Brave Search API, Tavily, and Ninelayer can all bring web context into AI systems.
They are optimized for different jobs.
Brave Search API
Brave Search API is a search API backed by Brave's web index.
It is useful when you want web search results and plan to build your own extraction, ranking, and agent-context layer.
Best for:
- general web search
- custom search products
- teams that want raw search API control
Tavily
Tavily is built for AI search and retrieval workflows.
It is useful when you want a search API with AI-app-friendly options like answers, extracted content, and domain controls.
Best for:
- AI assistants
- web research products
- search-plus-extraction use cases
Ninelayer
Ninelayer is built for AI agents.
It exposes search and URL extraction through MCP, so tools like Claude Code, Cursor, and custom agents can call retrieval directly.
Best for:
- coding agents
- MCP workflows
- source-aware evidence
- reducing token waste
- first-shot usefulness
The Decision
Use Brave when you want a search API foundation.
Use Tavily when you want AI-oriented search features.
Use Ninelayer when the consumer is an agent that needs compact evidence through MCP.
The Practical Takeaway
The best AI-agent search tool is not only the one with the broadest web index.
It is the one that returns context the agent can safely act on.
Sources
- Brave Search API docs: Getting started
- Tavily docs: Search endpoint
- Ninelayer: Full LLM reference