openserp 0.2.2

Python SDK for the OpenSERP self-hosted server and OpenSERP Cloud.

openserp

pip install openserp

Cloud:

import os
from openserp import OpenSERP

client = OpenSERP(api_key=os.environ["OPENSERP_KEY"])
resp = client.search(engine="google", text="openserp")

print(resp.results[0].title, resp.results[0].url)

Self-hosted:

from openserp import OpenSERP

client = OpenSERP(base_url="http://localhost:7000")
resp = client.search(engine="bing", text="openserp")

print(resp.results[0].title, resp.results[0].url)

Python SDK for the OpenSERP multi-engine SERP API — Google, Bing, Yandex, Baidu, DuckDuckGo, and Ecosia results in a single call. Works against the self-hosted open-source server and against OpenSERP Cloud with the same code.

Use it for AI grounding, RAG pipelines, LLM tool use, agent tool use, LangChain / LlamaIndex integrations, SEO rank tracking, competitor analysis, and search-powered automations. Open-source alternative to SerpAPI, DataForSEO, ScrapingBee, Bright Data SERP, Oxylabs SERP, and Zenserp.

Also available for TypeScript / JavaScript: @openserp/sdk.

Alpha — the API may change before 1.0.0. Pin a version in production.

Contents

• Install
• Quickstart — OSS (self-hosted)
• Quickstart — Cloud
• Why two backends?
• Search
• Extract
• Images
• Async
• Endpoint availability
• Telemetry
• Error handling
• Retry hook
• Use cases

Install

pip install openserp

DataFrame export is an optional extra:

pip install "openserp[pandas]"

Requires Python 3.10+.

Quickstart — OSS (self-hosted)

Run the open-source server locally, no API key required:

docker run -p 7000:7000 karust/openserp serve

from openserp import OpenSERP

client = OpenSERP(base_url="http://localhost:7000")

resp = client.search(
    engine="google",
    text="openserp",
    limit=10,
    region="US",
)

print(resp.results[0].title, resp.results[0].url)

If you pass no options, the client defaults to http://localhost:7000.

Quickstart — Cloud

Get an API key from the API keys section in the dashboard. When api_key is set, the SDK defaults base_url to https://api.openserp.org/v1 and sends Authorization: Bearer ... for you.

import os
from openserp import OpenSERP

client = OpenSERP(api_key=os.environ["OPENSERP_KEY"])

resp = client.search(engine="google", text="openserp")

print(resp.results[0].title)
print(client.last_response.credits)  # CreditInfo(used=..., remaining=...)

If both base_url and api_key are set, base_url wins and the key is still sent. Use this for an authenticated self-hosted deployment. Add backend="oss" when you also need OSS-only methods such as stats() or health().

Why two backends?

OpenSERP Cloud uses the same public HTTP contract as the OSS server, with a /v1/ prefix and bearer auth. The same SDK call works on both; you only change base_url / api_key. Start with OSS locally, then move to Cloud when you want the hosted API. See openserp.org/docs/oss-vs-cloud for the full comparison.

Search

single = client.search(engine="bing", text="golang", limit=10, region="US")

mega = client.mega_search(
    text="golang",
    engines=["google", "bing", "yandex"],
    mode="balanced",
    limit=20,
)

fast = client.fast_search(text="golang", engines=["google", "bing"])
any_ = client.any_search(text="golang", engines=["google", "yandex"])

mega_search aggregates multiple engines. mode is "balanced" (default, merged and deduplicated), "any" (first successful engine wins), or "fast" (engines reordered by recent health). fast_search / any_search are sugar for the matching mode.

To enrich top search results with cleaned page content, pass the extraction flags:

grounded = client.search(
    engine="google",
    text="openserp docs",
    extract=True,
    extract_top=3,
    extract_mode="auto",
    min_runes=500,
)

print(grounded.results[0].extracted.content)

Extract

page = client.extract(
    url="https://openserp.org/docs",
    mode="auto",
    clean=True,
)

print(page.markdown)

Use min_runes to set the auto-mode escalation floor, clean=False for whole-page readable extraction, and use_llms_txt=True to prefer /llms-full.txt or /llms.txt for site-root URLs. Non-JSON formats are returned as strings:

markdown = client.extract(url="https://openserp.org", format="markdown")

Images

images = client.image(engine="bing", text="golang logo", limit=20)

mega_images = client.mega_image(text="golang logo", engines=["bing", "google"])

Async

import asyncio, os
from openserp import AsyncOpenSERP


async def main() -> None:
    async with AsyncOpenSERP(api_key=os.environ["OPENSERP_KEY"]) as client:
        resp = await client.search(engine="google", text="openserp")
        print(resp.results[0].title)


asyncio.run(main())

Run hundreds of queries concurrently with a semaphore:

import asyncio
from openserp import AsyncOpenSERP


async def main() -> None:
    sem = asyncio.Semaphore(20)
    queries = [f"keyword {i}" for i in range(500)]

    async with AsyncOpenSERP() as client:
        async def run(query: str):
            async with sem:
                return await client.search(engine="google", text=query, limit=10)

        responses = await asyncio.gather(*(run(q) for q in queries))
        print(len(responses))


asyncio.run(main())

Endpoint availability

OSS-only operational methods raise OssOnlyError when the client is configured for Cloud:

client.parse_google(html="<html>...</html>")
client.stats()
client.health()

Cloud-only account methods raise CloudOnlyError when the client is configured for OSS:

client.me()
client.pricing()
client.engines_status()
client.engines_capabilities()

The backend is inferred from base_url and api_key. Pass backend="oss" or backend="cloud" to the constructor to override.

Telemetry

client.last_response is updated after every HTTP response:

client.last_response.credits          # Cloud — CreditInfo(used, remaining)
client.last_response.engine_used      # both — X-Engine-Used
client.last_response.fallback_engine  # OSS only
client.last_response.cache            # OSS only
client.last_response.headers          # raw response headers (lower-cased)

Some self-hosted operational headers are not part of the Cloud response contract, so expect those fields to be None against api.openserp.org. credits is Cloud-specific.

Error handling

from openserp import OpenSERP, RateLimitError, CaptchaError, SERPError

client = OpenSERP(api_key="...")

try:
    client.search(engine="google", text="openserp")
except RateLimitError:
    # slow down or queue the request
    ...
except CaptchaError:
    # inspect the upstream search failure and retry later
    ...
except SERPError as err:
    print(err.status, err.code, err.reason, err.request_id)

Retry hook

The SDK does not apply a retry policy. Provide a hook when you want one:

import os, random, time
from openserp import OpenSERP, SERPError

RETRYABLE = {408, 429, 500, 502, 503}
client: OpenSERP


def should_retry(err: Exception, attempt: int) -> bool:
    if attempt >= 3 or not isinstance(err, SERPError) or err.status not in RETRYABLE:
        return False
    headers = client.last_response.headers if client.last_response else {}
    retry_after = float(headers.get("retry-after", 0) or 0)
    wait = retry_after or min(2 ** attempt * 0.25, 8.0)
    time.sleep(wait + random.random() * 0.25)
    return True


client = OpenSERP(api_key=os.environ["OPENSERP_KEY"], retry=should_retry)
client.search(engine="google", text="openserp")

Use cases

• AI grounding / RAG — feed top-N results into an LLM prompt (OpenAI, Anthropic, Ollama) for up-to-date answers.
• LLM tool use — expose client.search as a tool to your agent.
• SEO monitoring — daily rank tracking across multiple engines and regions, export to a DataFrame or Sheets.
• Competitor analysis — weekly diff of top-10 results for a keyword set.
• Data pipelines — stream SERPs to ClickHouse, BigQuery, or a DataFrame for NLP on snippets.

Quick SEO rank report with pandas:

import pandas as pd
from openserp import OpenSERP

client = OpenSERP()
keywords = ["openserp", "serp api", "google search api"]
frames = []

for keyword in keywords:
    resp = client.search(engine="google", text=keyword, region="US", limit=10)
    frame = resp.to_pandas()
    frame["keyword"] = keyword
    frames.append(frame)

pd.concat(frames, ignore_index=True).to_csv("rank-report.csv", index=False)