Master Exa with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up for a free Exa account at exa.ai and get 1,000 free searches monthly Generate your API key from the Exa dashboard and review the documentation Install the Exa Python SDK (pip install exa_py) or use the REST API directly Test basic neural search with a simple query to understand semantic vs keyword results Integrate Exa search into your AI agent workflow using provided LangChain or custom integrations
💡 Quick Start: Follow these 1 steps in order to get up and running with Exa quickly.
Explore the key features that make Exa powerful for ai search workflows.
A purpose-built embeddings-based web index that ranks results by semantic meaning, allowing AI agents to issue natural-language queries and retrieve conceptually relevant pages even when keywords do not match.
Fetches clean, parsed text, summaries, and highlights from any URL returned by search (or supplied directly), removing the need for custom scraping, boilerplate stripping, and HTML cleanup in RAG pipelines.
Returns a synthesized natural-language answer with inline citations from the open web, enabling developers to embed Perplexity-style cited responses into their own applications via a single API call.
Higher-level product that turns a natural-language description of an entity type into a structured, spreadsheet-style dataset of matching results enriched with custom columns — useful for lead generation, recruiting, and market research.
Orchestrates multi-step search, browsing, and synthesis to produce long-form, cited research reports from a single high-level prompt, designed for agentic workflows that need depth rather than a single SERP.
Official Model Context Protocol server plus SDKs and integrations let MCP-aware clients (Claude, Cursor) and frameworks (LangChain, LlamaIndex) call Exa as a native tool without custom adapters.
Supports filtering by domain, date range, language, and content type, with an auto mode that dynamically chooses between neural and keyword search per query for optimal recall and precision.
Prompt builder, API dashboard with usage analytics, status page, and detailed documentation reduce time-to-first-query and make it easier to monitor production deployments.
Exa is built natively for AI consumption rather than human browsing. It offers neural/semantic search over a custom-built web index, returns clean parsed content (not HTML), and exposes endpoints like Answer, Contents, and Websets that are designed around RAG and agent workflows rather than around displaying SERPs to end users.
Yes. Exa provides a free tier with monthly API credits so developers can try the Search, Contents, and Answer endpoints before upgrading. Paid plans add higher rate limits, more credits, and access to advanced features like Websets and Deep Research.
Yes. Exa publishes official SDKs, integrates with popular agent frameworks, and ships an MCP (Model Context Protocol) server that lets MCP-aware clients such as Claude and Cursor call Exa as a native tool without custom glue code.
Websets is a higher-level product that turns a natural-language description of an entity type (for example, 'Series B fintech startups in Europe hiring engineers') into a structured dataset of matching results with enriched columns. The Search API returns ranked URLs for a single query, while Websets orchestrates many searches plus enrichment to produce a spreadsheet-style output.
Exa is positioned specifically for production RAG and agent use cases. It offers the Contents API for clean text retrieval, supports filtering by domain and date for grounding control, and provides citations from the Answer endpoint. Teams should still benchmark recall and latency against their specific corpus needs before relying on it as the sole retrieval layer.
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Tutorial updated March 2026