Search the open web,
built for LLM agents.

import os, requests

r = requests.get(
    "https://bitseek.com/v1/search",
    headers={"Authorization": f"Bearer {os.environ['BITSEEK_KEY']}"},
    params={"q": "fastapi tutorial", "limit": 5},
    timeout=15,
)
print(r.json()["results"][0])

const res = await fetch(
  "https://bitseek.com/v1/search?q=fastapi+tutorial&limit=5",
  { headers: { "Authorization": `Bearer ${process.env.BITSEEK_KEY}` } }
);
const data = await res.json();
console.log(data.results[0]);

import anthropic, os, requests

client = anthropic.Anthropic()

tools = [{
    "name": "bitseek_search",
    "description": "Search the open web. Use for current events, facts, or any info beyond the model's training cutoff.",
    "input_schema": {
        "type": "object",
        "properties": {
            "query": {"type": "string", "description": "The search query."},
            "limit": {"type": "integer", "default": 5},
        },
        "required": ["query"],
    },
}]

msg = client.messages.create(
    model="claude-3-5-sonnet-latest",
    max_tokens=1024,
    tools=tools,
    messages=[{"role": "user", "content": "Find recent papers on RAG-vs-long-context."}],
)

# When msg.stop_reason == "tool_use", call BitSeek and feed back:
for block in msg.content:
    if block.type == "tool_use" and block.name == "bitseek_search":
        results = requests.get(
            "https://bitseek.com/v1/search",
            headers={"Authorization": f"Bearer {os.environ['BITSEEK_KEY']}"},
            params=block.input,
        ).json()

from langchain_core.tools import tool
import os, requests

@tool
def bitseek_search(query: str, limit: int = 5) -> list:
    """Search the open web with BitSeek. Use for current events and facts."""
    r = requests.get(
        "https://bitseek.com/v1/search",
        headers={"Authorization": f"Bearer {os.environ['BITSEEK_KEY']}"},
        params={"q": query, "limit": limit},
        timeout=15,
    )
    return r.json()["results"]

# Bind to any LangChain model:
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o").bind_tools([bitseek_search])
print(llm.invoke("What happened at WWDC?"))

from llama_index.core.tools import FunctionTool
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
import os, requests

def bitseek_search(query: str, limit: int = 5) -> list:
    """Search the open web with BitSeek."""
    r = requests.get(
        "https://bitseek.com/v1/search",
        headers={"Authorization": f"Bearer {os.environ['BITSEEK_KEY']}"},
        params={"q": query, "limit": limit},
    )
    return r.json()["results"]

tool = FunctionTool.from_defaults(fn=bitseek_search)
agent = ReActAgent.from_tools([tool], llm=OpenAI(model="gpt-4o"))
print(agent.chat("Latest research on Mamba SSMs?"))

import { generateText, tool } from "ai";
import { openai } from "@ai-sdk/openai";
import { z } from "zod";

const bitseekSearch = tool({
  description: "Search the open web with BitSeek.",
  parameters: z.object({
    query: z.string(),
    limit: z.number().default(5),
  }),
  execute: async ({ query, limit }) => {
    const r = await fetch(
      `https://bitseek.com/v1/search?q=${encodeURIComponent(query)}&limit=${limit}`,
      { headers: { Authorization: `Bearer ${process.env.BITSEEK_KEY}` } }
    );
    return (await r.json()).results;
  },
});

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools: { bitseekSearch },
  prompt: "What's the latest on AI legislation in the EU?",
});