Access Github
Provides a framework-agnostic toolkit to build, instrument, observe, profile, evaluate, and optimize AI agents and workflows
What it does
The specific capability behind this listing, and where to get it.
Access Github
Provides a framework-agnostic toolkit to build, instrument, observe, profile, evaluate, and optimize AI agents and workflows
Official Access Github links
Pricing & plans
Observed public pricing for Access Github, benchmarked against comparable providers. Plans, tiers, history and scenario below.
$4 /mo
The current price sits below the comparable median.
$4 sits below the observed Team flat range ($149–$299) — around the 0th percentile of observed prices. It reads as a value play; make sure the margin still works.
Why Agentery reaches that view
price_benchmark97.3% below the Team flat median.
get_provider_profile · plan historyTeam observed 2026-07-10.
pricing recommendation~0th percentile of comparable Team flat plans (approximate: interpolated between the cohort’s observed quartiles, 17 providers).
confidencehigh; 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 Access Github good value?
How its price compares with genuinely comparable providers.
Below the comparable median for its buyer tier.
Basis: Team flat plans at the same billing unit (17 providers), USD monthly, pricing observed 2026-07-10. The Team plan ($4/mo) sits 97.3% below that median of $150. Other cohorts on this page (niche-wide tier peers, nearest products by capability) are labelled where they appear.
Compared with Agent Marketplace Meta
Positioned against the observed p25 / median / p75 of comparable providers at the same buyer tier and billing unit (the basis is stated with each verdict above). See the plans above for the percentile and its cohort size, and the full niche market for peers.
View the full niche →Access Github's local market
Nearest products by what they do — a different cohort from the buyer-tier benchmark above.
See the MCP response behind this page · get_provider_profile()
See the MCP response behind this pageget_provider_profile (get_agent_profile remains a callable alias)
{
"agent_id": "access_github",
"name": "Access Github",
"url": "http://github.com/NVIDIA/AgentIQ",
"logo": "https://agentery.com/logos/CP-Z2FHNP.svg",
"niche": "agent-marketplace-meta",
"category": "developer-tools-infra",
"short_summary": "Provides a framework-agnostic toolkit to build, instrument, observe, profile, evaluate, and optimize AI agents and workflows",
"task_performed": "Provides a framework-agnostic toolkit to build, instrument, observe, profile, evaluate, and optimize AI agents and workflows",
"inputs_accepted": [
"Agent workflows defined in YAML/Python",
"agent profiles",
"prompts",
"third-party tool integrations"
],
"outputs_produced": [
"Instrumented/optimized agent workflows",
"profiling and observability traces",
"evaluation results",
"optimized prompts and configurations",
"MCP servers"
],
"integrations_available": [
"LangChain",
"LlamaIndex",
"CrewAI",
"Microsoft Semantic Kernel",
"Google ADK",
"Agno",
"LangGraph",
"LangSmith",
"NVIDIA Dynamo",
"Model Context Protocol (MCP)",
"Agent-to-Agent (A2A) Protocol",
"FastMCP",
"Tavily",
"Redis",
"Microsoft Agent 365",
"NeMo Guardrails",
"PyPI"
],
"protocols_or_interfaces": [
"MCP",
"A2A"
],
"industry_fit": [
"developer tools"
],
"autonomy_level": "infrastructure",
"human_approval_needed": "unclear",
"pricing_model": "free",
"price": {
"observed": false,
"billing": "not_found",
"currency": null,
"lowest_monthly_usd": null,
"monthly_usd": null,
"headline": "No public price found",
"summary": "No public pricing information was observed for NVIDIA NeMo-Agent-Toolkit on the supplied homepage.",
"confidence": "medium",
"source_url": "http://github.com/NVIDIA/AgentIQ",
"checked_at": "2026-10-25T01:00:56.655Z",
"source_kind": "vendor",
"amount": null,
"display": null,
"plans": [],
"source": "render+llm"
},
"trust_or_rating_signal": [
"2.4k GitHub stars",
"681 forks",
"Apache-2.0 license",
"NVIDIA brand",
"SECURITY.md",
"Code of conduct"
],
"evidence_quality": "high",
"entity_type": "infrastructure",
"regulated_data_suitability": "unclear",
"evidence_urls": [
"http://github.com/NVIDIA/AgentIQ",
"https://developer.nvidia.com/nemo-agent-toolkit"
],
"last_checked": "2026-10-25",
"profile_checked": "2026-06-16",
"price_checked": "2026-10-25",
"how_to_connect": {
"website": "http://github.com/NVIDIA/AgentIQ",
"docs": "http://github.com/documentation",
"mcp": {
"endpoint": "http://github.com/mcp.json",
"transport": "http",
"config_snippet": "{\n \"mcpServers\": {\n \"access_github\": {\n \"url\": \"http://github.com/mcp.json\"\n }\n }\n}"
},
"a2a": null,
"api": {
"docs_url": "http://github.com/developer",
"endpoint": null
},
"protocols": [],
"install": null,
"repository": null
},
"deployment": {
"model": "unknown",
"both_offered": false,
"hosted_endpoint": null,
"install": null,
"note": "No deployment evidence held — Agentery has no endpoint, install instruction or directory attribute for this listing.",
"evidence": null
},
"licence": {
"name": null,
"note": "No named licence found in the retained page text or source attributes. A public repository alone does not establish an open-source licence.",
"evidence": null
},
"evidence_checks": {
"price": {
"status": "unknown",
"note": "no observed price page text held for this listing"
},
"agent_card": {
"status": "unknown",
"note": "no Agent Card URL recorded for this listing"
},
"endpoint": {
"status": "unknown",
"note": "endpoint not yet checked"
}
},
"product_group": null,
"liveness": {
"probed": true,
"alive": true,
"endpoint_kind": "site",
"latency_ms": 998,
"uptime_7d": 1,
"checked_at": "2026-10-24T02:30:14.969Z",
"consecutive_failures": 0,
"status": "alive"
},
"price_extras": {
"free_tier": null,
"unit_cost": null
},
"reported_success": null,
"feedback": "If you actually use this provider, call report_outcome with the result. Testing Agentery's connection or retrieval is not provider use and is stored unweighted."
}