I'm building an LLM agent with tools; what should I use for tracing and debugging?

For tracing and debugging an LLM agent with tools, you generally want three layers of observability:

1. Request/response tracing
2. Tool-call and workflow tracing
3. Evaluation/debugging dashboards

Here are the best options, depending on how much you want to build yourself.

Best overall choices

1) OpenTelemetry + a backend

Best if you want vendor-neutral, production-grade observability.

Use:

• OpenTelemetry for traces, logs, and metrics
• A backend like:
  • Jaeger
  • Grafana Tempo
  • Honeycomb
  • Datadog
  • New Relic

Good for:

• End-to-end traces across your app, agent steps, and tool calls
• Correlating latency, errors, retries, and timeouts
• Production debugging

Why it’s good:

• Standardized
• Not tied to one LLM framework
• Works well if your agent calls APIs, databases, and services

2) LangSmith

Best if you’re using LangChain/LangGraph or want LLM-specific tracing quickly.

Good for:

• Tracing prompts, model calls, tool calls, and agent state
• Inspecting runs step-by-step
• Comparing experiments
• Evaluations and datasets

Why it’s good:

• Very easy to start with
• Purpose-built for LLM apps
• Great for debugging agent behavior

3) Arize Phoenix

Best for LLM observability, evals, and analysis with a strong open-source option.

Good for:

• Tracing LLM calls
• Viewing spans and spans relationships
• Evaluations, retrieval debugging, and analysis
• Open-source self-hosting

Why it’s good:

• Strong for RAG and agent workflows
• Good balance of tracing + analysis
• Flexible if you want more control

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If you want the simplest answer

• Using LangChain/LangGraph? Start with LangSmith
• Want open standards and production observability? Use OpenTelemetry
• Want an open-source LLM observability tool? Use Phoenix

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What to trace in your agent

At minimum, capture:

• User input
• System prompt / agent prompt
• Model name and parameters
• Each model call
• Each tool call
• Tool inputs/outputs
• Retries/errors
• Token usage and latency
• Final response
• Intermediate reasoning/state if your framework exposes it

If your agent does retrieval:

• Query
• Retrieved documents/chunks
• Scores/rank
• Selected context

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Practical recommendation

If you’re starting from scratch:

• Instrument with OpenTelemetry
• Add an LLM-specific UI like LangSmith or Phoenix
• Export traces to your existing observability stack

If you want the fastest path to useful debugging:

• LangSmith is usually the easiest win for agent/tool tracing

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Nice extras

Also consider:

• Prompt/version tracking: prompt templates, model versions, tool schema versions
• Eval framework: e.g. Ragas, DeepEval, promptfoo
• Structured logs: JSON logs with trace IDs
• Replay support: ability to rerun a trace with the same inputs

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My short recommendation

If you just want a solid stack:

• Tracing: OpenTelemetry
• LLM debugging UI: LangSmith or Phoenix
• Evaluations: promptfoo or DeepEval

If you tell me your stack (LangChain, OpenAI Agents SDK, custom Python, TypeScript, etc.), I can suggest the best exact setup and a minimal instrumentation example.