Skill: aisadocs/openclaw-aisa-search-web-academic-tavily (version 1.0.0)

--- name: openclaw-search description: "Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API." homepage: https://openclaw.ai metadata: {"openclaw":{"emoji":"🔍","requires":{"bins":["curl","python3"],"env":["AISA_API_KEY"]},"primaryEnv":"AISA_API_KEY"}} --- # OpenClaw Search 🔍 **Intelligent search for autonomous agents. Powered by AIsa.** One API key. Multi-source retrieval. Confidence-scored answers. > Inspired by [AIsa Verity](https://github.com/AIsa-team/verity) - A next-generation search agent with trust-scored answers. ## 🔥 What Can You Do? ### Research Assistant ``` "Search for the latest papers on transformer architectures from 2024-2025" ``` ### Market Research ``` "Find all web articles about AI startup funding in Q4 2025" ``` ### Competitive Analysis ``` "Search for reviews and comparisons of RAG frameworks" ``` ### News Aggregation ``` "Get the latest news about quantum computing breakthroughs" ``` ### Deep Dive Research ``` "Smart search combining web and academic sources on 'autonomous agents'" ``` ## Quick Start ```bash export AISA_API_KEY="your-key" ``` --- ## 🏗️ Architecture: Multi-Stage Orchestration OpenClaw Search employs a **Two-Phase Retrieval Strategy** for comprehensive results: ### Phase 1: Discovery (Parallel Retrieval) Query 4 distinct search streams simultaneously: - **Scholar**: Deep academic retrieval - **Web**: Structured web search - **Smart**: Intelligent mixed-mode search - **Tavily**: External validation signal ### Phase 2: Reasoning (Meta-Analysis) Use **AIsa Explain** to perform meta-analysis on search results, generating: - Confidence scores (0-100) - Source agreement analysis - Synthesized answers ``` ┌─────────────────────────────────────────────────────────────┐ │ User Query │ └─────────────────────────────────────────────────────────────┘ │ ┌───────────────┼───────────────┐ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ Scholar │ │ Web │ │ Smart │ └─────────┘ └─────────┘ └─────────┘ │ │ │ └───────────────┼───────────────┘ ▼ ┌─────────────────┐ │ AIsa Explain │ │ (Meta-Analysis) │ └─────────────────┘ │ ▼ ┌─────────────────┐ │ Confidence Score│ │ + Synthesis │ └─────────────────┘ ``` --- ## Core Capabilities ### Web Search ```bash # Basic web search curl -X POST "https://api.aisa.one/apis/v1/scholar/search/web?query=AI+frameworks&max_num_results=10" \ -H "Authorization: Bearer $AISA_API_KEY" # Full text search (with page content) curl -X POST "https://api.aisa.one/apis/v1/search/full?query=latest+AI+news&max_num_results=10" \ -H "Authorization: Bearer $AISA_API_KEY" ``` ### Academic/Scholar Search ```bash # Search academic papers curl -X POST "https://api.aisa.one/apis/v1/scholar/search/scholar?query=transformer+models&max_num_results=10" \ -H "Authorization: Bearer $AISA_API_KEY" # With year filter curl -X POST "https://api.aisa.one/apis/v1/scholar/search/scholar?query=LLM&max_num_results=10&as_ylo=2024&as_yhi=2025" \ -H "Authorization: Bearer $AISA_API_KEY" ``` ### Smart Search (Web + Academic Combined) ```bash # Intelligent hybrid search curl -X POST "https://api.aisa.one/apis/v1/scholar/search/smart?query=machine+learning+optimization&max_num_results=10" \ -H "Authorization: Bearer $AISA_API_KEY" ``` ### Tavily Integration (Advanced) ```bash # Tavily search curl -X POST "https://api.aisa.one/apis/v1/tavily/search" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"query":"latest AI developments"}' # Extract content from URLs curl -X POST "https://api.aisa.one/apis/v1/tavily/extract" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"urls":["https://example.com/article"]}' # Crawl web pages curl -X POST "https://api.aisa.one/apis/v1/tavily/crawl" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com","max_depth":2}' # Site map curl -X POST "https://api.aisa.one/apis/v1/tavily/map" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url":"https://example.com"}' ``` ### Explain Search Results (Meta-Analysis) ```bash # Generate explanations with confidence scoring curl -X POST "https://api.aisa.one/apis/v1/scholar/explain" \ -H "Authorization: Bearer $AISA_API_KEY" \ -H "Content-Type: application/json" \ -d '{"results":[...],"language":"en","format":"summary"}' ``` --- ## 📊 Confidence Scoring Engine Unlike standard RAG systems, OpenClaw Search evaluates credibility and consensus: ### Scoring Rubric | Factor | Weight | Description | |--------|--------|-------------| | **Source Quality** | 40% | Academic > Smart/Web > External | | **Agreement Analysis** | 35% | Cross-source consensus checking | | **Recency** | 15% | Newer sources weighted higher | | **Relevance** | 10% | Query-result semantic match | ### Score Interpretation | Score | Confidence Level | Meaning | |-------|-----------------|---------| | 90-100 | Very High | Strong consensus across academic and web sources | | 70-89 | High | Good agreement, reliable sources | | 50-69 | Medium | Mixed signals, verify independently | | 30-49 | Low | Conflicting sources, use caution | | 0-29 | Very Low | Insufficient or contradictory data | --- ## Python Client ```bash # Web search python3 {baseDir}/scripts/search_client.py web --query "latest AI news" --count 10 # Academic search python3 {baseDir}/scripts/search_client.py scholar --query "transformer architecture" --count 10 python3 {baseDir}/scripts/search_client.py scholar --query "LLM" --year-from 2024 --year-to 2025 # Smart search (web + academic) python3 {baseDir}/scripts/search_client.py smart --query "autonomous agents" --count 10 # Full text search python3 {baseDir}/scripts/search_client.py full --query "AI startup funding" # Tavily operations python3 {baseDir}/scripts/search_client.py tavily-search --query "AI developments" python3 {baseDir}/scripts/search_client.py tavily-extract --urls "https://example.com/article" # Multi-source search with confidence scoring python3 {baseDir}/scripts/search_client.py verity --query "Is quantum computing ready for enterprise?" ``` --- ## API Endpoints Reference | Endpoint | Method | Description | |----------|--------|-------------| | `/scholar/search/web` | POST | Web search with structured results | | `/scholar/search/scholar` | POST | Academic paper search | | `/scholar/search/smart` | POST | Intelligent hybrid search | | `/scholar/explain` | POST | Generate result explanations | | `/search/full` | POST | Full text search with content | | `/search/smart` | POST | Smart web search | | `/tavily/search` | POST | Tavily search integration | | `/tavily/extract` | POST | Extract content from URLs | | `/tavily/crawl` | POST | Crawl web pages | | `/tavily/map` | POST | Generate site maps | --- ## Search Parameters | Parameter | Type | Description | |-----------|------|-------------| | query | string | Search query (required) | | max_num_results | integer | Max results (1-100, default 10) | | as_ylo | integer | Year lower bound (scholar only) | | as_yhi | integer | Year upper bound (scholar only) | --- ## 🚀 Building a Verity-Style Agent Want to build your own confidence-scored search agent? Here's the pattern: ### 1. Parallel Discovery ```python import asyncio async def discover(query): """Phase 1: Parallel retrieval from multiple sources.""" tasks = [ search_scholar(query), search_web(query), search_smart(query), search_tavily(query) ] results = await asyncio.gather(*tasks) return { "scholar": results[0], "web": results[1], "smart": results[2], "tavily": results[3] } ``` ### 2. Confidence Scoring ```python def score_confidence(results): """Calculate deterministic confidence score.""" score = 0 # Source quality (40%) if results["scholar"]: score += 40 * len(results["scholar"]) / 10 # Agreement analysis (35%) claims = extract_claims(results) agreement = analyze_agreement(claims) score += 35 * agreement # Recency (15%) recency = calculate_recency(results) score += 15 * recency # Relevance (10%) relevance = calculate_relevance(results, query) score += 10 * relevance return min(100, score) ``` ### 3. Synthesis ```python async def synthesize(query, results, score): """Generate final answer with citations.""" explanation = await explain_results(results) return { "answer": explanation["summary"], "confidence": score, "sources": explanation["citations"], "claims": explanation["claims"] } ``` For a complete implementation, see [AIsa Verity](https://github.com/AIsa-team/verity). --- ## Pricing | API | Cost | |-----|------| | Web search | ~$0.001 | | Scholar search | ~$0.002 | | Smart search | ~$0.002 | | Tavily search | ~$0.002 | | Explain | ~$0.003 | Every response includes `usage.cost` and `usage.credits_remaining`. --- ## Get Started 1. Sign up at [aisa.one](https://aisa.one) 2. Get your API key 3. Add credits (pay-as-you-go) 4. Set environment variable: `export AISA_API_KEY="your-key"` ## Full API Reference See [API Reference](https://aisa.mintlify.app/api-reference/introduction) for complete endpoint documentation. ## Resources - [AIsa Verity](https://github.com/AIsa-team/verity) - Reference implementation of confidence-scored search agent - [AIsa Documentation](https://aisa.mintlify.app) - Complete API documentation


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)