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 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'"
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 │
└─────────────────┘
# 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"
# 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"
# 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 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"}'
# 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"}'
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 |
# 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?"
| 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:
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]
}
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)
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.