What is LangChain?
The most popular framework for building LLM-powered applications. Provides chains, agents, and tools for composing AI workflows.
Searching X (Twitter) with LangChain
This integration lets your LangChain agent search X (Twitter) in real time via the Scavio API. The agent gets back structured JSON with post text, author handles, created_at timestamps, favorites, retweets, replies, quotes -- ready for reasoning and decision-making.
Setup
Bash
pip install langchain langchain-scavio langchain-openaiCode Example
Here is a complete LangChain agent that searches X (Twitter) using Scavio:
Python
from langchain_scavio import ScavioSearch
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
# scavio_api_key is the keyword the tool takes; SCAVIO_API_KEY also works.
tool = ScavioSearch(scavio_api_key="sk_live_your_key")
llm = ChatOpenAI(model="gpt-5.5")
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful research assistant."),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, [tool], prompt)
executor = AgentExecutor(agent=agent, tools=[tool])
result = executor.invoke({"input": "Search X (Twitter) for AI agents"})
print(result["output"])Full Working Example
A production-ready example with error handling:
Python
"""
Query X (Twitter) with LangChain + Scavio.
ScavioSearch calls POST /api/v1/x/search under the hood.
"""
import os
from langchain_scavio import ScavioSearch
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
os.environ.setdefault("SCAVIO_API_KEY", "sk_live_your_key")
tool = ScavioSearch()
llm = ChatOpenAI(model="gpt-5.5")
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant with access to real-time search."),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, [tool], prompt)
executor = AgentExecutor(agent=agent, tools=[tool], verbose=True)
result = executor.invoke({"input": "Search X (Twitter) for AI agents"})
print(result["output"])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 LangChain integration. Paid plans start at $30/month for higher volumes.