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Episodic memory — stores complete campaign trajectories on disk so the
Planner can retrieve "what worked last time on a similar repo".
Backed by SQLite for portability (no extra deps). Schema mirrors the
``memory_engine`` patterns already in the repo.
"""
from __future__ import annotations
import json
import os
import sqlite3
import time
from typing import Any
_DB = os.getenv("MYTHOS_EPISODIC_DB", "/data/mythos/episodic.sqlite")
class EpisodicMemory:
def __init__(self, path: str = _DB):
self.path = path
os.makedirs(os.path.dirname(path), exist_ok=True)
self._db = sqlite3.connect(path, check_same_thread=False)
self._db.execute(
"CREATE TABLE IF NOT EXISTS episodes ("
" id INTEGER PRIMARY KEY AUTOINCREMENT,"
" ts REAL,"
" repo TEXT,"
" iteration INTEGER,"
" cwes TEXT,"
" outcome TEXT,"
" payload TEXT"
")"
)
self._db.execute(
"CREATE INDEX IF NOT EXISTS idx_episodes_repo ON episodes(repo)"
)
self._db.commit()
def record(self, target: dict[str, Any], iteration: dict[str, Any]) -> int:
cwes = [h.get("cwe") for h in iteration.get("plan", {}).get("hypotheses", [])]
outcome = "exploit" if iteration.get("dynamic", {}).get(
"dynamic_report", {}).get("exploits") else "unconfirmed"
cur = self._db.execute(
"INSERT INTO episodes(ts, repo, iteration, cwes, outcome, payload) "
"VALUES (?, ?, ?, ?, ?, ?)",
(time.time(), target.get("repo", "?"), iteration.get("n", 0),
json.dumps(cwes), outcome, json.dumps(iteration, default=str)[:200_000]),
)
self._db.commit()
return cur.lastrowid or 0
def recall(self, repo: str, limit: int = 10) -> list[dict[str, Any]]:
cur = self._db.execute(
"SELECT ts, iteration, cwes, outcome FROM episodes "
"WHERE repo = ? ORDER BY id DESC LIMIT ?", (repo, limit),
)
return [
{"ts": ts, "iteration": it, "cwes": json.loads(cwes), "outcome": outcome}
for ts, it, cwes, outcome in cur.fetchall()
]
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