Using AgentFlare Social, Predictions and Crypto Data

This guide is for developers and agent builders who want structured social, prediction-market and crypto data from agentflare.org. You will learn which directories are metered and what each costs per request, which top-level and nested fields each .data.json file contains, how to authenticate with the crawler header, and how to fetch and parse one index file from each dataset in Python. Every field name and price below comes straight from the published field references and pricing.json.

How the flow works

Before you start

• A crawler token from AISA, shown as YOUR_CRAWLER_TOKEN below
• curl, or Python 3 with the requests package
• Basic familiarity with reading JSON

Step by step

1. Separate free pages from paid files

   AgentFlare keeps index pages, .html articles, .md teasers, llms.txt, sitemap.xml and robots.txt free (tier L1_free). Complete datasets and full analysis are the paid tier L2_paid: every *.data.json and *.full.md file, metered per request. In pricing.json the directory prices are social 0.005 USD, predictions 0.01 USD and crypto 0.01 USD. Because index.data.json ends in .data.json, it is billed like any other file in its directory.

   S5 · agentflare.org

2. Send the X-Crawler-Token header

   Identify your crawler with the X-Crawler-Token header. It replaces the earlier name X-AISA-Crawler-Token, which older AISA documentation pages still show. Never put the token in Authorization: Bearer, because that header is reserved for the gateway layer. The example fetches the Social Pulse index, a file priced at 0.005 USD per request. Replace YOUR_CRAWLER_TOKEN with your own token.

   curl -s 'https://agentflare.org/social/index.data.json' \
     -H 'X-Crawler-Token: YOUR_CRAWLER_TOKEN'

   S1 · esa.aisa.oneS2 · agentflare.orgS5 · agentflare.org

3. Read the Social Pulse category fields

   Every file carries the same envelope: type, tier, title, description, canonical, category, updated, generated_at, facts, data, analysis_md, sources, related, pricing and powered_by. In the social index, data.categories[] lists id, name, headline, url and a stats object with posts, likes, views, medLikes and verified, all numbers. Per-item files follow the pattern /social/<slug>.data.json.

   import requests
   
   url = 'https://agentflare.org/social/index.data.json'
   headers = {'X-Crawler-Token': 'YOUR_CRAWLER_TOKEN'}
   doc = requests.get(url, headers=headers).json()
   
   for c in doc['data']['categories']:
       s = c['stats']
       print(c['id'], c['name'], s['posts'], s['likes'],
             s['views'], s['medLikes'], s['verified'])

   S2 · agentflare.org

4. Pull prediction events, then one event file

   The predictions index has data.count, data.total_volume_usd and data.events[] with title, slug, leading, probability, change_pp_30d and volume_usd. Each event has its own file at /predictions/<slug>.data.json. That file adds data.event, data.slug, data.volume_usd, data.volume_24h_usd, data.end_date, data.tags and data.markets[], where each market has question, odds[] and volume_usd. Each request costs 0.01 USD.

   import requests
   
   base = 'https://agentflare.org/predictions/'
   headers = {'X-Crawler-Token': 'YOUR_CRAWLER_TOKEN'}
   idx = requests.get(base + 'index.data.json', headers=headers).json()
   
   for e in idx['data']['events']:
       print(e['slug'], e['leading'], e['probability'],
             e['change_pp_30d'], e['volume_usd'])
   
   first = idx['data']['events'][0]
   url = base + first['slug'] + '.data.json'
   ev = requests.get(url, headers=headers).json()
   for m in ev['data']['markets']:
       print(m['question'], m['odds'], m['volume_usd'])

   S3 · agentflare.orgS5 · agentflare.orgS1 · esa.aisa.one

5. Fetch crypto market and coin data

   The crypto index and the dated file /crypto/YYYY-MM-DD.data.json share one layout: data.total_market_cap_usd, data.coin_count, data.advancers_24h, data.stablecoin_share_pct, data.top[] and data.trending[]. A coin file such as /crypto/zcash.data.json holds price_usd, market_cap_rank, fdv_usd, change_pct, volume_24h_usd, ath_usd, atl_usd, supply, history_90d, analysis, faq and newswire. Each request costs 0.01 USD.

   import requests
   
   headers = {'X-Crawler-Token': 'YOUR_CRAWLER_TOKEN'}
   url = 'https://agentflare.org/crypto/zcash.data.json'
   d = requests.get(url, headers=headers).json()['data']
   
   print(d['name'], d['symbol'], d['price_usd'])
   print(d['market_cap_rank'], d['change_pct']['h24'], d['change_pct']['d7'])
   print(d['ath_usd'], d['ath_date'], d['atl_usd'], d['atl_date'])
   print(d['supply']['circulating'], d['supply']['max'])
   for p in d['history_90d'][:3]:
       print(p['date'], p['price'])

   S4 · agentflare.orgS5 · agentflare.orgS1 · esa.aisa.one

Datasets, key fields and price per request

Common pitfalls

• Do not send the crawler token in Authorization: Bearer. That header is reserved for the gateway layer; use X-Crawler-Token instead.
• Older AISA pages still show X-AISA-Crawler-Token. Use X-Crawler-Token, the name in the Open Verify specification.
• Prices differ by directory: social is 0.005 USD per request, predictions and crypto are 0.01 USD. Budget loops over many event or coin files accordingly.
• Dated files use a YYYY-MM-DD placeholder in their name, for example atp-fokina-tiafoe-YYYY-MM-DD.data.json. Substitute a real date before requesting.

Key takeaways

• Only .data.json and .full.md files are paid; index pages, .html articles and .md teasers stay free.
• Social costs 0.005 USD per request; predictions and crypto cost 0.01 USD each.
• Authenticate with X-Crawler-Token, then read data.categories, data.events or data.top from the index files.

FAQ

How this page is made. Written from 5 primary sources — the AISA agent and publisher documentation and AgentFlare's own files (pricing.json, robots.txt, llms.txt, live responses, .data.json field lists). Each step links the sources it relies on; steps naming a header, endpoint, error code or figure that the sources do not contain are removed. Refreshed weekly.