Production AI Workflows & Multi-Agent Blueprints

Architect a LangGraph-powered orchestrator that polls real-time wholesale energy markets (PJM, ERCOT), forecasts prices, and time-shifts or migrates AI workloads to the cheapest, greenest compute region - including SMR-powered data centers.

Give agents durable memory. Combine mem0 as the universal memory layer with LangGraph orchestration and a vector store to persist user, session, and agent-level context across sessions, channels, and devices, with adaptive updates that avoid memory bloat.

Engineer a real-time voice agent that streams speech-to-text, orchestrates an LLM with tool calling, and synthesizes speech so the conversational loop stays under 300ms perceived latency, with confidence thresholds and human escalation as safety nets.

Move beyond retrieve-then-generate. Build a self-correcting agentic graph RAG pipeline in LangGraph that fuses vector search with a knowledge graph, decomposes complex queries into sub-goals, performs multi-hop reasoning, grades every retrieved chunk, and rewrites queries in a retry loop until the evidence is solid.

Make LangGraph agents durable and auditable with Postgres checkpointing, Pydantic event-sourced state, crash recovery, and time-travel replay. This workflow does not just survive a crash, it rewinds to any past state and forks a new future.

Agents speak two protocols: MCP for tools and A2A for agents. This workflow builds a FastAPI interoperability gateway that bridges Google ADK and LangGraph, exposing A2A inbound while multiplexing shared MCP tool servers to both frameworks.

Production-grade observability for multi-agent systems needs three layers: OpenTelemetry for standardized signal emission, OpenInference for LLM-aware semantics, and Langfuse for trace analytics. This workflow wires all three into a CrewAI or LangGraph fleet without forking your agents.

Implement a stateful LangGraph pipeline for high-stakes financial auditing, featuring built-in human-in-the-loop approval gates and strict token budgeting controls.

Design self-healing Kubernetes infrastructure by deploying AutoGen 0.4 distributed multi-agent workflows for autonomous incident triage and remediation.

Architect a production-ready multi-agent RAG pipeline leveraging LlamaIndex and Qdrant for sophisticated hybrid vector-keyword search orchestration.

Architect a resilient, multi-agent LLM pipeline using LangGraph and Qdrant. Explore the Planner-Worker-Reviewer pattern for production-grade, stateful AI workflows with complex reasoning and long-term memory.

Create a cutting-edge self-healing infrastructure using AutoGen agents that continuously analyze Prometheus metrics to detect, diagnose, and remediate Kubernetes cluster anomalies in real time.