multi_session.py

"""
Multi-session example: Simulate an agent running over multiple days.

Shows how dream-memory consolidates knowledge across sessions,
getting sharper over time.
"""

import json
import time
from dream_memory import DreamEngine, DreamConfig
from dream_memory.store.sqlite_store import SQLiteStore
from dream_memory.llm import CallableLLM


# Simulate a smarter LLM that actually reads the context
call_count = 0

def simulated_llm(prompt: str, max_tokens: int = 2000) -> str:
    global call_count
    call_count += 1

    if "analyzing" in prompt:
        # Orient: detect redundancy if we have duplicate-ish memories
        redundant = []
        if "Python" in prompt and "python" in prompt.lower():
            redundant = [["m1", "m2"]]  # Hypothetical overlap
        return json.dumps({
            "stale_topics": [],
            "redundant_groups": [],
            "new_signals": ["New deployment workflow learned"],
            "contradictions": [],
            "health_score": 0.85,
        })
    elif "extracting" in prompt:
        return json.dumps({
            "memories": [
                {"content": "Agent learned Docker deployment workflow", "topic": "devops", "priority": 3},
                {"content": "Production uses PostgreSQL 16", "topic": "infrastructure", "priority": 2},
            ]
        })
    elif "pruning" in prompt:
        return json.dumps({"decisions": []})
    return json.dumps({"merged": [], "updated": []})


# Configure for frequent dreaming (demo purposes)
config = DreamConfig(
    min_sessions_between_dreams=2,
    min_hours_between_dreams=0,  # No cooldown for demo
    max_total_memories=50,
)

store = SQLiteStore("./multi_demo.db", config=config)
engine = DreamEngine(store, llm=CallableLLM(simulated_llm), config=config)

# Day 1: Initial sessions
print("=== Day 1 ===")
engine.add_memory("Project uses Python 3.12", topic="stack", priority=3)
engine.add_memory("Team lead is Sarah", topic="team", priority=4)
engine.log_session("Set up development environment. Installed Python 3.12, Docker, and PostgreSQL.")
engine.log_session("First code review. Team uses black formatter and pytest.")
result = engine.dream()
print(f"  Dream 1: {result.memories_before} -> {result.memories_after} memories")

# Day 2: More sessions
print("\n=== Day 2 ===")
engine.log_session("Deployed to staging. Used Docker Compose for orchestration.")
engine.log_session("Production incident: DB connection pool exhausted. Fixed by increasing max_connections.")
result = engine.dream()
print(f"  Dream 2: {result.memories_before} -> {result.memories_after} memories")

# Day 3: Even more
print("\n=== Day 3 ===")
engine.log_session("Implemented caching layer with Redis.")
engine.log_session("Code review feedback: prefer composition over inheritance.")
result = engine.dream()
print(f"  Dream 3: {result.memories_before} -> {result.memories_after} memories")

# Check final state
print("\n=== Final State ===")
status = engine.status()
print(f"  Total memories: {status['total_memories']}")
print(f"  Topics: {', '.join(status['topics'])}")
print(f"  Dreams completed: {status['dream_count']}")
print(f"  LLM calls made: {call_count}")
print("\nAll memories:")
for mem in store.list_memories():
    print(f"  [{mem.priority.name:<9}] ({mem.topic}) {mem.content}")

# Cleanup
import os, glob
for f in glob.glob("./multi_demo.db*"):
    os.remove(f)