exa-py 2.13.2

Python SDK for Exa API.

Exa Python SDK

The official Python SDK for Exa, the web search API for AI.

Documentation | Dashboard

Install

pip install exa-py

Requires Python 3.9+

Quick Start

from exa_py import Exa

exa = Exa(api_key="your-api-key")

# Search the web
results = exa.search(
    "blog post about artificial intelligence",
    type="auto",
    contents={"highlights": True}
)

# Ask a question
response = exa.answer("What is the capital of France?")

Search

results = exa.search(
    "machine learning startups",
    contents={"highlights": True}
)

results = exa.search(
    "climate tech news",
    num_results=20,
    start_published_date="2024-01-01",
    include_domains=["techcrunch.com", "wired.com"],
    contents={"highlights": True}
)

results = exa.search(
    "What are the latest battery breakthroughs?",
    type="auto",
    system_prompt="Prefer official sources and avoid duplicate results",
    output_schema={
        "type": "object",
        "properties": {
            "summary": {"type": "string"},
            "key_companies": {"type": "array", "items": {"type": "string"}},
        },
        "required": ["summary", "key_companies"],
    },
)
print(results.output.content if results.output else None)

for chunk in exa.stream_search(
    "What are the latest battery breakthroughs?",
    type="auto",
):
    if chunk.content:
        print(chunk.content, end="", flush=True)

Search output_schema modes:

• {"type": "text", "description": "..."}: return plain text in output.content
• {"type": "object", ...}: return structured JSON in output.content

system_prompt and output_schema are supported on every search type. Search streaming is available via stream_search(...), which yields OpenAI-style chat completion chunks.

For type: "object", search currently enforces:

• max nesting depth: 2
• max total properties: 10

Deep search variants that also support additional_queries:

• deep-lite
• deep
• deep-reasoning

Contents

results = exa.get_contents(
    ["https://docs.exa.ai"],
    text=True
)

results = exa.get_contents(
    ["https://arxiv.org/abs/2303.08774"],
    highlights=True
)

Answer

response = exa.answer("What caused the 2008 financial crisis?")
print(response.answer)

for chunk in exa.stream_answer("Explain quantum computing"):
    print(chunk, end="", flush=True)

Agent API (Beta)

run = exa.beta.agent.runs.create(
    betas=["agent-2026-05-07"],
    query="Find engineering leaders at AI infrastructure companies that raised a Series A or B in the last 6 months.",
    output_schema={
        "type": "object",
        "properties": {
            "people": {
                "type": "array",
                "maxItems": 10,
                "items": {
                    "type": "object",
                    "properties": {
                        "name": {"type": "string"},
                        "contact_email": {"type": "string", "format": "email"},
                        "linkedin_url": {"type": "string", "format": "uri"},
                    },
                    "required": ["name", "linkedin_url"],
                },
            }
        },
        "required": ["people"],
    },
    effort="auto",
)

run = exa.beta.agent.runs.poll_until_finished(
    run.id, betas=["agent-2026-05-07"]
)
print(run.output.structured if run.output else None)

Async

from exa_py import AsyncExa

exa = AsyncExa(api_key="your-api-key")

results = await exa.search("async search example", contents={"highlights": True})

More

See the full documentation for all features including websets, filters, and advanced options.