Web Search API (Exa)

Neural web search engine designed for AI agents. Semantic ranking via neural embeddings (not just keywords), similar page discovery for competitive research, full-text page content extraction, and AI-grounded answers with source citations. Filter by category: company, research paper, news, PDF, GitHub, LinkedIn profile, financial report.

Base URL: https://api.jarvisclaw.ai/v1/marketplace/exa

Endpoints

Method	Endpoint	Description
POST	`/search`	Semantic web search with neural ranking
POST	`/find-similar`	Find pages similar to a given URL
POST	`/contents`	Extract full-text content from URLs
POST	`/answer`	AI-grounded answer with source citations

Pricing

Endpoint	Price	Description
/search	$0.01/request	Neural semantic search
/find-similar	$0.01/request	Similar page discovery
/contents	$0.002/URL	Full-text page extraction
/answer	$0.01/request	AI answer with citations

POST /search

Search the web using neural semantic ranking. Returns results ranked by meaning, not just keyword matching.

Parameters

Parameter	Type	Required	Description
`query`	string	Yes	Search query (max 10,000 characters)
`numResults`	integer	No	Number of results to return. Default: `10`
`category`	string	No	Filter by category: `company`, `research paper`, `news`, `pdf`, `github`, `linkedin profile`, `financial report`
`startPublishedDate`	string	No	Filter results published after this date (ISO 8601)
`endPublishedDate`	string	No	Filter results published before this date (ISO 8601)

Request

json

{
  "query": "latest breakthroughs in protein folding AI models 2026",
  "numResults": 5,
  "category": "research paper",
  "startPublishedDate": "2026-01-01T00:00:00Z"
}

Response

json

{
  "results": [
    {
      "title": "AlphaFold 3: Predicting All Molecular Interactions",
      "url": "https://example.com/alphafold3-paper",
      "score": 0.95,
      "publishedDate": "2026-03-15T00:00:00Z",
      "author": "DeepMind Research"
    },
    {
      "title": "ESMFold Advances in Zero-Shot Structure Prediction",
      "url": "https://example.com/esmfold-advances",
      "score": 0.89,
      "publishedDate": "2026-02-20T00:00:00Z",
      "author": "Meta AI"
    }
  ],
  "query": "latest breakthroughs in protein folding AI models 2026"
}

POST /find-similar

Find web pages similar to a given URL. Useful for competitive research, finding related content, and discovering alternatives.

Parameters

Parameter	Type	Required	Description
`url`	string	Yes	Source URL to find similar pages for (must be publicly indexable)
`numResults`	integer	No	Number of similar results to return. Default: `10`

Request

json

{
  "url": "https://openai.com/research/gpt-4",
  "numResults": 5
}

Response

json

{
  "results": [
    {
      "title": "Claude 3.5 Sonnet: Technical Report",
      "url": "https://example.com/claude-report",
      "score": 0.92
    },
    {
      "title": "Gemini Ultra: Capabilities and Limitations",
      "url": "https://example.com/gemini-ultra",
      "score": 0.87
    }
  ],
  "sourceUrl": "https://openai.com/research/gpt-4"
}

POST /contents

Extract full-text content from one or more URLs. Returns clean, parsed page text suitable for LLM consumption.

Parameters

Parameter	Type	Required	Description
`urls`	string[]	Yes	Array of URLs to extract content from

Request

json

{
  "urls": [
    "https://example.com/article-1",
    "https://example.com/article-2"
  ]
}

Response

json

{
  "contents": [
    {
      "url": "https://example.com/article-1",
      "title": "Understanding Transformer Architectures",
      "text": "Transformer models have revolutionized natural language processing...",
      "publishedDate": "2026-05-10T00:00:00Z"
    },
    {
      "url": "https://example.com/article-2",
      "title": "Scaling Laws for Neural Language Models",
      "text": "We study empirical scaling laws for language model performance...",
      "publishedDate": "2026-04-22T00:00:00Z"
    }
  ]
}

POST /answer

Get an AI-generated answer to a query, grounded in web sources with citations.

Parameters

Parameter	Type	Required	Description
`query`	string	Yes	Question to answer (max 10,000 characters)

Request

json

{
  "query": "What are the main differences between GPT-4 and Claude 3.5?"
}

Response

json

{
  "answer": "GPT-4 and Claude 3.5 differ in several key areas: architecture design, context window size, multimodal capabilities, and safety approaches. GPT-4 supports up to 128k tokens of context while Claude 3.5 Sonnet supports 200k tokens...",
  "sources": [
    {
      "title": "GPT-4 Technical Report",
      "url": "https://example.com/gpt4-report"
    },
    {
      "title": "Claude 3.5 Model Card",
      "url": "https://example.com/claude-model-card"
    }
  ]
}

Errors

HTTP Status	Code	Description
400	`query_too_long`	Query exceeds the 10,000 character limit
400	`invalid_url`	The provided URL is malformed or not a valid web address
422	`url_unreachable`	The URL could not be fetched (not publicly accessible or blocked)

Code Examples

bash

# Semantic search
curl -X POST https://api.jarvisclaw.ai/v1/marketplace/exa/search \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "best open source LLM frameworks 2026",
    "numResults": 5,
    "category": "github"
  }'

# Find similar pages
curl -X POST https://api.jarvisclaw.ai/v1/marketplace/exa/find-similar \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://github.com/langchain-ai/langchain",
    "numResults": 5
  }'

# Extract page contents
curl -X POST https://api.jarvisclaw.ai/v1/marketplace/exa/contents \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "urls": ["https://example.com/article"]
  }'

# Get AI answer with citations
curl -X POST https://api.jarvisclaw.ai/v1/marketplace/exa/answer \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is retrieval augmented generation and how does it work?"
  }'

python

import requests

BASE = "https://api.jarvisclaw.ai/v1/marketplace/exa"
HEADERS = {
    "Authorization": "Bearer sk-your-api-key",
    "Content-Type": "application/json",
}

# Semantic search
resp = requests.post(f"{BASE}/search", headers=HEADERS, json={
    "query": "best open source LLM frameworks 2026",
    "numResults": 5,
    "category": "github",
})
for result in resp.json()["results"]:
    print(f"{result['title']}: {result['url']}")

# Find similar pages
resp = requests.post(f"{BASE}/find-similar", headers=HEADERS, json={
    "url": "https://github.com/langchain-ai/langchain",
    "numResults": 5,
})
for result in resp.json()["results"]:
    print(f"{result['title']} (score: {result['score']})")

# Extract page contents
resp = requests.post(f"{BASE}/contents", headers=HEADERS, json={
    "urls": ["https://example.com/article-1", "https://example.com/article-2"],
})
for content in resp.json()["contents"]:
    print(f"{content['title']}: {content['text'][:100]}...")

# AI-grounded answer
resp = requests.post(f"{BASE}/answer", headers=HEADERS, json={
    "query": "What is retrieval augmented generation and how does it work?",
})
data = resp.json()
print(data["answer"])
for source in data["sources"]:
    print(f"  Source: {source['url']}")

python

from jarvisclaw import SearchClient

# --- Option A: Base chain (EVM) ---
# Hex private key -> USDC on Base (Chain ID 8453)
search = SearchClient(private_key="0x<evm-private-key>")

# --- Option B: Solana ---
# Base58 keypair -> USDC SPL on Solana mainnet
# search = SearchClient(private_key="<solana-bs58-keypair>")

# SDK auto-detects chain from key format - no config needed

# Semantic search
results = search.search("best open source LLM frameworks 2026", num_results=5, category="github")
for r in results:
    print(f"{r.title}: {r.url}")

# Find similar pages
similar = search.find_similar("https://github.com/langchain-ai/langchain", num_results=5)
for r in similar:
    print(f"{r.title} (score: {r.score})")

# Extract page contents
contents = search.contents(["https://example.com/article-1", "https://example.com/article-2"])
for c in contents:
    print(f"{c.title}: {c.text[:100]}...")

# AI-grounded answer with citations
answer = search.answer("What is retrieval augmented generation?")
print(answer.text)
for source in answer.sources:
    print(f"  Source: {source.url}")

package main

import (
    "context"
    "fmt"
    jc "github.com/api-jarvisclaw/go-sdk"
)

func main() {
    ctx := context.Background()
    sc, _ := jc.NewSearchClient(jc.WithAPIKey("sk-your-api-key"))

    // Semantic search
    results, _ := sc.Search(ctx, "best open source LLM frameworks 2026",
        jc.WithNumResults(5), jc.WithCategory("github"))
    for _, r := range results {
        fmt.Printf("%s: %s\n", r.Title, r.URL)
    }

    // Find similar pages
    similar, _ := sc.FindSimilar(ctx, "https://github.com/langchain-ai/langchain",
        jc.WithNumResults(5))
    for _, r := range similar {
        fmt.Printf("%s (score: %.2f)\n", r.Title, r.Score)
    }

    // Extract page contents
    contents, _ := sc.Contents(ctx, []string{"https://example.com/article"})
    for _, c := range contents {
        fmt.Printf("%s: %s...\n", c.Title, c.Text[:100])
    }

    // AI-grounded answer
    answer, _ := sc.Answer(ctx, "What is retrieval augmented generation?")
    fmt.Println(answer.Text)
    for _, s := range answer.Sources {
        fmt.Printf("  Source: %s\n", s.URL)
    }
}

package main

import (
    "context"
    "fmt"
    jc "github.com/api-jarvisclaw/go-sdk"
)

func main() {
    ctx := context.Background()

    // x402 Agent wallet - pays per-call via USDC on Base (Chain ID 8453)
    sc, _ := jc.NewSearchClient(jc.WithPrivateKey("0x<evm-private-key>"))

    // Semantic search
    results, _ := sc.Search(ctx, "best open source LLM frameworks 2026",
        jc.WithNumResults(5), jc.WithCategory("github"))
    for _, r := range results {
        fmt.Printf("%s: %s\n", r.Title, r.URL)
    }

    // Find similar pages
    similar, _ := sc.FindSimilar(ctx, "https://github.com/langchain-ai/langchain",
        jc.WithNumResults(5))
    for _, r := range similar {
        fmt.Printf("%s (score: %.2f)\n", r.Title, r.Score)
    }

    // AI-grounded answer
    answer, _ := sc.Answer(ctx, "What is retrieval augmented generation?")
    fmt.Println(answer.Text)
}

Limitations

10,000 character query max — queries exceeding this length are rejected with query_too_long
Public pages only — cannot access content behind authentication or paywalls
100 requests/minute rate limit — per API key, across all Exa endpoints
find-similar requires indexable URL — the source URL must be publicly accessible and crawlable
answer takes 3-8 seconds — the AI synthesis step adds latency compared to raw search

Web Search API (Exa) ​

Endpoints ​

Pricing ​

POST /search ​

Parameters ​

Request ​

Response ​

POST /find-similar ​

Parameters ​

Request ​

Response ​

POST /contents ​

Parameters ​

Request ​

Response ​

POST /answer ​

Parameters ​

Request ​

Response ​

Errors ​

Code Examples ​

Limitations ​

Web Search API (Exa)

Endpoints

Pricing

POST /search

Parameters

Request

Response

POST /find-similar

Parameters

Request

Response

POST /contents

Parameters

Request

Response

POST /answer

Parameters

Request

Response

Errors

Code Examples

Limitations