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, authors, scores, 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 ScavioRedditPost
from langchain_openai import ChatOpenAI
tool = ScavioRedditPost(scavio_api_key="sk_live_your_key")
llm = ChatOpenAI(model="gpt-5.5")
agent = create_react_agent(llm, [tool])
result = agent.invoke({
"messages": [{"role": "user", "content": "Search Reddit Comments Tree for t3_1u1143i"}]
})
print(result["messages"][-1].content)Full Working Example
A production-ready example with error handling:
Python
"""
LangGraph agent that queries Reddit Comments Tree via Scavio.
ScavioRedditPost calls POST /api/v1/reddit/post/comments under the hood.
"""
import os
from langgraph.prebuilt import create_react_agent
from langchain_scavio import ScavioRedditPost
from langchain_openai import ChatOpenAI
os.environ.setdefault("SCAVIO_API_KEY", "sk_live_your_key")
tool = ScavioRedditPost()
llm = ChatOpenAI(model="gpt-5.5")
agent = create_react_agent(llm, [tool])
result = agent.invoke({
"messages": [{"role": "user", "content": "Search Reddit Comments Tree for t3_1u1143i"}]
})
for message in result["messages"]:
if hasattr(message, "content") and message.content:
print(message.type, str(message.content)[:200])Pricing
Scavio offers a free tier with 50 credits on signup (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.