What should I use for tracing multi-step LLM agent workflows?

For tracing multi-step LLM agent workflows, you generally want a tool that records:

• each model call
• tool/function calls
• intermediate state
• prompts and outputs
• latency, errors, retries
• the full chain of reasoning in an auditable way without exposing hidden chain-of-thought

Good options

1) OpenTelemetry + your observability stack

Best if you want a vendor-neutral, production-grade approach.

• Works well for distributed systems
• Lets you trace agent runs across services
• Can export to Datadog, Grafana Tempo, Honeycomb, Jaeger, etc.
• Good for correlating LLM calls with app logs and metrics

Use this if: you’re building production infrastructure and want standard observability.

2) LangSmith

Best if you’re using LangChain/LangGraph or want LLM-specific tracing out of the box.

• Captures chains, tools, prompts, outputs
• Great UI for debugging agent workflows
• Supports dataset/eval workflows too

Use this if: you want fast setup and are in the LangChain ecosystem.

3) Arize Phoenix

Best for LLM observability + evaluation with an open-source angle.

• Trace agent steps
• Inspect retrieval, embeddings, and model outputs
• Useful for debugging RAG and agent behavior

Use this if: you want open-source tracing plus quality/eval tooling.

4) OpenAI Tracing / built-in agent tracing

If you’re using an SDK or platform that offers native tracing, it can be the easiest route.

• Less setup
• Good visibility into tool calls and steps
• Often best for quick development loops

Use this if: you want the lowest-friction built-in experience.

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What I’d recommend

• Production, platform-agnostic: OpenTelemetry
• LLM debugging/evals: LangSmith or Phoenix
• Fastest setup: built-in tracing from your LLM/agent framework

Practical recommendation

If you’re building a real multi-step agent, a strong pattern is:

• OpenTelemetry for system-wide traces
• LLM-specific tracing (LangSmith/Phoenix/etc.) for prompt/tool inspection
• structured logs for inputs/outputs and error states

If you want, I can recommend the best tracing stack for your exact setup—for example:

• Python vs Node
• LangChain vs LlamaIndex vs custom agent
• production vs prototype
• RAG vs tool-using agents