Nebius Blueprints
Production-ready reference architectures for building agents on Nebius AI Cloud.
Each blueprint is a validated, composable stack you can run in under 5 minutes and adapt to your workload.
Agents Blueprint
Agent failures aren’t model problems, they’re system problems: lack of live grounding, wrong retrieval, missing observability, inference costs that don’t survive scale. The Nebius Agents Blueprint is an open reference architecture that connects proven components. Every component is independently deployable and replaceable.
Build, run, and continuously improve AI agents in production. Open at every layer, no lock-in.
Blueprint recipes
Runnable guides, one capability at a time.
GitHub repository
Complete end-to-end code implementations.
Architecture
Nebius Token Factory is the agent runtime. Every other component plugs in around it.
Agent Inference and Runtime — Nebius Token Factory
Dedicated endpoints, autoscaling, OpenAI-compatible API. 60+ open models.
Orchestration — LangChain Deep Agents
Multi-step workflows, persistent state, MCP-compatible tool connections.
Observability — LangSmith
Full execution traceability — every prompt, tool call, and retrieval step recorded.
Retrieval — Pinecone
Structured knowledge instead of raw documents. Every chunk source-traceable.
Grounding — Tavily by Nebius
Real-time web retrieval with source reliability filtering.
Simulation — Snowglobe by Guardrails AI
Synthetic testing before launch. Produces a labeled eval dataset, fine-tuning data, and a QA regression suite from the same runs.
Blueprint Recipes
Runnable recipes from first agent to production. Start at 01 or jump to the capability you need. All recipes include Token Factory as the inference layer.
Level
Title
Time
Case study: From prototype to production
What does it take to turn an AI agent into a production-ready system?
To find out, we built a compliance audit agent and applied Blueprint Recipes across grounding, orchestration, evaluation, and governance. We then benchmarked each stage to measure the impact on quality and cost.
The production agent achieved 20% higher precision while reducing cost by 72% compared to the prototype, with identical recall.
