LangChain
Orchestration in Action

Build chains, run RAG pipelines, and see why orchestrated AI beats raw API calls. Real inference, powered by Cloudflare Workers AI.

🔗 Chains 📚 RAG ⚡ Live Inference Cloudflare Edge

Build Your Chain

Ready
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Prompt Template
Write a {length} explanation of {topic} for a {audience} audience.
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System Prompt
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Model
Gemma 3 12B · Google · via Cloudflare Workers AI
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Output Parser
prompt | model | parser

Chain Execution

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Click "Run Chain" to execute the pipeline...
Steps: — Model: — Latency: —

Knowledge Base

8 document chunks

These chunks represent your company's internal documents. The RAG pipeline retrieves the most relevant ones to answer your question.

Ask a Question

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Choose a Task

Raw API Call

Latency: —
Raw response will appear here...
1 API call · No system prompt · No parsing

Chain (Orchestrated)

Latency: —
Chain response will appear here...
System prompt + template + output parsing
How It Works

What LangChain Actually Does

LangChain sits between your business logic and the AI model. Instead of writing raw API calls, you compose chains — reusable pipelines that handle prompt templating, model selection, output parsing, and error recovery. The demos above use the same Cloudflare Workers AI endpoint, but the chain wraps it with structure that makes the output reliable and consistent.

Composability

Chains snap together like Lego. Swap the model, change the parser, add memory — without rewriting the pipeline. The Chain Builder tab shows this in action.

RAG Pattern

Retrieval-Augmented Generation grounds AI responses in your data. The RAG Simulator shows how embedding-based retrieval + context injection produces answers that cite real sources.

Quality Gap

The Raw vs Chain tab proves the point: same model, same prompt, dramatically different output. System prompts, structured templates, and output parsing are the difference.

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