Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Linkup Research is live — ranked #1 on the SealQA-0 benchmark. Try it →
How to use Linkup in LlamaIndex
Get your Linkup API Key
Install dependencies
pip install llama-index llama-index-tools-linkup-research
Create your agent
from llama_index.core.agent import FunctionCallingAgent
from llama_index.llms.openai import OpenAI
from llama_index.tools.linkup_research.base import LinkupToolSpec
# Tool initialization
linkup_tool = LinkupToolSpec(
api_key="<YOUR LINKUP API KEY>",
depth="standard", # Options: "fast", "standard" or "deep"
output_type="searchResults", # Options: "searchResults", "sourcedAnswer", or "structured"
)
| Parameter | Options | Description |
|---|---|---|
depth | "fast", "standard", "deep" | Controls search depth. "fast" is the fastest with no LLM processing, "standard" leverages agentic search, "deep" performs more thorough research. |
outputType | "searchResults", "sourcedAnswer", "structured" | Determines the format of returned information. |
Initialize and run your agent
# Agent initialization
agent = FunctionCallingAgent.from_tools(
linkup_tool.to_tool_list(),
llm=OpenAI(
api_key="<YOUR OPENAI API KEY>",
model="gpt-4o-mini"
),
)
# Agent invocation
response = agent.chat("Can you tell me which women were awarded the Physics Nobel Prize")
print(response)
# Sample output
{
"response": "Marie Curie (1903), Maria Goeppert Mayer (1963), Donna Strickland (2018), and Andrea Ghez (2020) have been awarded the Nobel Prize in Physics.",
"sources": [
# Source information would appear here
]
}
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