exa-rag
Exa RAG enables you to construct retrieval-augmented generation systems that pull live web data into your AI applications. Integrate with LangChain, LlamaIndex, Vercel AI SDK, or Claude MCP to add grounded search capabilities, citations, and fact-checking to your agents.
Exa RAG helps you build retrieval-augmented generation pipelines with real-time web search integration.
AI-generated summary based on this skill's SKILL.md
Install
ejirocodes/agent-skills/exa-rag
git clone https://github.com/ejirocodes/agent-skills
cp -r agent-skills/exa/skills/exa-rag ~/.claude/skills/exa-ragFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I build RAG with Exa?
Exa RAG lets you construct retrieval-augmented generation systems by integrating live web search into your AI workflows. Start by initializing an Exa client with your API key, then use the ExaSearchRetriever to fetch real-time web results. Connect this retriever to LangChain, LlamaIndex, Vercel AI SDK, or Claude MCP to ground your model's responses in current web data and generate cited answers that reduce hallucinations.
Can I use Exa RAG with LangChain or LlamaIndex?
Yes, Exa RAG integrates seamlessly with both LangChain and LlamaIndex. For LangChain, use the ExaSearchRetriever to connect Exa web search into your retrieval chains. For LlamaIndex, Exa RAG provides compatible retriever patterns that work within LlamaIndex query engines. Both frameworks allow you to build end-to-end RAG pipelines that pull live web data and generate grounded, fact-checked responses.
How does Exa RAG reduce hallucinations in AI agents?
Exa RAG reduces hallucinations by grounding your AI agent's responses in real-time web data. Instead of relying solely on training data, the agent retrieves current, verified information from the web and generates citations. This fact-checking layer ensures your agent produces accurate, sourced answers rather than generating plausible-sounding but false content.
What web search capabilities can I add to my chatbot?
Exa RAG enables you to add real-time web search, semantic search, domain-filtered retrieval, and current-events awareness to your chatbot. You can fetch live results, extract highlights for context, generate grounded responses with citations, and integrate with Claude MCP, Vercel AI SDK, or other agent frameworks. This transforms your chatbot into a fact-aware assistant that accesses up-to-date information.
Is Exa RAG available under an open-source license?
Yes, Exa RAG is released under the MIT license, making it free to use, modify, and distribute in both open-source and commercial projects. The MIT license provides permissive terms with minimal restrictions, allowing you to integrate Exa RAG into your applications with confidence.
How do I set up an async RAG retriever pattern with Exa?
Exa RAG supports async retriever patterns for high-performance RAG pipelines. Initialize an async Exa client, define your retrieval queries, and use async/await syntax to fetch web results concurrently. This pattern integrates with LangChain's async chains and LlamaIndex's async query engines, enabling scalable, non-blocking retrieval that doesn't slow down your AI agent's response time.
SKILL.md
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Exa RAG Integration
Quick Reference
| Topic | When to Use | Reference |
|---|---|---|
| LangChain | Building RAG chains with LangChain | langchain.md |
| LlamaIndex | Using Exa as a LlamaIndex data source | llamaindex.md |
| Vercel AI SDK | Adding web search to Next.js AI apps | vercel-ai.md |
| MCP & Tools | Claude MCP server, OpenAI tools, function calling | mcp-tools.md |
Essential
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exa/skills/exa-rag/SKILL.md
exa/skills/exa-rag/references/langchain.md
exa/skills/exa-rag/references/llamaindex.md
exa/skills/exa-rag/references/mcp-tools.md
exa/skills/exa-rag/references/vercel-ai.md