LangDB offers a Rust‑compiled AI gateway that provides real‑time observability and debugging for agents built on any major LLM framework. It captures end‑to‑end traces, latency, token usage and cost, presenting the data through a unified analytics dashboard and a framework‑agnostic API supporting over 250 models. The platform enables MLOps engineers to monitor performance, detect anomalies, and enforce governance with sub‑millisecond request latency.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents built on frameworks such as LangChain, Google ADK, or OpenAI often operate without centralized visibility, making it hard to trace request flows, monitor LLM performance, and diagnose runtime issues. This lack of observability hampers optimization, increases operational cost, and limits the ability to enforce governance across heterogeneous model stacks.
Solution
LangDB delivers a Rust‑native AI gateway that provides real‑time observability and debugging for agents across all major LLM frameworks. The platform captures end‑to‑end traces, latency metrics, token usage, and cost data, then surfaces them through a unified analytics dashboard for rapid root‑cause analysis. Built on the open‑source vLLora stack, LangDB integrates with over 250 models via a single, framework‑agnostic API, eliminating the need for pip or npm installations. Advanced analytics apply streaming aggregation and statistical profiling to surface model drift, token‑level effectiveness, and usage anomalies, enabling data‑driven tuning. Because the core engine is compiled in Rust, the gateway achieves sub‑millisecond request latency and horizontal scalability suitable for enterprise workloads.
Target Audience
Primary users are MLOps engineers, AI platform teams, and developers building production AI agents who need scalable observability, performance monitoring, and governance across heterogeneous LLM ecosystems.
Features
- Rust‑compiled AI gateway delivering sub‑millisecond latency and high throughput for production‑grade LLM traffic
- Real‑time tracing of agent execution paths with per‑step latency, token count, and cost attribution
- Unified API that abstracts 250+ LLM providers, supporting seamless model switching without code changes
- vLLora open‑source observability stack offering plug‑and‑play instrumentation and on‑prem deployment options
- Streaming analytics dashboard with anomaly detection, drift monitoring, and customizable alerting rules
- Framework‑agnostic integration (LangChain, Google ADK, OpenAI, etc.) requiring no pip/npm dependencies
- Role‑based access control and end‑to‑end encryption for secure multi‑tenant operation