Langtrace — Open Source AI Observability Platform

What it is

Langtrace is an open-source observability platform purpose-built for LLM-powered applications. It hooks into your existing OpenAI, Anthropic, and LangChain calls and records every request, response, token count, and latency figure using OpenTelemetry-native instrumentation.

If you run AI features in production and need to understand cost, latency, or failure patterns without building your own tracing infrastructure, Langtrace gives you that visibility out of the box.

How it saves time or tokens

Without observability, debugging LLM applications means adding print statements or scrolling through logs. Langtrace captures structured traces automatically, so you skip the manual instrumentation step entirely. It also surfaces token usage per call, letting you spot prompt bloat before it inflates your bill.

How to use

1. Install the Langtrace SDK in your project and initialize it with your project API key.
2. Make LLM calls as usual through OpenAI, Anthropic, or LangChain SDKs. Langtrace patches these clients automatically.
3. Open the Langtrace dashboard to view traces, latency distributions, token usage, and error rates in real time.

Example

from langtrace_python_sdk import langtrace
import openai

# Initialize Langtrace
langtrace.init(api_key='your-api-key')

# Normal OpenAI call — automatically traced
client = openai.OpenAI()
response = client.chat.completions.create(
    model='gpt-4',
    messages=[{'role': 'user', 'content': 'Explain observability'}]
)
print(response.choices[0].message.content)

Related on TokRepo

• AI Gateway tools — Compare Langtrace with gateway-level observability solutions like Langfuse and Helicone.
• AI tools for monitoring — Browse other monitoring and tracing tools for AI workloads.

Common pitfalls

• Forgetting to call langtrace.init() before any LLM client is instantiated means traces never get captured.
• Running the dashboard locally without a persistent datastore loses traces on restart. Use a hosted setup or configure a durable backend.
• Assuming Langtrace replaces application-level logging. It traces LLM calls specifically; you still need standard logging for non-LLM code paths.