Deep research using Exa AI. Use when the user needs comprehensive research, literature review, multi-source synthesis, or asks to "research", "deep search", "look into", or "find out about" a topic.
Run comprehensive research using Exa AI's suite of search and research tools.
Parse the user's input from $ARGUMENTS:
fast:, pro:, or balanced:, extract it as the tier. Default tier is fast.| Prefix | Model | Typical time | Use case |
|---|---|---|---|
fast: (default) | exa-research-fast | ~15s | Quick overviews, simple questions |
balanced: | exa-research | 15-45s | Most research tasks |
pro: | exa-research-pro |
| 45s-3min |
| Complex topics needing depth |
Choose the right tool based on the query content:
crawling_exaIf the query contains a URL (starts with http:// or https://), use crawling_exa to extract the page content. Set maxCharacters to 10000 for full extraction. Then summarize the content for the user.
company_research_exaIf the query is specifically about a company (e.g., "research Stripe", "tell me about OpenAI"), use company_research_exa with the company name.
get_code_context_exaIf the query is about code, APIs, libraries, SDKs, or programming patterns, use get_code_context_exa. Set tokensNum to 8000 for thorough results.
web_search_exaIf the query is a simple factual question (who, what, when, where) that doesn't need synthesis, use web_search_exa for speed.
deep_search_exaIf the query needs multi-angle search but not a full report, use deep_search_exa:
deepdeep-reasoning for queries that need analysis (triggered by pro: prefix or complex phrasing)deep_researcher_start + deep_researcher_checkFor anything that needs a comprehensive report with synthesis — this is the default path for most research queries.
Steps:
deep_researcher_start with:
instructions: The full research query, enriched with specificity if the user's query is vaguemodel: Based on the tier prefix (see table above)deep_researcher_check with the returned researchId:
completed, wait 5 seconds, then call againcompletedWhen presenting results:
/exa-deep-research what is retrieval augmented generation
→ Routes to deep_researcher_start with exa-research-fast
/exa-deep-research pro: comparative analysis of transformer architectures for education
→ Routes to deep_researcher_start with exa-research-pro
/exa-deep-research https://arxiv.org/abs/2401.12345
→ Routes to crawling_exa
/exa-deep-research how does the Anthropic Python SDK handle streaming
→ Routes to get_code_context_exa
/exa-deep-research Notion company
→ Routes to company_research_exa
/exa-deep-research who won the 2024 Nobel Prize in Physics
→ Routes to web_search_exa