Connect LLMs to Octen Search

pip install octen

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)

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)

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)

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)