Structured tutorials exploring OpenAI Agents SDK, Google ADK, Smolagents, LangGraph, and CrewAI.
Demonstrates how to monitor LLM requests and responses using Google ADK interaction callbacks to track token consumption, calculate API costs, and record latency.
Demonstrates advanced handoff configuration including callbacks, structured inputs, and custom tool naming. cp ../env.example .env from agent import main.
Illustrates wrapping specialized agents as callable tools within the OpenAI Agents SDK, enabling hierarchical delegation between coordinator and worker agents.
Demonstrates multi-agent delegation architectures where a lead coordinator delegates tasks to domain-specific agent tools with custom schemas.
Demonstrates fundamental agent-to-agent task delegation using the OpenAI Agents SDK handoff system. cp ../env.example .env from agent import main.
Google ADK provides powerful pre-built tools that are optimized for performance and reliability. These tools integrate seamlessly with Gemini models and provide essential capabilities like web search and code execution.
Demonstrates using OpenAI Agents SDK built-in tools like WebSearchTool and CodeInterpreterTool. cp ../env.example .env from agents import Runner.
Demonstrates manual conversation threading with toinputlist() and automatic management with Sessions. cp ../env.example .env from agent import manualconversationexample, sessionconversationexample.
Demonstrates advanced tracing patterns including custom traces, spans, and workflow organization for complex multi-agent systems. cp ../env.example .env.
Demonstrates implementing a type-safe customer support ticketing system with Google ADK, generating structured Pydantic tickets with priority levels and resolution estimates.
Demonstrates the built-in automatic tracing system that captures all agent workflow events without any setup required. cp ../env.example .env.
A Google ADK tutorial demonstrating type-safe email generation using Pydantic schemas and Gemini models to guarantee structured subject and body outputs.
Demonstrates the three execution methods available in the OpenAI Agents SDK: sync, async, and streaming. cp ../env.example .env from agents import Runner.
def calculatecompoundinterest(principal: float, rate: float, years: int) -> dict: Calculate compound interest for an investment. Use this function when users ask about investment growth.
Demonstrates custom function tools creation using the @functiontool decorator. cp ../env.example .env from agents import Runner from agent import rootagent.
Demonstrates managing multiple concurrent sessions for different users, contexts, and conversation types. cp ../env.example .env from agent import multiusersessions, contextbasedsessions.
Demonstrates running multiple agents simultaneously using asyncio.gather() for improved performance and quality through diversity. cp ../env.example .env.
A basic personal assistant agent demonstrating the fundamental concepts of agent creation with the OpenAI Agents SDK. cp ../env.example .env from agents import Runner.
A complex structured output agent demonstrating advanced Pydantic schemas for product review analysis. cp ../env.example .env from agents import Runner.
A structured output agent demonstrating Pydantic schema-based responses for customer support ticket creation. cp ../env.example .env from agents import Runner.
An ADK agent is a programmable AI assistant that can: Think of it as creating a smart function that uses AI to handle complex tasks. The main building block for creating AI agents in ADK.
An OpenAI agent is a programmable AI assistant that can: Think of it as creating a smart function that uses AI to handle complex tasks.
Learn how to create agents that work with different AI models using OpenRouter. This example shows how ADK can use OpenAI and Anthropic models through separate agent implementations.
Teaches how to create type-safe AI agents using OpenAI Agents SDK and Pydantic models to guarantee consistent, validated JSON responses for application workflows.
Demonstrates integrating custom Python functions and pre-built tools into OpenAI Agents SDK agents, enabling autonomous task execution and external API interaction.
Covers execution methods in the OpenAI Agents SDK, explaining synchronous runs, streaming event handling, exception management, and the internal agent execution loop.
Context management allows you to pass custom data structures to your agents that persist throughout the entire agent execution. Think of context as a shared state container that.
Tool execution callbacks allow you to monitor when agents use tools, track their execution lifecycle, and analyze the results. This provides visibility into how agents interact with external systems and APIs.
Guardrails are automated safety mechanisms that validate inputs and outputs to ensure AI agents operate within acceptable boundaries. Think of guardrails as safety checkpoints that.
Plugins in Google ADK are custom code modules that can be executed at various stages of an agent workflow lifecycle using callback hooks. Unlike regular callbacks that are configured on individual agents or tools, plugins are registered...
Handoffs enable agent specialization and delegation where agents can transfer tasks to other agents with specific expertise. Think of handoffs as a smart routing system that.
A coordinator LlmAgent orchestrates three specialized agents in a sequential workflow: Research → Summarize → Critique. Each agent contributes to building a comprehensive research report.
Explains end-to-end monitoring and debugging in the OpenAI Agents SDK, capturing LLM generations, tool executions, and agent handoffs with traces and spans.
Business Topic → SequentialAgent → 4 Sub-agents (Sequential Execution) [Market Research + Web Search] → [SWOT Analysis] → [Strategy] → [Implementation].
Demonstrates iterative plan refinement using Google ADK's LoopAgent pattern, executing sequential sub-agents in a stateful loop until satisfaction conditions are met.
According to the ADK docs, Parallel Agents execute their sub-agents concurrently. Each child runs on its own invocation branch but shares the same session.state.
Multi-agent orchestration enables coordinated AI workflows where multiple specialized agents work together to solve complex problems. Think of orchestration as a conductor leading an orchestra where.
The batteries-included agent harness.
AI PDF chatbot agent built with LangChain & LangGraph.
Autonomous Hacking Agent for Red Team.
Full toolkit for running an AI agent service built with LangGraph, FastAPI and Streamlit.
A股多Agent投研框架 — 适配A股数据源(龙虎榜/游资/解禁等)，7位分析师基于A股规则的辩论决策，基于TradingAgents深度改造，适配大A。A-share multi-agent investment research framework — 7 AI analysts, bull/bear debate, risk assessment。.
