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Basic Information

LangDB AI Gateway is an open source, enterprise-grade API gateway built in Rust that provides a unified, OpenAI-compatible interface to multiple large language model providers. It is designed for teams and developers who need to govern, secure, and optimize AI traffic by centralizing access to models from providers such as OpenAI, Google Gemini, Anthropic, Mistral and others. The project exposes standard endpoints for chat completions, embeddings, model listing, and image generation, and supports local deployment via Docker, Cargo, or Docker Compose. It includes configuration via config.yaml and command line options, supports MCP tools for external servers, and integrates observability and analytics backends such as ClickHouse. The gateway is intended to sit between applications and multiple LLM backends to provide routing, rate limits, cost controls, logging, and developer-friendly controls for production AI deployments.

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App URL
https://github.com/langdb/ai-gateway
Github Repository
https://github.com/langdb/ai-gateway/blob/main/README.md

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App Details

Features
The repository emphasizes high performance and reliability by being implemented in Rust and offering seamless integration with frameworks like Langchain and Vercel AI SDK. It provides OpenAI-compatible endpoints, dynamic model routing including fallback, script, percentage and latency-based strategies, and supports many LLM providers out of the box. Enterprise capabilities include usage analytics, cost tracking, rate limiting, load balancing, failover, and evaluation tooling. Observability features include OpenTelemetry tracing with ClickHouse storage and example SQL for querying traces. Deployment and developer conveniences include Docker images, Cargo installation, Docker Compose for full stacks, a config file with mustache-style variables, command line overrides, and explicit support for cost and request rate limits.
Use Cases
The gateway helps organizations centralize and control AI usage by providing a single, consistent API surface for heterogeneous LLM providers, simplifying model switching and multi-provider fallbacks. Built-in rate limiting and cost control help prevent unexpected spending and abuse while routing and load balancing optimize latency and availability. Observability and logging give teams traceability and analytics for performance and usage accounting, and ClickHouse integration enables querying and storing traces. The project also supports hosted and enterprise offerings for managed deployments, and developer workflows are supported with Docker, Cargo, sample configs, CLI options, and tests, making it easier to deploy, monitor, and iterate on production-grade AI services.

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