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Aisa Multi Source Search

Intelligent search for agents.

Rating: 4.6 (165 reviews)
Downloads: 21,877 downloads
Version: 1.0.0

Overview

Intelligent search for agents.

Complete Documentation

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name: openclaw-search 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 - 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.



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
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

Installation

Terminal bash


openclaw install aisa-multi-source-search

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Overview

Complete Documentation

OpenClaw Search 🔍

🔥 What Can You Do?

Research Assistant

Market Research

Competitive Analysis

News Aggregation

Deep Dive Research

Quick Start

🏗️ Architecture: Multi-Stage Orchestration

Phase 1: Discovery (Parallel Retrieval)

Phase 2: Reasoning (Meta-Analysis)

Core Capabilities

Web Search

Basic web search

Full text search (with page content)

Academic/Scholar Search

Search academic papers

With year filter

Smart Search (Web + Academic Combined)

Intelligent hybrid search

Tavily Integration (Advanced)

Tavily search

Extract content from URLs

Crawl web pages

Site map

Explain Search Results (Meta-Analysis)

Generate explanations with confidence scoring

📊 Confidence Scoring Engine

Scoring Rubric

Score Interpretation

Python Client

Web search

Academic search

Smart search (web + academic)

Full text search

Tavily operations

Multi-source search with confidence scoring

API Endpoints Reference

Search Parameters

🚀 Building a Verity-Style Agent

1. Parallel Discovery

2. Confidence Scoring

3. Synthesis

Pricing

Get Started

Full API Reference

Resources

Installation

Tags

Quick Info

Ready to Install?

Resources

Related Skills

4claw

Aap Passport

Adaptive Suite

Adversarial Prompting