About
Langtrace AI is an open-source, OTEL-compatible observability and evaluations platform purpose-built for AI engineers and teams building production-grade LLM applications and AI agents. With just two lines of code, developers can instrument their GenAI stack using the Python or TypeScript SDK and immediately begin capturing detailed traces across their entire pipeline—from LLM calls and vector database queries to agent orchestration steps. The platform provides rich dashboards tracking vital metrics such as token usage, inference cost, latency, and evaluated accuracy, enabling teams to identify bottlenecks and regressions quickly. Langtrace automatically surfaces relevant metadata from API requests, making it easy to explore and debug complex multi-step agent workflows. Evaluations are a core part of the platform: teams can measure baseline performance, curate datasets, and run automated evaluations to iterate toward safer and higher-performing models. A built-in prompt version control system allows prompt engineers to store, compare, deploy, or roll back prompts across model versions with just a few clicks. Langtrace supports leading AI frameworks including LangChain, LlamaIndex, CrewAI, and DSPy, as well as a broad range of LLM providers and vector databases like Pinecone out of the box. It is ideal for AI engineering teams, ML researchers, and enterprises transitioning AI prototypes into production-ready products.
Key Features
- Automatic Tracing & Observability: Instrument your entire GenAI stack in two lines of code (Python or TypeScript) with OTEL-compatible tracing that captures LLM calls, vector DB queries, and agent steps automatically.
- Metric Dashboards: Real-time dashboards track token usage, inference cost, latency, and evaluated accuracy against configurable budgets and thresholds.
- Evaluations & Dataset Curation: Measure baseline model performance, curate labeled datasets, and run automated evaluations to systematically improve accuracy and safety over time.
- Prompt Version Control: Store, version, compare, and deploy prompts across model versions. Roll back to previous prompt versions in just a few clicks.
- Broad Framework & Provider Support: Out-of-the-box integrations with LangChain, LlamaIndex, CrewAI, DSPy, OpenAI, Pinecone, and many more LLM providers and vector databases.
Use Cases
- Monitoring production LLM applications for cost overruns, latency spikes, and accuracy degradation in real time.
- Debugging complex multi-step AI agent workflows by exploring full distributed traces and surfaced metadata.
- Running automated evaluations to establish performance baselines and measure the impact of model or prompt changes.
- Managing and versioning prompts across teams to ensure consistent, reproducible LLM behavior in production.
- Curating labeled datasets from production traces to support fine-tuning and continuous model improvement.
Pros
- Minimal Setup: Only two lines of code are required to start tracing your entire LLM application, dramatically reducing the time to first insight.
- Open Source & Transparent: Being fully open source means teams can self-host, inspect the codebase, and contribute, making it a trustworthy choice for enterprise deployments.
- Comprehensive Observability: Covers the full observability lifecycle—tracing, metrics, evaluations, and prompt management—in a single unified platform.
- OTEL Compatibility: Built on OpenTelemetry standards, ensuring compatibility with existing observability infrastructure and easy integration into current workflows.
Cons
- Self-Hosting Complexity: Teams opting for self-hosted deployments may need DevOps resources to configure, maintain, and scale the infrastructure.
- Narrower Ecosystem Than Mature APM Tools: As an AI-focused observability tool, it lacks the breadth of integrations found in general-purpose APM platforms like Datadog or New Relic.
- Early-Stage Ecosystem: As a newer platform, some advanced enterprise features and integrations may still be maturing compared to established observability vendors.
Frequently Asked Questions
How do I get started with Langtrace AI?
Create a project in the Langtrace dashboard to generate an API key, then install the Langtrace SDK (available for Python and TypeScript) and initialize it with your API key using just two lines of code. Traces will start appearing automatically.
What AI frameworks does Langtrace support?
Langtrace supports LangChain, LlamaIndex, CrewAI, and DSPy out of the box, along with a wide range of LLM providers (such as OpenAI) and vector databases (such as Pinecone).
Is Langtrace AI open source?
Yes, Langtrace is fully open source. You can self-host it, inspect the source code, and contribute to the project. A cloud-hosted version is also available for teams that prefer a managed experience.
What metrics can I track with Langtrace?
Langtrace tracks token usage (prompt and completion tokens), inference cost, latency, and evaluated accuracy. These metrics are displayed in real-time dashboards with configurable budgets and thresholds.
Does Langtrace support prompt versioning?
Yes. Langtrace includes a built-in prompt version control system that lets you store, compare, and deploy different prompt versions across model configurations, with the ability to roll back to any previous version.
