Featured tutorial
Build a multi-agent system from scratch in Python
Learn how to design, implement, and run an orchestration system with planning, tool use, memory, and agent coordination.
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Curated tutorials on building autonomous AI agents and multi-agent systems — frameworks, the A2A protocol, kernels, and production reliability.
Featured tutorial
Learn how to design, implement, and run an orchestration system with planning, tool use, memory, and agent coordination.
Read tutorial →Latest tutorials
Use Microsoft AutoGen to create conversational agents and multi-agent workflows.
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Design, build, and deploy a multi-agent system with agents, skills, tools, and orchestration.
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Build autonomous agents using LangChain agents, tools, and OpenAI models.
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Agents, tools, workflows, structured outputs, and multi-agent applications in Python.
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Enable agents to call APIs, run code, and use external tools effectively with MCP and A2A.
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Equip agents with long-term memory using embeddings and vector stores.
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Implement ReAct-style agents that reason, act, and observe iteratively.
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Containerize and deploy agents reliably with Docker and production best practices.
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Ground, evaluate, and add guardrails so agentic systems behave reliably in production.
Read tutorial →An AI agent is a system that can reason, use tools, remember context, and act toward a goal.
The tutorials may use the BeeAI Framework, LangChain, AutoGen, the A2A protocol, vector databases, Docker, and related tools.
Basic Python and familiarity with APIs are helpful. Some tutorials are beginner-friendly, while others focus on production patterns.