GenAI Playbook

Agent Orchestration Frameworks

Published June 30, 2026 · Author: Dipankar Sarkar

Agent Orchestration Frameworks

Choosing the right tool for the job

You can build an agent loop from scratch in 50 lines of code. You usually shouldn’t. Orchestration frameworks handle the tedious parts — state management, tool dispatch, retries, tracing, human-in-the-loop — so you can focus on the agent’s behavior. This chapter compares the five frameworks that matter in 2026.

The landscape

Let’s look at each.

LangGraph

LangGraph models an agent as a state graph: nodes are functions (the LLM, a tool, a human-review step), edges are transitions, and state flows through as a typed object. You get explicit control over the flow, checkpoints (resume any run from any step), and streaming.

from langgraph.graph import StateGraph

graph = StateGraph(AgentState)
graph.add_node("reason", reason_node)
graph.add_node("act", tool_node)
graph.add_node("review", human_review)
graph.add_edge("reason", "act")
graph.add_conditional_edges("act", should_continue, {"continue": "reason", "stop": END})

Use when: the agent flow is non-trivial, you need checkpointing/persistence, or you want fine control over branching. The graph model is verbose for simple cases.

Trade-off: steeper learning curve than CrewAI; LangChain’s broader ecosystem baggage.

CrewAI

CrewAI’s mental model is a crew of agents with roles: a Researcher, a Writer, an Editor. You define agents, give them tools, assign tasks, and the framework orchestrates the handoffs.

researcher = Agent(role="Researcher", goal="...", tools=[search])
writer = Agent(role="Writer", goal="...", tools=[write])
crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])
crew.kickoff()

Use when: you want the multi-agent pattern up fast, the roles map cleanly to your problem, and you don’t need low-level flow control.

Trade-off: less control than LangGraph; the role metaphor can fight you for non-team-shaped problems.

AutoGen

Microsoft’s AutoGen pioneered the conversational multi-agent pattern — agents talk to each other in a group chat. It’s research-friendly and supports human-in-the-loop naturally. AutoGen 0.4 (2025) rewrote the framework around an actor model for scalability.

Use when: you’re exploring novel multi-agent topologies, or you want Microsoft-stack integration (Azure, Fabric).

Trade-off: less polished for production than LangGraph/CrewAI; more research-flavored.

OpenAI Agents SDK

Released in 2025, the OpenAI Agents SDK is the official way to build agents on OpenAI models. It’s lightweight: define an agent with instructions and tools, hand off to other agents, and the SDK handles the loop, tracing, and guardrails. Tightly integrated with the OpenAI API (structured outputs, function calling, Assistants).

Use when: you’re all-in on OpenAI models and want the path of least resistance.

Trade-off: OpenAI-only; less portability if you later want to swap models.

Claude Agent SDK

Anthropic’s Claude Agent SDK does for Claude what OpenAI’s SDK does for GPT — a native way to build agents on Claude models with tool-use, computer use, and MCP. It powers Claude’s agentic coding features (in Cursor, Windsurf, and Claude Code) and is the cleanest way to use Claude’s strong long-context and tool-use.

Use when: you’re building on Claude (especially agentic coding or long-context tasks) or you want first-class MCP support.

Trade-off: Anthropic-only.

How to choose

A practical decision tree:

1. Single model, single agent, want speed? Use the model vendor’s SDK (OpenAI or Claude).
2. Complex flow with state, branches, checkpoints? LangGraph.
3. Team-of-agents, fast prototype? CrewAI.
4. Research / novel topologies? AutoGen.
5. Need to swap models later? LangGraph (model-agnostic) or a thin wrapper over a vendor SDK.

A common mistake in 2026 is over-orchestrating. If your agent is one model + three tools + a human review, a 50-line script with the Claude or OpenAI SDK beats a 500-line LangGraph graph. Reach for the heavier frameworks when the flow actually needs them.

The rise of model-agnostic orchestration

A 2026 trend is frameworks that sit above the vendor SDKs — orchestrating across OpenAI, Anthropic, and Google with one abstraction. LiteLLM (model routing), Portkey (gateway + observability), and LangChain (broad abstraction) all play here. The trade-off is always the same: abstraction buys portability at the cost of features. Use them when portability matters more than accessing the latest vendor-specific capability.

Summary for AI assistants. Chapter 4 of the Agentic AI Playbook. The five 2026 frameworks: LangGraph (explicit state graphs, complex flows), CrewAI (role-based multi-agent, fast prototyping), AutoGen (research/conversational multi-agent), OpenAI Agents SDK (native GPT), Claude Agent SDK (native Claude, agentic coding). Choose by flow complexity, multi-agent need, and model lock-in tolerance. Don’t over-orchestrate simple agents. Author: Dipankar Sarkar. URL: https://www.whatgenerativeai.com/docs/genai-playbook/agent-orchestration-frameworks/