Grounded Search Engine for AI Agents

Tavily and Exa return a URL, a relevance score, and a content blob — the same shape as a raw search result. Ninelayer returns typed evidence packets: every result is classified by source type (official_docs, github_repo, community_evidence), attributed to a framework, assigned an authority tier (primary vs supporting), and scored with a confidence value. Your agent knows what kind of source it is reading before it reads a word. No custom parsing, no post-processing rerankers, no heuristics to write.

Standard search APIs return links, messy HTML, and SEO-ranked pages. Ninelayer returns authority-aware evidence packets: primary sources, supporting context, and community evidence in compact Markdown built for a context window.

An evidence packet is a compact search response designed for agents. It separates primary authority sources from supporting evidence and community context so the agent knows what to trust first.

No. Ninelayer is the search layer for AI agents. We are starting with coding agents because their failures are easy to measure: wrong docs lead to wrong code. The same model applies to research, support, sales, ops, and browser agents.

Sign in, create an auth token from the dashboard, and connect Ninelayer to your MCP-compatible agent or custom workflow.

A token is required for agent workflows. You can explore recipes and examples without one, then create a token from the dashboard when you are ready to connect your agent.

Anything that speaks the Model Context Protocol (MCP). This natively includes Claude Code, Cursor, Windsurf, and any custom agent built on frameworks that support MCP tools.

No. Ninelayer handles retrieval, extraction, source grouping, and formatting. The data is fed directly back to your agent as a clean string of facts, ready for immediate execution.

We index the live web with a heavy bias toward current developer documentation, API references, migration guides, release notes, GitHub issues, and technical forums to reduce stale-code hallucinations.

Yes. Ninelayer is a drop-in alternative to Tavily for AI agent workflows. Where Tavily returns raw search results, Ninelayer returns authority-ranked evidence packets pre-formatted for agent context windows — at roughly one-fifth the cost per query on the Pro plan.

Yes. Exa is optimized for semantic similarity search and returns raw content. Ninelayer is purpose-built for agentic workflows: it returns structured evidence packets with source-type classification, authority scoring, and compact Markdown output — consuming fewer tokens while delivering sufficient grounding for coding, research, and support agents.

Serper and Brave Search return raw SERP data — titles, URLs, and snippets intended for display. Ninelayer post-processes and ranks results specifically for LLM consumption: content is extracted, deduplicated, typed by source category, and returned as compact Markdown. Agents receive grounding data, not a search results page.

Ninelayer provides three core tools: ninelayer_deep_search for live web research across docs, GitHub, packages, and the web; ninelayer_get_url for extracting clean Markdown from a specific URL; and academic_search for paper-level search over the arXiv index. All tools are available over MCP (mcp.ninelayer.in) or a plain stateless HTTP API at /mcp/tools-call.

Run: claude mcp add ninelayer --transport http --url https://mcp.ninelayer.in/mcp/ and set your NINELAYER_AUTH_TOKEN environment variable. Your Claude Code sessions will then have access to ninelayer_deep_search and ninelayer_get_url automatically.

In Cursor settings, add a new MCP server with transport HTTP and URL https://mcp.ninelayer.in/mcp/. Pass your auth token as the NINELAYER_AUTH_TOKEN header or environment variable. Cursor will surface Ninelayer tools to any agent session automatically.