LangChain – Quick Guide
Table Of Contents:
- Quick Start
- Build A Basic Agent
- Building A Real World Agent.
(1) Quickstart
pip install -U langchain deepagents
export OPENAI_API_KEY = "your-api-key"(2) Building A Basic Agent
from langchain.agents import create_agent
def get_weather(city:str)->str:
"""Get Weather For A Given City"""
return f"It's Always Sunny In {city}!"
agent = create_agent(
model = "openai:gpt-5.5",
tools = [get_weather],
system_prompt = "You Are A Helpful Assistant",
)
result = agent.invoke(
{"message":[{"role":"user", "content":"What Is The Weather In Bhubaneswar"}]}
)
print(result["message"][-1].content_blocks)(3) Building A Real World Agent
SYSTEM_PROMPT = """
You are a literary data assistant.
IMPORTANT RULES:
1. If a user provides a URL, ALWAYS call fetch_text_from_url.
2. Never say you cannot access URLs.
3. Never answer questions about a URL from memory.
4. Before answering any question about a URL:
- call fetch_text_from_url(url)
- read the returned text
- then answer.
5. The fetch_text_from_url tool is your ONLY way to access URL content.
Available tools:
fetch_text_from_url(url: str)
Fetches text from a URL.
You MUST use the tool whenever a URL is present.
"""import urllib.error
import urllib.request
from langchan.tools import tool
from langchain.chat_models import init_chat_model
from langgraph.checkpoint.memory import InMemorySaver
@tool
def fetch_text_from_url(url:str)-> str:
"""Fetch the document from a URL.
"""
req = urllib.request.Request(
url,
headers={"User-Agent": "Mozilla/5.0 (compatible; quickstart-research/1.0)"},
)
try:
with urllib.request.urlopen(req, timeout=120) as resp:
raw = resp.read()
except urllib.error.URLError as e:
return f"Fetch failed: {e}"
text = raw.decode("utf-8", errors="replace")
return textmodel = init_chat_model(
"ollama:qwen3:0.6b",
temperature=0.5,
timeout=300,
max_token=25000,
)from langgraph.checkpoint.memory import InMemorySaver
checkpointer = InMemorySaver()
from langchain.agents import create_agent
from langchain.deep_agents import create_deep_agent
agent = create_agent(
model = model,
tools = [fetch_text_from_url],
system_prompt = SYSTEM_PROMPT
)deep_agent = create_deep_agent(
model = model,
tools = [fetch_text_from_url],
system_prompt = SYSTEM_PROMPT,
)agent_result = agent.invoke(
{"messages": [{"role": "user", "content": content}]},
config={"configurable": {"thread_id": "great-gatsby-lc"}},
)deep_agent_result = deep_agent.invoke(
{"messages": [{"role": "user", "content": content}]},
config={"configurable": {"thread_id": "great-gatsby-da"}},
)print(agent_result["messages"][-1].content_blocks)