What should I use instead of CrewAI for production agents?

If you mean “CrewAI-style multi-agent orchestration, but something I’d trust more in production”, the best alternatives depend on what you need:

Good production-oriented alternatives

1. LangGraph

• Best choice if you want reliable, controllable agent workflows
• Built around state machines / graphs, so behavior is explicit
• Better for:
  • retries
  • branching
  • human-in-the-loop
  • long-running workflows
  • observability and debugging
• If you want agents in production, this is often the first thing I’d look at.

2. Semantic Kernel

• Strong if you’re in a Microsoft/.NET/enterprise ecosystem
• Good structure for tool use, planners, and orchestration
• Production-friendly and integrates well with enterprise systems

3. AutoGen

• Good for multi-agent conversation patterns
• More flexible than CrewAI for some scenarios
• Better for experimentation and collaborative agent setups
• Less “workflow-engine” and more “agent-to-agent coordination”

4. OpenAI Assistants / Responses API + your own orchestration

• Good if you want to keep the stack simpler
• Use the model provider’s tool calling, structured outputs, and state management
• Often the most production-safe approach is:
  • one agent
  • clear tools
  • explicit state
  • your own control flow
• This avoids overcomplicated “agent swarms” unless you truly need them

5. Temporal + LLM tools

• Best if you need durable workflows
• Temporal handles retries, timeouts, long-running jobs, and workflow state
• You add LLM calls/tools inside activities
• This is a very strong production pattern for mission-critical systems

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My practical recommendation

If you want:

• agent orchestration with real production reliability → LangGraph
• durable business workflows → Temporal + LLM tools
• simple, robust single-agent apps → OpenAI API + your own orchestration
• enterprise .NET stack → Semantic Kernel

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What I’d avoid for production

• “Autonomous” agent swarms without strong control flow
• frameworks that hide too much state
• systems that make debugging/reproducibility hard

In production, the winning pattern is usually: explicit workflow + small number of agents + strong state management + observability

If you want, I can also give you:

1. a CrewAI → LangGraph migration path, or
2. a decision matrix for choosing between LangGraph, AutoGen, Temporal, and Semantic Kernel.