# Building AI Agents with LangChain and Tool Use

> Build tool‑using AI agents with LangChain (ReAct/Plan‑and‑Execute), memory, tool safety, and error handling — from idea to working demo.

Published: 2026-03-01  
Canonical: https://aimastermindlab.com/tutorials/ai-agents-langchain

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AI Agents represent the leap from "Chatbots" to "Digital Workers." Instead of just generating text, an Agent plans a workflow and executes tools to accomplish goals. LangChain simplifies this via rigorous tool structuring.

## 1. Defining Tools

Tools must have explicit, strictly typed definitions using Pydantic. If you don't define exactly what the tool accepts, the LLM will hallucinate invalid API calls.

```python
from langchain.tools import tool
from pydantic import BaseModel, Field

class WeatherParams(BaseModel):
    city: str = Field(description="The city name, e.g., 'San Francisco'")
    units: str = Field(description="Temperature units: 'celsius' or 'fahrenheit'")

@tool("get_weather", args_schema=WeatherParams)
def get_weather(city: str, units: str) -> str:
    """Gets the current weather for a specific city."""
    # Fake API call
    return f"The weather in {city} is 72 {units}."
```

## 2. Structuring the Agent

Agents typically operate on a **ReAct** (Reason + Act) loop:
1. **Thought**: What do I need to do next?
2. **Action**: Choose a tool.
3. **Action Input**: Provide the JSON input.
4. **Observation**: What did the tool return?
5. Repeat until the task is complete.

```python
from langchain_openai import ChatOpenAI
from langchain.agents import create_openai_tools_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

tools = [get_weather]
llm = ChatOpenAI(model="gpt-4o", temperature=0)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with access to tools. Always use them when required."),
    ("placeholder", "{chat_history}"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

# Create and execute
agent = create_openai_tools_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = agent_executor.invoke({"input": "What's the weather like in Tokyo right now in celsius?"})
print(result["output"])
```

## Safety First
Never deploy agents with read/write database tools or email APIs without **Human-in-the-loop (HITL)** approvals. An LLM can easily misinterpret a command and autonomously drop a production table or send a sensitive email.
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Source: https://aimastermindlab.com/tutorials/ai-agents-langchain — AI Mastermind Lab (https://aimastermindlab.com)