
Aipricepatterns
AI Price Patterns provides historical market memory for AI agents through semantic vector search across years of financial market data. Use it to discover sim…
What it does
The specific capability behind this listing, and where to get it.
AI Price Patterns provides historical market memory for AI agents through semantic vector search across years of financial market data. Use it to discover similar market regimes, analyze historical price patterns, retrieve market context, and accelerate quantitative research. Designed for algorithmic trading, quantitative finance, reinforcement learning, and autonomous AI agents. Features: • Semantic vector search • Historical market memory • Pattern similarity discovery • Market regime analysis • Quantitative research • Remote HTTP MCP • Works with Claude, Cursor, ChatGPT, Codex, VS Code and other MCP-compatible clients.
Official Aipricepatterns links
Pricing & plans
Observed public pricing for Aipricepatterns, benchmarked against comparable providers. Plans, tiers, history and scenario below.
$0.00 /call
The current price is deliberately competitive.
$0.00 is positioned against a $0–$0 observed middle market.
Why Agentery reaches that view
price_benchmark100% below the median.
get_agent_profile · plan historyper call observed 2026-07-28.
pricing recommendationPercentile unavailable for this basis.
confidenceSource page rechecked daily.
Test a different price for this plan.
Move the proposed monthly price. Agentery recalculates the provider’s market position and explains the likely percentile.
Is Aipricepatterns good value?
How its price compares with genuinely comparable providers.
Below the niche median for its buyer tier.
Benchmarked against comparable providers at the same buyer tier and billing unit — the entry plan sits 100% below the observed median.
Compared with Market Data Agent
Positioned against the observed p25 / median / p75 of comparable providers at the same buyer tier and billing unit. See the plans above for the exact percentile and the full niche market for peers.
View the full niche →Comparable Market Data Agent
Alternatives in the same niche, with observed price and liveness where available.





See the MCP response behind this page · get_agent_profile()
See the MCP response behind this pageget_agent_profile
{
"agent_id": "aipricepatterns",
"name": "Aipricepatterns",
"url": "https://aipricepatterns.com",
"logo": "https://agentery.com/logos/CP-R2Z6VJ-ld256.png",
"niche": "market-data-agent",
"category": "financial-services",
"short_summary": "AI Price Patterns provides historical market memory for AI agents through semantic vector search across years of financial market data.\n\nUse it to discover sim…",
"task_performed": "AI Price Patterns provides historical market memory for AI agents through semantic vector search across years of financial market data.\n\nUse it to discover similar market regimes, analyze historical price patterns, retrieve market context, and accelerate quantitative research.\n\nDesigned for algorithmic trading, quantitative finance, reinforcement learning, and autonomous AI agents.\n\nFeatures:\n• Semantic vector search\n• Historical market memory\n• Pattern similarity discovery\n• Market regime analysis\n• Quantitative research\n• Remote HTTP MCP\n• Works with Claude, Cursor, ChatGPT, Codex, VS Code and other MCP-compatible clients.",
"inputs_accepted": [],
"outputs_produced": [],
"integrations_available": [
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],
"protocols_or_interfaces": [
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],
"industry_fit": [
"developer tools"
],
"autonomy_level": "infrastructure",
"human_approval_needed": "unclear",
"pricing_model": "unclear",
"price": {
"observed": true,
"billing": "usage",
"currency": "SOL",
"lowest_monthly_usd": null,
"monthly_usd": null,
"headline": "Paid (price not published)",
"summary": "AIPP offers a permanent free tier and SOL-priced usage units for paid agent capabilities, with exact checkout pricing handled through Manus.",
"confidence": "high",
"source_url": "https://aipricepatterns.com/billing",
"checked_at": "2026-07-28T05:09:03.466Z",
"amount": null,
"display": null,
"plans": [
{
"name": "Try the Engine",
"price": "◎ 0 forever",
"period": null,
"persona": "free",
"highlights": [
"100 free calls per day per service",
"No registration or wallet needed",
"Playground and demo routes included"
],
"price_annual": null
},
{
"name": "Pattern Search",
"price": "◎ 0.001 / 100 calls",
"usage": true,
"period": "usage",
"persona": "free",
"highlights": [
"Structural analog search",
"Match exports",
"Reusable access token"
],
"price_annual": null
},
{
"name": "Live Signals",
"price": "◎ 0.0005 / 500 calls",
"usage": true,
"period": "usage",
"persona": "free",
"highlights": [
"Live probability and Polymarket signal calls",
"Token reuse for automated routing"
],
"price_annual": null
},
{
"name": "Backtesting",
"price": "◎ 0.002 / 50 calls",
"usage": true,
"period": "usage",
"persona": "free",
"highlights": [
"Walk-forward and selector backtests",
"Strategy validation and optimization"
],
"price_annual": null
},
{
"name": "Dataset Ops",
"price": "◎ 0.003 / 20 calls",
"usage": true,
"period": "usage",
"persona": "free",
"highlights": [
"Dataset refresh, gaps, and expansion",
"Explicit agent-controlled spend"
],
"price_annual": null
}
],
"source": "render+llm"
},
"trust_or_rating_signal": [],
"evidence_quality": "medium",
"entity_type": "infrastructure",
"regulated_data_suitability": "unclear",
"evidence_urls": [
"https://aipricepatterns.com"
],
"last_checked": "2026-07-07",
"how_to_connect": {
"website": "https://aipricepatterns.com",
"docs": null,
"mcp": null,
"a2a": null,
"api": null,
"protocols": [
"MCP"
],
"note": "Speaks MCP but publishes no endpoint we could verify — check the docs/website."
},
"liveness": {
"probed": true,
"alive": true,
"endpoint_kind": "site",
"latency_ms": 410,
"uptime_7d": 1,
"checked_at": "2026-07-28T02:30:57.395Z",
"consecutive_failures": 0,
"status": "alive"
},
"price_extras": {
"free_tier": {
"amount": 100,
"unit": "calls",
"per": "day",
"raw": "100 free calls per day",
"plan": "Try the Engine"
},
"unit_cost": null
},
"reported_success": null,
"feedback": "If you use this listing, call report_outcome afterwards — it sharpens rankings for everyone including you."
}