What is LangGraph?
A framework for building stateful, multi-step AI agent workflows as graphs. Built on top of LangChain for complex agentic applications.
Searching Reddit Comments Tree with LangGraph
This integration lets your LangGraph agent search Reddit Comments Tree in real time via the Scavio API. The agent gets back structured JSON with comment text, author, score, depth — ready for reasoning and decision-making.
Setup
Bash
pip install langgraph langchain-scavio langchain-openaiCode Example
Here is a complete LangGraph agent that searches Reddit Comments Tree using Scavio:
Python
from langgraph.prebuilt import create_react_agent
from langchain_scavio import ScavioSearch
from langchain_openai import ChatOpenAI
tool = ScavioSearch(api_key="your_scavio_api_key")
llm = ChatOpenAI(model="gpt-4o")
agent = create_react_agent(llm, [tool])
result = agent.invoke({
"messages": [{"role": "user", "content": "Search Reddit Comments Tree for r/ChatGPT best prompt for summarizing long articles"}]
})
print(result["messages"][-1].content)Full Working Example
A production-ready example with error handling:
Python
"""
LangGraph agent that searches Reddit Comments Tree via Scavio.
"""
from langgraph.prebuilt import create_react_agent
from langchain_scavio import ScavioSearch
from langchain_openai import ChatOpenAI
tool = ScavioSearch(api_key="your_scavio_api_key")
llm = ChatOpenAI(model="gpt-4o")
agent = create_react_agent(llm, [tool])
result = agent.invoke({
"messages": [{"role": "user", "content": "Search Reddit Comments Tree for r/ChatGPT best prompt for summarizing long articles"}]
})
for message in result["messages"]:
if hasattr(message, "content") and message.content:
print(f"{message.type}: {message.content[:200]}")Pricing
Scavio offers a free tier with 500 credits/month (1 credit per search). No credit card required. This is enough to build and test your LangGraph integration. Paid plans start at $30/month for higher volumes.