17 articles
Complete guide to the OpenAI Assistants API for building stateful AI agents with persistent threads, file search, code interpreter, and function calling. Includes Python examples from setup to production.
Step-by-step tutorial on OpenAI function calling (tool use) with Python examples. Covers tool definition, parallel calls, multi-step agent loops, and structured data extraction.
A comprehensive guide to building autonomous LLM agents with tool use, covering the ReAct pattern, function calling, memory systems, and multi-agent coordination — with production-ready Python examples. For ML engineers and backend developers building agentic AI systems.
A comprehensive guide to Microsoft AutoGen for building multi-agent AI systems, covering agent setup, GroupChat, code execution, custom tools, and production deployment patterns. For ML engineers building collaborative agent pipelines.
A complete guide to CrewAI for building teams of AI agents that collaborate on complex tasks, covering agents, tasks, tools, sequential and hierarchical processes, and real-world workflow examples. For developers building production multi-agent pipelines.
A comprehensive guide to LangGraph for building stateful, multi-step AI agents using directed graphs with conditional routing, persistence, and human-in-the-loop support — with full Python examples. For engineers building production-grade agentic workflows.
A comprehensive guide to Microsoft Semantic Kernel for building enterprise AI applications, covering kernel setup, plugins, planners, memory, and agent patterns — with Python examples. For enterprise developers integrating LLMs into production systems.
Master the art of designing tools that LLMs can reliably use. Learn schema patterns, error handling, idempotency, and production tool registries.
Design APIs for AI agents: structured errors, idempotency keys, verbose context, bulk operations, OpenAPI specs, token-based rate limiting, and version stability.
Deploy CrewAI multi-agent systems to production. Learn crew composition, memory systems, custom tools, and scaling patterns for reliable AI teams.
Master LangGraph for production AI agents. Learn stateful workflows, checkpointing, human-in-the-loop patterns, and deployment strategies.
Build production-grade stateful agents with LangGraph: graph definitions, Postgres checkpointing, human interrupts, streaming, error handling, and deployment.
Design bulletproof LLM agents with structured tool definitions, parallel execution, result validation, human-in-the-loop gates, and comprehensive observability.
Learn how Anthropic''s Model Context Protocol enables AI agents to securely share tools and context. We explore the open standard, build an MCP server, and compare it to function calling.
Build scalable multi-agent systems using the orchestrator-worker pattern. Learn task routing, state management, error recovery, and production deployment patterns.
Learn the Plan-and-Execute pattern for slashing AI inference costs. Use frontier models for planning, cheap models for execution, and optimally route tasks by type.
Learn how agentic RAG systems use reasoning and iterative retrieval to outperform static RAG pipelines, including CRAG, FLARE, and self-ask decomposition patterns.