The search layer for AI agents.
Ninelayer markets a search layer for agent memory, public-web search, and a customer's private corpus, delivered as cited, context-ready evidence packets through MCP and REST API.
Search infrastructure for agents and RAG workflows, focused on structured and attributed evidence rather than browser-style search results.
Teams building MCP-compatible agents or RAG systems that need public and private context in one search layer.
It is also a poor fit for buyers who only need a browser-oriented search API rather than a developer integration for agents or RAG pipelines.
No curated rival set covers this tool yet, so these are simply other tools in the same architectural layer and product form. Treat it as a starting point, not a shortlist.
It describes memory, public search, and a customer's proprietary corpus as its three surfaces.
The vendor says results are typed evidence packets with source type, authority tier, and confidence information.
The site names Claude Code, Cursor, ChatGPT, LangChain, Windsurf, VS Code, Vercel AI, LlamaIndex, and others.
The vendor says requests use mandatory account filters and project content is not used for fine-tuning or model improvement.
The changelog records an MCP server launch on March 29, 2026 and Extract plus Scoped Search tools on April 20, 2026.
This page tells you whether NineLayer fits. Cheetah builds and runs the system it goes into — enrichment, sequencing, CRM and the joins between them.
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