agent_py.py

# -*- coding: utf-8 -*-
"""Agent.py

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1aYmc6hcd6JKLK6sdFwPS-yC89WliBwhh
"""

pip install langchain langchain-openai pydantic openai

pip install langchain-community

"""# Library and Framework imports
- ChatOpenAI: This is the LLM backend (OpenAI’s GPT model).

- ConversationBufferMemory: This stores past messages, enabling Memory (MCP).

- initialize_agent: Initializes the agent, which decides how to use the LLM and tools.

- Tool: Represents a single tool the agent can call
"""

from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool
import os

"""# Build the Memory Layer (MCP)

- The memory ensures the agent doesn’t “forget” previous context, which is crucial for MCP (Memory + Chain-of-Thought).

- return_messages=True means the memory will store entire message objects (input and output).
"""

def build_memory() -> ConversationBufferMemory:
    """Create memory so the agent can remember past messages"""
    return ConversationBufferMemory(memory_key="chat_history", return_messages=True)

"""# Prompt engineering:
- SystemPrompt: Sets up the agent's persona as an experienced platform architect. This is the agent's guide.

- HumanPrompt: Defines what the agent needs (modernization plan, resilience strategy).

- CoT Behavior: The agent will reason step-by-step and expose only the reasoning summary at the end.
"""

from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate

def build_prompt() -> ChatPromptTemplate:
    """Create a system+human prompt that encourages explicit planning (CoT-style)."""
    system = SystemMessagePromptTemplate.from_template(
        "You are InfraResilience, a senior platform architect.\n"
        "Task: given a legacy stack, propose modernization options AND a resilience test plan.\n"
        "Be concrete, AWS-aware (Airflow/Step Functions/ECS/Lambda/S3/SQS/SNS), and security-conscious.\n"
        "Output concise, actionable bullet points.\n"
        "When planning, think step-by-step internally; expose only a brief reasoning summary."
    )

    human = HumanMessagePromptTemplate.from_template(
        "Legacy stack description:\n{legacy_stack}\n\n"
        "Outage scenario (optional): {outage_scenario}\n\n"
        "Provide:\n"
        "1) Modernization plan\n"
        "2) Resilience test strategy\n"
        "3) Short reasoning summary"
    )

    return ChatPromptTemplate.from_messages([system, human])

"""# GPT-4o-Mini LLM build
- temperature=0.2: This controls the randomness of the LLM. A lower value makes the LLM more deterministic (less random).

- LLM (OpenAI's GPT) that the agent will use for reasoning.
"""

from typing import Optional
def build_llm(model_name: Optional[str] = None, temperature: float = 0.2) -> ChatOpenAI:
    """Instantiate the chat LLM with sane defaults."""
    chosen = model_name or os.getenv("LLM_MODEL_NAME", "gpt-4o-mini")
    return ChatOpenAI(model=chosen, temperature=temperature)

"""So far, we have Memory, Prompts, LLM initialized. Now, let's build the agent with tools.

The agent is now connected to tools (we'll define in tools.py), memory, and the prompt template. It's ready to think through problems.
"""

from typing import List
def create_agent(tools: List[Tool], llm: Optional[ChatOpenAI] = None, memory: Optional[ConversationBufferMemory] = None):
    llm = llm or build_llm()  # LLM backend (can override)
    memory = memory or build_memory()  # Memory (MCP)
    prompt = build_prompt()  # Prompt

    agent = initialize_agent(
        tools=tools,  # List of tools (what actions the agent can take)
        llm=llm,  # Language model (the brain)
        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,  # React-style agent
        memory=memory,  # Memory
        verbose=False,  # Toggle logs on/off
        agent_kwargs={"extra_prompt_messages": [prompt]},  # Include custom prompt
    )

    return agent  # This returns the agent, which can be invoked later