| 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
- Define tools (search, calculator, API)
- Setup system prompt with agent role
- Add memory (buffer, summary)
- 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
Validation
- Agent selects correct tools
- Memory preserves dialogue context
- Reasoning chain is logical and complete
