AgentBurn vs LangSmith: Observability vs FinOps for AI Agents

LangSmith is LangChain's observability and evaluation platform. It logs traces, lets you debug chains, run evaluations, and monitor production LLM applications. Cost tracking is a feature, not the focus.

LangSmith's Strengths

• Deep tracing — See every step in a LangChain/LangGraph chain with input/output at each node
• Evaluation — Run automated evals against datasets to measure quality
• Prompt playground — Test prompt variations with side-by-side comparison
• LangChain integration — First-class support if you're in the LangChain ecosystem

AgentBurn's Strengths

• Cost-first design — Every feature is built around understanding and reducing spend
• Framework-agnostic — Works with any agent framework (CrewAI, AutoGen, custom)
• Multi-provider tracking — LLMs, compute, tool calls, everything in one view
• Budget governance — Per-agent alerts and spend limits
• Self-hosted and MIT — No vendor dependency

The Key Difference

LangSmith answers: "Why is my agent giving bad responses?" AgentBurn answers: "Why is my agent costing so much?"

LangSmith is a debugging and quality tool. AgentBurn is a financial management tool. They solve different problems.

Comparison

Using Both

LangSmith for debugging and quality. AgentBurn for cost management. Many teams use both — LangSmith tells you if your agent is working correctly, AgentBurn tells you if you can afford to keep running it.