OrioSearch: Your AI Agent need WebSearch
OrioSearch is an open-source, self-hosted web search and content extraction API that serves as a free, Tavily-compatible alternative for AI agents with unlimited queries, complete data control, and no rate limits.
https://www.oriosearch.org/?ref=producthunt
Informazioni sul Prodotto
Aggiornato:Mar 9, 2026
Cos'è OrioSearch: Your AI Agent need WebSearch
OrioSearch is designed as a comprehensive solution for developers and AI builders who need reliable web search capabilities integrated into their AI agents and applications. It's an MIT-licensed open-source platform that can be deployed via Docker in just 30 seconds, offering a drop-in replacement for paid services like Tavily. The platform combines powerful search functionality across 70+ search engines with content extraction and AI answer generation capabilities, all while keeping your data and infrastructure under your control.
Caratteristiche principali di OrioSearch: Your AI Agent need WebSearch
OrioSearch is an open-source, self-hosted web search and content extraction API designed as a free alternative to paid services like Tavily. It offers Tavily-compatible APIs, integrates with 70+ search engines through SearXNG, provides content extraction capabilities, AI-generated answers with citations, and features like SSE streaming and Redis caching. The platform can be deployed via Docker in 30 seconds and allows unlimited searches without API costs or rate limits.
Comprehensive Search Integration: Aggregates results from 70+ search engines including Google, Bing, and DuckDuckGo with automatic DuckDuckGo fallback functionality
Advanced Content Processing: Multi-tier content extraction pipeline with high accuracy (F1: 0.958) and AI-powered answer generation with source citations
Production-Ready Infrastructure: Includes Redis caching, SSE streaming, circuit breakers, rate limiting, and result reranking capabilities for reliable performance
Seamless Integration: Drop-in replacement for Tavily API with identical response formats and OpenAI function-calling compatibility
Casi d'uso di OrioSearch: Your AI Agent need WebSearch
AI Agent Development: Power AI agents with real-time web search capabilities without worrying about API costs or rate limits
Enterprise Search Solutions: Deploy as an internal search service for companies wanting to maintain data privacy and control over their search infrastructure
Research Automation: Enable automated content extraction and summarization for research tools and knowledge management systems
Content Aggregation Platforms: Build content aggregation services with reliable search and extraction capabilities while maintaining full control over the infrastructure
Vantaggi
Free and open-source with no API costs
Complete control over infrastructure and data privacy
No rate limits and unlimited scalability
Easy deployment with Docker
Svantaggi
Requires self-hosting and maintenance
May need additional infrastructure for large-scale deployments
Depends on external search engines' availability
Come usare OrioSearch: Your AI Agent need WebSearch
Clone and Deploy OrioSearch: Run these commands in terminal:
1. git clone https://github.com/vkfolio/orio-search
2. cd oriosearch
3. docker compose up --build
This will automatically start 3 services: API, SearXNG, and Redis
Verify Installation: Check if OrioSearch is running properly by making a health check request:
curl http://localhost:8000/health
You should receive: {"status": "ok", "service": "orio-search"}
Basic Search Usage: Make a POST request to search endpoint:
curl -X POST http://localhost:8000/search \
-H "Content-Type: application/json" \
-d '{"query": "latest AI news", "max_results": 5}'
Advanced Search with AI Answers: Make a POST request with additional parameters:
curl -X POST http://localhost:8000/search \
-H "Content-Type: application/json" \
-d '{"query": "what is docker", "include_answer": true, "search_depth": "advanced"}'
Configure LLM Integration: Edit config.yaml to set up LLM integration:
llm:
enabled: true
provider: "ollama" # or "openai", "groq"
base_url: "http://ollama:11434/v1"
model: "llama3.1"
api_key: "ollama" # use real API key for cloud services
Replace Existing Tavily Integration: If you're currently using Tavily, simply change the base URL in your code:
From:
base_url = "https://api.tavily.com"
api_key = "tvly-xxxxxxxxxxxxxxxx"
To:
base_url = "http://localhost:8000"
api_key = "" # optional
FAQ di OrioSearch: Your AI Agent need WebSearch
OrioSearch is an open-source web search and content extraction API that you can self-host. It's Tavily-compatible and can be deployed with Docker in 30 seconds. It provides web search capabilities for AI agents without any API costs.
Video di OrioSearch: Your AI Agent need WebSearch
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