Developer Resources
Tutorials, benchmarks, and deep dives on RAG retrieval, web extraction, and AI agent tooling.
How to Build a RAG Pipeline with Fetchium and LangChain
A step-by-step guide to replacing a naive web search + scrape setup with Fetchium's federated, token-budgeted retrieval in your LangChain pipeline.
Read more →Token-Budgeted Extraction: Why Context Size Matters for LLM Cost
A 100-page website has 60,000+ tokens. Your LLM needs 2,000. The QATBE algorithm bridges that gap — here's how it works and why it saves 60–90% on context costs.
Read more →Web Scraping for AI vs. Web Scraping for Humans: What's Different
Traditional web scraping delivers HTML. AI needs semantic content, structured citations, and token-budgeted output. This post explains the architectural differences.
Using Fetchium as an MCP Tool in Claude Desktop and Cursor
The Model Context Protocol lets AI clients call external tools without code. This tutorial shows how to configure Fetchium's MCP server in Claude Desktop and Cursor in under 5 minutes.
Search API Benchmark 2025: Latency, Accuracy, and Cost Compared
We tested Fetchium, Tavily, Exa, SerpAPI, and Brave Search with 50 standardized queries. Here are the results: latency, success rate, content quality, and total cost per 1K queries.