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openclaw-search Intelligent search for agents. Multi-source retrieval with confidence scoring - web, academic, and Tavily in one unified API.
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ZIP herunterladen Herunterladen... Mehr aus diesem Repository Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
Persistent memory system for AI agents following Model Context Protocol (MCP). Use for storing long-term memories across sessions, semantic search of past knowledge, building knowledge graphs, auto-injecting context, deduplicating memories, syncing to cloud storage. Essential for agents that need to remember decisions, solutions, preferences, and learned patterns over time.
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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":"๐","requires":{"bins":["curl","python3"],"env":["AISA_API_KEY"]},"primaryEnv":"AISA_API_KEY"}}
OpenClaw Search ๐
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.
๐ฅ 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"
๐๏ธ 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 retrievalWeb : Structured web searchSmart : Intelligent mixed-mode searchTavily : 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
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โ User Query โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโ
โผ โผ โผ
โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ
โ Scholar โ โ Web โ โ Smart โ
โโโโโโโโโโโ โโโโโโโโโโโ โโโโโโโโโโโ
โ โ โ
โโโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโ
โผ
โโโโโโโโโโโโโโโโโโโ
โ AIsa Explain โ
โ (Meta-Analysis) โ
โโโโโโโโโโโโโโโโโโโ
โ
โผ
โโโโโโโโโโโโโโโโโโโ
โ Confidence Scoreโ
โ + Synthesis โ
โโโโโโโโโโโโโโโโโโโ
Core Capabilities
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 "
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
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 "
Smart Search (Web + Academic Combined)
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)
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"}'
Explain Search Results (Meta-Analysis)
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"}'
๐ 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
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?"
API Endpoints Reference Endpoint Method Description /scholar/search/webPOST Web search with structured results /scholar/search/scholarPOST Academic paper search /scholar/search/smartPOST Intelligent hybrid search /scholar/explainPOST Generate result explanations /search/fullPOST Full text search with content /search/smartPOST Smart web search /tavily/searchPOST Tavily search integration /tavily/extractPOST Extract content from URLs /tavily/crawlPOST Crawl web pages /tavily/mapPOST 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)
๐ Building a Verity-Style Agent Want to build your own confidence-scored search agent? Here's the pattern:
1. Parallel Discovery 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 def score_confidence (results ):
"""Calculate deterministic confidence score."""
score = 0
if results["scholar" ]:
score += 40 * len (results["scholar" ]) / 10
claims = extract_claims(results)
agreement = analyze_agreement(claims)
score += 35 * agreement
recency = calculate_recency(results)
score += 15 * recency
relevance = calculate_relevance(results, query)
score += 10 * relevance
return min (100 , score)
3. Synthesis 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" ]
}
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
Resources Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
