Exa Research
Automate in-depth web research and receive structured JSON results with citations.
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:
-
Planning – Your natural-language
instructions
are parsed by an LLM that decomposes the task into one or more research steps. -
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.
-
Reasoning & synthesis – Reasoning models combine facts across sources and return structured JSON (if you provide
output.schema
) or a detailed markdown report. -
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.
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.
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:
Model | p50 Time (seconds) | p90 Time (seconds) |
---|---|---|
exa-research | 20 | 40 |
exa-research-pro | 60 | 90 |
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.
Operation | exa-research | exa-research-pro | Notes |
---|---|---|---|
Search | $5/1,000 searches | $5/1,000 searches | Each unique search query issued by the agent |
Page read | $5/1,000 pages read | $10/1,000 pages read | One “page” = 1,000 tokens from the web |
Reasoning tokens | $5/1M tokens | $5/1M tokens | Specific LLM tokens used for reasoning and synthesis |
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:
Examples
1. Competitive Landscape Table Goal: Compare the current flagship GPUs from NVIDIA, AMD, and Intel and extract pricing, TDP, and release date.
2. Market Size Estimate Goal: Estimate the total global market size (USD) for battery recycling in 2030 with a clear methodology.
3. Timeline of Key Events Goal: Build a timeline of major OpenAI product releases from 2015 – 2023.