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Documentation Index

Fetch the complete documentation index at: https://docs.linkup.so/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

Linkup can be used with LlamaIndex to create advanced AI agents and workflows based on internal and web data.

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Getting Started with Linkup in LLamaIndex

1

Get your Linkup API Key

Get your API key

Create a Linkup account for free to get your API key.
2

Install dependencies

pip install llama-index llama-index-tools-linkup-research
3

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"
)
ParameterOptionsDescription
depthfast, standard, deepControls search depth. fast is the fastest with no LLM processing, standard leverages agentic search, deep performs more thorough research.
output_typesearchResults, sourcedAnswer, structuredDetermines the format of returned information.
4

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)

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Example 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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