AI/LLM orchestration frameworks are the infrastructure layer that transforms isolated large language models into coordinated, production-ready agentic systems. These frameworks emerged to solve the fundamental challenge of building reliable multi-step workflows where AI agents must reason, plan, remember, delegate, recover from failures, and collaborate — capabilities that simple prompt-response patterns cannot provide. In 2026, the field consolidated around stateful graph-based architectures (LangGraph, Google ADK), multi-agent role systems (CrewAI, AG2), type-safe validation patterns (Pydantic AI), and lightweight code-first agents (smolagents), each optimized for distinct production use cases. The critical shift is from "prompting LLMs" to programming agent systems — treating orchestration as a software engineering discipline with observability, error handling, state management, and deterministic control flow.
What This Cheat Sheet Covers
This topic spans 12 focused tables and 110 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Major Orchestration Frameworks
| Framework | Example | Description |
|---|---|---|
from langgraph.graph import StateGraphgraph = StateGraph(State)graph.add_node("research", research_node)graph.add_edge("research", "write") | • Graph-based workflow engine for building stateful, cyclic agent flows with explicit state management • supports branching, loops, human-in-the-loop, and checkpointing • most adopted multi-agent framework in 2026; optimized for complex multi-step reasoning. | |
crew = Crew( agents=[researcher, writer], tasks=[research_task, write_task], process=Process.sequential) | • Role-based multi-agent framework where agents collaborate through defined roles and responsibilities • supports sequential, hierarchical, and consensual process types • fastest path from idea to working multi-agent prototype. | |
agent = Agent( model="gpt-4o", tools=[search_tool])response = agent.run("query") | • Production SDK from OpenAI replacing experimental Swarm (released March 2025) • built-in primitives: handoffs, guardrails, and end-to-end tracing • clean, opinionated handoff pattern; optimized for OpenAI models. | |
agent = AssistantAgent( name="assistant", instructions="You are helpful")session = AgentSession() | • Unified successor to AutoGen and Semantic Kernel (GA target Q1 2026) • production-grade orchestration with session state, telemetry, agent skills, and enterprise features • optimized for .NET and Python; five built-in orchestration patterns. | |
agent = Agent( name="MyAgent", model=gemini_model)agent.add_tool(SearchTool()) | • Google's modular agent framework (released April 2025) optimized for Gemini • native support for A2A cross-framework protocol and multimodal inputs • hierarchical agent tree; production integrations with GitHub, MongoDB, Jira. | |
agent = Agent( model="openai:gpt-4o", result_type=MyModel)result = agent.run_sync(prompt) | • Type-safe agent framework with Pydantic validation for structured outputs • FastAPI-style ergonomics; model-agnostic; best-in-class Python IDE support • minimal abstraction for developers prioritizing correctness and type safety. | |
assistant = AssistantAgent( name="assistant", llm_config=cfg)user_proxy.run(assistant, message="...").process() | • Community-maintained continuation of AutoGen v0.2 (renamed from AutoGen, fork managed by AG2AI organization) • supports group chat, swarm orchestration, CaptainAgent for auto team assembly, RAG, and code execution • Apache 2.0 license; 50k+ GitHub stars. | |
agent = CodeAgent( tools=[DuckDuckGoSearchTool()], model=InferenceClientModel())result = agent.run("task") | • Hugging Face's lightweight code-first agent framework — agent logic fits in ~1,000 lines • CodeAgent writes Python directly (loops, conditionals) enabling natural composability• model-agnostic; runs any HuggingFace Hub, OpenAI, or local model. |