Pattern 7 — Comprehensive Context Engineering demo

snippet17_comprehensive_demo.py

from dataclasses import dataclass
from typing import List

try:
    from strands import Agent
    from strands.tools import calculator, mem0_memory, tavily_search, tavily_extract, use_llm
    from strands.managers import SlidingWindowConversationManager
except Exception:
    class Agent:
        def __init__(self, **kwargs): pass
    def calculator(*a, **k): return None
    def mem0_memory(*a, **k): return None
    def tavily_search(*a, **k): return None
    def tavily_extract(*a, **k): return None
    def use_llm(*a, **k): return None
    class SlidingWindowConversationManager:
        def __init__(self, window_size:int): self.window_size=window_size

@dataclass
class ContextState:
    task: str
    constraints: List[str]
    memory_bullets: List[str]
    citations: List[str]
    budget_tokens: int = 500

def build_prompt(state: ContextState) -> str:
    slots = [
        "# Task\n" + state.task,
        "# Constraints\n" + "\n".join(f"- {c}" for c in state.constraints),
        "# Memory\n" + "\n".join(f"- {b}" for b in state.memory_bullets[:8]),
        "# Citations (ref only)\n" + "\n".join(state.citations[:5]),
    ]
    prompt = "\n\n".join(slots)
    if len(prompt.split()) > state.budget_tokens:
        prompt = "\n\n".join(slots[:-1])
    return prompt

def run_comprehensive_demo():
    agent = Agent(
        system_prompt=(
            "You are an advanced AI assistant that uses context engineering best practices. "
            "Always structure outputs, use tools, and include citations."
        ),
        tools=[calculator, mem0_memory, use_llm, tavily_search, tavily_extract],
        conversation_manager=SlidingWindowConversationManager(window_size=30)
    )
    task = "Research the latest trends in AI context management and calculate the efficiency gains"
    state = ContextState(
        task=task,
        constraints=[
            "Use recent sources (2024-2025)",
            "Provide calculations where applicable",
            "Include proper citations",
            "Keep responses under 200 tokens",
        ],
        memory_bullets=["Previous research showed 30% improvement in context efficiency"],
        citations=[],
    )
    prompt = build_prompt(state)
    print("Packed Context Prompt:\n" + (prompt[:300] + "..." if len(prompt) > 300 else prompt))
    # In a real run, you would now call the agent with the prompt.
    return {"prompt": prompt, "note": "Invoke agent(prompt) in real environment."}

if __name__ == "__main__":
    print(run_comprehensive_demo())