Building an Open-Source AI Gateway in Go with Support for 10 LLM Providers
The article discusses AegisFlow, an open-source AI gateway built in Go that supports 10 different large language model (LLM) providers, allowing seamless integration and fallback across multiple AI services.
Why it matters
AegisFlow provides a flexible and secure way to leverage multiple AI/LLM providers, enabling teams to easily switch between services and handle failures without disrupting their applications.
Key Points
- 1AegisFlow is a single Go binary that sits between applications and LLM providers, handling authentication, rate limiting, policy enforcement, and caching
- 2It supports 10 different LLM providers, including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, and more
- 3The policy engine scans requests for potential issues like jailbreak attempts and PII detection, blocking or warning before reaching the LLM provider
- 4AegisFlow caches identical requests to improve performance and reduce costs, and provides a unified API endpoint for all supported providers
Details
AegisFlow was created to address the common challenge of managing multiple AI providers and SDKs in production environments. It provides a single API endpoint that can work with any OpenAI SDK, handling the complexity of communicating with different LLM providers behind the scenes. This allows teams to easily switch between providers or use multiple providers without rewriting application code. The architecture includes middleware components for authentication, rate limiting, policy enforcement, caching, and fallback routing. Adding a new LLM provider requires implementing just six methods, and new policy filters can be added by implementing a single function. The policy engine scans requests for potential issues like jailbreak attempts and PII, blocking or warning before the request reaches the provider. AegisFlow also caches identical requests to improve performance and reduce costs. Overall, AegisFlow aims to simplify the integration and management of large language models in production environments.
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