Integrating Real-Time Web Search with Search Router

Practical Summary and Purpose

The Search Router API provides a centralized interface for retrieving web search results, including titles, URLs, and snippets, which are essential for grounding Large Language Models (LLMs) and powering agentic tool calls. The simple-search repository serves as an open-source reference application built on top of this API. It is designed to demonstrate how to integrate search capabilities into a production-shaped environment using Python 3.12+, FastAPI, and Pydantic v2. By utilizing this reference, developers can implement pluggable backend adapters, manage search results with Redis caching, and ensure system reliability through circuit breakers and structured logging. This architecture is specifically intended for developers building search or RAG-adjacent UIs and agentic tool-calling systems.

Prerequisites and Initial Setup

To begin, ensure your environment supports Python 3.12 or higher. The project is designed to be containerized, requiring Docker and Docker Compose for the most straightforward deployment. For local development without Docker, you must create a virtual environment and install the necessary dependencies. The application relies on a .env file for configuration, where you can define environment-level knobs such as APP_ENV and LOG_LEVEL. While the application functions out of the box using deterministic mocks, obtaining a real API key from search-router.com is required to access live search data from the upstream service. The system is built to be modular, allowing for easy integration of custom backends.

Deployment and Configuration Workflow

The workflow for deploying the reference application is streamlined for both containerized and local environments. After cloning the repository, you should copy the provided example environment file to initialize your configuration. The system uses a config.yaml file for declarative backend wiring and feature flags. When running the application, the system automatically detects the presence of a SEARCH_ROUTER_API_KEY. If the key is missing, the application transparently swaps real backend adapters for mock implementations, allowing for immediate UI development and testing without requiring active credentials. This design ensures that developers can iterate on the frontend and API logic independently of the upstream search provider.

git clone https://github.com/search-router/simple-search.git
cd simple-search
cp .env.example .env
docker compose up --build
# open http://localhost:8000/

API Interaction and Data Retrieval

The Search Router API exposes several endpoints for interacting with search services. The primary endpoints include /api/v1/search/web for general web queries and /api/v1/search/images for image-specific results. These endpoints accept JSON payloads containing parameters such as q (query), backend, language, region, and limit. The API returns structured data suitable for programmatic consumption by AI agents. Additionally, the service provides introspection endpoints like /api/v1/backends and /api/v1/health, which are gated by an ADMIN_TOKEN in production environments to monitor the status of the service, Redis cache, and individual backend adapters. This structured approach ensures consistent data handling across different search providers.

Extensibility and Backend Adapters

One of the core strengths of the simple-search architecture is its support for pluggable backends. Developers can extend the system by creating new providers without needing to fork the main repository. To add a new backend, you must subclass app.backends.base.BaseBackend and implement the required methods: search_web, search_images, and capabilities(). Once implemented, the new backend can be registered in config.yaml or exposed via the search_service.backends entry point group. This modular approach allows teams to integrate proprietary search providers or custom RAG-adjacent data sources while maintaining a consistent API schema for the rest of the application, ensuring that no public route or response schema needs to change when adding new providers.

Limitations and Production Considerations

It is important to note that the simple-search repository is a reference implementation and not a production-ready drop-in solution. Before exposing the service to public traffic, developers must consult the documentation regarding deployment hardening, specifically concerning security headers, CSRF protection, and rate limiting. The included ads cabinet is a demo surface and should not be considered part of the core Search Router API. Furthermore, while the system supports Redis for caching, it will gracefully degrade to a NullCache if Redis is not configured. Always review the security implications of your specific deployment environment before moving from dev to prod mode, as the reference implementation requires specific configuration for production safety.

Testing and Quality Assurance

The project includes a comprehensive testing suite to ensure reliability during development. Developers can run pytest -q to execute unit and integration tests. Unit tests cover critical components such as schemas, i18n, the normalizer, the Search Router adapter, the circuit breaker, and the cache key builder. Integration tests are designed to round-trip through the mock backends to validate the API and UI rendering, including RTL support. Additionally, the project enforces code quality through ruff and type checking via mypy. This testing infrastructure is essential for maintaining the integrity of the search service when integrating custom backends or modifying the core application logic.

Choosing When to Use This Tool

This reference implementation is ideal for developers who need to ground LLM answers with fresh web data, implement agent tool calls, or build search-adjacent UIs. It is particularly useful for teams that want to avoid the complexity of building a search service from scratch while maintaining the flexibility to swap backend providers. By using the simple-search template, developers gain access to a production-shaped architecture that includes caching, circuit breaking, and structured logging out of the box. It is recommended for those who require a robust starting point for integrating the Search Router API into their own applications, provided they are prepared to handle the necessary production hardening steps.