Exa Websets Search

Comprehensive webset management including creation, search, imports, items, and enrichments.

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
webset_id=$(exa-ai webset-create --search '{"query":"tech startups","count":1}' | jq -r '.webset_id')

# More reliable: run directly, then parse
exa-ai webset-create --search '{"query":"tech startups","count":1}'
# Then in a follow-up command if needed:
webset_id=$(cat output.json | jq -r '.webset_id')

Critical Requirements

Universal rules across all operations:

1. Start with minimal counts (1-5 results): Initial searches are test spikes to validate quality. ALWAYS default to count:1 unless user explicitly requests more.
2. Three-step workflow - Validate, Expand, Enrich: (1) Create with count:1 to test search quality, (2) Expand search count if results are good, (3) Add enrichments only after validated, expanded results.
3. No enrichments during validation: Never add enrichments when testing with count:1. Validate search quality first, expand count second, add enrichments last.
4. Avoid --wait flag: Do NOT use

   --wait

   flag in commands. It's designed for human interactive use, not automated workflows.
5. Maintain query AND criteria consistency: When scaling up or appending searches, use the EXACT same query AND criteria that you validated. Omitting criteria causes Exa to regenerate them on-the-fly, producing inconsistent results.

Credit Costs

Pricing: $50/month = 8,000 credits ($0.00625 per credit)

Cost per operation:

• Each webset item: 10 credits ($0.0625)
• Standard enrichment: 2 credits ($0.0125)
• Email enrichment: 5 credits ($0.03125)

Why start with count:1: Testing with 1 result costs 10 credits ($0.0625). A failed search with count:100 wastes 1,000 credits ($6.25) - 100x more expensive.

Why enrich last: Enriching bad results wastes credits. Always validate first, expand second, enrich last.

Quick Command Reference

exa-ai --help

Output Formats

All exa-ai webset commands support output formats:

• JSON (default): Pipe to

  jq

  to extract specific fields (e.g.,

  | jq -r '.webset_id'

  )
• toon: Compact, readable format for direct viewing
• pretty: Human-friendly formatted output
• text: Plain text output

Webset Management

Core operations for managing webset collections.

Entity Types

• company

  : Companies and organizations

• person

  : Individual people

• article

  : News articles and blog posts

• research_paper

  : Academic papers

• custom

  : Custom entity types (define with --entity-description)

Create Webset from Search

webset_id=$(exa-ai webset-create \
  --search '{"query":"AI startups in San Francisco","count":1}' | jq -r '.webset_id')

Create with Detailed Search Criteria

exa-ai webset-create \
  --search '{
    "query": "Technology companies focused on developer tools",
    "count": 1,
    "entity": {
      "type": "company"
    },
    "criteria": [
      {
        "description": "Companies with 50-500 employees indicating growth stage"
      },
      {
        "description": "Primary product is developer tools, APIs, or infrastructure"
      }
    ]
  }'

Create with Custom Entity

exa-ai webset-create \
  --search '{
    "query": "Nonprofits focused on economic justice",
    "count": 1,
    "entity": {
      "type": "custom",
      "description": "nonprofit"
    },
    "criteria": [
      {
        "description": "Primary focus on economic justice"
      },
      {
        "description": "Annual operating budget between $1M and $10M"
      }
    ]
  }'

Create from CSV Import

import_id=$(exa-ai import-create companies.csv \
  --count 100 \
  --title "Companies" \
  --format csv \
  --entity-type company | jq -r '.import_id')

exa-ai webset-create --import $import_id

Three-Step Workflow: Validate → Expand → Enrich

Step 1: VALIDATE - Create with count:1 (NO enrichments)

webset_id=$(exa-ai webset-create \
  --search '{"query":"tech startups","count":1}' | jq -r '.webset_id')

exa-ai webset-item-list $webset_id

⚠️ REQUIRED: Manually verify the result is relevant before continuing. If not, adjust the query and start over.

---

Step 2: EXPAND - Gradually increase count with verification at each stage

# Expand to 2 results (use same query and criteria from validation)
exa-ai webset-search-create $webset_id \
  --query "tech startups" \
  --behavior override \
  --count 2

exa-ai webset-item-list $webset_id

⚠️ REQUIRED: Check quality at this scale. Repeat with larger counts (5, 10, 25, 50, 100) until you reach your target.

Loop this step: Keep expanding gradually (2 → 5 → 10 → 25 → 50 → 100) with verification between each expansion.

---

Step 3: ENRICH - Add enrichments only after confirming quality

exa-ai enrichment-create $webset_id \
  --description "Company website" --format url --title "Website"

exa-ai enrichment-create $webset_id \
  --description "Employee count" --format text --title "Team Size"

Interpreting Criterion Success Rates

CRITICAL: Criteria are evaluated conditionally - when one criterion fails, others may not run. A low success rate doesn't indicate that criterion is restrictive; it means OTHER criteria are filtering results first. Only interpret a low success rate as "restrictive" when OTHER criteria have high success rates (>80%).

Manage Websets

exa-ai webset-list
exa-ai webset-get ws_abc123
exa-ai webset-update ws_abc123 --metadata '{"status":"active","owner":"team"}'
exa-ai webset-delete ws_abc123

---

Search Operations

Run searches within a webset to add new items.

Search Behavior

Control how new search results are combined with existing items:

• append (default): Add new items to existing collection

  • Requires previous search results to exist
  • Error if webset has no previous search: "No previous search found"
  • Default behavior when

    --behavior

    is omitted

• override: Replace entire collection with search results

  • REQUIRED for first search on a webset
  • Use when starting fresh or completely replacing results

CRITICAL - First search requirement: The first

webset-search-create

on a webset MUST explicitly use

--behavior override

. Since the default is append, omitting

--behavior

will fail with "No previous search found" error. Subsequent searches can omit the flag (defaults to append).

Query and Criteria Consistency

CRITICAL: When appending or scaling up searches, maintain IDENTICAL query and criteria from your validated search.

Why This Matters

Using different criteria causes Exa to generate new search parameters on-the-fly, which:

• Violates consistency and produces mismatched results
• Reduces result quality compared to validated criteria
• Makes it impossible to reproduce or debug issues

Complete Example

# Step 1: Test search with criteria (MUST use override for first search)
exa-ai webset-search-create ws_abc123 \
  --query "Progressive nonprofits in California" \
  --behavior override \
  --count 1 \
  --criteria '[
    {"description": "Annual budget between $1M and $10M"},
    {"description": "Primary focus on economic justice, affordability, living wages, or worker power"},
    {"description": "Established communications, narrative strategy, or messaging function"}
  ]'

# Verify quality, then append MORE results with IDENTICAL query and criteria
exa-ai webset-search-create ws_abc123 \
  --query "Progressive nonprofits in California" \
  --behavior append \
  --count 5 \
  --criteria '[
    {"description": "Annual budget between $1M and $10M"},
    {"description": "Primary focus on economic justice, affordability, living wages, or worker power"},
    {"description": "Established communications, narrative strategy, or messaging function"}
  ]'

Best Practice: Save Criteria to File

# Create criteria file once
cat > criteria.json <<'EOF'
[
  {"description": "Annual budget between $1M and $10M"},
  {"description": "Primary focus on economic justice, affordability, living wages, or worker power"},
  {"description": "Established communications, narrative strategy, or messaging function"}
]
EOF

# Use consistently across all searches (first search needs override)
exa-ai webset-search-create ws_abc123 \
  --query "Progressive nonprofits in California" \
  --behavior override \
  --count 1 \
  --criteria @criteria.json

exa-ai webset-search-create ws_abc123 \
  --query "Progressive nonprofits in California" \
  --behavior append \
  --count 5 \
  --criteria @criteria.json

Basic Search Operations

# First search on webset (must use override)
exa-ai webset-search-create ws_abc123 \
  --query "AI startups in San Francisco" \
  --behavior override \
  --count 1

# Append to collection
exa-ai webset-search-create ws_abc123 \
  --query "SaaS companies Series B" \
  --behavior append \
  --count 1

# Override collection
exa-ai webset-search-create ws_abc123 \
  --query "top tech companies" \
  --behavior override \
  --count 1

Monitor Search Progress

webset_id="ws_abc123"
search_id=$(exa-ai webset-search-create $webset_id \
  --query "fintech startups" \
  --behavior override \
  --count 1 | jq -r '.search_id')

exa-ai webset-search-get $webset_id $search_id
exa-ai webset-search-cancel $webset_id $search_id

---

CSV Imports

Upload CSV files to create websets from existing datasets.

CSV Format Requirements

1. First row contains column headers
2. Each row represents one entity
3. Include at minimum a name or identifier column

Basic Import Workflow

# Create import
import_id=$(exa-ai import-create companies.csv \
  --count 100 \
  --title "Tech Companies" \
  --format csv \
  --entity-type company | jq -r '.import_id')

# Create webset from import
webset_id=$(exa-ai webset-create --import $import_id | jq -r '.webset_id')

Custom Entity Type

exa-ai import-create products.csv \
  --count 5 \
  --title "Product List" \
  --format csv \
  --entity-type custom \
  --entity-description "Consumer electronics products"

Manage Imports

exa-ai import-list
exa-ai import-get imp_abc123

---

Import vs Search Scope

--import

loads data for enrichment.

search.scope

filters searches to specific sources.

⚠️ NEVER use same ID in both - returns 400:

# ❌ INVALID
exa-ai webset-create --import import_abc \
  --search '{"scope":[{"source":"import","id":"import_abc"}]}'

# ✅ Scoped search only
exa-ai webset-create \
  --search '{"query":"CEOs","scope":[{"source":"import","id":"import_abc"}]}'

# ✅ Relationship traversal
exa-ai webset-search-create ws_abc --query "investors" --behavior override \
  --scope '[{"source":"webset","id":"webset_abc","relationship":{"definition":"investors of","limit":5}}]'

---

Item Management

Manage individual items in websets.

Basic Operations

# List items
exa-ai webset-item-list ws_abc123
exa-ai webset-item-list ws_abc123 --output-format pretty

# Get item details
exa-ai webset-item-get item_xyz789

# Delete item
exa-ai webset-item-delete item_xyz789

Extract Item Data

# Get all item IDs
exa-ai webset-item-list ws_abc123 --output-format json | jq -r '.[].id'

# Count items
exa-ai webset-item-list ws_abc123 --output-format json | jq 'length'

---

Enrichments

Add structured data fields to all items in a webset using AI extraction.

Enrichment Formats

• text: Free-form text extraction (employee count, description, technology stack)
• url: Extract URLs only (website, LinkedIn, GitHub)
• options: Categorical data with predefined options (industry, funding stage, size range)

Key Concepts

• description: The primary AI prompt that drives extraction. This tells the enrichment WHAT to extract. (Can be updated)
• instructions: Optional additional guidance on HOW to extract or format. (Creation-only, cannot be updated)
• Use

  exa-ai enrichment-create --help

  and

  exa-ai enrichment-update --help

  to see all available parameters

Create Enrichments

# Text enrichment
exa-ai enrichment-create ws_abc123 \
  --description "Number of employees as of latest data" \
  --format text \
  --title "Team Size"

# URL enrichment
exa-ai enrichment-create ws_abc123 \
  --description "Primary company website URL" \
  --format url \
  --title "Website"

# Options enrichment
exa-ai enrichment-create ws_abc123 \
  --description "Current funding stage" \
  --format options \
  --options '[
    {"label":"Pre-seed"},
    {"label":"Seed"},
    {"label":"Series A"},
    {"label":"Series B"},
    {"label":"Series C+"},
    {"label":"Public"}
  ]' \
  --title "Funding Stage"

Use Options from File

cat > industries.json <<'EOF'
[
  {"label": "SaaS"},
  {"label": "Developer Tools"},
  {"label": "AI/ML"},
  {"label": "Fintech"},
  {"label": "Healthcare"},
  {"label": "Other"}
]
EOF

exa-ai enrichment-create ws_abc123 \
  --description "Primary industry or sector" \
  --format options \
  --options @industries.json \
  --title "Industry"

Add Instructions for Precision

exa-ai enrichment-create ws_abc123 \
  --description "Technology stack" \
  --format text \
  --instructions "Focus only on backend technologies and databases. Ignore frontend frameworks." \
  --title "Backend Tech"

Manage Enrichments

# List enrichments
exa-ai enrichment-list ws_abc123
exa-ai enrichment-list ws_abc123 --output-format pretty

# Get details
exa-ai enrichment-get ws_abc123 enr_xyz789

# Update extraction prompt (description)
exa-ai enrichment-update ws_abc123 enr_xyz789 \
  --description "Exact employee count from most recent source"

# Update format and options
exa-ai enrichment-update ws_abc123 enr_xyz789 \
  --format options \
  --options '[{"label":"Small"},{"label":"Medium"},{"label":"Large"}]'

# Update metadata
exa-ai enrichment-update ws_abc123 enr_xyz789 \
  --metadata '{"source":"manual","updated":"2024-01-15"}'

# Note: Cannot update --instructions or --title (creation-only parameters)
# To change instructions, delete and recreate the enrichment

# Delete
exa-ai enrichment-delete ws_abc123 enr_xyz789

# Cancel running enrichment
exa-ai enrichment-cancel ws_abc123 enr_xyz789

Common Enrichment Patterns

Company websets: Website (url), Team Size (text), Funding Stage (options), Industry (options)

Person websets: LinkedIn (url), Job Title (text), Company (text), Location (text)

Research papers: Publication Year (text), Authors (text), Venue (text), Research Area (options)

---

Best Practices

1. Start small, validate, then scale: Always use count:1 for initial searches
2. Follow three-step workflow: Validate → Expand → Enrich
3. Never enrich during validation: Only enrich after validated, expanded results
4. Avoid --wait flag: Do NOT use

   --wait

   in commands. It's designed for human interactive use, not automated workflows.
5. Maintain query AND criteria consistency: When appending or scaling up, use IDENTICAL query and criteria from validated search. Save criteria to file for consistency.
6. CRITICAL - First search must use override: The library defaults to

   --behavior append

   . First search on a webset MUST explicitly use

   --behavior override

   or it will fail with "No previous search found" error.
7. Use correct parameter names:
   • Use

     --behavior append

     or

     --behavior override

     (NOT

     --mode

     )
   • Commands like

     webset-search-get

     require both webset_id and search_id
8. Choose specific entity types: Use company, person, etc. for better results
9. Save IDs: Use

   jq

   to extract and save IDs for subsequent commands

---

Detailed Reference

For complete command references, syntax, and all options, consult REFERENCE.md and component-specific reference files.

Shared Requirements

<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.

</shared-requirements>