Jobs to Be Done
- Attach a live-search tool to LangChain, LlamaIndex, and custom agents
- Ground LLM responses in up-to-date information to reduce hallucinations
- Let agents research products, news, and videos autonomously
- Keep agent tool calls fast and cheap at production scale
- Expose the same search primitive across multiple agents and MCP servers
Common Workflows
MCP server with search tools
Wrap Scavio behind an MCP server that exposes google_search, amazon_search, and youtube_search tools. Any Claude, Cursor, or custom client can then plug in and query live results without writing new integration code for each agent.
Example: mcp.server.tool('google_search', handler=lambda q: scavio.google(q).results)
Agent research loop
Build an autonomous research agent that decomposes a user question, calls Scavio for each sub-query, reads the top results, and iterates until it has enough evidence to answer. Scavio returns structured JSON so the agent never wastes tokens parsing HTML.
Example: agent.plan(question) -> [scavio.google(sub_q) for sub_q in plan] -> synthesize
Grounded chat with citations
In a customer-facing chatbot, every factual claim triggers a Scavio call in parallel with generation. Final answers weave live snippets into the response with inline citations linking back to original sources, turning a stock LLM into a grounded assistant.
Example: on_claim: scavio.google_news(claim, recency='24h') -> cite
Pain Points Scavio Solves
- Agents hallucinate when they cannot see post-training information
- Raw HTML scraping blows up context windows and burns tokens
- Rate limits on unofficial search APIs kill agent reliability in production
- Different agents need different search semantics but shouldn't each reimplement scraping
Tools AI Engineers Pair With Scavio
LangChain, LlamaIndex, Anthropic SDK, OpenAI SDK, MCP, Pinecone. Scavio returns structured JSON that fits into any of these tools.
Quick Start
import requests
response = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": "your_scavio_api_key"},
json={"query": "scavio.google('latest anthropic model release notes', recency='7d')"},
)
data = response.json()
# Analyze results for your workflow
for result in data.get("organic_results", [])[:10]:
print(result["title"], "-", result["link"])Platforms You Will Use
Web search with knowledge graph, PAA, and AI overviews
Google News
News search with headlines and sources
YouTube
Video search with transcripts and metadata
Amazon
Product search with prices, ratings, and reviews
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