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OpenClaw

openclaw-search

Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.

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Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install chaimengphp/openclaw-aisa-search

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

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  │
        └─────────┘     └─────────┘     └─────────┘
              │...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/chaimengphp/openclaw-aisa-search
Version
—
Commit
680ea1db80be79cdb3235a6e8d5ef75c65ddfb11
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

README

OpenClaw Search 🔍

Intelligent search for autonomous agents with confidence scoring.

Inspired by AIsa Verity - A next-generation search agent with trust-scored answers.

Features

  • Web Search: Structured web results
  • Scholar Search: Academic paper retrieval
  • Smart Search: Intelligent hybrid search
  • Tavily Integration: External validation
  • Verity Mode: Multi-source search with confidence scoring

Quick Start

export AISA_API_KEY="your-key"

# Single source search
python scripts/search_client.py web --query "AI frameworks"
python scripts/search_client.py scholar --query "transformer models"

# Multi-source with confidence scoring
python scripts/search_client.py verity --query "Is quantum computing enterprise-ready?"

Confidence Scoring

The Verity-style search returns a deterministic confidence score (0-100):

Score Level Meaning
90-100 Very High Strong consensus
70-89 High Good agreement
50-69 Medium Mixed signals
30-49 Low Conflicting sources
0-29 Very Low Insufficient data

Documentation

See SKILL.md for complete API documentation.

Contenu extrait

SKILL.md
# 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  │
        └─────────┘     └─────────┘     └─────────┘
              │...
README
# OpenClaw Search 🔍

Intelligent search for autonomous agents with confidence scoring.

Inspired by [AIsa Verity](https://github.com/AIsa-team/verity) - A next-generation search agent with trust-scored answers.

## Features

- **Web Search**: Structured web results
- **Scholar Search**: Academic paper retrieval
- **Smart Search**: Intelligent hybrid search
- **Tavily Integration**: External validation
- **Verity Mode**: Multi-source search with confidence scoring

## Quick Start

```bash
export AISA_API_KEY="your-key"

# Single source search
python scripts/search_client.py web --query "AI frameworks"
python scripts/search_client.py scholar --query "transformer models"

# Multi-source with confidence scoring
python scripts/search_client.py verity --query "Is quantum computing enterprise-ready?"
```

## Confidence Scoring

The Verity-style search returns a deterministic confidence score (0-100):

| Score | Level | Meaning |
|-------|-------|---------|
| 90-100 | Very High | Strong consensus |
| 70-89 | High | Good agreement |
| 50-69 | Medium | Mixed signals |
| 30-49 | Low | Conflicting sources |
| 0-29 | Very Low | Insufficient data |

## Documentation

See [SKILL.md](SKILL.md) for complete API documentation.

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