What should I use for AI agent orchestration and tool use?

It depends on how much control you want, how complex the workflow is, and what model/provider you’re using. Here’s a practical way to choose.

Quick recommendations

Use LangGraph if:

• You want robust agent workflows
• You need stateful multi-step orchestration
• You want loops, branching, retries, human-in-the-loop
• You’re building something production-ish and want explicit control

Best for: complex agents, tool chains, multi-agent flows, workflow-heavy systems.

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Use OpenAI Assistants / Responses + tools if:

• You want the simplest path to tool use
• You’re already using OpenAI models
• You don’t need heavy custom orchestration
• You want built-in function calling / structured outputs

Best for: straightforward agent apps, internal tools, fast prototypes, simple copilots.

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Use Semantic Kernel if:

• You’re in the Microsoft/.NET ecosystem
• You want a more enterprise-friendly abstraction
• You need orchestration plus plugin/tool patterns
• You prefer C# or multi-language support with structured architecture

Best for: enterprise apps, .NET teams, Microsoft stack.

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Use CrewAI if:

• You want a high-level multi-agent framework
• You like role-based agent patterns
• You want to prototype multi-agent collaboration quickly

Best for: demos, experimentation, simple multi-agent setups.

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Use LlamaIndex if:

• Your main problem is RAG / data retrieval
• You need agents that interact with indexed data sources
• You want document-centric workflows with tools

Best for: knowledge assistants, search over private data, data-heavy apps.

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Use custom orchestration if:

• Your workflow is specific and stable
• You need maximum reliability and observability
• You don’t want framework complexity
• You only need a few tools and simple control flow

Best for: production systems where the agent logic is predictable.

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A simple decision rule

• Simple tool-calling agent → OpenAI tool use / Responses API
• Complex workflow with states and branches → LangGraph
• Multi-agent roleplay/prototype → CrewAI
• Knowledge/RAG-heavy app → LlamaIndex
• .NET / Microsoft enterprise → Semantic Kernel
• Very custom, very reliable → build your own orchestration

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What I’d recommend in practice

For most new projects:

1. Start with direct model tool calling
   Keep it simple until you hit real orchestration needs.

2. If you need more control, move to LangGraph
   It gives you a good balance of flexibility and production readiness.

3. If your app is mostly retrieval over documents, use LlamaIndex with tool calling.

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Things to evaluate

When choosing a framework, check:

• State management: can it persist and resume?
• Tool calling: function calling, structured outputs, retries
• Control flow: branching, loops, conditional execution
• Observability: tracing, logs, debugging
• Human approval steps
• Memory / conversation state
• Deployment complexity
• Vendor lock-in
• Community maturity

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My default suggestion

If you’re unsure: use OpenAI tool calling for the first version, then LangGraph if the agent gets complicated.

If you tell me:

• your language stack,
• whether you need RAG,
• whether this is a single agent or multi-agent system,
• and what “tool use” means in your case,

I can recommend a specific stack.