The world of AI agents is evolving from exciting demos to practical applications. The challenge now lies in ensuring they work together seamlessly across various tools, models, and runtime environments without having to start over from scratch. If you’re exploring LangChain, LlamaIndex, NVIDIA NIM microservices, or looking into safety and governance with NeMo Guardrails, this upcoming livestream will unveil practical strategies for building a scalable cross-framework agent ecosystem.

What the livestream covers

In this session, NVIDIA experts will guide you through patterns, code samples, and deployment strategies for integrating popular open-source frameworks with production-grade inference and tooling. Get ready for a hands-on journey that includes:

• Design patterns for multi-agent workflows and tool usage (routing, orchestration, and handoffs)
• Interoperability using widely-supported APIs and adapters (like OpenAI-style chat and tool endpoints)
• Retrieval-augmented generation (RAG) that functions across frameworks and vector databases
• Safety and governance strategies using guardrails, grounding, and content filters
• Operationalizing agents with containerized inference, autoscaling, and observability

Who should attend

• ML and software engineers developing intelligent applications
• Data engineers and architects standardizing AI services
• Product leaders, founders, and researchers looking into multi-agent systems

Key outcomes

By the end of the session, you will understand how to:

• Connect frameworks like LangChain, LlamaIndex, Haystack, and Microsoft Semantic Kernel to model and embedding services
• Utilize NVIDIA NIM microservices for standardized, production-ready inference and embeddings
• Compose multi-agent workflows with graph-based orchestration (such as LangGraph)
• Create portable RAG pipelines compatible with Milvus, FAISS, or pgvector
• Implement NeMo Guardrails for policy control, tool invocation, and safer outputs
• Deploy and scale using NVIDIA Triton Inference Server, containers, and Kubernetes
• Instrument your applications with OpenTelemetry to track agent behavior and latency

Why cross-framework matters now

Agent ecosystems are naturally diverse: one framework might be best for document retrieval, another for tool orchestration, and yet another for safety. Trying to standardize a single stack can hold teams back, especially as new advancements occur weekly in the AI space.

Interoperability enables you to select the best component for each task and connect them through consistent interfaces. For instance, using services that offer OpenAI-compatible chat completions and tools endpoints simplifies integrating different frameworks into the same model or embedding backend. NVIDIA NIM microservices provide containerized, optimized inference for leading models with standardized APIs, allowing you to move from prototype to production rapidly while keeping your options open for future models and workflows (NIM overview).

Sample agenda

• The agent stack in 2025: frameworks, services, and deployment patterns
• Interoperability tactics: adapters, schemas, and event buses
• Live demo: connecting a RAG-enabled, tool-using agent across two frameworks
• Safety and governance: adding guardrails and evaluations
• Transitioning from laptop to cluster: packaging, serving, monitoring
• Q&A

What you will see in practice

To maintain clarity, the walkthrough will showcase:

• Framework adapters and OpenAI-style interfaces for chat, tools, and function calling (API reference)
• RAG constructed with framework-native nodes plus shared services for embeddings and vector search
• Guardrails for prompt injection resistance, tool authorization, and output constraints
• Model packaging behind NIM or Triton for low-latency, GPU-optimized inference
• Observability through traces, spans, and metrics for diagnosing bottlenecks

How to prepare

You don’t need extensive experience with any specific framework, but some familiarity with Python will be beneficial. To follow along later with the provided examples, ensure you have:

• Python 3.10 or later and a recent version of pip or Conda
• Docker or Podman for running local services
• Access to a GPU is optional, but recommended for testing high-throughput inference

Register and save your spot

Seats are limited! Register on the NVIDIA Developer Events page, and you’ll receive the livestream link and calendar invite.

Register on NVIDIA Developer Events

Why this approach is credible

The patterns discussed are in line with industry best practices and widely utilized tools:

• NVIDIA NIM microservices standardize inference with optimized, containerized endpoints, assisting teams in scaling from POC to production (NIM).
• Agent-oriented frameworks such as LangChain, LlamaIndex, and Haystack offer mature building blocks for tools, RAG, and orchestration (LangChain), (LlamaIndex), (Haystack).
• NeMo Guardrails enforces policy-driven, auditable controls for LLM applications (repo).
• Triton Inference Server is widely adopted for high-performance model serving in production (Triton).
• OpenTelemetry has become the go-to standard for distributed tracing across services (OpenTelemetry).

FAQs

Do I need an NVIDIA GPU to benefit from this session?

No, the concepts and patterns shared will apply regardless of the hardware you have. However, having access to GPUs will be beneficial if you’re looking to run high-throughput inference locally or in production.

Will code samples be provided?

Absolutely! The session will include links to examples and reference integrations that you can adapt for your own stack.

Can I use my existing framework or vector database?

Yes, in most cases. The focus will be on interoperability and standard interfaces that simplify integrating your preferred components.

How does this relate to safety and governance?

You’ll see how to implement guardrails for tool usage, data access, and output filtering, including suggestions for evaluations and logging.

Will this cover deployment at scale?

Yes, expect insights on containerized serving, scaling strategies, and observability for production workloads.

Sources

1. NVIDIA NIM microservices
2. LangChain documentation
3. LlamaIndex documentation
4. Haystack documentation
5. NeMo Guardrails
6. NVIDIA Triton Inference Server
7. OpenTelemetry
8. OpenAI API reference – Chat Completions
9. Milvus, FAISS, and pgvector
10. NVIDIA Developer Events