Langfuse
Open-source LLM observability, tracing & prompt management platform
Langfuse provides full-lifecycle tracing, evaluation, and monitoring for AI agents executing complex multi-step tool calls.
Quickstart Execution
trace.ts
Get started with Langfuse in your project.
import { Langfuse } from 'langfuse'
const langfuse = new Langfuse()
const trace = langfuse.trace({ name: 'danish-cvr-lookup' })
const span = trace.span({ name: 'fetch-cvr-api' })
// Perform API request...
span.end({ output: { company: 'Novo Nordisk A/S', cvr: '24256790' } })
await langfuse.shutdownAsync()About Langfuse
When AI agents execute tool calling across Danish APIs, scrapers, and MCP servers, Langfuse captures complete execution traces, token consumption, latency, and tool input/output payloads. Developers can debug agent failures, track costs, and evaluate output quality in real time.
DK
Danish Product & Data Recipes
How developers and agents use Langfuse to connect with Danish digital infrastructure.
Trace and Debug Danish Agent Tool-Calling Workflows
Wrap your agent in Langfuse to inspect every API call made to CVR, DAWA, or DMI, monitoring payload correctness and API latency.
Key Capabilities
Detailed distributed tracing of agent tool calls & model steps
Self-hostable via Docker or managed cloud platform
Cost tracking, latency monitoring, and token analytics
Integrations with Vercel AI SDK, LangChain, LlamaIndex, and OpenAI