all-agentic-architectures

LangGraph multi-agent systems are implemented using structured patterns for meta-controller routing, memory architectures, and safety workflows provided in production-ready notebooks. The architectures enable scalable multi-agent orchestration and reproducible execution.

Available LangChain agentic patterns include Reflection, Tool Use, ReAct, and Planning for single agents, alongside multi-agent systems, memory architectures, safety, metacognition, and self-improvement workflows. These 17+ patterns provide structured scalable templates.

Add memory and self-improvement to AI agents via architectures covering memory retention, metacognition, and self-improvement workflows using LangChain. These patterns ensure agents store context and iteratively refine reasoning processes in production notebooks.

These agentic architectures suit production environments by delivering production-ready notebooks and implementation guidance for robust, extensible AI agents. They include safety patterns and evaluation workflows ensuring reproducible, scalable deployment.

Build a ReAct agent with LangChain using provided single-agent pattern notebooks covering reasoning and acting loops. These templates accelerate implementation by providing structured, reproducible workflows for robust agent deployment.

LangGraph is required for multi-agent routing patterns to implement meta-controller routing and complex multi-agent systems. LangChain and LangGraph together provide the foundational framework for scalable agentic architectures and reproducible workflows.