Exa Research

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How It Works

The Research API is an asynchronous, multi-step pipeline that transforms open-ended questions into structured JSON answers backed by citations. You provide natural-language instructions (e.g. “Compare the hardware roadmaps of the top GPU manufacturers”) and an optional JSON Schema describing the output you want.

Under the hood, Exa agents perform multiple steps:

1. Planning – Your natural-language instructions are parsed by an LLM that decomposes the task into one or more research steps.

2. Searching – Specialized search agents issue semantic and keyword queries to Exa’s search engine, continuously expanding and refining the result set until they can fulfil the request.

3. Reasoning & synthesis – Reasoning models combine facts across sources and return structured JSON (if you provide output.schema) or a detailed markdown report.

4. Citations – Generates a citations object mapping each root-level field to its supporting source passages so you can surface inline sources to your users.

Because tasks are asynchronous, you submit a request and immediately receive a task_id. You can poll the task until it is complete or failed, or list all tasks to monitor progress in bulk.

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Best Practices

• Be explicit – Clear, scoped instructions lead to faster tasks and higher-quality answers.
• Keep schemas small – 1-5 root fields is the sweet spot. If you need more, create multiple tasks.
• Use enums – Tight schema constraints improve accuracy and reduce hallucinations.

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Models

The Research API offers two advanced agentic researcher models that break down your instructions, search the web, extract and reason over facts, and return structured answers with citations.

• exa-research (default) adapts to the difficulty of the task, using more or less compute for individual steps. Recommended for most use cases.
• exa-research-pro maximizes quality by using the highest reasoning capability for every step. Recommended for the most complex, multi-step research tasks.

Typical completion times:

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Pricing

The Research API now uses variable usage-based pricing. You are billed based on how much work and reasoning the research agent does.

NOTE: You are ONLY charged for tasks that complete successfully.

Example:
A research task with exa-research that performs 6 searches, reads 20 pages of content, and uses 1,000 reasoning tokens would cost:

For exa-research-pro, the same task would cost:

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Examples

1. Competitive Landscape Table Goal: Compare the current flagship GPUs from NVIDIA, AMD, and Intel and extract pricing, TDP, and release date.

import os
from exa_py import Exa

exa = Exa(os.environ["EXA_API_KEY"])

instructions = "Compare the current flagship GPUs from NVIDIA, AMD and Intel. Return a table of model name, MSRP USD, TDP watts, and launch date. Include citations for each cell."
schema = {
    "type": "object",
    "required": ["gpus"],
    "properties": {
        "gpus": {
            "type": "array",
            "items": {
                "type": "object",
                "required": ["manufacturer", "model", "msrpUsd", "tdpWatts", "launchDate"],
                "properties": {
                    "manufacturer": {"type": "string"},
                    "model": {"type": "string"},
                    "msrpUsd": {"type": "number"},
                    "tdpWatts": {"type": "integer"},
                    "launchDate": {"type": "string"}
                }
            }
        }
    },
    "additionalProperties": False
}

task = exa.research.create_task(
    model="exa-research",
    instructions=instructions,
    output_schema=schema
)

# Poll until completion
result = exa.research.poll_task(task.id)
print(result)

2. Market Size Estimate Goal: Estimate the total global market size (USD) for battery recycling in 2030 with a clear methodology.

import os
from exa_py import Exa

exa = Exa(os.environ["EXA_API_KEY"])

instructions = "Estimate the global market size for battery recycling in 2030. Provide reasoning steps and cite sources."
schema = {
    "type": "object",
    "required": ["estimateUsd", "methodology"],
    "properties": {
        "estimateUsd": {"type": "number"},
        "methodology": {"type": "string"}
    },
    "additionalProperties": False
}

task = exa.research.create_task(
    model="exa-research",
    instructions=instructions,
    output_schema=schema
)

# Poll until completion
result = exa.research.poll_task(task.id)
print(result)

3. Timeline of Key Events Goal: Build a timeline of major OpenAI product releases from 2015 – 2023.

import os
from exa_py import Exa

exa = Exa(os.environ["EXA_API_KEY"])

instructions = "Create a chronological timeline (year, month, brief description) of major OpenAI product releases from 2015 to 2023."
schema = {
    "type": "object",
    "required": ["events"],
    "properties": {
        "events": {
            "type": "array",
            "items": {
                "type": "object",
                "required": ["date", "description"],
                "properties": {
                    "date": {"type": "string"},
                    "description": {"type": "string"}
                }
            }
        }
    },
    "additionalProperties": False
}

task = exa.research.create_task(
    model="exa-research",
    instructions=instructions,
    output_schema=schema
)

# Poll until completion
result = exa.research.poll_task(task.id)
print(result)

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FAQs