ModelRefs / Best AI Workflows with LangGraph

Best AI Workflows with LangGraph

LangGraph encodes stateful multi-step workflows as a graph. Use it when you need explicit planner loops, retries and human-in-the-loop checkpoints.

Overview

LangGraph encodes stateful multi-step workflows as a graph. Use it when you need explicit planner loops, retries and human-in-the-loop checkpoints.

Workflows in this collection

6 workflows match this collection.

Ordered by the authority score ModelRefs computes from evidence breadth — model, provider and benchmark coverage, and implementation maturity. That is a measure of how much evidence exists, not a recommendation or a performance ranking.

  • Autonomous Workflow Automation

    Autonomous workflow automation chains agents, tool calls and human-approval gates to execute multi-system business processes end-to-end. The canonical stack emphasises reliability, audit trails and graceful failure so enterprises can trust the system in production.

    enterprise maturity · category: agentic-models

  • Agentic RAG

    Agentic RAG upgrades a standard single-shot retrieval pipeline with a planning agent that can reformulate queries, switch retrieval strategies, validate the retrieved evidence against the question and iterate until the answer meets a confidence threshold. The agent maintains a scratchpad of retrieval attempts, surfaces the chain of retrieval decisions alongside the final answer, and falls back gracefully when no confident answer can be constructed. This pattern is recommended over single-shot RAG for complex multi-hop questions and research-grade applications where source traceability is critical.

    production maturity · category: agentic-models

  • Multi-Agent Orchestration

    Plan, route and supervise multi-agent workflows with typed message contracts and budget circuit-breakers. Multi-Agent Orchestration is a provisional implementation reference with candidate models, providers, tools, benchmarks and deployment patterns to validate on the target workload. Designed for ML engineering teams with evaluation pipelines, model observability dashboards, and deployment governance controls. Self-hosted cluster deployment allows custom inference stacks and hardware-accelerated embedding generation. All pipeline stages emit structured telemetry for cost tracking, latency profiling, and drift detection.

    production maturity · category: agentic-models

  • Agent Assist

    Agent assist surfaces real-time support to human agents during live customer conversations. As the conversation progresses, a streaming language model monitors the dialogue, retrieves relevant knowledge-base articles, suggests next-best-reply options and flags policy violations before the agent sends a response. The system operates with sub-second latency without interrupting the agent's flow. All suggestions are logged with retrieved sources so quality reviewers can audit decisions and improve the knowledge base.

    production maturity · category: llms

  • Voice Support Agent

    A voice support agent handles inbound tier-1 calls with speech-to-text transcription, real-time intent detection, and a RAG layer that retrieves policy and product information to ground responses. A sentiment model monitors the caller's emotional tone and triggers an escalation to a human agent when frustration or urgency signals exceed a threshold. All calls produce a full timestamped transcript, an intent classification and a structured action-item log, giving QA teams auditable records and enabling automated CSAT prediction.

    production maturity · category: audio-models

  • Agentic Systems

    Agentic systems give an LLM the ability to plan, call tools, and execute multi-step tasks autonomously. They combine reasoning models with structured tool schemas, memory, and circuit breakers to convert language into action while keeping cost and reliability in budget.

    production maturity · category: agentic-models

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