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LangChain is the go-to framework for building LLM applications. CrawlForge MCP provides the web data layer that LangChain apps often need. Together, they're a powerful combination for AI engineers.
This tutorial shows you 5 practical integration patterns with working code examples.
Prerequisites
Get your CrawlForge API key at crawlforge.dev/signup - 1,000 free credits included.
1. Web-Augmented RAG Pipeline
The most common use case: enhance your RAG system with fresh web data.
The Problem
Static RAG systems can't answer questions about:
- Current events
- Updated documentation
- Real-time pricing
- Recent releases
The Solution
Use CrawlForge to fetch and index web content on-demand.
Credit Cost: 2 credits per URL fetched
2. Research Agent with Tool Calling
Build an agent that can search and research topics autonomously.
Credit Cost: 5 credits per search + 2 credits per extraction
3. Competitive Intelligence Pipeline
Monitor competitors and extract structured data.
Credit Cost: 2 credits per competitor
4. Document Processing Chain
Process PDFs and documents from the web.
Credit Cost: 2 credits per document
5. Real-Time Monitoring Chain
Track changes and react to updates.
Credit Cost: 2-5 credits per check
Best Practices
1. Cache Aggressively
2. Batch When Possible
3. Handle Rate Limits Gracefully
Get Started
- Sign up at crawlforge.dev/signup
- Get your API key (1,000 free credits)
- Install LangChain JS and start building
Need help? Check our API documentation or reach out on GitHub.
