", (scene,)
).fetchone()
def upsert_scene(self, scene, summary):
self.db.execute("""
INSERT INTO mem_scenes VALUES(?,?,?)
ON CONFLICT(scene) DO UPDATE SET
summary=excluded.summary,
updated_at=excluded.updated_at
""", (scene, summary, datetime.utcnow().isoformat()))
self.db.commit()
def retrieve_scene_context(self, query, limit=6):
tokens = re.findall(r"(a-zA-Z0-9)+", query)
if not tokens:
return ()
fts_query = " OR ".join(tokens)
rows = self.db.execute("""
SELECT scene, content FROM mem_cells_fts
WHERE mem_cells_fts MATCH ?
LIMIT ?
""", (fts_query, limit)).fetchall()
if not rows:
rows = self.db.execute("""
SELECT scene, content FROM mem_cells
ORDER BY salience DESC
LIMIT ?
""", (limit,)).fetchall()
return rows
def retrieve_scene_summary(self, scene):
row = self.get_scene(scene)
return row("summary") if row else ""We focus on memory retrieval and view maintenance logic. We implement secure full-text search by streamlining user queries and adding a fallback strategy if no literal match is found. We also disclose helpful methods for obtaining aggregated visual summaries for long-horizon context building.
class MemoryManager:
def __init__(self, db: MemoryDB):
self.db = db
def extract_cells(self, user, assistant) -> List(Dict):
prompt = f"""
Convert this interaction into structured memory cells.
Return JSON array with objects containing:
- scene
- cell_type (fact, plan, preference, decision, task, risk)
- salience (0-1)
- content (compressed, factual)
User: {user}
Assistant: {assistant}
"""
raw = llm(prompt)
raw = re.sub(r"```json|```", "", raw)
try:
cells = json.loads(raw)
return cells if isinstance(cells, list) else ()
except Exception:
return ()
def consolidate_scene(self, scene):
rows = self.db.db.execute(
"SELECT content FROM mem_cells WHERE scene=? ORDER BY salience DESC",
(scene,)
).fetchall()
if not rows:
return
cells = (json.loads(r("content")) for r in rows)
prompt = f"""
Summarize this memory scene in under 100 words.
Keep it stable and reusable for future reasoning.
Cells:
{cells}
"""
summary = llm(prompt, temperature=0.05)
self.db.upsert_scene(scene, summary)
def update(self, user, assistant):
cells = self.extract_cells(user, assistant)
for cell in cells:
self.db.insert_cell(cell)
for scene in set(c("scene") for c in cells):
self.consolidate_scene(scene)We implement a dedicated memory management component responsible for structuring the experience. We extract compact memory representations from interactions, store them, and periodically consolidate them into stable visual summaries. We ensure that the memory evolves sequentially without interfering with the agent’s reaction flow.
class WorkerAgent:
def __init__(self, db: MemoryDB, mem_manager: MemoryManager):
self.db = db
self.mem_manager = mem_manager
def answer(self, user_input):
recalled = self.db.retrieve_scene_context(user_input)
scenes = set(r("scene") for r in recalled)
summaries = "n".join(
f"({scene})n{self.db.retrieve_scene_summary(scene)}"
for scene in scenes
)
prompt = f"""
You are an intelligent agent with long-term memory.
Relevant memory:
{summaries}
User: {user_input}
"""
assistant_reply = llm(prompt)
self.mem_manager.update(user_input, assistant_reply)
return assistant_reply
db = MemoryDB()
memory_manager = MemoryManager(db)
agent = WorkerAgent(db, memory_manager)
print(agent.answer("We are building an agent that remembers projects long term."))
print(agent.answer("It should organize conversations into topics automatically."))
print(agent.answer("This memory system should support future reasoning."))
for row in db.db.execute("SELECT * FROM mem_scenes"):
print(dict(row))We define a worker agent that reasons while being memory-aware. We retrieve relevant sequences, gather relevant summaries, and generate responses based on long-term knowledge. We then close the loop by moving the interaction back to the memory manager so that the system can continue to improve over time.
In this tutorial, we showed how an agent can actively curate its memory and turn past interactions into stable, reusable knowledge rather than ephemeral chat logs. We have enabled memory to be developed through consolidation and selective recall, which supports more coherent and grounded reasoning across sessions. This approach provides a practical foundation for building long-lived agentic systems, and as the complexity of the system increases, it can be naturally extended with forgetting mechanisms, rich relational memory, or graph-based orchestration.
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