Open-source observability platform for LLM applications and AI agents with OpenTelemetry-based tracing, cost tracking, and performance analytics.
Open-source monitoring for AI apps — see exactly what your AI is doing with detailed tracing and performance metrics.
Langtrace is an open-source observability platform purpose-built for monitoring LLM applications and AI agents. Built on the OpenTelemetry standard, Langtrace provides distributed tracing, cost tracking, and performance analytics that give developers complete visibility into how their agents behave in production. The platform captures every LLM call, tool invocation, and chain step with detailed telemetry data.
The SDK integrates with minimal code changes — typically a single initialization line — and automatically instruments popular frameworks including LangChain, LlamaIndex, CrewAI, DSPy, and Anthropic's SDK. This auto-instrumentation captures prompts, completions, token counts, latency, model parameters, and costs without manual logging code.
Langtrace's tracing dashboard shows the complete execution flow of agent requests with waterfall visualizations, making it easy to identify bottlenecks, failed tool calls, and unexpected agent behaviors. Each trace includes detailed information about LLM interactions, retrieval steps, and tool executions, enabling root cause analysis when agents produce incorrect or slow results.
Cost tracking is a standout feature — Langtrace automatically calculates costs for every LLM call based on model pricing, providing per-request, per-user, and per-feature cost breakdowns. This is essential for teams managing agent budgets and optimizing token usage.
The platform supports both self-hosted deployment (via Docker) and a managed cloud service. Self-hosted deployment uses ClickHouse for efficient trace storage and provides full data sovereignty. The evaluation features enable teams to rate agent outputs and build datasets for systematic quality assessment. Langtrace represents the OpenTelemetry-native approach to LLM observability, complementing general APM tools with agent-specific insights.
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Built on the OpenTelemetry standard for vendor-neutral distributed tracing, compatible with existing observability infrastructure.
Use Case:
Single-line SDK initialization automatically instruments LangChain, LlamaIndex, CrewAI, DSPy, and other frameworks — no manual logging needed.
Use Case:
Automatic cost calculation for every LLM call with per-request, per-user, and per-feature breakdowns based on model pricing.
Use Case:
Complete execution flow visualization showing LLM calls, tool invocations, and chain steps with timing and dependency information.
Use Case:
Deploy with Docker using ClickHouse for efficient storage, providing full data sovereignty and control over observability data.
Use Case:
Rate agent outputs, build evaluation datasets, and track quality metrics for systematic agent performance assessment.
Use Case:
Free
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View Pricing Options →Monitoring production AI agent performance and costs
Teams with existing OpenTelemetry infrastructure adding LLM observability
Cost optimization for agent systems with high LLM usage
Self-hosted observability for data-sensitive agent deployments
We believe in transparent reviews. Here's what Langtrace doesn't handle well:
Both are open-source LLM observability tools. Langtrace is built on OpenTelemetry standards for better interoperability with existing observability stacks. Langfuse has a larger community and more integrations.
Yes. Langtrace uses OpenTelemetry, so traces can be exported to Jaeger, Grafana Tempo, Datadog, and other OTLP-compatible backends alongside agent-specific analysis.
By default yes, for debugging purposes. You can configure the SDK to redact or exclude sensitive content from traces.
Langtrace adds minimal overhead through async trace collection. The SDK is designed to not impact agent response latency.
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