Building a single AI agent is a solved problem, but creating a multi-agent system that runs in production requires a different approach. This article answers infrastructure questions and provides working code for a real system.
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This article discusses building a multi-agent AI system using LangGraph, MCP, and A2A. It covers the engineering layer required for a reliable system, including state recovery, standardized tool access, and agent coordination.
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freecodecampHyDE uses a language model to generate hypothetical documents and an unsupervised dense retriever to map those documents into an embedding space, improving the accuracy of RAG systems.
freecodecampIn this tutorial, we'll expose a local AI agent over HTTP using FastAPI and add a Streamlit UI for a chat-style frontend interface.
freecodecampLearn how to build a lightweight evaluation setup to test AI agents before making changes, using a Python script, test cases, rule-based checks, and an LLM-as-judge.
freecodecampLearn how to create a multi-agent AI system in Python and LangGraph, and discover the benefits of using a workflow framework for building complex AI systems.
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