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Langfuse

Traces, evals, prompt management and metrics to debug and improve your LLM application.

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LangFuse

Video Demos​

Below is an outline of the steps involved in a simple Math Agent. Key elements illustrated include:

  • A visual breakdown of each step—e.g., when the agent invokes a tool and when control returns to the agent
  • Inputs and outputs at each stage of the process:
    • User to Agent: The user asks a natural language question — e.g., What is 1 + 1?
    • Agent to Tool: The agent decides to call a calculator tool with structured arguments — e.g., args: { a: 1, b: 1 }.
    • Tool to Agent: The tool executes the operation and returns the result — e.g., 2.
    • Agent to User: The agent responds with the final answer in natural language — e.g., 1 + 1 = 2.
  • The full chat history throughout the interaction
  • Latency and cost associated with each node

Step 1: Math Agent (User to Agent → Agent to Tool)​


This section shows:

  • User to Agent: The user asks a natural language question — e.g., What is 1 + 1?
  • Agent to Tool: The agent decides to call a calculator tool with structured arguments — e.g., args: { a: 1, b: 1 }.
  • Full Chat History Throughout the Interaction: You can inspect earlier user-agent messages. For instance:
User: reply only no
Agent: No.

In this example, the agent responded directly without calling any tools.

Step 2: Tool Call (Tool to Agent)​


This section shows:

  • Tool to Agent: The tool executes the operation and returns the result — e.g., 2.

Step 3: Math Agent (Agent to User)​


This section shows:

  • Agent to User: The agent responds with the final answer in natural language — e.g., 1 + 1 = 2.