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AIsa Multi Source Search

Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.

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Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.

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Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, scripts/search_client.py

Validation

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: AIsaPay
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 29 sections Open source page

OpenClaw Search 🔍

Intelligent search for autonomous agents. Powered by AIsa. One API key. Multi-source retrieval. Confidence-scored answers. Inspired by AIsa Verity - A next-generation search agent with trust-scored answers.

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

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 │ └─────────────────┘

Web Search

# 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

# 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)

# 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)

# 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)

# 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

FactorWeightDescriptionSource Quality40%Academic > Smart/Web > ExternalAgreement Analysis35%Cross-source consensus checkingRecency15%Newer sources weighted higherRelevance10%Query-result semantic match

Score Interpretation

ScoreConfidence LevelMeaning90-100Very HighStrong consensus across academic and web sources70-89HighGood agreement, reliable sources50-69MediumMixed signals, verify independently30-49LowConflicting sources, use caution0-29Very LowInsufficient or contradictory data

Python Client

# 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

EndpointMethodDescription/scholar/search/webPOSTWeb search with structured results/scholar/search/scholarPOSTAcademic paper search/scholar/search/smartPOSTIntelligent hybrid search/scholar/explainPOSTGenerate result explanations/search/fullPOSTFull text search with content/search/smartPOSTSmart web search/tavily/searchPOSTTavily search integration/tavily/extractPOSTExtract content from URLs/tavily/crawlPOSTCrawl web pages/tavily/mapPOSTGenerate site maps

Search Parameters

ParameterTypeDescriptionquerystringSearch query (required)max_num_resultsintegerMax results (1-100, default 10)as_ylointegerYear lower bound (scholar only)as_yhiintegerYear 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

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

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

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.

Pricing

APICostWeb search~$0.001Scholar search~$0.002Smart search~$0.002Tavily search~$0.002Explain~$0.003 Every response includes usage.cost and usage.credits_remaining.

Get Started

Sign up at aisa.one Get your API key Add credits (pay-as-you-go) Set environment variable: export AISA_API_KEY="your-key"

Full API Reference

See API Reference for complete endpoint documentation.

Resources

AIsa Verity - Reference implementation of confidence-scored search agent AIsa Documentation - Complete API documentation

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

1 Docs1 Scripts

SKILL.md Primary doc
scripts/search_client.py Scripts