openclaw-aisa-search-website-academic-tavily-serp-exaIntelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.
Install via ClawdBot CLI:
clawdbot install 0xjordansg-yolo/openclaw-aisa-search-website-academic-tavily-serp-exaIntelligent 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.
```
"Search for the latest papers on transformer architectures from 2024-2025"
```
```
"Find all web articles about AI startup funding in Q4 2025"
```
```
"Search for reviews and comparisons of RAG frameworks"
```
```
"Get the latest news about quantum computing breakthroughs"
```
```
"Smart search combining web and academic sources on 'autonomous agents'"
```
```bash
export AISA_API_KEY="your-key"
```
OpenClaw Search employs a Two-Phase Retrieval Strategy for comprehensive results:
Query 4 distinct search streams simultaneously:
Use AIsa Explain to perform meta-analysis on search results, generating:
```
┌─────────────────────────────────────────────────────────────┐
│ User Query │
└─────────────────────────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────┐
│ Scholar │ │ Web │ │ Smart │
└─────────┘ └─────────┘ └─────────┘
│ │ │
└───────────────┼───────────────┘
▼
┌─────────────────┐
│ AIsa Explain │
│ (Meta-Analysis) │
└─────────────────┘
│
▼
┌─────────────────┐
│ Confidence Score│
│ + Synthesis │
└─────────────────┘
```
```bash
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"
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"
```
```bash
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"
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"
```
```bash
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"
```
```bash
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"}'
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"]}'
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}'
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"}'
```
```bash
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"}'
```
Unlike standard RAG systems, OpenClaw Search evaluates credibility and consensus:
| 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 | 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 |
```bash
python3 {baseDir}/scripts/search_client.py web --query "latest AI news" --count 10
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
python3 {baseDir}/scripts/search_client.py smart --query "autonomous agents" --count 10
python3 {baseDir}/scripts/search_client.py full --query "AI startup funding"
python3 {baseDir}/scripts/search_client.py tavily-search --query "AI developments"
python3 {baseDir}/scripts/search_client.py tavily-extract --urls "https://example.com/article"
python3 {baseDir}/scripts/search_client.py verity --query "Is quantum computing ready for enterprise?"
```
| 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 |
| 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) |
Want to build your own confidence-scored search agent? Here's the pattern:
```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]
}
```
```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)
```
```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.
| 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.
export AISA_API_KEY="your-key"See API Reference for complete endpoint documentation.
Generated Mar 1, 2026
Researchers can use the skill to search for recent academic papers and web articles on specific topics, with confidence scoring to prioritize high-quality sources. It supports year filters for scholarly searches, enabling comprehensive reviews of emerging fields like transformer architectures.
Business analysts can leverage the skill to retrieve web articles and Tavily data on market trends, such as AI startup funding, with meta-analysis to assess source agreement. This aids in competitive analysis and strategic decision-making based on reliable information.
Journalists or media professionals can use the skill to aggregate latest news from multiple sources, with confidence scoring to evaluate credibility. It helps in synthesizing reports on topics like quantum computing breakthroughs while minimizing misinformation.
Developers can perform smart searches combining web and academic sources to explore RAG frameworks or other technologies. The confidence scoring provides insights into source reliability, supporting informed tool selection and implementation.
Content creators can utilize the skill to gather information from diverse sources for articles or videos, with Tavily integration for advanced web crawling. The meta-analysis helps synthesize accurate and up-to-date content on topics like autonomous agents.
Offer the search functionality as a paid API subscription, charging based on usage tiers or number of queries. This model targets developers and businesses needing integrated multi-source retrieval with confidence scoring for their applications.
Provide customized enterprise packages with enhanced features like dedicated support, higher query limits, and on-premise deployment. This caters to large organizations requiring robust research tools for internal teams.
Offer a free tier with basic search capabilities and limited queries, while premium tiers unlock advanced features such as higher confidence scoring details, Tavily integrations, and priority access. This attracts a broad user base and converts heavy users.
💬 Integration Tip
Ensure the AISA_API_KEY is securely set as an environment variable and use the provided curl commands to test endpoints before full integration into agent workflows.
Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).
AI-optimized web search via Tavily API. Returns concise, relevant results for AI agents.
This skill should be used when users need to search the web for information, find current content, look up news articles, search for images, or find videos. It uses DuckDuckGo's search API to return results in clean, formatted output (text, markdown, or JSON). Use for research, fact-checking, finding recent information, or gathering web resources.
Web search and content extraction via Brave Search API. Use for searching documentation, facts, or any web content. Lightweight, no browser required.
Search indexed Discord community discussions via Answer Overflow. Find solutions to coding problems, library issues, and community Q&A that only exist in Discord conversations.
Multi search engine integration with 17 engines (8 CN + 9 Global). Supports advanced search operators, time filters, site search, privacy engines, and WolframAlpha knowledge queries. No API keys required.