---
name: openclaw-search-0702aba46b03
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


---
Source attribution: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/0xjordansg-yolo/openclaw-aisa-search-website-academic-tavily-serp-exa/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 clawdbot

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/0xjordansg-yolo/openclaw-aisa-search-website-academic-tavily-serp-exa/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Education & Research, Marketing, Product Management
Roles: Content Marketer, Data Analyst, Marketing Analyst, Product Manager, Researcher
Specialties: API
Tools: Aisa
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: d59e95361289411b15deee5810536a6f2ac8e20ab68016c4d62f23c6ece9a085