Looking for langgraph workflows? These agent skills help developers automate setup, coding, and delivery tasks.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
12 skills found
Operational hub for LLM system lifecycle, architecture, and deployment. Features include PEFT/LoRA fine-tuning, RAG pipelines, vLLM throughput optimization, automated drift detection, and CI/CD-integrated evaluation frameworks.
Guideline for extending the Agent V4 system by registering custom tools in the ToolRegistry for automated execution.
LiteLLM-RS A2A Protocol: A robust Rust-based framework for autonomous agent communication, utilizing JSON-RPC 2.0 to orchestrate multi-provider agent workflows and task management.
Autonomous multi-agent LinkedIn system using LangGraph and Claude Opus 4.5 for trend research, content creation, voice profiling, and analytics-driven optimization.
Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.
Implement production-grade AI agents with LangGraph, tool-calling guardrails, SSE streaming, and episodic memory. Includes anti-patterns, fix pairs, and stateful architecture patterns.
A structured repository of Agent Skills for context engineering, multi-agent architectures, and production-grade agent system optimization.
Master multi-agent orchestration with LangGraph. Build stateful, fault-tolerant AI workflows using supervisor-worker patterns, conditional routing, and advanced state management.
A systematic workflow to instrument, evaluate, and monitor LLM applications using TruLens, supporting frameworks like LangChain, LangGraph, and LlamaIndex.
Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.
Architect production-grade LLM applications using LangChain 1.x and LangGraph. Implement stateful AI agents, multi-step workflows, and custom memory systems for complex conversational and automation tasks.
Expert LangGraph architect skill for designing stateful, multi-actor AI agent workflows with robust persistence, conditional branching, and ReAct patterns.