Langtrace is an open-source, OpenTelemetry-based end-to-end observability tool for LLM applications that captures and analyzes real-time traces, evaluations and metrics across LLM APIs, vector databases and LLM frameworks, and it is aimed at developers and LLMOps teams who need to see what their LLM-backed applications are actually doing in production.

What it is

Langtrace is an open-source observability software that captures, debugs and analyzes traces and metrics from applications that leverage LLM APIs, vector databases and LLM based frameworks. Its traces adhere to OpenTelemetry (OTEL) standards, and the project is developing semantic conventions for the span attributes it emits, with current definitions published in the langtrace-trace-attributes repository. Client libraries ship for two ecosystems: a TypeScript SDK (@langtrase/typescript-sdk) and a Python SDK (langtrace-python-sdk). A managed SaaS version runs at langtrace.ai, and the same software can be self-hosted on the user's own infrastructure.

The concrete problem it addresses is fragmentation: a single user request through an LLM framework fans out into many provider calls, vector operations and retrieval steps, each emitting output in a different place with its own format and its own delay. Langtrace pulls those events into one trace-oriented view where latency, cost and usage patterns are analyzed together, so a slow or expensive response can be followed step by step instead of reconstructed from scattered provider dashboards and framework logs. Evaluations and datasets round this out by attaching quality measurement to the same spans that carry the runtime data.

Key capabilities

• OpenTelemetry-based tracing built on the OTEL standard, with semantic conventions for LLM span attributes under active development in the langtrace-trace-attributes schemas.
• Real-time monitoring of LLM API calls, vector database operations and LLM framework usage as they happen.
• Performance insights covering latency, cost and usage patterns per trace.
• Debug tooling for stepping through LLM application workflows.
• Analytics with detailed metrics and visualizations over collected traces.
• Evaluations and datasets for measuring application output quality alongside runtime telemetry.
• Self-hosting option for deploying the full stack on infrastructure the user controls.

Who uses it and how

• Application teams building on LLM frameworks such as LangChain, who need per-call traces instead of a single opaque chain invocation.
• Teams calling OpenAI and GPT-family models, who need to attribute latency and cost back to individual API calls.
• Teams running vector database operations, who need retrieval steps visible in the same trace as the generation step that consumed them.
• LLMOps groups tracking cost and latency trends across deployments, using the analytics views rather than manual log inspection.
• Teams with data-handling constraints that self-host rather than send traces to the managed service, deploying through the Railway template or the Northflank stack.

Getting started

Install the client library with npm i @langtrase/typescript-sdk for TypeScript or pip install langtrace-python-sdk for Python, then call Langtrace.init({ api_key: ' ' }) in TypeScript or langtrace.init(api_key=' ') in Python with an API key obtained at langtrace.ai. Self-hosters can deploy the same stack through the Railway template or the Northflank stack linked from the README.

How it compares

This registry provides no list of paid products that Langtrace replaces, and the facts name no direct alternative tools, so it stands alone in this registry as the OpenTelemetry-based LLM observability entry. The only comparable surface the facts describe is its own split between the hosted service at langtrace.ai and the self-hosted deployment of the same software.

When to use it — and when not to

Self-hosting means operating the observability stack on infrastructure the user manages, which suits teams with data-handling constraints but is overhead for anyone who just wants traces collected. Teams that will not accept the AGPL-3.0 copyleft terms, or that need stable frozen semantic conventions rather than ones the project describes as ongoing development, should look elsewhere. The README excerpt is thin on operational detail — backing storage, authentication and data retention are not documented in the facts provided, and sections such as Getting Started are truncated — so evaluate those directly before committing.

# For TypeScript/JavaScript
npm i @langtrase/typescript-sdk

# For Python
pip install langtrace-python-sdk

// TypeScript
import * as Langtrace from '@langtrase/typescript-sdk'
Langtrace.init({ api_key: '<your_api_key>' }) // Get your API key at langtrace.ai

# Python
from langtrace_python_sdk import langtrace
langtrace.init(api_key='<your_api_key>') # Get your API key at langtrace.ai

npm i @langtrase/typescript-sdk

import * as Langtrace from '@langtrase/typescript-sdk' // Must precede any llm module imports
Langtrace.init({ api_key: <your_api_key> })

import * as Langtrace from "@langtrase/typescript-sdk"; // Must precede any llm module imports
LangTrace.init(); // LANGTRACE_API_KEY as an ENVIRONMENT variable

pip install langtrace-python-sdk

from langtrace_python_sdk import langtrace
langtrace.init(api_key=<your_api_key>)

from langtrace_python_sdk import langtrace
langtrace.init() # LANGTRACE_API_KEY as an ENVIRONMENT variable

docker compose up

docker compose down -v