AI Agents with LangGraph
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Project Demo
In lesson 1, you started an AI Agent project to localize app strings from English into Spanish. So far, all that it does is translate a few words. You won’t add any functionality to your agent today, but you’ll modify it to use LangGraph.
def extract(user_input):
prompt = """Analyze if the user is asking for a translation.
If so, respond with only the text to translate.
Do not translate the text yourself. Otherwise, respond normally.
For example, if the user says 'How do you say hello in Spanish?'
you should respond 'hello' """
completion = llm.chat.completions.create(
model="gpt-4o",
messages=[
{"role": "system", "content": prompt},
{"role": "user", "content": user_input}
]
)
return completion.choices[0].message.content
from langgraph.graph import Graph
graph = Graph()
graph.add_node("extractor", extract)
graph.add_node("translator", translate)
graph.add_edge("extractor", "translator")
graph.set_entry_point("extractor")
graph.set_finish_point("translator")
app = graph.compile()
user_input = "How do you say goodbye in Spanish?"
result = app.invoke(user_input)
print(result)