Memory Concept

# Simple agent memory management concept

class AgentMemory:
    """Simple agent working memory implementation."""

    def __init__(self):
        self.short_term = []  # Current task steps and observations
        self.notes = {}       # Key findings the agent wants to remember

    def add_observation(self, step, observation):
        """Record what happened at each step."""
        self.short_term.append({
            "step": step,
            "observation": observation
        })
        print(f"Memory: Recorded step {step} -> {observation[:60]}...")

    def save_note(self, key, value):
        """Save a key finding for later use in the task."""
        self.notes[key] = value
        print(f"Memory: Saved '{key}'")

    def recall(self, key):
        """Retrieve a previously saved note."""
        return self.notes.get(key, "Not found in memory")

    def get_context_summary(self):
        """Get a summary of everything done so far."""
        summary = f"Steps completed: {len(self.short_term)}\n"
        summary += f"Key notes: {list(self.notes.keys())}\n"
        return summary

# Simulate an agent using memory during a research task
memory = AgentMemory()

memory.add_observation("Step 1", "Found 10 Python visualization libraries via web search")
memory.save_note("top_libraries", ["Matplotlib", "Seaborn", "Plotly", "Bokeh", "Altair"])

memory.add_observation("Step 2", "Retrieved GitHub star counts for all 5 libraries")
memory.save_note("stars", {"Matplotlib": 18000, "Plotly": 14000, "Seaborn": 12000})

print()
print("Agent memory summary:")
print(memory.get_context_summary())
print()
print("Recalling top libraries:", memory.recall("top_libraries"))