# 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.

## 🔥 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

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

# 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

| 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

# 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

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

| 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

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 for complete endpoint documentation.

## Resources

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

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