Langtrace — Open Source AI Observability Platform

What is Langtrace?

Langtrace is an open-source observability platform for LLM applications. It auto-instruments calls to OpenAI, Anthropic, LangChain, LlamaIndex, and 20+ providers using OpenTelemetry — giving you traces, latency metrics, token usage, and cost tracking in a real-time dashboard.

Answer-Ready: Langtrace is an open-source AI observability platform that auto-instruments LLM calls to OpenAI, Anthropic, LangChain, and 20+ providers with OpenTelemetry-native tracing, latency metrics, and cost tracking.

Best for: Teams running LLM apps in production who need observability. Works with: OpenAI, Anthropic, LangChain, LlamaIndex, Cohere, Pinecone. Setup time: Under 2 minutes.

Core Features

1. Auto-Instrumentation

One line to trace all LLM calls:

langtrace.init()  # That's it — all calls auto-traced

Supported providers: OpenAI, Anthropic, Google, Cohere, Mistral, Groq, LangChain, LlamaIndex, Pinecone, ChromaDB, Weaviate.

2. OpenTelemetry Native

Export traces to any OTel-compatible backend:

langtrace.init(
    api_key="langtrace-key",
    # Or export to your own collector
    custom_remote_exporter=OTLPSpanExporter(endpoint="http://localhost:4317"),
)

3. Real-Time Dashboard

Self-hosted or cloud dashboard showing:

• Request traces with full input/output
• Latency percentiles (p50, p95, p99)
• Token usage per model
• Cost breakdown per endpoint
• Error rates and patterns

4. Evaluation & Testing

from langtrace_python_sdk import with_langtrace_root_span

@with_langtrace_root_span("test-summarization")
def test_summary_quality():
    result = summarize("long text...")
    # Trace includes test metadata
    return result

5. Prompt Management

Track prompt versions alongside traces:

from langtrace_python_sdk import inject_additional_attributes

with inject_additional_attributes({"prompt_version": "v2.3", "experiment": "temp-0.7"}):
    response = client.messages.create(...)

Self-Hosting

git clone https://github.com/Scale3-Labs/langtrace
docker compose up -d
# Dashboard at http://localhost:3000

FAQ

Q: How does it compare to LangFuse? A: Both are open-source LLM observability tools. Langtrace is OpenTelemetry-native (standard traces), LangFuse uses custom tracing. Langtrace has broader auto-instrumentation.

Q: Does it add latency? A: Traces are sent asynchronously. Overhead is < 1ms per call.

Q: Can I use it without the cloud? A: Yes, fully self-hostable with Docker Compose.