On this tutorial, we construct a self-organizing reminiscence system for an agent that goes past storing uncooked dialog historical past and as a substitute constructions interactions into persistent, significant data models. We design the system in order that reasoning and reminiscence administration are clearly separated, permitting a devoted element to extract, compress, and manage data. On the similar time, the primary agent focuses on responding to the person. We use structured storage with SQLite, scene-based grouping, and abstract consolidation, and we present how an agent can preserve helpful context over lengthy horizons with out counting on opaque vector-only retrieval.
import sqlite3
import json
import re
from datetime import datetime
from typing import Record, Dict
from getpass import getpass
from openai import OpenAI
OPENAI_API_KEY = getpass("Enter your OpenAI API key: ").strip()
consumer = OpenAI(api_key=OPENAI_API_KEY)
def llm(immediate, temperature=0.1, max_tokens=500):
return consumer.chat.completions.create(
mannequin="gpt-4o-mini",
messages=[{"role": "user", "content": prompt}],
temperature=temperature,
max_tokens=max_tokens
).selections[0].message.content material.strip()We arrange the core runtime by importing all required libraries and securely gathering the API key at execution time. We initialize the language mannequin consumer and outline a single helper operate that standardizes all mannequin calls. We be certain that each downstream element depends on this shared interface for constant era habits.
class MemoryDB:
def __init__(self):
self.db = sqlite3.join(":reminiscence:")
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 material TEXT,
created_at TEXT
)
""")
self.db.execute("""
CREATE TABLE mem_scenes (
scene TEXT PRIMARY KEY,
abstract TEXT,
updated_at TEXT
)
""")
self.db.execute("""
CREATE VIRTUAL TABLE mem_cells_fts
USING fts5(content material, 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 outline a structured reminiscence database that persists data throughout interactions. We create tables for atomic reminiscence models, higher-level scenes, and a full-text search index to allow symbolic retrieval. We additionally implement the logic to insert new reminiscence entries in a normalized and queryable type.
def get_scene(self, scene):
return self.db.execute(
"SELECT * FROM mem_scenes WHERE scene=?", (scene,)
).fetchone()
def upsert_scene(self, scene, abstract):
self.db.execute("""
INSERT INTO mem_scenes VALUES(?,?,?)
ON CONFLICT(scene) DO UPDATE SET
abstract=excluded.abstract,
updated_at=excluded.updated_at
""", (scene, abstract, datetime.utcnow().isoformat()))
self.db.commit()
def retrieve_scene_context(self, question, restrict=6):
tokens = re.findall(r"[a-zA-Z0-9]+", question)
if not tokens:
return []
fts_query = " OR ".be a part of(tokens)
rows = self.db.execute("""
SELECT scene, content material FROM mem_cells_fts
WHERE mem_cells_fts MATCH ?
LIMIT ?
""", (fts_query, restrict)).fetchall()
if not rows:
rows = self.db.execute("""
SELECT scene, content material FROM mem_cells
ORDER BY salience DESC
LIMIT ?
""", (restrict,)).fetchall()
return rows
def retrieve_scene_summary(self, scene):
row = self.get_scene(scene)
return row["summary"] if row else ""We give attention to reminiscence retrieval and scene upkeep logic. We implement protected full-text search by sanitizing person queries and including a fallback technique when no lexical matches are discovered. We additionally expose helper strategies to fetch consolidated scene summaries for long-horizon context constructing.
class MemoryManager:
def __init__(self, db: MemoryDB):
self.db = db
def extract_cells(self, person, assistant) -> Record[Dict]:
immediate = f"""
Convert this interplay into structured reminiscence cells.
Return JSON array with objects containing:
- scene
- cell_type (truth, plan, desire, determination, process, threat)
- salience (0-1)
- content material (compressed, factual)
Consumer: {person}
Assistant: {assistant}
"""
uncooked = llm(immediate)
uncooked = re.sub(r"```json|```", "", uncooked)
strive:
cells = json.hundreds(uncooked)
return cells if isinstance(cells, listing) else []
besides Exception:
return []
def consolidate_scene(self, scene):
rows = self.db.db.execute(
"SELECT content material FROM mem_cells WHERE scene=? ORDER BY salience DESC",
(scene,)
).fetchall()
if not rows:
return
cells = [json.loads(r["content"]) for r in rows]
immediate = f"""
Summarize this reminiscence scene in underneath 100 phrases.
Maintain it steady and reusable for future reasoning.
Cells:
{cells}
"""
abstract = llm(immediate, temperature=0.05)
self.db.upsert_scene(scene, abstract)
def replace(self, person, assistant):
cells = self.extract_cells(person, 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 the devoted reminiscence administration element liable for structuring expertise. We extract compact reminiscence representations from interactions, retailer them, and periodically consolidate them into steady scene summaries. We be certain that reminiscence evolves incrementally with out interfering with the agent’s response movement.
class WorkerAgent:
def __init__(self, db: MemoryDB, mem_manager: MemoryManager):
self.db = db
self.mem_manager = mem_manager
def reply(self, user_input):
recalled = self.db.retrieve_scene_context(user_input)
scenes = set(r["scene"] for r in recalled)
summaries = "n".be a part of(
f"[{scene}]n{self.db.retrieve_scene_summary(scene)}"
for scene in scenes
)
immediate = f"""
You might be an clever agent with long-term reminiscence.
Related reminiscence:
{summaries}
Consumer: {user_input}
"""
assistant_reply = llm(immediate)
self.mem_manager.replace(user_input, assistant_reply)
return assistant_reply
db = MemoryDB()
memory_manager = MemoryManager(db)
agent = WorkerAgent(db, memory_manager)
print(agent.reply("We're constructing an agent that remembers initiatives long run."))
print(agent.reply("It ought to manage conversations into matters robotically."))
print(agent.reply("This reminiscence system ought to assist future reasoning."))
for row in db.db.execute("SELECT * FROM mem_scenes"):
print(dict(row))We outline the employee agent that performs reasoning whereas remaining memory-aware. We retrieve related scenes, assemble contextual summaries, and generate responses grounded in long-term data. We then shut the loop by passing the interplay again to the reminiscence supervisor so the system repeatedly improves over time.
On this tutorial, we demonstrated how an agent can actively curate its personal reminiscence and switch previous interactions into steady, reusable data fairly than ephemeral chat logs. We enabled reminiscence to evolve by consolidation and selective recall, which helps extra constant and grounded reasoning throughout classes. This strategy supplies a sensible basis for constructing long-lived agentic methods, and it may be naturally prolonged with mechanisms for forgetting, richer relational reminiscence, or graph-based orchestration because the system grows in complexity.
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