Multi-Agent-AI-System and AI-Agents-in-LangGraph
These two projects are competitors, as both are educational repositories demonstrating how to build AI agent systems using LangGraph, offering similar learning paths and examples.
About Multi-Agent-AI-System
FareedKhan-dev/Multi-Agent-AI-System
Building a Multi-Agent AI System with LangGraph and LangSmith
Implements a supervisor-based multi-agent architecture using LangGraph's state management to orchestrate specialized subagents, with LangSmith providing real-time tracing and debugging capabilities. Integrates SQLite database access through LangChain's SQL tools, enabling agents to query structured data while maintaining conversation history and supporting human-in-the-loop intervention. Features long-term memory persistence and includes evaluation frameworks to measure agent performance and reduce hallucinations in complex agentic workflows.
About AI-Agents-in-LangGraph
ksm26/AI-Agents-in-LangGraph
Master the art of building and enhancing AI agents. Learn to develop flow-based applications, implement agentic search, and incorporate human-in-the-loop systems using LangGraph's powerful components.
Covers stateful agent design with multi-threaded persistence for conversation management and state reloading, plus structured agentic search for retrievable knowledge augmentation. Teaches LangGraph's core graph-based architecture for decomposing agent logic into reusable nodes and edges, with practical patterns for essay-writing workflows. Integrates LangChain's LLM ecosystem and Tavily's search capabilities to demonstrate production-grade agent composition.
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