AI Agent Orchestration Tools, Compared Honestly
Most AI agent orchestration platforms are frameworks for building multi-agent systems in Python. A smaller set of tools supervise the coding agents you already run. This guide separates the two, names what's in each, and helps you work out which problem you have.
“AI agent orchestration tools” covers two different products. Most of them, including LangGraph, CrewAI, and Microsoft Agent Framework, are libraries for building multi-agent systems in Python or .NET. A much smaller set supervise coding agents like Claude Code or Codex that you already run. Buying from the wrong category is the most common mistake here.
Checked July 31, 2026. Every description below was read from the project’s own documentation or repository on that date, and quoted claims are linked to the source. These projects move fast, so re-check anything you are about to depend on.
the two categories
| build agents | supervise agents | |
|---|---|---|
| What it is | a library you import | an app you run |
| What you produce | an application containing agents | nothing, you’re watching work happen |
| Who writes the agent logic | you do | the agent vendor did |
| The hard problem it solves | state, routing, retries, handoffs | which agent is blocked, finished, or colliding |
| Examples | LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK | tmux, Cursor cloud agents, crystl |
There’s a third meaning of “orchestration tools” that shows up in the same searches and belongs to neither: workflow and data orchestration. More on that below, because it will keep appearing in your results.
category one: frameworks for building multi-agent systems
These are what almost every “agent orchestration platform” article is about. You write code against them. The output is software.
LangGraph
LangGraph describes itself as “a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents.” You define a graph: nodes do work, edges decide what runs next, and a typed state object is passed along. Its two headline capabilities are durable execution, so agents “persist through failures and can run for extended periods, resuming from where they left off,” and human-in-the-loop, “inspecting and modifying agent state at any point.”
Python and JavaScript/TypeScript, MIT licensed. The hosted side is separate and commercial: what was LangGraph Platform was renamed to LangSmith Deployment in October 2025, and it’s framework-agnostic, so you can deploy agents built with other libraries on it too.
Pick it if you want explicit control over control flow and state, and you’re comfortable that the price of that control is writing the graph yourself.
CrewAI
CrewAI is “an open-source Python framework with high-level abstractions and low-level APIs for building production-ready multi-agent workflows.” It models a team: role-based Agents take Tasks, grouped into a Crew. The Process decides execution shape, either Sequential, which “executes tasks sequentially, ensuring tasks are completed in an orderly progression,” or Hierarchical, which “organizes tasks in a managerial hierarchy” with a manager agent delegating and validating.
Alongside Crews it has Flows for event-driven, precisely controlled automation, and the docs frame the pairing as a Flow being the process definition while Crews do the heavy lifting. Python only (3.10 to 3.13), MIT licensed, with a hosted enterprise offering and a control plane on top. Its README states plainly that it’s standalone, “a standalone Python framework with its own primitives,” rather than a LangChain layer.
Pick it if the role-and-task metaphor matches how you already think about the work. It gets you to a running crew faster than a graph library does.
Microsoft Agent Framework
The consolidation story matters more than the feature list here. Microsoft announced Agent Framework on October 1, 2025 as a merge of two of its own projects, taking the multi-agent orchestration of AutoGen and combining it with the enterprise foundations of Semantic Kernel. It reached 1.0 GA in April 2026 with stable APIs, long-term support, and migration assistants for both predecessors.
The repo calls it “an open, multi-language framework for building production-grade AI agents and multi-agent workflows in .NET and Python,” MIT licensed, with Azure AI Foundry as the managed hosting path. A Go version exists in public preview and is not at parity.
Pick it if you’re in the Microsoft stack already, or you want .NET as a first-class target rather than an afterthought.
AutoGen
Read this one before you start a project on it. AutoGen is in maintenance mode. Its README states it “will not receive new features or enhancements and is community managed going forward,” with contributions “limited to bug fixes, security patches, and documentation improvements,” and directs new users to Microsoft Agent Framework instead. The announcement, from October 2, 2025, is worth reading in full if you have AutoGen in production: it commits to critical bug fixes and security patches while ruling out significant new features, and links a migration guide.
Architecturally it’s three layers: a Core API for event-driven message passing between agents, AgentChat for rapid prototyping, and Extensions for model clients and tools. Plus AutoGen Studio, a no-code GUI for assembling multi-agent apps. Much of the research vocabulary in this space, group chat included, comes from here.
Pick it if you already run it and it works. Otherwise start on Agent Framework.
OpenAI Agents SDK
The Agents SDK is described by OpenAI as “a production-ready upgrade of our previous experimentation for agents, Swarm.” Its primitives are small and easy to hold in your head: Agents (an LLM with instructions and tools), Handoffs (delegation to another agent), and Guardrails (validation on inputs and outputs), plus Sessions for memory and built-in tracing. Python and TypeScript.
Pick it if you want the least framework possible between you and the model.
the other orchestration: workflows and data
These will land in your search results because they’ve owned the word “orchestration” for a decade, and several are now marketing toward agents as well.
- Apache Airflow is “a platform created by the community to programmatically author, schedule and monitor workflows.” Batch DAGs, data engineering heritage.
- Prefect now bills itself as “Workflow Orchestration for Data, ML, and Agents.”
- Dagster is asset-oriented, “the operational layer that structures how data is built, observed, and delivered, so both teams and AI agents can rely on it.”
- Temporal sells durable execution, currently positioned as “the Durable Execution layer that keeps AI applications and agents running reliably in production.”
The distinction that survives their own marketing is the primitive, not the intent. These give you durable task and DAG execution. If your agent needs to survive a process crash halfway through a twelve-hour job, this category is genuinely relevant. If your problem is which of your nine terminals is blocked, it is not.
category two: tools for supervising the agents you already run
This is a much thinner shelf, because it’s a newer problem. You’re not building anything. You started Claude Code in four repos, and the work is watching them.
a plain terminal plus tmux
The default, and it goes further than people expect. tmux gives you named sessions, windows, and panes that survive a disconnect, plus scripting to lay out a whole workspace in one command. Our tmux for AI coding agents guide covers the setup properly.
Where it stops: tmux moves bytes. It has no idea what’s in the stream, so it cannot tell you that pane 3 is blocked on an approval while pane 4 is still thinking. You can bolt on notifications with agent-side hooks, which helps a lot, and past that you’re building a supervision tool by hand.
Cursor cloud agents
Cursor’s cloud agents, previously called background agents, run in isolated VMs in the cloud with full development environments. They clone your repo, work on a branch, and open a PR, and you can start them from the editor, the web, Slack, GitHub comments, Linear, mobile, or an API. Cursor states you can run as many in parallel as you want.
They run Cursor’s own agent. There’s no documented way to launch or supervise the Claude Code or Codex CLI through them. We compare this in detail in Cursor background agents vs crystl.
Warp
Warp is the other terminal built around AI agents, and by 2026 it’s a cloud agent platform with team governance, seat management, and metered credits. It’s aimed at organizations standardizing how a team ships with agents. Warp vs crystl has the full comparison.
crystl
crystl is a Mac terminal built for supervising several running agents at once, and it sits in this second category on purpose. It doesn’t compete with LangGraph and isn’t trying to.
The design bet is that terminals treat every session as the same undifferentiated character stream, which is why nothing in a normal terminal can tell you which agent needs you. crystl keeps each session as structured data instead, so state is something the system knows:
- Approval requests and agent questions surface as floating panels color-coded by project, and push to your phone.
- Sessions group by project as gems and shards, with an agent activity panel showing every agent’s live state across every project at once.
- Isolated shards are git worktrees, so two agents on one repo don’t collide.
- Any CLI agent is first class. Claude Code, Codex, and others run side by side, which is a thing no single agent vendor will do for you.
- For actual multi-agent shape there’s fanout, where one session manages a worker per project, and crystl quest, where independent agents coordinate as peers in a shared chat.
Honest limits: crystl is macOS only, it’s closed source, and the deepest surfacing (approval panels via hooks) is Claude Code first, with Codex partial and other agents supervised at terminal level. If you’re on Linux or Windows, this isn’t your tool. Our Claude Code GUI comparison covers the open-source and cross-platform options.
working out which one you need
Three questions settle it almost every time.
Are you writing code that calls a model? If yes, you want category one. Nothing in category two helps you, because there’s no application for it to supervise.
Did you start these agents, or did your program? If you typed claude in a terminal, you want category two. A graph library has nothing to route.
What’s the sentence that made you search? “I need agent A to hand off to agent B based on the result” is a framework problem. “I don’t know which of these is waiting on me” is a supervision problem. “This job needs to survive a crash six hours in” is a durable execution problem, and that’s the workflow category.
It’s entirely normal to have two of these at once. Someone building an agent product on LangGraph is also, right now, running four Claude Code sessions to write it.
the honest summary
The orchestration platform market is mostly a developer tools market for people building multi-agent applications, and it’s a good one. LangGraph, CrewAI, and Microsoft Agent Framework are all serious, well-documented, actively maintained projects with real users.
The market for supervising coding agents you already run is small, young, and where crystl lives. If your day involves several agents working in several repos and the hard part is keeping track of them, start with managing multiple AI coding sessions, then agent orchestration for what crystl does about it.
crystl is free. Sign up at crystl.dev/login.