Langtrace AI
Langtrace AI is a comprehensive toolset for monitoring, evaluating, and optimizing large language models (LLMs). It is designed to enhance AI applications by pr...
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What is Langtrace AI?
Langtrace is an open‑source observability and evaluations platform purpose‑built for AI agents and LLM applications. It automatically captures OpenTelemetry‑compatible traces from your generative stack, including LLM calls, vector‑DB queries, and framework operations, and surfaces detailed metadata such as prompts, completions, token counts, model parameters, latency, and cost. The platform gives developers a dashboard to monitor these metrics over time, identify performance bottlenecks, and optimize prompt performance and agent behavior.
Key features include end‑to‑end tracing of popular LLM frameworks like LangChain, LlamaIndex, CrewAI, and DSPy, as well as integrations with major LLM providers and vector databases. The dashboard provides built‑in metrics for tokens, cost, latency, and evaluation scores, plus tools for dataset curation, prompt versioning, and A/B‑style prompt comparison across models. Langtrace is designed to help teams move from quick LLM prototypes to production‑ready, auditable AI agents with clear performance and cost visibility.
Langtrace is aimed at developers, ML engineers, and AI product teams building agent‑based or LLM‑driven applications who need observability without heavy infrastructure overhead. It suits both small teams experimenting with AI agents and larger enterprises that require SOC 2‑compliant, secure, and customizable observability. The platform is especially useful for teams that want to standardize on OpenTelemetry, track and control LLM spend, and systematically improve model accuracy and response quality using automated evaluations and prompt management.
Langtrace AI pricing
Pricing model: Freemium
Langtrace offers a Starter plan that is free forever, including 1 user per month and up to 10,000 spans per month, plus prompt management and evaluations. The Growth plan is priced at $39 per user per month, includes everything in the free tier plus evaluations in the cloud, team collaboration features, and security guardrails, with usage‑based pricing of $0.005 per additional span ingested per month above 50,000 spans. The Scale plan is custom‑priced for enterprises and includes tailored retention policies, SLAs, security guardrails, team collaboration, and SOC 2 Type II compliance. The Self‑Hosted option is free to get started and includes tracing, prompt management, and evaluations when you run the platform locally, without a per‑month span cap.
Langtrace AI pros
- OpenTelemetry‑compatible tracing out of the box
- Open‑source core with available source code on GitHub
- Supports popular LLM frameworks such as LangChain, LlamaIndex, CrewAI, and DSPy
- Integrates with multiple LLM providers and vector databases
- Collects detailed trace metadata including prompts, completions, tokens, and model parameters
- Tracks token usage, cost, latency, and success rate for each span
- Provides dashboards to visualize and filter LLM‑specific metrics over time
- Includes built‑in evaluations to measure model accuracy and response quality
- Enables dataset creation and management for testing and fine‑tuning
- Offers prompt version control and prompt‑set management directly in the platform
- Allows A/B‑style prompt comparison across different models and prompt versions
- Supports Python and TypeScript SDKs with minimal setup code
- Can export traces to existing observability stacks like Grafana or Datadog without a Langtrace API key
- Offers self‑hosted deployment for full data control and on‑prem use
- Designed for enterprise‑grade security including SOC 2 Type II compliance for higher tiers
Langtrace AI cons
- Currently supports only Python and TypeScript SDKs, limiting broader language ecosystems
- Complex applications can generate large numbers of spans, increasing cost and storage needs
- Advanced features such as SOC 2 compliance and custom SLAs are tied to enterprise plans
- Self‑hosted option requires operational overhead to run and maintain the dashboard and storage
- Documentation and SDKs are still evolving, which can lead to occasional breaking changes
- Cloud‑based growth plan usage is metered per span beyond 50K, making scaling potentially expensive
- Some evaluations and dataset features may require manual configuration or extra plumbing
- No native UI or management layer for non‑LLM microservices in the same observability view
Frequently asked questions about Langtrace AI
What is Langtrace and what does it do?
Langtrace is an open‑source observability and evaluations platform designed specifically for AI agents and LLM applications. It collects OpenTelemetry‑compatible traces from your generative stack, captures prompts, completions, token counts, costs, model hyperparameters, and latency, and provides tools to analyze, evaluate, and improve the performance and accuracy of your LLM apps.
Which LLM frameworks and backends does Langtrace support?
Langtrace supports AI‑agent frameworks such as LangChain, LlamaIndex, CrewAI, and DSPy, and it integrates with a wide range of LLM providers as well as vector databases out of the box. The platform auto‑traces operations from these frameworks and databases, surfacing relevant metadata for each step in the agent workflow.
How does Langtrace help with cost and performance monitoring?
Langtrace tracks token usage, monetary cost, inference latency, and success rates for each span in your LLM application. The dashboard lets you visualize these metrics over time, identify high‑cost or slow operations, and optimize prompts or architecture to reduce latency and stay within budget.
How do evaluations and datasets work in Langtrace?
Langtrace provides evaluation tools that let you define metrics and criteria for measuring model accuracy and response quality. You can curate labeled datasets, run automated evaluations over your traces, and store prompt sets linked to these datasets to iteratively improve models and prompt designs.
What is prompt version control in Langtrace?
Prompt version control in Langtrace allows you to store, tag, and version‑control different prompts and prompt variants. You can deploy new prompt versions, roll back to previous ones, and compare evaluation results across versions and models directly from the platform.
How easy is it to set up Langtrace in an existing project?
Langtrace is designed for simple integration with just a few lines of code. You sign up, generate an API key, install the Langtrace SDK for Python or TypeScript, initialize it with the key, and then run your existing LLM app to start exporting traces to the Langtrace dashboard or other OpenTelemetry backends.
What languages and SDKs does Langtrace support?
Langtrace currently provides official SDKs for Python and TypeScript, with documentation for integrating those SDKs into your projects. The platform is built to be extensible, and the team is working on adding support for additional languages over time.
Can I use Langtrace without sending data to the cloud?
Yes, Langtrace offers a Self‑Hosted option that lets you run the dashboard and storage locally so your data never leaves your infrastructure. The OpenTelemetry‑based SDK can still export traces to Langtrace or to other observability tools without requiring a Langtrace API key if you only want to use your own backend.
What are Langtrace’s security and compliance features?
Langtrace emphasizes enterprise‑grade security and privacy, including SOC 2 Type II compliance for its higher‑tier plans. The open‑source nature of the platform allows teams to audit and customize the code, and the Self‑Hosted option enables full control over data location, access, and encryption.
How does Langtrace pricing work for teams and enterprises?
Langtrace offers a Starter plan that is free forever with 1 user per month and 10,000 spans per month, plus prompt management and evaluations. The Growth plan costs $39 per user per month and includes cloud evaluations, team collaboration, and security guardrails, with usage‑based pricing above 50,000 spans. The Scale plan is custom‑priced for large enterprises and includes tailored retention, SLAs, and compliance features, while the Self‑Hosted option is free to start and can be run entirely on your own infrastructure.