Anthropic
Educational/engineering guidance on how to build effective LLM agents and agentic systems (workflows and agents)
What it does
The specific capability behind this listing, and where to get it.
Anthropic
Educational/engineering guidance on how to build effective LLM agents and agentic systems (workflows and agents)
Official Anthropic links
Pricing & plans
Observed public pricing for Anthropic, benchmarked against comparable providers. Plans, tiers, history and scenario below.
$17.41 /mo
The current price is deliberately competitive.
$19.85 sits below the observed Team flat range ($201.88–$273.13) — around the 25th percentile of observed prices. It reads as a value play; make sure the margin still works.
Why Agentery reaches that view
price_benchmark92.7% below the Team flat median.
get_agent_profile · plan historyUnspecified pricing item observed 2026-08-30.
pricing recommendation~25th percentile of comparable Team flat plans.
confidencethin (thin cohort); source 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 Anthropic good value?
How its price compares with genuinely comparable providers.
Premium-priced for its buyer tier.
Benchmarked against comparable providers at the same buyer tier and billing unit — the entry plan sits 335% above the observed median.
Compared with Agent Framework Open Source
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 →Anthropic's local market
Nearest products by what they do.
See the MCP response behind this page · get_agent_profile()
See the MCP response behind this pageget_agent_profile
{
"agent_id": "anthropic_engineering",
"name": "Anthropic",
"url": "https://www.anthropic.com/engineering/building-effective-agents",
"logo": "https://agentery.com/logos/CP-YBA2XD-256.png",
"niche": "agent-framework-open-source",
"category": "developer-tools-infra",
"short_summary": "Educational/engineering guidance on how to build effective LLM agents and agentic systems (workflows and agents)",
"task_performed": "Educational/engineering guidance on how to build effective LLM agents and agentic systems (workflows and agents)",
"inputs_accepted": [],
"outputs_produced": [],
"integrations_available": [
"Model Context Protocol",
"Claude Agent SDK",
"Strands Agents SDK",
"Rivet",
"Vellum",
"LLM APIs"
],
"protocols_or_interfaces": [
"SDK"
],
"industry_fit": [
"developer tools"
],
"autonomy_level": "infrastructure",
"human_approval_needed": "unclear",
"pricing_model": "unclear",
"price": {
"observed": false,
"billing": "unknown",
"currency": null,
"lowest_monthly_usd": null,
"monthly_usd": null,
"headline": "No public price found",
"summary": "The pricing page visibly lists €0, €15, €180, €18, and €90, but the supplied context does not identify their plans or billing periods.",
"confidence": "low",
"source_url": "https://www.anthropic.com/pricing",
"checked_at": "2026-08-30T04:25:54.974Z",
"amount": null,
"display": null,
"plans": [],
"source": "render+llm"
},
"trust_or_rating_signal": [
"Worked with dozens of teams building LLM agents across industries"
],
"evidence_quality": "medium",
"entity_type": "content-community",
"regulated_data_suitability": "unclear",
"evidence_urls": [
"https://www.anthropic.com/engineering/building-effective-agents"
],
"last_checked": "2026-06-17",
"how_to_connect": {
"website": "https://www.anthropic.com/engineering/building-effective-agents",
"docs": "https://www.anthropic.com/docs",
"mcp": null,
"a2a": null,
"api": {
"docs_url": "https://www.anthropic.com/api",
"endpoint": null
},
"protocols": []
},
"liveness": {
"probed": true,
"alive": true,
"endpoint_kind": "site",
"latency_ms": 417,
"uptime_7d": 1,
"checked_at": "2026-09-02T02:31:13.932Z",
"consecutive_failures": 0,
"status": "alive"
},
"price_extras": {
"free_tier": null,
"unit_cost": null
},
"reported_success": null,
"feedback": "If you use this listing, call report_outcome afterwards — it sharpens rankings for everyone including you."
}