- Updated: March 17, 2026
- 8 min read
Building a Python client library for the OpenClaw Plugin Rating API
Answer: This guide walks you through building a fully functional Python client library for the OpenClaw Plugin Rating API, covering environment setup, authentication, core request handling, packaging, testing, and real‑world usage examples.
1. Introduction
The OpenClaw Plugin Rating API enables developers to submit, retrieve, and manage ratings for plugins hosted on the OpenClaw marketplace. While the API is REST‑ful and language‑agnostic, a dedicated Python client library streamlines integration, reduces boilerplate, and enforces consistent error handling across projects.
Why build a Python client?
- Encapsulate authentication logic in one place.
- Provide typed helper methods for common rating operations.
- Facilitate unit testing with mockable request layers.
- Allow distribution via
pipfor reuse across teams.
2. Prerequisites
Before you start, ensure your development environment meets the following requirements:
| Component | Minimum Version |
|---|---|
| Python | 3.9+ |
| pip | 22.0+ |
| virtualenv (optional but recommended) | 20.0+ |
3. Project Setup
3.1 Create a virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows use .venv\Scripts\activate3.2 Install core dependencies
We’ll rely on requests for HTTP calls and pydantic for data validation.
pip install requests pydantic4. Designing the Client Library
Good library design follows the MECE principle: each component has a single responsibility and does not overlap with others.
4.1 API Authentication
OpenClaw uses token‑based authentication. The token is passed in the Authorization header as Bearer <TOKEN>. Store the token securely (environment variable or secret manager).
4.2 Core request handling class
The OpenClawClient class will centralise request construction, response parsing, and error handling.
4.3 Helper methods for rating operations
submit_rating(plugin_id, score, comment=None)get_ratings(plugin_id, limit=10, offset=0)delete_rating(rating_id)
5. Implementing the Library
Below is a complete, runnable implementation. Save it as openclaw_client.py.
import os
import logging
from typing import List, Optional
import requests
from pydantic import BaseModel, Field, ValidationError
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
API_BASE_URL = "https://api.openclaw.io/v1"
class Rating(BaseModel):
id: int
plugin_id: str = Field(..., alias="pluginId")
user_id: str = Field(..., alias="userId")
score: int
comment: Optional[str] = None
created_at: str = Field(..., alias="createdAt")
class OpenClawClient:
def __init__(self, api_token: str, base_url: str = API_BASE_URL):
self.base_url = base_url.rstrip("/")
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_token}",
"Content-Type": "application/json",
"Accept": "application/json"
})
logger.info("OpenClawClient initialized with base URL %s", self.base_url)
def _handle_response(self, response: requests.Response):
try:
response.raise_for_status()
return response.json()
except requests.HTTPError as exc:
logger.error("HTTP error %s: %s", response.status_code, response.text)
raise exc
def submit_rating(self, plugin_id: str, score: int, comment: Optional[str] = None) -> Rating:
payload = {"pluginId": plugin_id, "score": score, "comment": comment}
logger.debug("Submitting rating payload: %s", payload)
resp = self.session.post(f"{self.base_url}/ratings", json=payload)
data = self._handle_response(resp)
try:
rating = Rating(**data)
logger.info("Rating submitted with ID %s", rating.id)
return rating
except ValidationError as ve:
logger.error("Response validation failed: %s", ve)
raise ve
def get_ratings(self, plugin_id: str, limit: int = 10, offset: int = 0) -> List[Rating]:
params = {"pluginId": plugin_id, "limit": limit, "offset": offset}
logger.debug("Fetching ratings with params: %s", params)
resp = self.session.get(f"{self.base_url}/ratings", params=params)
data = self._handle_response(resp)
ratings = []
for item in data.get("items", []):
try:
ratings.append(Rating(**item))
except ValidationError as ve:
logger.warning("Skipping invalid rating entry: %s", ve)
logger.info("Retrieved %d ratings", len(ratings))
return ratings
def delete_rating(self, rating_id: int) -> bool:
logger.debug("Deleting rating ID %s", rating_id)
resp = self.session.delete(f"{self.base_url}/ratings/{rating_id}")
if resp.status_code == 204:
logger.info("Rating %s deleted successfully", rating_id)
return True
else:
self._handle_response(resp)
return False
# Helper to load token from environment
def get_client_from_env() -> OpenClawClient:
token = os.getenv("OPENCLAW_API_TOKEN")
if not token:
raise EnvironmentError("OPENCLAW_API_TOKEN not set in environment")
return OpenClawClient(api_token=token)
5.1 Error handling and logging
The client uses Python’s built‑in logging module. Adjust the log level in production to WARNING or ERROR to reduce noise.
6. Publishing the Library
6.1 Packaging with setuptools
Create a setup.py at the project root:
from setuptools import setup, find_packages
setup(
name="openclaw-client",
version="0.1.0",
description="Python client for the OpenClaw Plugin Rating API",
author="Your Name",
packages=find_packages(),
install_requires=[
"requests>=2.28.0",
"pydantic>=1.10.0"
],
python_requires=">=3.9",
url="https://github.com/yourusername/openclaw-client",
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
)
6.2 Uploading to PyPI (optional)
After building the distribution, push it to PyPI so teammates can install with pip install openclaw-client:
python -m pip install --upgrade build twine
python -m build
twine upload dist/*7. Example Usage
Below is a short script that demonstrates authenticating, submitting a rating, and retrieving the latest ratings for a plugin.
from openclaw_client import get_client_from_env
def main():
client = get_client_from_env()
# Submit a new rating
new_rating = client.submit_rating(
plugin_id="com.example.myplugin",
score=5,
comment="Excellent functionality and great docs!"
)
print(f"Submitted rating ID: {new_rating.id}")
# Retrieve the most recent 5 ratings
recent = client.get_ratings(plugin_id="com.example.myplugin", limit=5)
for r in recent:
print(f"[{r.score}] {r.comment or 'No comment'} (by {r.user_id})")
if __name__ == "__main__":
main()
8. Testing the Library
8.1 Unit tests with pytest
Install pytest and responses for HTTP mocking:
pip install pytest responsesCreate tests/test_client.py:
import os
import json
import pytest
import responses
from openclaw_client import OpenClawClient
API_TOKEN = "test-token"
client = OpenClawClient(api_token=API_TOKEN, base_url="https://api.openclaw.io/v1")
@responses.activate
def test_submit_rating_success():
responses.add(
responses.POST,
"https://api.openclaw.io/v1/ratings",
json={"id": 123, "pluginId": "p1", "userId": "u1", "score": 4, "comment": "Good", "createdAt": "2024-01-01T00:00:00Z"},
status=201
)
rating = client.submit_rating("p1", 4, "Good")
assert rating.id == 123
assert rating.score == 4
@responses.activate
def test_get_ratings_pagination():
payload = {
"items": [
{"id": 1, "pluginId": "p1", "userId": "u1", "score": 5, "comment": "Great", "createdAt": "2024-01-02T00:00:00Z"},
{"id": 2, "pluginId": "p1", "userId": "u2", "score": 3, "comment": None, "createdAt": "2024-01-03T00:00:00Z"}
]
}
responses.add(
responses.GET,
"https://api.openclaw.io/v1/ratings",
json=payload,
status=200
)
ratings = client.get_ratings("p1", limit=2)
assert len(ratings) == 2
assert ratings[0].score == 5
8.2 Mocking API responses
The responses library intercepts HTTP calls, allowing you to test edge cases (e.g., 401 Unauthorized, 500 Server Error) without hitting the live API.
9. Deploying in Real‑World Projects
When you integrate the client into a larger SaaS product, consider the following best practices:
- Store the API token in a secret manager (AWS Secrets Manager, HashiCorp Vault, etc.).
- Wrap the client in a singleton or dependency‑injection container to avoid repeated session creation.
- Implement exponential back‑off for transient network errors.
- Log audit trails for rating submissions to comply with data‑privacy regulations.
10. Extending the Library
The core client is deliberately minimal. You can extend it with additional endpoints such as plugin metadata, user profiles, or webhook registration. For rapid prototyping, the UBOS templates for quick start provide ready‑made UI components that consume the client library directly from a Flask or FastAPI backend.
11. Pricing and Platform Considerations
If your application scales to thousands of rating requests per minute, review the UBOS pricing plans for API gateway throughput and rate‑limit options. The UBOS platform overview also outlines how to host the client as a micro‑service within a Kubernetes cluster for high availability.
12. Related UBOS Capabilities
Beyond rating management, UBOS offers a suite of AI‑enhanced tools that can enrich your plugin ecosystem:
- AI marketing agents can automatically promote top‑rated plugins.
- The Web app editor on UBOS lets you build admin dashboards without writing front‑end code.
- Leverage the Workflow automation studio to trigger notifications when a rating falls below a threshold.
13. Hosting OpenClaw on UBOS
For a seamless deployment experience, you can host the OpenClaw service directly on UBOS using the dedicated OpenClaw hosting solution. This integration provides built‑in scaling, monitoring, and one‑click SSL, letting you focus on business logic rather than infrastructure.
14. Conclusion
Building a Python client library for the OpenClaw Plugin Rating API equips developers with a reusable, testable, and publishable component that accelerates integration across SaaS products. By following the step‑by‑step guide—setting up a virtual environment, designing a clean request layer, packaging with setuptools, and writing comprehensive tests—you’ll deliver a robust solution that scales with your user base.
Next steps include publishing the package to an internal PyPI repository, wiring it into your CI/CD pipeline, and exploring UBOS’s AI‑driven extensions to turn rating data into actionable insights.
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