Valyu — Real-Time Web Search & Specialised Data Access

Use this skill when you need current, authoritative information that is not in your training data or is behind paywalls. Valyu connects to 36+ specialised sources in addition to quality web search.

When to Use This Skill

• Financial analysis requiring SEC 10-K/10-Q filings
• Biomedical research needing PubMed, ChEMBL, or clinical trial data
• Economic analysis needing FRED or BLS indicators
• Patent research and prior art searches
• Any query requiring information published after your knowledge cutoff
• Fact-checking against authoritative primary sources

Install

npx skills add https://github.com/valyuai/skills --skill valyu-best-practices
pip install valyu

Set your API key:

export VALYU_API_KEY=your-api-key

Core API

Web + specialised search

from valyu import Valyu

client = Valyu(api_key="your-key")

result = client.search(
    query="your search query",
    search_type="all",        # "all" = web + proprietary, "web" = web only, "proprietary" = sources only
    max_num_results=10,
    relevance_threshold=0.5,  # 0-1, higher = stricter
)

for r in result.results:
    print(r.title, r.url, r.content)

Targeted source search

# SEC filings
result = client.search(
    query="risk factors in latest 10-K for semiconductor companies",
    search_type="proprietary",
    included_sources=["valyu/valyu-sec-filings"],
    max_num_results=5,
)

# Biomedical cross-search
result = client.search(
    query="GLP-1 receptor agonists drug interactions clinical outcomes",
    search_type="all",
    included_sources=[
        "valyu/valyu-pubmed",
        "valyu/valyu-chembl",
        "valyu/valyu-clinical-trials",
    ],
    max_num_results=10,
)

# Economic data
result = client.search(
    query="US inflation rate Q1 2026 CPI components",
    search_type="proprietary",
    included_sources=["valyu/valyu-fred"],
    max_num_results=5,
)

Direct cited answer (Answer API)

Use when you need a grounded, cited response rather than raw documents:

answer = client.context(
    query="What were the key risk factors disclosed by NVIDIA in their most recent 10-K?",
    search_type="proprietary",
    max_num_results=5,
)
print(answer.context)   # Synthesised answer with citations

Available Specialised Sources

Category	Sources
Financial	SEC filings (10-K, 10-Q, 8-K), earnings transcripts
Biomedical	PubMed, ChEMBL (2.5M compounds), ClinicalTrials.gov
Economic	FRED, BLS, World Bank indicators
Legal/Patent	USPTO, EPO patent databases
Academic	CrossRef, arXiv, Semantic Scholar
Web	Quality-filtered live web search

Best Practices

1. Be specific about sources — use included_sources to target the data type you need
2. Always surface citations — return source URLs to users so they can verify
3. Use search_type="all" for broad research; "proprietary" for authoritative data
4. Set relevance_threshold higher (0.7+) when you need precise matches
5. Handle empty results gracefully — fall back to broader query if needed

Error Handling

try:
    result = client.search(query=query, search_type="all", max_num_results=5)
    if not result.results:
        # Widen the query or try a different source
        ...
except Exception as e:
    # Log and inform the user; do not hallucinate a substitute answer
    ...

Performance Benchmarks

Benchmark	Valyu	Google	Exa
FreshQA (600 time-sensitive queries)	79%	39%	24%
Finance-specific queries	73%	55%	—
MedAgent (562 medical queries)	48%	—	—

Tips

• Never fabricate data when Valyu returns no results — tell the user and suggest alternative queries
• For streaming large result sets, use pagination parameters
• Cite sources inline in your response: "According to Source Title…"