exa-research

Exa Research & Answer API

Quick Reference

Topic	When to Use	Reference
Answer API	Q&A with citations, grounded responses	answer-api.md
Deep Search	Smart query expansion, high-quality summaries	deep-search.md
Citations	Source attribution, verification	citations.md

Essential Patterns

Answer API (Python)

from exa_py import Exa

exa = Exa()

response = exa.answer(
    "What are the key features of Python 3.12?",
    text=True
)

print(response.answer)
for citation in response.citations:
    print(f"Source: {citation.url}")

Streaming Answers

stream = exa.answer(
    "Explain the benefits of microservices architecture",
    stream=True
)

for chunk in stream:
    print(chunk.text, end="", flush=True)

# Access citations after streaming
print("\nSources:", stream.citations)

Deep Search with Summaries

results = exa.search_and_contents(
    "latest developments in quantum computing",
    type="neural",
    num_results=10,
    summary=True,
    use_autoprompt=True  # Smart query expansion
)

for result in results.results:
    print(f"{result.title}")
    print(f"Summary: {result.summary}")

When to Use

Feature	Use Case	Output
Answer API	Direct Q&A needing citations	Answer + source URLs
Deep Search	Query expansion + summaries	Enhanced search results
Exa Research	Long-form async reports	Structured JSON/Markdown

Common Mistakes

1. Not using streaming for long answers - Use stream=True for better UX on complex questions
2. Ignoring citations - Always include response.citations for verifiable responses
3. Missing text=True - Answer API needs content access; include text=True
4. Over-complex queries - Answer API works best with clear, focused questions
5. Not validating citations - Check citation.url exists before displaying to users