For AI agents: docs.octen.ai/integrations/connect-llms-to-octen-search.md
Give your LLM access to real-time web information. Octen Web Search can be used as a tool with any LLM provider that supports function calling.Get API Key
Get your API key from the API Platform
API Reference
View the full Web Search API reference
Install
pip install octen
OpenAI
Define Octen Web Search as a tool and let GPT decide when to search.import json
from openai import OpenAI
from octen import Octen
openai = OpenAI(
api_key="your-openai-api-key",
base_url="https://api.openai.com/v1", # or your custom endpoint
)
octen = Octen(api_key="your-octen-api-key")
tools = [
{
"type": "function",
"function": {
"name": "web_search",
"description": "Search the web for real-time information.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query"
}
},
"required": ["query"]
}
}
}
]
messages = [{"role": "user", "content": "What are the latest AI news today?"}]
response = openai.chat.completions.create(
model="gpt-5.4",
messages=messages,
tools=tools,
)
# Handle tool calls
if response.choices[0].message.tool_calls:
tool_call = response.choices[0].message.tool_calls[0]
args = json.loads(tool_call.function.arguments)
# Call Octen Web Search
search_results = octen.search.search(query=args["query"], count=5)
# Feed results back to GPT
messages.append(response.choices[0].message)
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": json.dumps(search_results.results)
})
final = openai.chat.completions.create(
model="gpt-5.4",
messages=messages,
tools=tools,
)
print(final.choices[0].message.content)
Anthropic
Use Octen Web Search as a tool with Claude.import json
import anthropic
from octen import Octen
client = anthropic.Anthropic(
api_key="your-anthropic-api-key",
base_url="https://api.anthropic.com", # or your custom endpoint
)
octen = Octen(api_key="your-octen-api-key")
tools = [
{
"name": "web_search",
"description": "Search the web for real-time information.",
"input_schema": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query"
}
},
"required": ["query"]
}
}
]
messages = [{"role": "user", "content": "What are the latest AI news today?"}]
response = client.messages.create(
model="claude-opus-4-6",
max_tokens=4096,
messages=messages,
tools=tools,
)
# Handle tool use
if response.stop_reason == "tool_use":
tool_block = next(b for b in response.content if b.type == "tool_use")
# Call Octen Web Search
search_results = octen.search.search(
query=tool_block.input["query"], count=5
)
# Feed results back to Claude
messages.append({"role": "assistant", "content": response.content})
messages.append({
"role": "user",
"content": [
{
"type": "tool_result",
"tool_use_id": tool_block.id,
"content": json.dumps(search_results.results)
}
]
})
final = client.messages.create(
model="claude-opus-4-6",
max_tokens=4096,
messages=messages,
tools=tools,
)
print(final.content[0].text)
Google Gemini
Use Octen Web Search as a tool with Gemini.from google import genai
from google.genai import types
from octen import Octen
client = genai.Client(api_key="your-gemini-api-key")
octen = Octen(api_key="your-octen-api-key")
web_search_tool = types.Tool(
function_declarations=[
types.FunctionDeclaration(
name="web_search",
description="Search the web for real-time information.",
parameters=types.Schema(
type="OBJECT",
properties={
"query": types.Schema(
type="STRING",
description="The search query",
),
},
required=["query"],
),
)
]
)
response = client.models.generate_content(
model="gemini-3.1-pro-preview",
contents="What are the latest AI news today?",
config=types.GenerateContentConfig(tools=[web_search_tool]),
)
# Handle function call
part = response.candidates[0].content.parts[0]
if part.function_call:
query = part.function_call.args["query"]
# Call Octen Web Search
search_results = octen.search.search(query=query, count=5)
# Feed results back to Gemini
response = client.models.generate_content(
model="gemini-3.1-pro-preview",
contents=[
types.Content(
role="user",
parts=[types.Part.from_text("What are the latest AI news today?")],
),
response.candidates[0].content,
types.Content(
role="tool",
parts=[
types.Part.from_function_response(
name="web_search",
response={"results": search_results.results},
)
],
),
],
config=types.GenerateContentConfig(tools=[web_search_tool]),
)
print(response.text)
LangChain
Use Octen Web Search as a LangChain tool.from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
from langgraph.prebuilt import create_react_agent
from octen import Octen
octen = Octen(api_key="your-octen-api-key")
@tool
def web_search(query: str) -> str:
"""Search the web for real-time information."""
results = octen.search.search(query=query, count=5)
return str(results.results)
llm = ChatOpenAI(
model="openai/gpt-5.4",
api_key="your-api-key",
base_url="https://api.openai.com/v1", # or your custom endpoint
)
agent = create_react_agent(llm, [web_search])
response = agent.invoke(
{"messages": [HumanMessage(content="What are the latest AI news today?")]}
)
print(response["messages"][-1].content)