Examples
Performs agentic research, producing quick cited answers and in-depth report-style research with multi-step planning
What it does
The specific capability behind this listing, and where to get it.
Examples
Performs agentic research, producing quick cited answers and in-depth report-style research with multi-step planning
Source repository available · no commercial pricing observed.
No price does not imply the product is free. Any code-host platform pricing is excluded.
Is Examples good value?
Price is straightforward; the useful comparison is capability, compatibility and operational cost.
Source repository available · no commercial pricing observed.
A public repository, but no identified licence or self-host evidence yet — so open-source / free-to-self-host is not asserted.
Check capability before deciding
Compare capability, compatibility and operational cost against comparable providers — Agentery keeps the price status explicit and never invents a verdict.
View the full niche →Comparable Academic Research 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": "examples",
"name": "Examples",
"url": "https://github.com/NVIDIA-AI-Blueprints/aiq-research-assistant",
"logo": "https://agentery.com/logos/CP-QD6TBZ.svg",
"niche": "academic-research-agent",
"category": "research-knowledge-work",
"short_summary": "Performs agentic research, producing quick cited answers and in-depth report-style research with multi-step planning",
"task_performed": "Performs agentic research, producing quick cited answers and in-depth report-style research with multi-step planning",
"inputs_accepted": [
"Chat messages/prompts",
"selectable data sources (web",
"paper",
"enterprise",
"collaboration",
"knowledge-layer)",
"API calls",
"YAML configs"
],
"outputs_produced": [
"Cited answers",
"long-form citation-backed research reports",
"async deep research jobs",
"tokenomics/cost analysis reports",
"evaluation benchmark results"
],
"integrations_available": [
"NVIDIA NeMo Agent Toolkit",
"LangChain Deep Agents",
"Tavily Search API",
"Serper Search API",
"Modal sandbox",
"NVIDIA NIM",
"REST API",
"Docker Compose",
"Helm",
"Jupyter Notebooks"
],
"protocols_or_interfaces": [
"API"
],
"industry_fit": [
"developer tools / enterprise research"
],
"autonomy_level": "workflow automation",
"human_approval_needed": "unclear",
"pricing_model": "free",
"price": {
"observed": false,
"billing": "not_found",
"currency": null,
"lowest_monthly_usd": null,
"monthly_usd": null,
"headline": null,
"summary": null,
"confidence": "high",
"source_url": "https://github.com/pricing",
"checked_at": "2026-07-26T07:58:38.525Z",
"amount": null,
"display": null,
"plans": [],
"source": "render+llm"
},
"trust_or_rating_signal": [
"736 GitHub stars",
"203 forks",
"Apache-2.0 license",
"DeepResearch Bench leaderboard benchmarks",
"Security policy/considerations"
],
"evidence_quality": "high",
"entity_type": "infrastructure",
"regulated_data_suitability": "unclear",
"evidence_urls": [
"https://github.com/NVIDIA-AI-Blueprints/aiq-research-assistant",
"https://developer.nvidia.com/nemo-agent-toolkit"
],
"last_checked": "2026-06-16",
"how_to_connect": {
"website": "https://github.com/NVIDIA-AI-Blueprints/aiq-research-assistant",
"docs": "https://github.com/documentation",
"mcp": {
"endpoint": "https://github.com/mcp.json",
"transport": "http",
"config_snippet": "{\n \"mcpServers\": {\n \"examples\": {\n \"url\": \"https://github.com/mcp.json\"\n }\n }\n}"
},
"a2a": null,
"api": {
"docs_url": "https://github.com/developer",
"endpoint": null
},
"protocols": []
},
"liveness": {
"probed": true,
"alive": true,
"endpoint_kind": "site",
"latency_ms": 893,
"uptime_7d": 1,
"checked_at": "2026-07-28T02:33:27.817Z",
"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."
}