Description
Langtrace offers an open-source observability and evaluation platform specifically built for AI agents. Its primary goal is to empower developers to move AI prototypes from development to enterprise-grade production by providing critical insights into performance, security, and cost. The platform enables users to measure and iterate on AI agent performance, ensuring better safety and efficiency.
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Setting up Langtrace is designed to be straightforward. Users begin by creating a project and generating an API key, followed by installing the appropriate SDK and instantiating Langtrace with the provided key. The platform supports a range of popular AI frameworks, including CrewAI, DSPy, LlamaIndex, and Langchain, along with a wide array of LLM providers and VectorDBs out-of-the-box.
Key capabilities include tracking vital metrics through dedicated dashboards, which monitor token usage, cost, latency, and evaluated accuracies. Langtrace automatically traces GenAI stacks, surfacing relevant metadata to help understand API requests. The evaluation features allow users to measure baseline performance, curate datasets for automated evaluations, and facilitate finetuning. Prompt management is also a core component, offering prompt version control to store, deploy, and roll back prompts with ease. A playground feature enables comparison of prompt performance across different models.
Langtrace is built with enterprise-grade security in mind, employing private and secure, industry-leading security protocols and enterprise-grade encryption. It is SOC2 Type II certified, meeting stringent compliance requirements for robust data protection. As an open-source solution, Langtrace encourages customization, auditing, and community contribution, with its source code fully accessible on GitHub.
The platform is ideal for AI engineers, ML engineers, and developers working with AI agents and LLM applications who need to monitor, debug, and optimize their systems. The value proposition lies in its ability to provide deep visibility, facilitate rapid iteration, and ensure the security and compliance of AI deployments, all within an open-source framework.
- CategoryMLOps & Model Deployment
- Pricing ModelOpen Source
- DeploymentCloud, In-Browser (Langtrace lite)
- PlatformsWeb
Langtrace's Core Features
Open-source observability and evaluation platform for AI agents
Supports CrewAI, DSPy, LlamaIndex, and Langchain frameworks
Integrates with a wide range of LLM providers and VectorDBs
Tracks token usage, cost, latency, and evaluated accuracies
Automatic tracing of GenAI stacks and relevant metadata
Features for measuring baseline performance and automated evaluations
Prompt version control for storing, deploying, and rolling back prompts
Playground for comparing prompt performance across models
Enterprise-grade security with private and secure protocols
SOC2 Type II certified for data protection compliance
Non-intrusive setup with SDK integration in Python and TypeScript
Lightweight, in-browser OTEL-compatible observability dashboard (Langtrace lite)
How to use Langtrace?
Configure: Install the appropriate SDK (Python/TypeScript) and instantiate Langtrace with your API key.
Trace: Integrate the Langtrace SDK with just two lines of code into your AI application.
Monitor: Access dashboards to track vital metrics like token usage, cost, and inference latency.
Evaluate: Measure baseline performance, curate datasets, and perform automated evaluations.
Optimize: Use insights from observability and evaluations to iterate and improve AI agent performance.
Version Control: Store, deploy, and manage prompts effectively using the prompt versioning feature.
Langtrace's Use Cases
- AI Agent Observability
- LLM Application Debugging
- Performance Optimization
- Cost Management
- Prompt Engineering & Testing
- Security Monitoring
- AI Agent Evaluation
- Enterprise AI Deployment
