exa-research

name	exa-research
description	Use when the user mentions Exa research OR when the workflow benefits from complex, multi-step research and other exa-ai approaches are not yielding satisfactory results.

Exa Research Tasks

Manage asynchronous research tasks with exa-ai for complex, multi-step research workflows.

Use --help to see available commands and verify usage before running:

exa-ai <command> --help

Working with Complex Shell Commands

When using the Bash tool with complex shell syntax, follow these best practices for reliability:

1. Run commands directly: Capture JSON output directly rather than nesting command substitutions
2. Parse in subsequent steps: Use jq to parse output in a follow-up command if needed
3. Avoid nested substitutions: Complex nested $(...) can be fragile; break into sequential steps

Example:

# Less reliable: nested command substitution
results=$(exa-ai research-start --instructions "query" | jq -r '.result')

# More reliable: run directly, then parse
exa-ai research-start --instructions "query"
# Then in a follow-up command if needed:
exa-ai research-get research_id | jq -r '.result'

Cost Optimization

Pricing

Research is the most expensive Exa endpoint:

• Agent search: $0.005 per search operation
• Standard page read: $0.005 per page
• Pro page read: $0.010 per page (2x standard)
• Reasoning tokens: $0.000005 per token

Cost strategy:

• Avoid research unless required: Most expensive option (2-10x cost premium over other endpoints)
• Use only for autonomous, multi-step reasoning tasks that justify the cost
• For simpler queries, use search, answer, or get-contents instead
• Consider using exa-research (standard) instead of exa-research-pro unless you need the higher quality

Research Overview

Research tasks are asynchronous operations that allow you to:

• Run complex, multi-step research workflows
• Process large amounts of information over time
• Monitor progress of long-running research
• Get structured output from comprehensive research

When to Use Research vs Search

Use research-start when:

• The research requires multiple steps or complex reasoning
• You need comprehensive analysis of a topic
• The task will take significant time to complete
• You want structured, synthesized output

Use search (from exa-core) when:

• You need immediate results
• The query is straightforward
• You want quick factual information

Commands

research-start

Initiate a new research task with instructions.

exa-ai research-start --instructions "Find the top 10 Ruby performance optimization techniques"

For detailed options and examples, consult REFERENCE.md.

research-get

Check status and retrieve results of a research task.

exa-ai research-get research_abc123

For detailed options and examples, consult REFERENCE.md.

research-list

List all your research tasks with pagination.

exa-ai research-list --limit 10

For detailed options and examples, consult REFERENCE.md.

Research Models

• exa-research (default): Balanced speed and quality
• exa-research-pro: Higher quality, more comprehensive results
• exa-research-fast: Faster results, good for simpler research

Quick Examples

Simple Research

exa-ai research-start \
  --instructions "Find the latest breakthroughs in quantum computing"

Research with Structured Output

exa-ai research-start \
  --instructions "Compare TypeScript vs Flow for type checking" \
  --output-schema '{
    "type":"object",
    "properties":{
      "typescript":{
        "type":"object",
        "properties":{
          "pros":{"type":"array","items":{"type":"string"}},
          "cons":{"type":"array","items":{"type":"string"}}
        }
      },
      "flow":{
        "type":"object",
        "properties":{
          "pros":{"type":"array","items":{"type":"string"}},
          "cons":{"type":"array","items":{"type":"string"}}
        }
      }
    }
  }'

Background Research Workflow

# Start research
research_id=$(exa-ai research-start \
  --instructions "Analyze competitor landscape for project management tools" | jq -r '.research_id')

# Check status later
status=$(exa-ai research-get $research_id | jq -r '.status')

# Get results when complete
if [ "$status" = "completed" ]; then
  exa-ai research-get $research_id | jq -r '.result'
fi

Use Pro Model for Comprehensive Research

exa-ai research-start \
  --instructions "Comprehensive analysis of microservices vs monolithic architecture with case studies" \
  --model exa-research-pro \
  --events

Shared Requirements

Schema Design

MUST: Use object wrapper for schemas

Applies to: answer, search, find-similar, get-contents

When using schema parameters (--output-schema or --summary-schema), always wrap properties in an object:

{"type":"object","properties":{"field_name":{"type":"string"}}}

DO NOT use bare properties without the object wrapper:

{"properties":{"field_name":{"type":"string"}}}  // ❌ Missing "type":"object"

Why: The Exa API requires a valid JSON Schema with an object type at the root level. Omitting this causes validation errors.

Examples:

# ✅ CORRECT - object wrapper included
exa-ai search "AI news" \
  --summary-schema '{"type":"object","properties":{"headline":{"type":"string"}}}'

# ❌ WRONG - missing object wrapper
exa-ai search "AI news" \
  --summary-schema '{"properties":{"headline":{"type":"string"}}}'

---

Output Format Selection

MUST NOT: Mix toon format with jq

Applies to: answer, context, search, find-similar, get-contents

toon format produces YAML-like output, not JSON. DO NOT pipe toon output to jq for parsing:

# ❌ WRONG - toon is not JSON
exa-ai search "query" --output-format toon | jq -r '.results'

# ✅ CORRECT - use JSON (default) with jq
exa-ai search "query" | jq -r '.results[].title'

# ✅ CORRECT - use toon for direct reading only
exa-ai search "query" --output-format toon

Why: jq expects valid JSON input. toon format is designed for human readability and produces YAML-like output that jq cannot parse.

SHOULD: Choose one output approach

Applies to: answer, context, search, find-similar, get-contents

Pick one strategy and stick with it throughout your workflow:

1. Approach 1: toon only - Compact YAML-like output for direct reading

   • Use when: Reading output directly, no further processing needed
   • Token savings: ~40% reduction vs JSON
   • Example: exa-ai search "query" --output-format toon

2. Approach 2: JSON + jq - Extract specific fields programmatically

   • Use when: Need to extract specific fields or pipe to other commands
   • Token savings: ~80-90% reduction (extracts only needed fields)
   • Example: exa-ai search "query" | jq -r '.results[].title'

3. Approach 3: Schemas + jq - Structured data extraction with validation

   • Use when: Need consistent structured output across multiple queries
   • Token savings: ~85% reduction + consistent schema
   • Example: exa-ai search "query" --summary-schema '{...}' | jq -r '.results[].summary | fromjson'

Why: Mixing approaches increases complexity and token usage. Choosing one approach optimizes for your use case.

---

Shell Command Best Practices

MUST: Run commands directly, parse separately

Applies to: monitor, search (websets), research, and all skills using complex commands

When using the Bash tool with complex shell syntax, run commands directly and parse output in separate steps:

# ❌ WRONG - nested command substitution
webset_id=$(exa-ai webset-create --search '{"query":"..."}' | jq -r '.webset_id')

# ✅ CORRECT - run directly, then parse
exa-ai webset-create --search '{"query":"..."}'
# Then in a follow-up command:
webset_id=$(cat output.json | jq -r '.webset_id')

Why: Complex nested $(...) command substitutions can fail unpredictably in shell environments. Running commands directly and parsing separately improves reliability and makes debugging easier.

MUST NOT: Use nested command substitutions

Applies to: All skills when using complex multi-step operations

Avoid nesting multiple levels of command substitution:

# ❌ WRONG - deeply nested
result=$(exa-ai search "$(cat query.txt | tr '\n' ' ')" --num-results $(cat config.json | jq -r '.count'))

# ✅ CORRECT - sequential steps
query=$(cat query.txt | tr '\n' ' ')
count=$(cat config.json | jq -r '.count')
exa-ai search "$query" --num-results $count

Why: Nested command substitutions are fragile and hard to debug when they fail. Sequential steps make each operation explicit and easier to troubleshoot.

SHOULD: Break complex commands into sequential steps

Applies to: All skills when working with multi-step workflows

For readability and reliability, break complex operations into clear sequential steps:

# ❌ Less maintainable - everything in one line
exa-ai webset-create --search '{"query":"startups","count":1}' | jq -r '.webset_id' | xargs -I {} exa-ai webset-search-create {} --query "AI" --behavior override

# ✅ More maintainable - clear steps
exa-ai webset-create --search '{"query":"startups","count":1}'
webset_id=$(jq -r '.webset_id' < output.json)
exa-ai webset-search-create $webset_id --query "AI" --behavior override

Why: Sequential steps are easier to understand, debug, and modify. Each step can be verified independently.