agent-design

name	agent-design
description	Designs LLM agents with memory, tools, and reasoning cycle. Use for creating autonomous AI assistants.
category	ai
tags	["agent","llm","tools","memory","reasoning"]
models	["opus"]
version	1.0.0
created	"2026-04-29T00:00:00.000Z"

Agent Design

Design LLM agents with memory and tools.

Quick Start

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.tools import tool
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

@tool
def search(query: str) -> str:
    """Search the web for information."""
    return f"Results for: {query}"

tools = [search]

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])

llm = ChatOpenAI(model="gpt-4o", temperature=0)
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
executor.invoke({"input": "Find information about Python"})

When to Use

• ✅ Autonomous AI assistants
• ✅ Need reasoning and tool usage
• ❌ Not for simple Q&A tasks

Step-by-Step Instructions

1. Define tools (search, calculator, API)
2. Setup system prompt with agent role
3. Add memory (buffer, summary)
4. Test reasoning cycle

Dependencies

pip install langchain langchain-openai langchain-core

Examples

Input: "What's the weather in Moscow?" → Output: Agent calls weather API, returns answer

Resources

• LangChain Agents
• Examples

Validation

1. Agent selects correct tools
2. Memory preserves dialogue context
3. Reasoning chain is logical and complete