Can you recommend an LLM observability platform for tracing prompts and responses during agent failures?

Yes — a few strong options depend on how much you want to instrument, self-host, and analyze.

Top recommendations

1) LangSmith

Best if you’re using LangChain or want an LLM-native tracing/debugging experience.

Why it’s good:

• Excellent tracing of prompts, tool calls, agent steps, and final responses
• Very good for debugging agent failures and chain-of-thought-like execution paths
• Supports evaluations, datasets, and regression testing
• Clean UI for inspecting spans and comparing runs

Best for:

• LangChain / LangGraph users
• Teams actively iterating on prompts and agent workflows

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2) Helicone

Best if you want LLM API observability at the gateway/proxy layer.

Why it’s good:

• Easy to add to OpenAI/Anthropic/etc. calls
• Tracks prompts, completions, latency, token usage, cost
• Good request/response logging and filtering
• Useful for production monitoring and debugging failures

Best for:

• Teams wanting quick integration without heavy framework coupling
• Production API-level tracing and cost analysis

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3) Arize Phoenix

Best if you want open-source observability plus evaluation.

Why it’s good:

• Open-source and flexible
• Strong tracing for LLM apps and agent workflows
• Good for debugging, retrieval evaluation, and experiment analysis
• Can be self-hosted

Best for:

• Teams that want control and open-source deployment
• RAG-heavy applications and evaluation workflows

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4) Langfuse

Best all-around option for tracing, metrics, and prompt management with a strong open-source story.

Why it’s good:

• Traces prompts, outputs, tool calls, and metadata
• Good dashboards for latency, token usage, and cost
• Prompt versioning and experiment tracking
• Self-host or cloud

Best for:

• Teams that want a practical production observability platform
• Open-source preference with good UX

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Quick picks by scenario

• Using LangChain/LangGraph: LangSmith
• Want proxy-style logging with minimal code changes: Helicone
• Want open-source + evaluation-focused: Arize Phoenix
• Want balanced open-source production observability: Langfuse

What to look for specifically for agent failures

Make sure the platform supports:

• Full trace trees across agent steps
• Tool call inputs/outputs
• Prompt/response capture with metadata
• Correlation IDs across retries and subcalls
• Token usage, latency, and cost tracking
• Replay or run comparison
• Error logging and exception capture
• Optional redaction for sensitive data

My practical recommendation

If you want the safest default:

• Langfuse if you want a strong general-purpose platform
• LangSmith if you’re already in the LangChain ecosystem
• Helicone if you mainly need request-level logging and fast setup

If you want, I can also give you:

1. a side-by-side comparison table, or
2. a recommendation based on your stack (OpenAI/Anthropic, LangChain, crewAI, custom agent, etc.).